Adapting financial rationality: Is a New Paradigm Emerging?

Mona Soufian1, William Forbes2, Robert Hudson3

1Newcastle Business School, Northumbria University

2School of Business and Economics, Loughborough University

3Newcastle University Business School, Newcastle University

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Abstract

We discuss the implications of an alternative to the efficient market hypothesis (EMH) the

adaptive market hypothesis (AMH). The AMH may give a theoretical basis for a new financial

paradigm which can better model such phenomena as the recent financial crisis. The AMH

regards the financial market order as evolving, tentative and defined by creative destruction

in which trading strategies are introduced, mutate to survive or face abandonment. The

concept of investor rationality is less helpful than the distinction between investment

strategies which are more or less well adapted to the prevailing market environment in

which they are deployed. We outline how a more systematic and grounded basis for

behavioural finance can be developed in line with the later approach. Based on this we

develop testable hypotheses allowing the AMH to be distinguished from the EMH. Finally we

discuss how the AMH can aid our understanding of important issues in finance. A crucial

feature is that in the But in this survival of richest, as opposed to fittest, implied by the AMH

there is much room for misallocation of resources as price and value uncouple. In this

evolution of the financial market order the regulatory State features as a further market in

which the vote market verifies orand disrupts settled market conditions.

AcknowledgementsWe would like to acknowledge very insightful and helpful comments by the editor and three anonymous referees on an earlier draft of this paper which have considerably improved the work.

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1. Introduction

The recent global financial crisis has dealt a huge and largely unanticipated shock to the

world economy. The Queen of England surely expressed the thoughts of much of the

world’s population in November 2008 when she asked in a visit to the London School of

Economics why no one had seen the crisis coming. We believe the fundamental answer to

her question lies in the dominance of the neoclassical financial paradigm. A few far-sighted

people had seen problems ahead but were largely ignored i. The idea that markets rationally

price assets and risk was so entrenched amongst influential academics, practitioners,

regulators and politicians that dissenting views were completely marginalised.

Recent years have seen an almost continuous succession of financial crises including the

emerging markets crisis of the late 1990s, LTCM, the bursting of the ‘dot com’ bubble, the

accounting scandals at Enron and Worldcom, cumulating in the near collapse of the global

banking system in 2008 and on going problems with sovereign debt. As Hyman Minksy

(1986) has taught us financial crises are a recurring theme of economic history. What is so

disturbing is the escalating frequency and intensity of the crises we now observe. Galbraith

(1990, p viii) states the case thus “Recurrent speculative insanity and the associated financial

deprivation and larger devastation are, I am persuaded, inherent in the system. Perhaps it is

better this can be recognised and accepted.” Recently Ferguson (2012) has portrayed the

2008 Crisis as a critical point in a “great degeneration” of Western capitalist economises as

they enter a “stationary state” characterised by the rule of law being replaced by the rule of

lawyers and an intense rent-seeking amongst market participants for shares of a pie that has

ceased to grow or has even entered decline.

Mainstream finance theory has clearly failed to anticipate, or even convincingly explain, the

recent crises. Indeed to a large extent it might be considered to have caused them by giving

intellectual authority to the ideal of unrestrained financial markets and dogmatically

suppressing dissenting views. There seems a vital need to address this situation with new

research programmes to better model reality. In the terms of Kuhn’s seminal work on the

structure of scientific revolutions the crises are anomalies; that is a failure of the current

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paradigm to take into account observed phenomena (Kuhn, 1962). An accumulation of

anomalies eventually leads to a change of paradigm. The current crisis may act as the

breakpoint enabling serious consideration of different theoretical approaches.

Perhaps we can finally now accept the cyclical nature of financial crises and move on to

explaining the function they serve. Periods of trauma and destruction may intensify the

speed at which differentiation based on evolutionary fitness proceeds. This suggests seeking

to build financial institutions that can withstand further crises may be a misplaced effort. It

may be better to try to put in place bank resolution procedures that are both easily triggered

and avoid capable of protecting the taxpayer’s purse. Taleb (2012) identifies a category of

trader/entrepreneurs who thrive of volatile, if not destructive times. Such innovators are

“antifragile” in the sense that they enter their own in periods when pressures to survive are

there most intense.

As one would expect a crisis of the magnitude we are experiencing has given rise to

enormous debate. There have been hundreds of popular and academic articles and books

on the subjectii. Different authors have emphasised different perspectives. Much of the

popular coverage has personalised the issues. Often individuals have become scapegoats for

behaviour they personify, for example Richard Fuld, Sean Fitzpatrick and Fred Goodwin, the

CEOs who presided over the demise of Lehman Brothers, Anglo-Irish, and the need for the

government rescue of RBS, respectively. A simple assertion that we have a flawed and

greedy banking culture is now commonplace as a result.

Whilst it is surely prudent to rapidly address particular flaws in financial practices and

regulations much of the post crisis response has been very piecemeal and ad-hoc in nature.

This type of response is inevitable given the evident lack of an appropriate and credible

theoretical basis to inform policy. The deficit in theory has been recognised even in some

essentially practical and hard-headed assessments of the crisis. This is one of the primary

insights of the UK's Turner Review into the failure of regulatory authorities to head off the

burgeoning securitised debt crisis (Turner, 2009). Turner concludes (Turner, 2009, pp 85)

“the conventional wisdom relating to the global financial system – that risks had been

diversified – was widely accepted and was wrong”. If the ability to diversify as a risk

reduction strategy now looks tarnished in the face of systemic risk then the very

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fundamentals of received professional wisdom are in doubt. In a related review of the UK’s

equity market’s John Kay explicitly demures from a view (Kay, 2012), that “public policy

should proceed as if these ‘irrational’ behaviours did not exist: such an approach would not

be consistent with the fundamental goals of performing high performance companies.” This

insight echoes Jean-Claude Trichet’s (Governor of ECB) view that many of the economic

models used by advisors during the financial crisis (Trichet, 2010) “seemed incapable of

explaining what is happening in the economy in a convincing manner.” Thus the EMH seems

to have been discarded as the basis of sound public policy and the race is now on to gain

acceptance of a credible alternative. The AMH could become such an alternative source of

guidance.

In this paper we question the core assumption of the EMH which is the ‘rationality’ of

economic agentsiii. Following Gigerenzer et al (2003) we argue that true evolved rationality

emerges when the response when investors’ cognition is a good match to the demands of

the environment in which they find themselves trading. The distinction here is between the

investment strategy and its cognitive and external context, as opposed to a proposed

statistical property which is conjectured to prevail regardless of context or cognition. Based

on the above, in this paper we discuss directions for future research which offer some hope

to build a more persuasive and useful theorisation of financial decision-making. Initially we

propose replacement of the concept of the Efficient Markets Hypothesis (EMH) that financial

markets always act to set prices ‘rationally’ by an understanding that prices change as

investors’ constantly adapt their behaviour as markets evolve their own internal order. The

latter process is known as the Adaptive Markets Hypothesis (AMH) and was initially

proposed by Lo (2004, 2005). The AMH recognises the importance of behavioural finance

and was partly designed to offer a way to reconcile this emergent literature with the

mainstream. Our second, and complimentary, proposal is to work towards a more

systematic and theoretically grounded basis for behavioural finance, building on the work of

Herbert Simon on bounded rationality, and the research programme of Gigerenzer on

heuristics. At the moment behavioural finance is somewhat fragmented from a theoretical

point of view and can be criticised as often being an arbitrary catalogue of observed

departures from rationality without a unifying theoretical vision to explain those anomalies.

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Rebuilding financial theory is a huge task and it would be naive to over-commit to a specific

way forward at this stage. However, the research agenda we propose is far from exclusive in

scope. The AMH is much less theoretically restrictive than the existing paradigm in that it

recognises the possibility of a wide range of responses by players in the markets rather than

taking a particular view of investor rationality as axiomatic. An advantage of the approach is

that it may reconcile understandings from various research traditions, including neo-classical

economics, behavioural finance and psychology. It can also accommodate different

modelling approaches. This is important as the rapid development of computing is

introducing radically new research tools such as the direct modelling of economic agents

(agent-based modelling). These can be characterised as a ‘bottom-up’ approaches as they

focus on the behaviour of the individual agents in the market and then aggregate this

behaviour to deduce the implications for the overall market. This is, of course, a largely

meaningless activity within the neo-classical world as the agents are axiomatically assumed

to be both homogeneous and rational, according to the normative construction of the

“representative agent” model. In contrast to the agent-based approach most research to

date, both neo-classical and behavioural, takes a modelling approach that can be

characterised as being ‘top-down’ with an emphasis on observing movements in the prices

of financial instruments rather than the underlying behaviour of market participants. In

general, the motivation of the participants cannot be directly observed. This creates

difficulties for practitioners of behavioural finance as motivations can only be deduced from

indirect evidence. From a methodological point of view it would be premature to definitely

favour the new agent-based approaches over much more established methods but equally it

would be foolish to reject them out of hand. They certainly permit the testing of hypotheses

that relate much more directly to the behaviour and motivations of individual market

participants.

While the AMH seems like a radical new departure to standard finance theorists evolution-

ary theory has a history of use within transaction cost perspectives on both the develop-

ment of corporate organisational form and governance structures operating within any

chosen form (Nelson and Winter, 1982). This evolutionary theory of economic change shif-

ted the analytical focus from managers maximising a given objective of profits, or sales, etc,

to the selection of “routines” appropriate to a fluctuating and uncertain environment.

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Rather than invoking calculus to solve for implicit maxima the evolutionary perspective fa-

vours processes, such asseems to parameterise markov-switching processes, for rotating

across alternative “routines”, selected to fit the trading environment the company faces.

Such routines include “well specified technical routines for producing things, … research and

development or advertising and business strategies about product diversification and over-

seas investment.” (Nelson and Winter, 1982, p 14). In tranquil times old established routines

are likely to remain unchallenged and comfortably embedded. But in more turbulent peri-

ods, rapid technological, or regulatory, change induces threats to survival requiring major re-

visions to the set of routines adopted. In such critical periods, as Nelson and Winter (1982, p

58) state “it is more natural to represent large scale motivational forces as a kind of persist-

ent pressure on decisions, a pressure to which the response is sluggish, halting and some-

times inconsistent…. an … evolutionary purging of motives that diverge excessively from sur-

vival requirements.” However, within these bounds, imposed by the need to survive, estab-

lished, comfortable, if sub-optimal, routines abound. So while within the Nelson and Winter

(1982) schema environmental changes initiate switches in prevailing routines. Realised

learning (RL) methods characterise routine rotations as emerging from the diffusion of indi-

vidual successful adaptions through a broader investment community.

The evolutionary perspective on organisational form reflects a much older interest in

the analogy between economic and biological processes which dates back to Bernard Madi-

ville’s satirical sonnet “The Fable of the Bees” (1714) where the Bee hive mimics the market

by allowing the struggle for individual survival (or private vices) to produce a perfect, if bru-

tal, social order (of questionable public virtue). Indeed Charles Darwin was much inspired in

conceiving of “The Origin of Species” by Robert Malthus’s “Essay on the Principle of Popula-

tion” suggesting Economics and the life sciences shared a common analytical frame in much

the same way as Econophysics marries finance and the natural sciences now (Ferguson,

2012 p 63).

In the next section of the paper, Section 2, we initially outline the background to current

mainstream theory with its emphasis on perfect rationality and follow this with a discussion

of an alternative approach which assumes that rationality is bounded and the resulting

implications of this for our proposed conceptual approach. The remainder of the paper is

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structured as follows: Section 3 reviews the Adaptive Expectations Hypothesis (AMH)

followed by an examination of the role of bounded rationality in recent theoretical

developments and considers how the AMH may be tested using ‘top-down’ approaches.

Section 4 examines how a more systematic and theoretically grounded basis for behavioural

finance can be developed. The internal and external bounds on financial decision-making are

examined in sub-section 4.1. Heuristics and adaptive behaviour are examined in sub-section

4.2, followed by discussions on satisficing and investor heterogeneity in sub-section 4.3.

Finally, sub-section 4.4 outlines a tentative modelling strategy for an evolutionary

perspective on financial innovation based on agent-based modelling. The various sub-

sections of Section 4 imply testable hypotheses, allowing us to distinguish the predictive

power of the AMH from the EMH using the ‘bottom-up’ methods associated with agent-

based modelling. Section 5 considers some practical examples of how the AMH and EMH

differ by considering some current areas of finance research. The paper concludes with

overall remarks and suggestions for future work in Section 6.

2. Background

2.1 Mainstream Theory and Perfect Rationality

The classical assumptions of Finance theory are broadly that individuals are rational, seek to

maximise the expected utility of their wealth, are risk averse and follow the tenets of

subjective probability. Capital markets in turn are perfect and generate financial returns

which are not predictable. Despite broad critiques, not least in this journal, (see, for

example, Hudson et al, 1999; Keasey and Hudson, 2007; Hudson and Maioli, 2010; Shiller,

2000; Clarkeson, 2009; Krugman, 2009 and Akerlof and Shiller, 2009) this mainstream

approach has remained very dominant. There are reasons for this rigidity, all the elite

finance departments and academic journals are overwhelmingly dominated by scholars

steeped in the mainstream approach (see Whitley, 1986 and Fox, 2010 for accounts of the

rise of this dominance). In addition, the mainstream approach is very closely allied to the

philosophical belief that free markets are the best way to allocate resources. This was

almost a point of patriotic faith in the US during the Cold War era when mainstream finance

was developed and certainly an easy and powerful argument to make after the collapse of

the Soviet Union. However, it may be that, like reason itself, markets are a good servant but

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a poor master. Below we discuss the concept of rationality as employed in mainstream

theory and then outline how the concept has become so associated with free market

policies.

The standard notion of rationality employed by economists is that of purposive rationality.

There are no value judgements of the desired ends simply whether the methods used to

achieve them are optimal. This notion lends itself to logical axioms which can be used to

determine rationality. For example, in his seminal multi-million copy selling text book, Paul

Samuelson, often considered the most influential post war economist, codified four

principles of rationality: completeness, transitivity, non-satiation and convexity (Samuelson,

1948, p45)iv. From this viewpoint the human being is seen as a utility maximising,

calculating machine (or “Laplacean Demon”) which raises obvious difficulties once we reflect

upon studies of actual human behaviour or merely our own social observation. Even

Kenneth Arrow, a Nobel Prize winning economist whose work is synonymous with the logical

analysis of economic issues, admitted that human beings could not be rational in this sense

(Arrow, 1986).

In practice the notion of rationality described above is often blurred in the literature around

financial markets with the prominence of the concept that financial markets are rational in

the sense that they produce the best social outcome. In much of the literature dealing with

finance the various notions of rationality are used in a rather cavalier and inter-changeable

manner often to make the rhetorical point that free markets are the best (and most rational)

way to allocate resources. For clarity in this paper we try to distinguish precisely what we

mean by rationality.

The academic theory most strongly justifying the approach of leaving allocative decisions to

the market is the EMH. The history of the development of the EMH is very revealing and can

be seen as not a disinterested scientific endeavour but one strongly influenced by ideological

considerations and the need to preserve the core methodological approaches of neo-

classical economics (see Fox, 2010, for an excellent account of its development). In broad

terms, by the 1950s mainstream economics had largely adopted the still dominant neo-

classical approach based on utility maximisation by rational agents. The methodology of this

approach draws very heavily on deterministic optimisation methods similar to those used in

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classical (pre – quantum theory) natural sciences such as physics and chemistry. Paul

Samuelson in particular drew heavily on methods from thermodynamics in his writings (see

Samuelson, 1986). Securities markets were problematic within this framework. In popular

discourse their behaviour had long been a byword for irrationality and the random nature of

their behaviour was becoming mathematically formalised by the early 1960s (see, for

example, Kendall, 1953; Mandlebrot, 1963; Cootner, 1964). Samuelson rose to the challenge

and wrote an ingenious article seemingly squaring the circle by reconciling randomness with

rationality (Samuelson, 1965). The very title of the article ‘Proof that Properly Anticipated

Prices Fluctuate Randomly’ and the liberal use of mathematics in the article gave an air of

dispassionate scientific rigor to the exercise.

The reasoning in the article is in fact very straightforward and superficially persuasive. Since

it is hard to predict stock prices then surely they incorporate all available information and

therefore are rationally determined. It is hard to overestimate the influence of this

argument on subsequent financial theory and practice. A very substantial body of academic

work was seen as supportive of Samuelson’s view. This work was built up by a number of

scholars through the 1960s. The general research pattern was to find empirical evidence that

prices were hard to predict by some metric and then tacitly or explicitly assume that this

showed that prices were properly anticipated. The scholar that is most closely associated

with this enterprise is Eugene Fama of the University of Chicago which personified fervent

support of free markets and positive economics. Fama developed the term ‘efficient

markets’. In 1965 in an article in the Financial Analysts Journal he wrote “In an efficient

market, however, the actions of the many competing participants should cause the actual

price of a security to wander randomly about its intrinsic value” Fama (1965, p56).

But the meaning of randomness was left unclear and thus left open to opportunistic use by

EMH advocates in later discussion (Mlodinow, 2008, p 84-85). One interpretation of

randomness is that any of the conceivable outcomes are equally likely, rain or shine for

example. This meaning to the word is sometimes termed the frequency interpretation of

randomness. Another is that rain or shine are not equally likely but I cannot predict which

will occur with any reasonable degree of accuracy. This is sometimes termed the subjective

definition of randomness. The frequency interpretation judges the randomness of the

sample ex-post, by the frequency of outcomes., where rain and shine days equally spread

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across the calender? But the subjective interpretation of randomness judges its presence ex-

ante by simply asking whether I could know if it will rain tomorrow or not. Flipping between

these meanings gives EMH advocates considerable scope for avoiding unwelcome

refutations of their preferred theory.

Fama subsequently continued to refine the Efficient Markets Hypothesis to classify different

types of efficiency (Fama, 1970). The term ‘efficient market’ was a brilliantly persuasive

choice of words. Efficiency is one of the highest social values in the West and generally

assumed to be a good thing, albeit that in the context efficiency is given a meaning far

different from its normal usage. People who are not scholars of finance invariably and

understandably assume that an efficient market refers to the operational efficiency of

market trading, for example, how fast or how cheaply trades can be processed. The main

achievement, and perhaps purpose, of the efficient market project was, however, to show

that intrinsic value and market value of traded assets were one and the same with profound

implications. If market prices are a perfect guide to intrinsic value the market can then be

taken as an infallible guide to human affairs. This view of the world is aptly summed up by

mathematician David Orrell who describes the market under the EMH as “..some kind of

hyper-rational being that can outwit any speculator or government regulator” (Orrell, p

265).

Only a few mavericks have raised dissenting voices against the EMH project. Many years

before our present difficulties Robert Shiller pointed out a basic logical mistake in this

reasoning. Just because prices are difficult to predict it does not imply that they are rational

in the sense that they represent intrinsic value. Unpredictability is a necessary but not

sufficient condition for market prices to be rational. “This argument for the efficient markets

hypothesis represents one of the most remarkable errors in the history of economic

thought. It is remarkable in the immediacy of its logical error and in the sweep and

implications of its conclusion” (Shiller, 1984, p. 459). Shiller’s critique, however, has largely

fallen on deaf ears and the mainstream view remains dominant.

There has been much institutionalised inertia in the research approaches adopted within

Finance. Findlay and Williams (1985) argue that in conventional Finance the positivist

assertion that ‘‘assumptions do not matter if the model works’ has been subverted into the

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notion that assumptions cannot be criticised so long as the model cannot be shown not to

work’’. In the relatively benign US financial environment between 1950 and the late 1990s,

the years of the “great moderation”, it was hard to show that the mainstream model was

clearly not working, despite the short sharp shock of the 1987 market crash. But in the

subsequent, more tumultuous, years its shortcomings have become transparent. It is

untenable to argue that the stock market in the dot-com years, or the US housing and

financial market in the run up to the financial crisis in 2008, were acting in an way that

reflected underlying intrinsic values. Even Alan Greenspan, the well known advocate of free

markets and the Federal Reserve Chairman from 1987 to 2006, was forced to admit in

testimony to a House of Representative Committee that his pro-market ideology was flawed

(Greenspan, 2008).

The development of finance theory is certainly not merely an “academic” issue in the

pejorative sense that few outside the academy know or care about such developments. In a

now well developed body of work Donald MacKenzie has illustrated the “performative”

nature of modern finance theory and especially the Black-Scholes-Merton option pricing

theory and the portfolio insurance schemes that the formula underpinned (MacKenzie,

2004, 2006 (a) and (b)). While the Black-Scholes-Merton model was “performative” in the

sense of moving the quoted price of options to be more like those implied by the model it

also embedded the possibility of a “counter-performative” pricing distortions. This counter-

performative feature of options pricing theory was perhaps most dramatically revealed by

the October 1987 Crash. Portfolio insurance, undertaken by creating “synthetic puts” in the

form of sell orders against the futures’ index contract, created positive feedback when sales

of the spot market contract became too heavy for the futures contract price to be speedily

constructed and quoted. Large sell orders were greatly amplified once effective hedging of

downside risk was nullified because market-makers were either unwilling or unable to

execute investors’ trades. Finance theory is rarely tested for validity upon data unaffected by

its own development. Rather the role of finance theory as an “engine not a camera”

(MacKenzie, 2004) means financial theories often create new market realities which the

theory was not initially designed to describe or understand.

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2.2 Bounded Rationality and its Incorporation into a New Conceptual Framework

For some years there has been on going work into the implications of relaxing the

mainstream assumptions about the behaviour of individuals (see Daniel et al, 2002). Recent

events add impetus to these efforts and raise many more questions about the realism of

current mainstream approaches to finance. The recent global financial crisis has certainly

encouraged some scholars to re-examine the foundations upon which economics and

finance are based. Mallard (2011), among others, challenges the assumption of perfect

rationality in reviewing recent developments in modelling bounded rationality. As discussed

above, mainstream finance theory relies on the assumption of perfect rationality of market

participants and hence it is obvious that this is necessary for markets to be efficient.

However, the existence of efficient markets is not consistent with empirical evidence.

Evidence of departures from efficiency include the high volatility of asset prices and

apparent over-reaction and under reaction in the stock markets (see early studies by, for

example, Shiller, 1981 and De Bondt and Thaler, 1985).

In the growing behavioural finance literature departures from market efficiency are generally

attributed to behavioural biases amongst investors. Much of the mainstream approach is

tacitly retained in that investors are assumed to have purposive rationality and departures

from the fully rational behaviour in the mainstream models are due to the biases and

cognitive limitations of the individuals involved in executing their purpose. Thus most work

in behavioural finance has tended to focus on the bounded rationality of individuals leading

to departures from the optimum solution given by the mainstream model.

Other approaches to investigating bounded rationality offer more positive assessments of

alternatives to perfect rationality and its offspring the EMH. This has been reflected in the

recent development of the AMH combining Simon’s notion of bounded rationality (Simon,

1955) and evolutionary cognitive theories (see Lo, 2004, 2011). The investigation of

bounded rationality is also the central theme of Gert Gigerenzer’s research programme

which has provided a better understanding of modelling heuristic decision-making.

Gigerenzer’s heuristic studies (1996, 2009, 2011) show that by employing specifically chosen

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heuristics agents/investors can make accurate, fast decisions while expending little effort in

the decision-making process.

We advance a general conceptual framework that incorporates bounds on both an

individual's internal cognition and the external environment within which it is formed. Our

discussion considers how rationality emerges from the dynamic interplay between individual

internal cognition and its external environment. Environments are constructed and shaped

by the cognition of decision-makers who act within them. This process envisages an

evolutionary chain in which heuristics arise, mutate and thrive, or die out, according to the

progeny they yield. Successful heuristics/mental frames may become entrenched amongst a

wealthy trading elite, while others become marginalised to a dwindling impoverished few,

only to to be reborn in some new guise. A moment’s reflection on the history of

chartism/technical trading, now reborn in the guise of high-frequency trading, reflects this

evolutionary thread (Lo and Hasanhodzic, 2010). Similar evolutionary mechanisms to those

we observe in the natural world are also observed in our social and economic lives. But

crucially no one person observes all the changing heuristics and the role of the price system

is then to transmit a diverse range of value signals no single investor can possess. A primary

benefit of heuristic driven choices is that no one pretends they are either optimal or

anything other than expedient. Hence discarding them in the face of a preferred alternative

is fairly costless and unproblematic (Taleb, 2012, p 11).

We argue that a true evolved rationality emerges when the response from investors is a

good match to the demands of the environment in which they find themselves trading.

Heuristics aid increasingly rational/optimal choice by investors in a stable trading

environment. But financial markets rarely remain stable and fractures in the external

environment rarely leave existing mental-frames pre-eminent, or perhaps even prominent,

for long. Hence the norm for financial markets is the fluctuation of partially adopted failing,

or retrieved and recycled, mental frames which allow a partial rationalisation of the market

setting. In order for investors to adapt their decisions using heuristic devices it is crucial to

match the joint effect of the structure of their internal cognitions to their environment.

This alignment can be compared to two blades of a pair of scissors (Newall and Simon, 1972,

p 55). The first blade represents the agent’s internal cognition (the heuristic

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mechanism/framing) while the second blade captures the market environment. An

appropriate conjunction of an investor's mental frame and the environment induces

boundedly rational behaviour. The constraints in our environment support the development

of simple strategies, using heuristics/frames that exploit the benefits/constraints on offer as

opportunities and guides. Such fast and frugal decision-making is suitable for an evolved and

still evolving market order. But this evolved order is rarely grasped by any single intelligence,

as Hayek states (1973, p 49-50)

“...The more complex the order aimed at, the greater will be that part of separate actions which will have to be determined by circumstances not known to those who direct the whole, and the more dependent control will be on rules rather than specific commands.”

Hence investors must always choose an investment strategy based on partial, imperfect and

fragmented information recognising that competing traders, with their own flawed

cognition, do the same. A constantly revised, revolving, set of heuristics enable an organic

order to emerge but equally allow for it to dissemble until a new transient order prevails.

Even skills acquired by continuous interaction with others, with all its punishments and

rewards, are often wrongly rationalised as deriving from our individual skill or talent (Smith,

2008, p 8). If heuristics/frames can truly aid decision-making the question arises as to when

and why do some heuristics perform well for investors while others prove misleading or

even harmful? The evolution of a bounded rationality, selected for fitness of purpose in

some particular market context, is athe focus of this paper. Some testable hypotheses

derived from such an evolutionary perspective on investor decision-making are discussed in

the later sections of the paper. In a penultimate section these hypotheses are applied to

established research problems in Finance.

A conceptual framework is developed that incorporates internal and external bounds upon

rational choice, building upon the key characteristics of bounded rationality. Three key

characteristics are taken into account in modelling bounded rationality. These are (a) the

evolution of heuristics and mental-frames by adaptive decision-making, (b) the impact of

investor heterogeneity, and, finally, (c) the concept of investor satisficing implied by

bounded rationality. Often both satisficing and optimizing behaviour predict almost the

same investment choices when investor heterogeneity is taken into account. We show

bounded rationality provides a promising perspective for refining the foundations of the

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AMH in an age when theory and practice have so wildly diverged and finance theory is

deemed unhelpful for evaluating policy reform.

Financial markets are subject to uncertainty, risk and ever changing events; these aspects

make such markets a particularly interesting field to examine market participants’ behaviour

and decision-making processes. Financial innovation has often increased such uncertainty in

financial markets. The multiple triggers for recapitalisation of Special Purpose Entities

(SPE’s), which housed securitised debt, or its division into tranches, illustrate this trend.

Price/quantity choices are made by investors within a trading environment itself in a

constant state of flux. A theory of expectation formation is a crucial part of any theory of

investment and asset pricing. Most theories in finance rely on the assumption that investors

form rational expectations. Professors Christopher Sims and Thomas Sargent were awarded

the Nobel Prize in 2011 for contributions to this work. In this area rationality is usually held

to imply an evaluation of risky outcomes in conformity with the von Neumann-Morgenstern

axioms. These axioms imply agents consistently rank alternative risk outcomes/gambles

based on an accurate projection of probability weighted future cash-flows. These axioms

are themselves largely a normative theory of how decisions should be made in uncertain

environments rather than providing a positive theory of how they are made. To quote

Vernon Smith, the 2002 Nobel Prize winner (2008, p. xv)

“Practitioners are into problem solving and do not relate naturally to discussions driven by economic theory...but they can appreciate working models when they see and experience them.”

As discussed above, despite the clear behavioural biases of investors standard finance

commonly invokes perfect “rationality” in the sense described by the von Neumann-

Morgenstern axioms.

3. The AMH as an alternative framework to perfect rationality, and its

implementation

This section reviews a recent alternative to the EMH’s assumption of perfect individual

rationality and the role of bounded rationality in the development of this theory and its

implementation.

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Behavioural finance attempts to provide evidence that the market is not efficient and that

market participants are not rational. Many empirical findings indeed jar with standard

Finance theory. Investors tend to overreact to private information signals and under-react to

public information signals, such as earnings announcements (Bernard and Thomas, 1990).

Stock market overreaction and consequent longer-run mean-reversion are foundational

findings for the behavioural perspective on investment. Further, the equity premium puzzle,

of equity's relatively high return as an asset class, remains puzzling even after the on going

financial crisis has partially dented its edge (Mehra and Prescott, 2008).

To date, however, no integrated theory of investment behaviour to rival standard theory has

yet emerged. As critics state behavioural finance can often seem a rag bag of ad hoc models

to explain individual anomalies with no overarching structure (see, for example, Fama,

1998).

One attempt to develop such an integrating framework is the AMH (Lo, 2004). While such

models are currently in their infancy, they provide hope of salvaging the best of the efficient

markets theorem while excising some of that theory’s worst follies. AMH explains loss

aversion, overreaction and other behavioural biases by the fact that investors react to a

changing market environment by invoking new heuristics/mental frames. The processes of

learning, heuristics management and adaptive decision-making are central to AMH.

Accordingly individual agents are regarded as not perfectly, but rather boundedly, rational or

“satisficers”. Hence a rational decision is always evolving, in construction and contention as

mental schemata are invoked, adapted for purpose, and discarded as market conditions

change. Hence we cannot understand market outcomes through the eyes of one

representative investor. Rather it is the very exchange, rejection and promulgation of mental

frames upon valuation that evolves the market condition we observe at any given point (Lo,

2004, p. 21). Investors satisfy, as opposed to optimise, by truncating search at varying points,

trading and observing the consequences.

Great, or perhaps just lucky, traders choose good truncation points and come to dominate

the market, poor traders truncate their search too early or too late and are eased out of the

market. But this difference can only be observed after the event with successful traders’

confidence being fuelled by the downfall of their peers. Trading strategies, high-frequency,

17

contrarian, etc. can be thought of as mutating species within an overall market ecology. The

growing weight of money flowing towards the favoured mental frame earns a declining rate

of return restarting the cycle of mental frame selection, via the search for yield in a

Darwinian selection process in which trading species grow, mutate and thrive or face

extinction. In this process there are no fixed valuation rules, but rather an array of

constantly recycled and sometimes outdated market plays of temporary opportunistic use.

Market plays are here neither rational nor irrational, but rather adapted or maladapted to

the context within which they are deployed. So trading profits guide the evolving chain of

market strategies, “By viewing economic profits as the ultimate food source on which

market participants depend for their survival, the dynamics of market interactions and

financial innovation can be readily derived.”(Lo, 2004, p. 23). This “survival of the richest”

rather than the fittest allows for substantial noise to surround the signals about asset value

conveyed by prices. Further successful actions speak louder than disastrous omissions, as

one feted movie executive put it “If I had said yes to all the projects I turned down and no to

all the other ones I took, it would of worked out much the same” (Molodinow, 2009, p 12).

Availability bias means few traders learn from even their most damaging omissions. So

learning is very history dependent with little inference being made from unused, but

potentially profitable, alternative trading strategies.

Scholars of psychology and behavioural finance are aware that beyond a certain level of

complexity human cognition is limited, the question is how bounded rationality can be more

realistically modelled in finance. There are two main approaches to modelling bounded

rationality (a) optimising agents who face constraints-with the focus placed upon external

bounds, and (b) satisficing decision-making agents- which focus upon internal bounds to

cognition.

Heuristics work by using small, but highly relevant, sets of information to resolve immediate

problems quickly. Investors apply heuristics to adapt to new information, for example as

market conditions change. New opportunities arise from the old heuristics. Think of the

“weightless economy” of the late 90's that made reported losses almost seem virtuous as

internet players struggled to “dominate the space” at all costs (Brynjolfsson and Kahn, 2000).

18

Since market conditions are subject to continuous and abrupt changes, investors will

continue to adjust and adapt, as the evolutionary ebb and flow of markets progresses (Lo,

2004).

According to the AMH arbitrage opportunities appear and disappear as a result of adaptive

responses to changing economic conditions; as they do so investors adapt new heuristics to

match new challenges. The most basic requirement of any trading strategy is its survival

value, its profitability and risk reduction value come after this most fundamental market

test. Strategies that “blow up” their adopters are unlikely to live long enough to

appropriately adapt to the market environment in which they are deployed. So where do

new market strategies or ways of seeing the market come from?

De Bondt (2004) interviewed over 500 European investors with between Euro 100,000 and

Euro 1 million to invest and concluded “individual clusters of attitudes and belief, often

associated with national character, gender, age and religion influence portfolio choice.

[Hence] Culture matters.” As Polyani pointed out (1944) financial markets are embedded

within a broader social and political settlement whose transformation (in a revolutionary

episode) can breach the control of such heuristic tools control. So mental frames influence

investment strategies and are shared and communally formed. Such cognitive schema are

inherited, adapted and eventually abandoned, cannibalised or recycled in the face of new

realities. Crucially our financial and personal lives are integrated and mutually enforcing and

not sealed off purely economic calculations

But how does this happen? What do we know about the evolution of the behavioural norms

we all share? One norm we all rely on is some, perhaps minimal, degree of co-operation

from others. One setting in which this collaboration is sorely needed is within the context of

a prisoner's dilemma where a failure to co-operate induces losses for all. Axelrod (1984)

reports the results of a competition to devise a computer program capable of optimally

solving the prisoner's dilemma problem. The winning program always initially uses a co-

operative strategy until the opposing player defects and then retaliates until the opponent

co-operates again. This ‘tit-for-tat’ strategy turns out to be optimal almost whatever the

opponent does. If my opponent is continually uncooperative he gets one period of grace

before my retaliation kicks in. If my opponent always cooperates so will I. Interestingly the

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strategy that performed worst is the most seemingly “sophisticated” invoking learning and

probability distributions to constantly update behaviour. Within financial markets the

problem of counter-party risk, so vividly displayed during the recent sub-prime debt crisis

reflects such a choice (Khanani and Lo, 2007 and Lo, 2009). Heiner (1983, p563) notes a

rather similar pattern of development in the evolution of blackjack play amongst

professional players. Card counting techniques, requiring immense memory skills, have given

way to very simple structured response strategies. It appears simple structured strategies

dominate more explicit “maximisation” strategies in many competitive environments.

The evolving order of the AMH contrasts with the EMH for which efficiency is seen as a static

concept. It is important, however, to consider how the two hypotheses can be tested

empirically. Direct tests of the AMH are quite easy to conceptualise using the ‘top-down’

approach. A large number of tests have been designed over the years to test for the EMH

and usually these involve some sort of quantification of efficiency. In broad terms the AMH

can be tested by investigating whether the level of efficiency in a market varies significantly

over time. An increasing number of papers report results consistent with the AMH. Neely et

al (2009) report regularity in appearing and disappearing profit opportunities in the foreign

exchange market in the 1970's through early 1990's. Lo (2004) and Kim et al (2011) report

fluctuating levels of market efficiency over very long historical periods. In addition, quite a

number of studies report fluctuations and even reversals in well known stock market

anomalies (see, for example, Brusa et al 2005; Chong et al, 2005; Marquering et al, 2006 and

Moller and Zilca, 2008). Yet the AMH must presently be regarded more as a way of seeing,

rather than a fully specified alternative model to the EMH. In Section 5 we adopt that way of

seeing to re-evaluate some classic problems in Finance.

The following section of the paperThis paper outlines conceptual frameworks incorporating

both internal and external bounds. ItThe following section reviews the inclusion of internal

and external bounds separately in the modelling of bounded rationality, highlighting the

importance of taking the two sets of constraints upon effective decision-making into account

when modelling the limitations upon rationality. These conceptual frameworks allow direct

testing of the AMH using ‘bottom-up’ approaches.

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4. A more systematic and theoretically grounded basis for behavioural finance and its links

to AMH

In this section we examine how a more systematic and theoretically grounded basis for

behavioural finance can be developed. The internal and external bounds on financial

decision-making are examined in sub-section 4.1. Heuristics and adaptive behaviour are

examined in sub-section 4.2, followed by discussions of satisficing and investor

heterogeneity in sub-section 4.3. We show how each section implies testable hypotheses

allowing us to distinguish the predictive power of the AMH from the EMH using ‘bottom-up’

methods which is discussed in sub-section 4.4.

4.1. Internal and external bounds upon investment decisions

Both environmental context and cognitive boundaries are crucial factors when modelling

decision-making under uncertainty. There are two types of model of bounded rationality.

One type of model focuses on the external bounds on the investor. These models are based

upon investor optimisation under externally imposed constraints (Lee, 2011, p 514). Here

investors are assumed to be rational in terms of maximising some objective, whereas

satisficing is interpreted as non-optimal and simply accepting some, at least tolerable,

outcome given the external constraints. Following this external constraint approach, some

studies use an optimal search model framework, where investors do not possess full

information about the choices set they face. But Weitzman's (1979) allows investors to

invoke heuristics in deciding when to stop their search. Asymmetric information is often

invoked as investors are often not equally informed about the distribution and location of

the prices on offer.

The second type of model of investor satisficing is based on internal cognitive limitations

which tend to induce errors in the judgements of boundedly rational agents (Salop and

Stiglitz, 1977, Bekiros, 2010). The focus of this type of model is therefore on the internal

cognitive bounds upon the investor. Following this approach, such models imbue

consumers/investors with heterogeneous response functions recognising that, for example,

investor's cognition may be reduced through habitual behaviour such as dividing price

quotes into, high, medium and low bands. In this example, traders with low cognitive ability

can only form a small number of partitions (for example the single partition into low and

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high prices), whereas the more cognitively gifted traders are able to form a greater number

of partitions (say low, medium and high) and are thus able to more astutely assess the price

distribution.

Heiner (1983) portrays the origins of predictable investor behaviour as deriving from the

need of investors to assess the reliability of information before it is acted upon. Actions in

this view are only undertaken if they cross a threshold for the reliability of the information

eliciting the possible action. More frequently deployed actions are by their nature more

reliable, if only because their effect has been played out more. The reliability of information

is a function of both an investor’s perception and environment. For a given environment less

perceptive investors will be able to assess the reliability of a smaller range of actions,

because of their inability to recall them being used. This makes sense when we compare the

variety of behaviour we observe in humans compared to animals, or cats compared to

baboons. The more developed the mind the less repetitive and predictable the agent’s

behaviour. So in Heiner’s (1983) framework task uncertainty reduces the scope of actions

adopted by challenging investors’ to assess the reliability of possible actions. It is this very

contraction of the feasible action set, induced by the need to select a reliable action, that

produces predictability in observed investor behaviour. So when reliability of actions is an

issue uncertainty may be a boon for the social scientist/researcher, by rendering investor

behaviour predictable.

The above two models of bounded rationality present an incomplete picture of the notion of

bounded rationality since they only apply one type of bound. In a more complete version of

bounded rationality, both internal and external bounds intertwine in determining the

agents’ decision-making. When only one set of bounds is taken into account, bounded

rationality is seen as a barrier to an optimal solution whereas if the two sets of bounds are

taken into account, as Simon’s scissors metaphor shows, a richer concept of rational action is

born. This fit between internal and external bounds highlights the evolving nature of limits

upon investor rationality.

According to this holistic view of decision-making our environment contains both potential

problems and solutions. This is because environments are constructed by decision-makers’

behaviour and the emerging structure of our environment interacts with the heuristics each

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trader adopts in order to manage and remould that environment. Therefore, the structure of

the environment maps onto and is redrawn by to the structure of individuals' cognition. This

implies the following hypothesis:

Hypothesis 1: The structures for both internal cognition and the external environment within which it arises define rationality for investors within an evolving market order.

The inclusion of both sorts of bounds allows an evolved form of rationality to emerge

amongst investors. Successful investors respond to their changing external environment

adaptively and appropriately. This matching process of environment to its cognition is

undertaken by the construction and adaption of heuristics/mental-frames aimed at limiting

cognitive errors without incurring unacceptably high costs in the struggle. This hypothesis

can be tested using Artificial Intelligence and agent based computational approaches. In the

next section we discuss the cost-benefit calculus that underlies this evolutionary process.

4.2 Heuristic and adaptive behaviour

The use of decision-making heuristics and the construction of mental frames is perhaps the

most influential departure from standard expected utility theory, it plays an important

function in prospect theory (Kahnneman and Tversky, 1979, 1992), the theory of mental

accounting Thaler (1985, 1999) and asset pricing Bernartzi and Thaler (1995).

Early studies of heuristics were associated with error-prone intuitions or apparent

irrationality (Kehneman and Tversky (1992). In the literature, heuristics are also viewed

positively as a natural feature of decision choice requiring a combination of learning and

adaptation (Gigerenzer and Brighton, 2009). Heuristics that are not rational if cognitive

processing was limitless may be understood as a form of constrained rationality given our

true mental abilities.

Recent studies attempt to model heuristics using an adaptive tool box built on three decision

blocks of (a) searching, (b) stopping rules and (c) final decision criterion. These studies show

that employing specific heuristics in an adaptive way helps to make accurate, fast and frugal

decisions. The frugality derives from adapting least cost filters to screen out clearly inferior

options and settle upon good enough outcomes. In this view, heuristics are the internal

mechanisms that guide search and determine when it should end. Using heuristics in this

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adaptive way means that the two sources of bounds upon rationality, from internal and

external sources, nest neatly. So Gigerenzer et al (1999, p 13) state

“A heuristic is ecologically rational to the degree that it is adapted to the structure of the environment.”

Here “'ecological' is just another word for the occurrence of a rule-governed, self-organized

order” (Smith 2008, p. 6). Decision-makers can make good decisions, by using their mind to

best infer future value given by trading in their environment. If external bounds are fairly

immutable from the investors’ standpoint, then internal bounds can be evolved to take

advantage of the structure of the external environment. A relatively stable environment

combined with a constantly adapting investor understanding of that environment makes for

better decision-making.

Heuristic search involves identifying a search procedure requiring limited computational

capacity to render complex decisions solvable. This involves an “editing” of decisions to

leave a feasible choice set capable of fairly simple, rule-based, evaluation. So, for example,

we sort stocks into value or glamour, small or big, bins prior to final selection. Heuristic

rules/mental frames are used to manage the consequences of cognitive limitations and

especially so in situations where there are constraints in time, cost and skills or when

information is limited or ambiguous. There are two reasons for rationality to break down:

firstly, beyond a certain level of complexity our logic fails us. Secondly, in the cut and thrust

of market trading investors cannot rely on the rationality of other agents, so they are forced

to make guesses regarding the behaviour of others.

Consider the game of chess. On the first move each player can move any of their 8 pawns or

either of their 2 Knights. This implies 40 moves are available to play in total at the opening

move. This fact implies at the end of first move 400 board configurations are possible. On

the second move this expands to 71,582 possible board configurations (Shenk, 2006, p. 69).

So good Chess players, like great traders, must make fast and frugal decisions focussing

directly on likely evolutionary paths of play and resulting threats and opportunities. Similarly

Rubic’s cube has 43 trillion possible initial starting points (Heiner, 1983, p 563). So recalling

the best way to unscramble the cube from each of them is impossible. So good players

simply sequentially adopt a few basic unscrambling strategies pursuing them until they

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prove fruitless. While this involves far more manipulations of the cube than a

perfect/optimal strategy, for any given starting point, it is hugely superior to any random

search strategy. This results in subjective beliefs and the assumption of managed risks.

Given the possible outcomes of play are so numerous co-ordinating the editing of possible

outcomes, prior to decision-making, becomes impossible even if players/traders were to

attempt it. Hence investors are almost certain to adopt very different mental frames for

trading purposes and it is only the evolutionary process of trading itself that can remove the

most unhelpful frames and promote the power of the most successful frames. In this way

each investor “promote(s) an end which is no part of his intentions.” (Smith, 1776, p. 421).

This is achieved by unconsciously sifting and perfecting new, more appropriate, mental

schemata thrown into prominence by changing market conditions.

Adaptive agents use specific heuristics that seem appropriate to the changing environment.

In this context adaptive decision-making is rational. Successful heuristics rotate over time,

with no fool-proof trading rule being on offer. As Lo (2004) points out rationality in the

context of EMH and irrationality in the context of behavioural finance are two sides of the

same coin i.e. agents adjust and adapt to new information. This is why Lo favours replacing

the polarities of rationality and irrationality with a more nuanced concept of adaptive

behaviour in complex, evolving, markets. This is consistent with a definition of an evolved

rationality, where investors try out, adapt and reject heuristics as they assess new market

opportunities and withdraw from past loss-making positions. From the above the following

hypothesis is derived:

Hypothesis 2: Adaptive decision-making enhances alignment between the structures of internal cognition and the external environment.

Here the reliability of a strategy, and hence its practical use, depends on both the

decision-makers environment and their perception of it. Heiner (1983) reminds us that the

most sophisticated agent’s may seek to change their environment as well as improve their

ability to perceive it. Of course such possibilities are limited, which may explain the

extinction of many animals with poorer perceptual abilities than humans.

More appropriate/adaptive responses to the trading environment enhances the match

between internal and external bounds. This hypothesis accords with the fact that cognitive

25

limits are not necessarily a curse, but may rather be a blessing in guiding investors to fast,

frugal, yet profitable, trading decisions in a world where time is money and the sources of

money change rapidly. Again this is not just a theoretical hypothesis but one that can be

tested using the Artificial Intelligence and agent based computational approach by designing

agents with different decision-making processes and observing how they respond to their

environment.

4.3. Satisficing and heterogeneity

Satisficing, as opposed to optimising, might be thought of as the first fruit of bounded

rationality. Satisficing embeds the art of the possible in a constrained environment with

sparse information and tight deadlines on decision-making. Satisficing fulfils feasible

aspirations while accepting money will always be left on the table. Often we must choose

the least bad option. But investors’ ability to conceive of and rank options will differ. For

some investors a tech stock is a tech stock, but some prescient investors, like John Doerr of

Kleiner Peabody Caulfield Byers realise Amazon, Netscape and Google are just that little bit

special. It is from such refined mental processes that fortunes are made.

Satisfaction of investors’ desires is limited by their cognitive abilities and powers of self-

control and these will differ across investors. Investors differ in their psychological makeup

and cognitive brain function, as well as their risk-profiles and investment horizons. From

the above, the following hypothesis is derived:

Hypothesis 3: Optimising agents who utilise their best internal capacity to adapt to their observed environmental structure are satisficers.

Investors optimise their investment portfolios subject to their personal cognitive bounds and

ability to match their cognition to objective market conditions. Putting this interpretation of

satisficing into a financial context implies that investors differ in their trading strategies in

addition to setting different goals according to their skills and knowledge. This hypothesis

can be investigated/tested in experiments using agents with different levels of decision-

making capacity and empirical research is now following this path quite fruitfully.

From the above discussion we identify four main features of bounded rationality for

incorporation into a successful modelling framework for investment. These features, listed

below, will be discussed within this section and applied in Section 6.

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1) Investors’ cognitions are bounded in different ways and to different degrees.

2) The goal of a satisficer is to achieve an acceptable solution by using their current

cognition, taking the heterogeneity of other agents into account.

3) Through adapting appropriate heuristics, the investor's own cognitive bounds and the

bounds set by their trading environment are matched.

4) Agents’ satisfy their goals through adaptive behaviour and the adoption, recasting and

rejection of decision heuristics.

The interaction between investors as decision-makers within financial markets is illustrated

in Figure 1. Here markets are in the short run “voting machines” not weighing machines as

the famous value investor Benjamin Graham pointed out (Graham and Dodd, 1934, p 23).

What matters is not what a trader believes but what traders think others believe. The

market reflects the instability of Keynes' “beauty contest” which yields an unstable

equilibrium in investors' expectations. Expectations and decisions by investors shape market

outcomes, the resulting structure of the market order is then reflected in individual

investor’s assessments of the effectiveness of their chosen trading strategy. This drives

iterative rounds of adoption, adjustment and rejection of mental frames by investors. This

adaptive process can be both costly and slow as a new order tentatively emerges.

Figure 1 about here

4.4. Implementation of Artificial Intelligent Agents

As we discussed in section 3, in general, the adaptive behaviour of investors suggested by

the AMH can be tested by estimating a markov switching process to estimate the transition

probabilities between environmental states. This sub-section outlines how artificial

Intelligence models can be constructed in order to test the hypotheses of the prior

subsections.

In response to the highly centralised, top-down and deductive approach that characterises

the mainstream neoclassical economic approach, agent-based modelling using artificial

intelligent agents is decentralised, bottom-up, and inductive, within dynamic environments

27

Agent-based modelling was developed in the early 1980s and is now a rapidly growing

approach to modelling economic behaviour (Duffy, 2006). While looking at the advantages

and disadvantages of this computational approach to economic behaviour Duffy (2006),

shows that these models often provide a better fit to experimental data and he suggests the

possibility of combining an agent-based methodology productively with human

subjects/experimental research methods.

Agent-based models have the considerable advantage that controlled experiments can be

run with the attributes of both the population of agents and market being capable of being

adjusted to see the effects on the resulting market dynamics (see, for example, Cincotti et

al., 2003; Yeh and Yang, 2010; Tseng et al., 2010)). Thus it is possible to directly test

hypotheses such as those developed in the three subsections above. An interesting sub set of

this work is that looking at ‘zero-intelligence’ agents who have no trading skill at all to see

how they fare in markets sometime in interactions with more skilled or faster learning

agents (see, Yeh, 2008). In these exercises the roles of skill and chance can be explicitly

investigated in a way that is very difficult in the traditional paradigm.

Machine learning refers to systems capable of independent learning; that is integrating and

acquiring knowledge. Researchers in artificial intelligence are actively engaged in developing

learning algorithms applicable in a real world complex system context. Modelling a financial

market is an ideal application for this field because financial markets are huge and complex.

Investors’ behaviour is complex and conflicting and so far standard linear asset pricing

models have struggled to describe it adequately. Therefore, the methodology and modelling

that allows agents to learn and adapt may become a crucial part of modelling financial

market behaviour.

Reinforcement Learning is a sub-field of machine learning and now commonly used in

artificial intelligence originated from psychology. Investor behaviour is heuristically

determined and depends on agents’ cognitive ability to learn and adapt, therefore RL

methodology appears to provide an ideal approach to model agents’ learning, experience,

intuition, knowledge and beliefs.

RL provides a formal framework for defining the interactions between goal-directed learning

agents within a dynamic environment in terms of states, actions and reward (Sutton and

28

Barto, 1998). Sutton and Barto (1998, p.3) describe RL as learning how to map actions into

the environment in order to maximise reward. They continue their definition as follow: “The

learner is not told which action to take, as in most forms of machine learning, but instead

must discover which actions yield the most reward by trying them out. In the most

interesting and challenging cases, actions may affect not only the immediate reward faced

but also the next situation encountered and, through that, all subsequent rewards. These

two characteristics-trial and error search and delayed reward- are the most important

distinguishing features of RL”.

Moreover, the dynamics of RL can be reduced to four main elements: a policy, a reward

function, a value function, and a model of the environment. An agent searches for an

optimal policy which maximises total reward. The agent is continuously analysing the

consequences of their actions through trial and error interactions with the environment. In

more recent RL frameworks agents simultaneously learn by trial and error a model of the

environment and, the use of that model for planning (Sutton and Barto, 1998).

RL consists of a set of environmental states, S, a set of agent actions, A, and a scalar

reward/reinforcement function, R, mapping environments and actions into pay-offs. The

environment is typically modelled as a finite-state Markov decision process (MDP), this make

RL algorithms highly related to dynamic programming techniques.

Greif and Laitlin (2004) show reinforced learning within institutions can make the difference

between continued survival and collapse. Comparing the dominant post-war socialist parties

in Italy and Sweden and the medieval cities of Genoa and Venice they trace how an inability

to learn to adapt to a radically changing environment lead to the demise of the Italian left

and the Genoese podesteria. These authors show how reinforcement learning can be built

into a simple repeated-game structure. They conclude, after considering a variety of case-

studies of institutional reform, that while initially institutions reinforce their structure over

time exogenous shocks tend to undermine this trend by posing an existential threat to the

increasingly atrophied institution. According to a survey done by Kaelbling et al. (1996) RL

has been successfully applied to complex problem-solving and decision making.

RL can be applied for the purpose of getting an optimal policy that resolves control problems

as well as prediction problem. Different versions of RL have been used in the context of

29

financial markets. For example, Lee (2001) adopts a RL algorithm which learns only from

past data for stock market prediction using the Korean stock market. He finds RL provides a

more useful indicator than conventional indicators of stock trading and he recommends

extending this approach by applying other RL algorithms where the definition of reward on

offer is slightly different from that in his paper.

An adaptive fuzzy RL approach, which combines the adaptive action selection policy of RL

and fuzzy logic, is employed by Bekiros (2010). In this approach agents beliefs are

represented by fuzzy inference rules to advance the literature on heterogeneous agents

based market. He finds that the predictive ability of the adaptive fuzzy RL is significantly

higher than other competing models.

5. EMH or AMH? - Some applications.

In this section we try to illustrate how a shift to viewing financial markets through the lens of

the AMH, as opposed to the traditional EMH, might aid our understanding and improve re-

search methods in finance. The greatest effect of such a shift would be to broaden the type

of questions that can legitimately be asked in finance, a prospect we discuss in our conclu-

sion. However, an AMH focus would illuminate many existing issues and the specific ways

they are conceptualised and addressed.

To illustrate the AMH approach we discuss three specific and important issues out of many

in the existing literature that can be illuminated by the AMH approach. We revisit these

standard research questions through the lens of our three proposed research hypotheses

developed above. We ask how those hypotheses might be used to enhance current model

building and financial decision-making. We later show the AMH helps us to understand State

intervention in financial markets not as a constraint on the operation of those markets but

as part of the evolution of financial institutions’ market power into full expression in the

political arena.

30

5.1 Three examples of applying the AMHThe first example we deal with concerns the random walk. Perhaps the most basic implica-

tion of the EMH is that market prices follow a random walk. Tomorrow’s price will only differ

from today’s price in random unpredictable ways. As stated above, an array of empirical

studies have rejected this claim in favour of under-reaction/momentum at relatively short in-

tervals of a year or so and longer-term correction/reversals implying earlier market overre-

action. Such refutations have emerged as a set of “anomalies” which form much of the grist

to the current mill of behavioural finance studies.

i) Momentum

Recently Barberis, Sheifer and Vishny (1998) have advanced a theoretical model in which

earnings (and hence stock values) always follow a random-walk, as the EMH implies, but in-

vestors perceive them to either revert to trend or exhibit momentum with some predeter-

mined probability. The transition between an investor focus upon momentum and reversion

regimes with a corresponding effect on prices, in clear denial of the random-walk property

of observed prices, is an obvious anomaly/irrationality within the EMH framework. These

agents fail to comply with the requirement of Hypothesis 3 above in matching their percep-

tions of value creation to the observed environment; thus they fail the test hypothesis 3

poses for boundededly rational agents. The distinction here, as in Hypothesis 1 above, is

between the investment strategy and its cognitive and external context, as opposed to a pro-

posed statistical property, which is conjectured to prevail regardless of context or cognition.

The AMH cautions us that it is not primarily value characteristics themselves that drive price

movements but rather the mental frames and heuristics through which value is perceived.

Hence the irrationality of investors, as suggested by the EMH, in the Barberis et al (1998)

framework is simply their adaptability, or evolving rationality, to those adopting the AMH

lens on the same phenomena.

The success of momentum based trading strategies is known to be a function of the

extent of informational asymmetry prevailing in the financial market (Easley et al, 2002),

suggesting the need to adapt trading strategies according to the environment into which

they are deployed. Neither momentum nor reversion/correction based strategies are domin-

ant universally but rather are revealed to be so by deployment into a favourable trading en-

vironment. So either the transition probability, underpinning the markov-switching process

31

between regimes in the Barberis Shleifer and Vishy model tracks changes in the regime

probabilities (as investors flip between momentum/reversion “routines”) or the choices

made by competing autonomous agents are allowed to aggregate to determine overall mar-

ket asset demand and supply schedules.

Seru et al (2010) show market learning consists of two types of correction. Firstly in-

vestors discover their true market ability in the process of trading. Secondly those who real-

ise they have little aptitude for trading, having been punished with losses, exit the market in

order to passively hold a diversified portfolio. Those who so exit may be largely individual in-

vestors whom Hur et al (2010) identify as being particularly associated with the presence of

profits to following momentum based strategies.

ii) The disposition effect

It is now well established that investors seem to sell winners (stock that have had recent

rises in price) too quickly and hold losers (stocks with recent price declines) too long. In any

market with transaction costs longer trades are, on average, more profitable trades. The

costs of trading opens up a range of prices between which arbitraging differences in prices of

assets of equivalent value makes no sense (Constantinides, 1983). In their seminal study

Schlarbaum et al (1978) document this fact. Returns to short holding periods are highest

amongst their sample of individual investors, although surprisingly good more generally. So

why do the returns of individual investors decline in the holding period over which they

trade?

Shrefrin and Statman (1985) argue that this occurs because such investors have a disposition

to sell winning stocks, whose price has just risen, too quickly and hold on to losing stocks,

whose price has just fallen, too slowly. They do this to avoid the “regret” of realising paper

losses or missing out on easily envisaged gains. This pattern of trading is all the more difficult

to understand given tax incentives to sell stocks whose value has fallen to enable the real-

ised loss to be used as a tax shelterv (Constantinides, 1984) . This is because the tax rate

levy/relief on capital gains/losses are equal so it makes sense to realise losses in order to

help harvest longer-term post-tax returns on your winning stocks. It appears individual in-

vestors at least do not seem to trade to maximize risk-adjusted returns, leaving tax subsidies

from the government on the table. In contradiction of hypothesis 3 they fail to match their

32

cognitive frame to the tax environment they find themselves in, suggesting they are not ra-

tional in this bounded sense.

What about more sophisticated traders, such as hedge funds and investment banks? Hur et

al (2010) confirm that the presence of both momentum and the disposition effect is directly

proportional to the percentage of individual traders present in each market segment. As

with momentum strategies the intensity of a heuristic and its explanatory power varies with

the environment into which it is deployed. While institutional investor dominated markets

offer little scope for arbitraging the disposition effect more “noisy” markets, in which indi-

vidual investors have a greater share of trades, offer greater opportunities for counter-dis-

position effect arbitrage profits to be reaped. Hence the disposition effect flourishes in indi-

vidual investor dominated markets but is arbitraged away elsewhere.

Yet it is not only the traders’ characteristics that determine the intensity of the observed dis-

position effect. Kumar (2009) shows firstly that the intensity of the disposition effect is

strongly related to various proxies for the uncertainty involved in valuing the stock and that

there is also evidence informed traders recognize such pockets of “hard to value” stocks and

that they proactively trade to arbitrage out such mispricing. Using standard metrics of the in-

tensity of informed tradingvi Kumar (2009) reports evidence that informed traders actively

predate upon the relatively uniformed who misguidedly trade in hard to value stocks. This

evidence brings us closer to direct observation of the elimination of noise-traders in a pro-

cess of Darwinian competition to implement profitable investment strategies.

iii) corporate governance and organisational form.

A tragic example of this process of a mismatch of cognition within a changing environment is

the way in which British Petroleum’s “upstream” exploration and extraction unit, BPX, was

focussed, by its then Head John Browne, on identifying very large hydrocarbon deposits of

oil and natural gas; so called “elephants” in the industry (Roberts, 2007). In the 90’s BP had

lost ground to smaller, niche player, independent explorers/extractors. So Browne cut com-

peting “asset managers” loose within BPX to compete as they saw fit. This replaced central-

ised control with information-sharing amongst “peer group” autonomous asset managers

within BPX (Roberts, 2007, p 25-27). The Deepwater Horizon rig explosion (in the Gulf of

Mexico on April 20th 2010) and the subsequent inability of BP to cap the underground well

33

until mid September that year exposed the dangers of such intense internal rivalry. Safety

thresholds were pushed to breaking point under such intense pressure. Following the spill

survival required hyper-vigilence with regard to rig safety as politicians made clear their will-

ingness, if not eagerness, to react to public fury. Hence corporate history matters, especially

in times of economic crisis by determining the nature and scope of corporate routines avail-

able to enable survival.

This “path dependence”, deriving from the effect of history of the array of corporate

routines is most starkly illustrated by the claim that shareholder rights are better protected

in common law jurisdictions (largely the UK and its former colonies, US, India, Malaysia, etc)

than in civil law ones (France, Germany or their former colonies) (La Porta, et al, 1998,

2000). Yet, as other others have pointed out in critiquing the “path dependency” story of

national shareholder protection arrangements, the civil law retains much of the plasticity the

common law offers by differential enforcement of a unified set of codified company laws

(Pistor et al, 2002, Pistor and Xu, 2003). The AMH allows us to understand both the “path

dependency” of governance arrangements and how legal enforcement of shareholder’s

rights operates as an evolutionary selection strategy within seemingly divergent governance

paths.

The examples given above have been drawn directly from the existing finance and

accounting literature and the insights gained can be fitted reasonably easily into the

standard methodological approach of creating analytically tractable models. However, the

traditional analytical methods are very limited in their scope to model interactions between

large numbers of heterogeneous agents when decision systems evolve though time. In so

far as papers in the literature deal with these issues, most either use either single

representative agent models with non-constant belief systems (see, for example, Barberis

and Huang, 2008) or models with two types of agent, one rational and the other non-

rational (see the literature on noise trader models, for example, De Long et al , 1990).

Models with much more complexity than this quickly become intractable analytically. It is

possible, however, to use computer simulations to directly model the interactions of large

numbers of heterogeneous agents with varying degrees of intelligence. This use of

intelligent agent models is a very fast growing field (see, the special issue of the Journal of

34

Economic Surveys in 2011 devoted to this and closely related topics) but is currently

largelyalmost totally disconnected from mainstream academic finance. Work of this nature

is oftenlargely done by computer scientists, mathematicians and physicists and receives very

little almost no coverage in the most prestigiousmajor mainstream finance journals. Given

that the assumptions adopted in this field are evidently a much closer depiction of reality

this field of research looks very promising and its dismissal by the mainstream all the more

troubling.

5.2 The AMH and financial reformThe AMH allows us to see the relationship between the markets and the State in a new, per -

haps more helpful, way. A keystone of the EMH is the belief that markets know best and the

State can at best only re-allocate wealth produced by the gains from the trade that charac-

terise free-markets. The recent financial crisis makes a mockery of such claims. In reality the

primary role of the TARP, UK bailout of RBS, EU bailout of Dexia, Bankia, etc, has been to re-

capitalise bankrupt private sector institutions at the tax-payers expense, in a “socialism for

the rich” parody of the welfare state. In reality two parallel, and often complementary, mar-

kets explain these facts. The market for votes is itself as much central to effective financial

hegemony as any financial market exchange. Increasingly political and financial power has

been fused in a way that makes the prudential role of banking authorities much in doubt

(Johnson and Kwak, 2010). The central figures in the management of the financial crisis,

Hank Paulson, Timothy Geithner and Larry Summers all had previous lives in investment

banking or headed straight to such an institution slightly after their tenure in office. The

close integration between the State and financial power suggests a symbiotic, and perhaps

even mutually parasitic, existence. The AMH recognises the need of investment institutions

to adapt to pressures from vote markets within democratic polities. Political preferment is

thus just another survival/advancement strategy of those using vote markets to buttress

their position within allegedly “efficient” financial markets. State intervention is not neces-

sarily a constraint on market forces but rather one further manifestation of market forces

power.

5.3 The AMH and nature of Darwinian economic competition.

35

The AMH with its acknowledgement of evolutionary behaviour makes clear some of

the problems of equating competition with automatic improvements in social welfare. Much

economic competition is truly Darwinian in that it is being ranked first as an individual, not

increasing the overall social product, that counts. Frank (2011, page 7) states the problem

thus

“Charles Darwin was one of the first to perceive the problem clearly. One of his cent -

ral insights was that natural selection favours traits and behaviours primarily according to

their effect on individual organisms not larger groups.”

A tension arises between the interest of individuals and the broader social group because

natural selection implies traits which confer individual fitness thrive regardless of their con-

sequence for the broader species. Frank (2011, p12) gives the example of drug cheats in

the world of professional sports. All cyclists may agree that a “clean” race is the only race

that counts. But each may feel forced to use drugs in a World where “everyone is doing it”.

The need to stretch to reach the highest possible rank means we may achieve the worst of

all possible worlds, with no worthy Champions to hail, rather than the best.

The proposed financial transaction/Tobin tax (FTT) 0.1% tax on all bond and equity

transactions now adopted by ten EU member states might be seen as a helpful attempt to

bring about a community-wide benefit in the face of individual nation states incentives to

act as a home to “flight capital”. While access to such capital clearly benefits whichever State

in which it chooses to domicile the need to appease it may serve to reduce community-wide

wealth and social cohesion. Such a “public good” might be seen as sufficient justification for

State intervention to dampen speculative trades. Indeed the costs of an individual State ad-

opting a FTT is already well known from Swedish experiments in the 1990’s. So it is clear any

benefits from the FTT can only be reaped by transnational co-operation rather than competi-

tion. An evolutionary perspective helps us understand that the problem is not that financial

markets are insufficiently competitive, but rather that the intensity of competition we ob-

serve is damaging to broader social objectives of stability. Hence the State has a legitimate

role in constraining individual competitive effort which damages broader social policy

This symbiotic relationship between the State and the market has been known at

least since Karl Polyani, explained how the “laissez-faire” system of free-market capitalism

was planned by England’s political ruling elite. In a similar way today intense and resource in-

tensive “rent seeking” is deployed by the wealthy to divert resources from the middle-class

36

to themselves (Stiglitz, 2010). The predatory lending practices and credit card services mar-

keting of financial institutions are examples of legal, but clearly immoral, transfers from the

poor to the wealthy. But in this the banks follow a well recognised path in procurement,

privatisation and regulatory practice which facilitates State mandated transfers from the

middle–class to a wealthy elite. The impact of such transfers is already very clear in the data.

In 2007, just before the Crisis hit America, the top 0.1% of American households had an in-

come 220 times higher than the average for the remaining 90% of American households

(Stiglitz, 2012, p 2)

More recently Rajan (2010) has traced the roots of the sub-prime crisis to the in-

creasing impoverishment of the uneducated and unskilled, often black, citizenry in the US.

From 2001 onwards economic recovery, even when it came, often was of the jobless recov-

ery form, where new jobs went to the young, better educated, more flexible elements of the

workforce leaving behind a surplus to requirement residue. The absence of any long-term

social welfare provision in the US means such victims of past restructuring enter a lower

paid, more transient, workforce more exposed to the vagaries of the market.

The obvious way of alleviating the most pressing needs of the swollen ranks of the

working poor, short of State provision, was easier access to credit. While this stoked up

problems for the future it did at least ameliorate the anxiety of current poverty. Rajan (2010,

p 35) finds no shortage of evidence that US government policy promoted the indebtedness

of the working poor. For example the Federal Housing Enterprise and Housing Enterprise

Safety and Soundness Act of 1992 instructed the Department of Housing and Urban Devel-

opment (HUD) to develop affordable housing goals specifically targeting ethic minorities and

those living in zip/post code areas rarely attracting mortgages. This initial encouragement to

the development of the sub-prime lending market was reinforced later by President George

W. Bush as part of his vision of expanding the American dream via a homeowners’ demo-

cracy.

Recently Ferguson (2012) has diagnosed Western capitalism as entering an era of a

“great degeneration” as market participants are driven to use rent-seeking to derive benefits

from the State over claims to a constant, or even declining, national product. This descent

into “stationary state” allows the rule of law to be replaced by the rule of lawyers as distribu-

tional conflicts dominate attempts to create a more efficient, productive, society. Within the

modern “beehive” of financial networks extreme complexity allows small perturbations

37

within the system to swiftly gather pace with disastrous consequences. While such network

externalities are now being addressed by finance theorists (Easley and Kleinberg, 2010) and

implemented tentatively by regulatory authorities (Gai et al, 2011) they remain irreconcil-

able with the standard finance theory of price-taking trades amongst anonymous atomistic

agents.

Financial institutions are the embodiment of the “rules of the game” of financial

markets (Heiner, 1983) and it is these very rules which have now clearly broken down. So

much of the post crisis challenge lies in appropriate post-crisis institutional reform. While

many commentators portray almost any institutional reform as an attempt to traduce mar-

ket allocations this ignores the role of financial institutions in maintaining the integrity of fin-

ancial markets and hence securing gains from trade. A competitive strategy which gives an

individual an edge ahead may nudge the society he lives in backwards. If free-market order

takes much planning, as Polansky suggests (1944), it is financial institutions which must

both bring a free market into existence and maintain its integrity once it exists. As Greif and

Laitlin (2004) show such institutions are limited in their capabilities of internal renewal and

so likely to being overwhelmed by historical events and hence collapse. This seems to have

been the fate of many of the existing regulatory authorities discredited by the crisis (the UK’s

FSA being perhaps the most obvious victim). Such bodies are unable to survive the compet-

itive process due to an inability to adapt to the greatly intensified rent-seeking behaviour the

financial crisis brought.

6. Conclusion

In the light of recent events which have exposed the shortcomings of the current financial

paradigm this paper discusses the implications of moving to an approach based on the AMH.

The AMH is much less theoretically restrictive than the existing paradigm as it does not

assume that market participants uniformly act in accordance with the rationality axioms of

neo-classical economics. Instead more realistic notions of bounded rationality are totally

consistent with the AMH approach.

This paperWe presents the main features of bounded rationality suggesting three testable

hypotheses to determine the degree to which observed trading behaviour conforms to the

tenets of bounded rationality. This reflects an evolutionary concept of bounded rationality

38

that seems more psychologically plausible than that commonly invoked by Finance

researchers in its EMH form.

As we have seen in the previous section the move to this approach would enable many

specific issues in the discipline to be addressed in a more constructive and meaningful way.

Many empirically established facts would cease to be troublesome anomalies to be ignored

or marginalised but phenomena to be integrated into the mainstream of the subject.

Yet it is in forming policy regarding financial markets that the transition from an EMH to an

AMH paradigm may yet have it biggest impact. Possibly the greatest effect might be at a

conceptual level by making acceptable discussion about and research into issues that would

previously have been regarded as resolved. It is clear that more questioning of this nature

might well have helped to avoid or alleviate the present crisis.

The AMH is still a very tentative and untested competitor to its EMH rival. But we have

shown some reasons to be excited about its sphere of application both in theory testing and

policy discussion. The evolving financial crisis may provide the context for such alternatives

to the prevailing orthodoxy to at last be taken seriously.

39

References:

Akerlof, G, Shiller, R.Animal Spirits: How human psychology drives the economy and why it matters for global capitalism:Princeton University Press New Jersey, USA: 2009.

Armstrong, M, Vickers, J Zhou, J. Prominence and Consumer Search. RAND Journal of Economics2009:209-233.

Arrow, K.J. Rationality of Self and Others in an Economic System. The Journal of Business. 1986: Vol. 59, No. 4: 385-399.

Arthur, W. Inductive Reasoning and Bounded Rationality. The American Economic Review, 1994 No. 2 (Papers and Proceedings of the Hundred and Sixth Annual Meeting of the American Economic Association 1994:(May) 406-411.

Axelrod, R.The evolution of cooperation: Basic Books Cambridge, Massachussetts, USA: 1984.

Barberis, N, Shleifer, A,Vishny, R.A Model of Investor sentiment, Journal of Financial Economics 1998: 49(December) 307-343.

Basov, S. Bounded rationality: static versus dynamic approaches. Economic Theory 2005:25(4), 871-886. Available at: http://works.bepress.com/suren_basov/3.

Bekiros, S. Heterogeneous trading strategies with adaptive fuzzy Actor-Critic reinforcement learning: A behavioral approach. Journal of Economic Dynamics and Control2010:34(6) 1153-1170.

Benartzi, S, Thaler, R.Myopic loss aversion and the equity premium puzzle. The Quarterly Journal of Economics1995: 110, 73-92.

Bernard, V. and Thomas, J. Evidence that stock prices do not fully reflect the implications of current earnings for future earnings. Journal of Accounting and Economics:1990 13: 305-340.

British Academy, Open letter to Her Majesty the Queen. 2009: http://www.britac.ac.uk/events/archive/forum-economy.cfm

Brock, W,Hommes, C.A rational route to randomness. Econometrica: Journal of the Econometric Society1997:651059-1095.

Brown, A, Chua, Z,Camerer, C. Learning and visceral temptation in dynamic saving experiments.Quarterly Journal of Economics2009: 124(1) 197-232.

Brusa, J, Pu, L, Schulman, C. Weekend Effect, ‘Reverse’ Weekend Effect, and Investor Trading Activities.Journal of Business Finance &Accounting2005: 32(7/8) 1495-1517.

40

Chang, E, Lewellen, W.Market Timing and Mutual Fund Performance. Journal of Business1984: 57 57-72.

Cincotti, S. ,Focardi S. M., Marchesi M., Raberto M., Who wins? Study of long-run trader survival in an artificial stock market. Physica A 2003: 324 1–2 227–233.

Chong, R, Hudson, R, Keasey, K, Littler, K.Pre-Holiday Effects: International Evidence on the Decline and Reversal of a Stock Market Anomaly. Journal of International Money and Finance 2005: 24 1226-1236.

Clarkeson, R. Actuarial insights into the global banking catastrophe. Journal of Financial Regulation and Compliance 2009:17(4) 381-397.

Cootner, P. (Ed) The Random Character of Stock Market Prices. Cambridge MA: MIT Press, 1964, 297-337.Critical Perspectives on International Business, Special Issue on the Financial Crisis 2009: 5(1/2).

Critical Perspectives on International Business, Special Issue on the Financial Crisis 2009: 5(1/2).

Daniel, K, Hirshleifer, D,Teoh, S. Investor psychology in capital markets: Evidence and policy implications, Journal of Monetary Economics2002: 49(1) 139-209.

Daniel, K, Hirshleifer, D, Subrahmanyam, A. Investor psychology and security market under-and overreactions. Journal of Finance1998: 53(6) 1839-1885.

De Bondt, W, Thaler, R. Does the stock market overreact? Journal of Finance1985:40(3) 793--805.

De Bondt, W,Thaler, R. Further evidence on investor overreaction and stock market seasonality. Journal of Finance1987:42(3), 557-581.

De Bondt, W. A portrait of the individual Investor, European Economic Review 1998:42 831-844.

De Bondt, W. The value and beliefs of European investors. In Knorr Cetina, K, Preda A., eds. The Sociology of Financial Markets, Oxford University Press: 2005.

De Long, B., Shleifer, A. Summers L. Waldman R. Noise trader risk in financial markets. Jour-nal of Political Economy, 1990: 98(4): 703-738.

Easley, D., S. Hvidkjaer, O’Hara, M. Is information risk a determinant of asset returns? Journal of Finance 2002:57(5): 2185-2221.

Easley, D. and J. Kleinberg. Networks, Crowds and Markets: Reasoning about a highly connected world. New York, USA, Cambridge University Press. 2012

41

Fama, E. Random Walks in Stock Market Prices. Financial Analysts Journal. Sept-Oct 1965. 55-59.Fama, E. Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 1970:25(2) 383–417.

Fama, E.Market efficiency, long term returns and behavioral finance. The Journal of Financial Economics 1998:49 283-306.

Ferguson, N. The Great Degeneration: How institutions decay and economies die. London, England, Allen Lane. 2012.

Findlay, M, Williams, E. A Post Keynesian View of Modern Financial Economics: In Search of Alternative Paradigms, Journal of Business Finance and Accounting1985:12(1) Spring.

Fox, J.The Myth of the Rational Market:Harriman House. Petersfield, UK: 2010.

Frank, R., The Darwin Economy: Liberty competition and the public good. Princeton, Princeton University Press. New Jersey, USA, 2011

Gai, P., Haldane, A. Kapedia, S. Complexity, concentration and contagion. Journal of Monetary Economics 58:July 453-470. 2011

Gabaix, X,Laibson, D. A boundedly rational decision algorithm. The American Economic Review2000:90(2) 433-438.

Gigerenzer, G, Brighton, H. Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science2009:1(1) 107-143.

Gigerenzer, G, Gaissmaier, W. Heuristic Decision Making. Annual Review of Psychology, 2011:62 451–482.

Gigerenzer, G, Goldstein, D. Reasoning the fast and frugal way: models of bounded rationality. Psychological Review 1996:103(4), pp. 650-669.

Gigerenzer, G, Selten, R. Bounded Rationality: The Adaptive Toolbox: MA MIT PressCambridge : 2001.

Gigerenzer, G, Todd, P, Group, A. Simple Heuristics That Make Us Smart: Evolution and Cognition Series Oxford University Press: 1999.

Graham, B, Dodd, D. Security Analysis: McGraw-Hill New York, USA: 1937.

Greenspan, A. Testimony to House of Representatives Committee on Government Oversight and Reform Oct 23: 2008.

Greif, A. and D. Laitlin, A Theory of Endogenous political change. American Political Science Review 2004:98(4): 633-652.

42

Heiner, R. The Origins of predictable behavior. American Economic Review 73(4): 560-595. 1983.

Hao, S, Jin, Q, Zhang, G. Investment Growth and the relation between equity value, earnings and equity book value. Accounting Review2011:86(2) 605-635.

Hommes, C. Financial markets as nonlinear adaptive evolutionary systems. Quantitative Finance2001:1(1) 149-167.

Hudson, R,Keasey, K, Littler, K, Dempsey, D. Time diversification: an essay on the need to revisit finance theory. Critical Perspectives on Accounting 1999: 10501–19.

Hudson, R, Maioli, S. A response to a ‘Reflections on a global financial crisis’. Critical Perspectives on International Business 2010:6, 1 53-71.

Hulbert, M.Timing and selection within the bond market: A look at the record, American Association of Individual Investors(AAII) Journal, 1996:(November).

Hur, J., M. Pritamani, et al. Momentum and the disposition effect: The role of individual investors. Financial Management 2010:39(3): 1155-1175.

Journal of Economic Surveys, Special Issue on Nonlinearity, Randomness and Complexity, 2011:25(3).

Journal of Financial Regulation and Compliance, 2009, Special Issue on the Financial Crisis, Vol 17, Issue 4.

Johnson, S, Kwak, J.13 Bankers: The Wall Street takeover and the next financial meltdown: Pantheon Books, New York, USA: 2010.

Kahneman, D, Slovic, P,Tversky, A. Judgment under uncertainty: Heuristics and biases:Cambridge University Press: 1982.

Kahneman, D, Tversky, A. Prospect theory: An analysis of decision under risk. Econometrica1979:47 263–291.

Kay, J. Review of UK equity markets and long-term decision-making. London, England, De-partment of Business Innovation and Science. 2012.

Keasey, K, Hudson, R. Finance theory a house without windows. Critical Perspectives on Accounting2007:18 932-951.

Kendall, M. The Analysis of Economic Time Series – Part I: Prices. Journal of the Royal Statistical Society, Series A (General) 116, no. 1 11-34.

43

Kim, J,Shamsuddin, A, Lim K. Stock return predictability and the adaptive markets hypothesis: Evidence from century-long U.S. data. Journal of Empirical Finance 2011: forthcoming.

Khandani, A, Lo, A. What happened to the quants in August 2007? Journal of Investment Management2007:5 5-54.

Krugman, P.How Did Economists Get It So Wrong? The New York Times, September 2nd, 2009: http://www.nytimes.com/2009/09/06/magazine/06Economict.html?pagewanted=1&_r=2.

Kuhn, T. The Structure of Scientific Revolutions. 1st. ed., Chicago: Univ. of Chicago Press: 1962.

Kumar, A. Hard to value stocks, behavioural biases and informed trading. Journal of Finan-cial and Quantitative Analysis 2009:44(6): 1375-1401.

Lee, C. Bounded Rationality and The Emergence of Simplicity Amidst Complexity. Journal of Economic Surveys2011: 25507-526.

Lo, A. The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management,2004: 30th Anniversary Issue 15-29.

Lo, A. Reconciling efficient markets with behavioural finance: the adaptive markets hypothesis. Journal of Investment Consulting2005: 7 (2) 21-44.

Lo, A. Regulatory reform in the wake of the financial crisis of 2007-2008. Journal of Financial Economic Policy2009:1(1) 4-43.

Lo, A, Hasanhodzic, J. The evolution of technical analysis: financial prediction from Babylonian tablets to Bloomberg terminals: Bloomberg Press, Hoboken, New Jersey, USA: 2010.

Lo, A, Fear, Greed and financial crises: A cognitive neurosciences perspective, MIT Working Paper, https://mitsloan.mit.edu/finance/pdf/Lo-20120109d.pdf, 2011.

Mandeville, B. The Fable of the Bees: Or private vices and public virtues. London, England, Penquin Books. 1714/1970.

Mandelbrot, B. The Variation of Certain Speculative Prices. The Journal of Business 1963 36 (4) 394-419

Mallard, G. Modelling cognitively bounded rationality: an evaluative taxonomy. Journal of Economic Surveys,2011: forthcoming.

44

Marquering, W, Nisser, J, Valla, T. Disappearing anomalies: a dynamic analysis of the persistence of anomalies, Applied Financial Economics 2006: 16(4) 291-302.

McLean, B, Nocera, J. All the devils are here: The hidden history of the financial crisis: Portfolio-Penguin, London, England: 2010.

Mehra, R, Prescott, E. The equity premium: A puzzle. Journal of Monetary Economics1985:15(2), 145-161.

Mehra, R, Prescott, E. The equity premium: ABC’s. Handbook of the Equity Risk-Premium:North Holland, Elsevier, Amsterdam, The Netherlands: 2008.

Merton, R. On Market Timing and Investment Performance, I. An Equilibrium Theory of the Value for Market Forecasts. Journal of Business1981:54 263-290.

Minsky, H. Stabilising an unstable economy: McGraw Hill Professional New York USA: 1986.

Mlodinow, L. The drunkard's walk: How randomness rules our lives: First Vintage Books New York, USA:, 2009

Moller, N, Zilca, S. The evolution of the January effect. Journal of Banking & Finance2008:32 (3), 447-457.

Neely, C, Weller, P, Ulrich, J. The Adaptive Markets Hypothesis: Evidence from the Foreign Exchange Market. Journal of Financial and Quantitative Analysis2009:44 467-488.

Newell, A,Simon, H. Human Problem Solving: Englewood Cliffs, NJ: Prentice-Hall: 1972.

Polyani, K. The great transformation: The political and economic origins of our time Beacon Press. Boston Massachusetts, USA 1944.

Rajan, R., Fault lines: How Hidden fractures still threaten the world economy. Princeton, New Jersey, USA. Princeton University Press. 2010

Rogers, R,Sahakian, B, Hodges, J, Polkey, C, Kennard C, Robbins,T. Dissociating executive mechanisms of tasks control following frontal lobe damage and Parkinson's disease. Brain.1998:121 815-842.

Rubinstein, A.On Price Recognition and Computational Complexity in a Monopolistic. Journal of Political Economy1993:39 83-96.

Rubinstein, A.On price recognition and computational complexity in a monopolistic model.Journal of Political Economy, 1993:101(3) 473-484.

Salop, S, Stiglitz, J.Bargains and Ripoffs: A Model of Monopolistically Competitive Price Dispersion. The Review of Economic Studies1977:44 493-510.

45

Samuelson, P. Economics. 1948. New York. McGraw-Hill.

Samuelson, P. Proof that Properly Anticipated Prices Fluctuate Randomly. Industrial Management Review 1965 6:2 41-49.Sapra, S, Zak, P.Neurofinance: Bridging Psychology, Neurology, and Investor Behavior. 2008:Working Paper Series, Issue Available at SSRN: http://ssrn.com/abstract=1323051.

Shenk, D.The immortal game:Anchor Books, New York, USA: 2006.

Seru, A., T. Shumway, Stoffman N. Learning by Trading. Review of Financial Studies 2010:(23): 705-739.

Shiller, R.Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends?American Economic Review 1981: 71(3): 421–436.

Shiller, R.Stock Prices and Social Dynamics.Brookings Papers on Economic Activity, 1984:(2) 457-510.

Shiller, R.Irrational Exuberance.2nd Edition: Princeton University Press, New Jersey, USA: 2000.

Shleifer, A, and Summers, L.The Noise Trader Approach to Finance.Journal of Economic Perspectives 1990:4(2) 19-33.

Simon, H.A behavioral model of rational choice.The Quarterly Journal of Economics, 1955:1 99-118.

Simon, H. Studying human intelligence by creating artificial intelligence.American Scientist1981:69(3) 300-309.

Simon, H.A mechanism for social selection and successful altruism.Science, 1990:250 1665-1668.

Simon, H.Invariants of human behavior. Annual Review of Psychology, 1990:41(1), 1-20.

Sorkin, R.Too big to fail:Penguin, London, England: 2009.

Smets, F, Wouters, R. An estimated dynamic stochastic general equilibrium model of the euro area.Journal of the European Economic Association2003:1(5), 1123-1175.

Smith, V. Rationality in Economics: Constructivist and Ecological forms. Cambridge University Press, New York, USA: 2008.

Stiglitz, J., The Price of Inequality. London, England, Allen Lane, 2012.

Taleb, N., Antifragile: How to live in a world we don't understand. London, England, Allen Lane. 2012.

46

Thaler, R. Mental Accounting and Customer Choice. Marketing Science1985:4(3), 199-21.

Thaler, R,Mental Accounting Matters.Journal of Behavioral Decision Making.1999: 12(3) 183-206.

Todd, P.Fast and frugal heuristics for environmentally bounded minds. In: G. Gigerenzer & R. Selten, eds. Bounded rationality: The adaptive toolbox (Dahlem Workshop Report):Cambridge, MA: MIT Press, 51-70: 2001.

Todd, P,Gigerenzer, G. Bounding rationality to the world. Journal of Economic Psychology2003:24(2) 143-165.

Trichet, J.-C. Reflections on the nature of monetary policy non-standard measures and finance theory., European Central Bank Press Release. http://www.ecb.int/press/key/date/2010/html/sp101118.en.html, 2010

Tseng, J., Lin C., Lin C., Wang S., Li S., Statistical properties of agent-based models in markets with continuous double auction mechanism. Physica A 2010: 389 8 1699–1707.

Tseng, K.Behavioral finance, bounded rationality, neuro-finance, and traditional finance. Investment Management and Financial Innovations, 3(4) 7-18.

Tversky, A, Kahneman, D.Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and uncertainty1992:5(4) 297-323.

Turner, A.The Turner Review: A regulatory response to the global banking crisis: Financial Services Authority: 2009.

Veit, E, Cheney, J.Are mutual funds market timers? Journal of Portfolio Management, 1982:8(Winter) 35-42.

Von Neumann, J., and Morgenstern, O. Theory of Games and Economic Behavior, Princeton, NJ, Princeton University Press, second ed. 1947.

Weitzman, M.Optimal Search for the Best Alternative.Econometrica1979: 47, 641-654.

Whitley, R.The transformation of business finance into financial economics: the roles of academic expansion and changes in U.S. capital markets. Accounting, Organisations and Society 1986: 11 171–92.

Yeh, C. The Effects of Intelligence on Price Discovery and Market Efficiency. Journal of Economic Behaviour & Organization, 2008: 68.613-625.

Yeh, C. and Yang, C. Examining the Effectiveness of Price Limits in an Artificial Stock Market. Journal of Economic Dynamics and Control, 2010: 34.pp.2089-2108.

47

Zhang, G.Accounting information, capital investment decisions and equity valuation: Theory and empirical implications, Journal of Accounting Research 2000:Vol 38 271-295.

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i A good example of this is Rajan, 2005. This paper was delivered in August 2005 to a gathering of top economists at Jackson Hole which that year was honouring the retirement of Alan Greenspan as Chairman of the Federal Reserve. In hindsight this paper seems remarkably prescient but it was attacked quite violently at the time. Lawrence Summers, for example, told the audience he found "the basic, slightly lead-eyed premise of (Rajan's) paper to be misguided." (Wall Street Journal, 2009). A paper by Bezemer, 2011 outlines a number of research studies that did forecast a crisis and concludes that they commonly adopted accounting or flow-of-funds models rather than neo-classical equilibrium models.

ii Books by McLean and Nocera, 2010 and Sorkin, 2009, are popular examples of the genre. One of the better high level overviews is a letter to the Queen seeking to answer her question written by members of the British Academy (British Academy, 2009). For diverse collections of academic papers on the crisis see special issues of the Journal of Financial Regulation and Compliance, 2009 and Critical Perspectives on International Business, 2009

iii Rationality is a rather ill-defined and potentially loaded concept as we discuss later in the paper.

iv Very similar axioms of rationality were set out by von-Neumann and Morgenstern in their work on the theory of games and economic behaviour.(von-Neumann and Morgenstern, 1947). vRepurchasing after 30 days if the asset is expected to rally, to avoid the sale being classified as a “wash sale” and discarded for tax purposes.

vi The PIN metric of Easley et al (2002).