financial time series analysis with wavelets rishi kumar baris temelkuran
TRANSCRIPT
Financial Time Series Analysis with Wavelets
Rishi Kumar
Baris Temelkuran
Agenda
Wavelet Denoising Threshold Selection Threshold Application
Applications Asset Pricing Technical Analysis
Denoising Techniques
4 choices to make Wavelet
Haar, Daub4 Threshold Selection Application of Thresholding Depth of Wavelet Decomposition
1, 2
Threshold Selection
Universal Threshold Minimax Stein's Unbiased Risk Hybrid of Stein’s and Universal
Threshold Selection
Universal Threshold
Let z1,…,zN be IID N(0,σε2) random
variables
NU log2ˆ
N
NzPt
t
as
1log2)max(
Threshold Selection
Minimax Does not have a closed formula. Tries to find an estimator that attains the
minimax risk
Does not over-smooth by picking abrupt changes
),ˆ( sup inf)(~
ˆxxRF
xx
Threshold Selection
Stein's Unbiased Risk
Threshold minimizes the estimated risk
),(min}:{#2),(1
2
k
iii zzikzSURE
),(minarg0
zSURES
Threshold Application
Hard Thresholding
Soft Thresholding
otherwise 0
if
ttH oo
, ) )(( ttS oosign
0 if0
0 if)(
x
xxx
Asset Pricing
Fama French Framework Cross sectional variation of equity returns Sensitivity to various sources of risk
Market Risk (1 factor) Systematic Factor Risk (2 factors)
Factors should be proxies for real, macroeconomic, aggregate, nondiversifiable risk
Asset Pricing
Fama French Framework Pricing Relation
Regression
HMLi
SMBi
Mi
ft
it hsRRE )(
it
HMLti
SMBti
ft
Mtii
ft
it RhRsRRRR )(
Wavelet Denoising
High Frequency Data: daily Use Denoising to Clean
Predictor Variables Response Variables
Goals Improve Regression Fit Decrease Out-of-Sample Error of
Expected Excess Return
Data
Daily returns: 19630701 to 20021231 Factors:
market return - risk free return (small - big) market cap returns (high - low) book to market returns
Assets IBM, GE, 6 Fama-French portfolios
Model Fit Tests
R-square Regress using sliding window (e.g. 2 year) Compute Rsquare
Mean Square Error in forecasting Regress using sliding window Forecast using regression Betas for 14 days Compare MSE of with actuals
Pricing Relation Test Compute mean of excess return for out-of-sample data
(e.g. 1 year forward) Compare with estimated expected excess return
Results
Expected Soft thresholding will work better Daub4 will work better than Haar
Empirical General: no statistically significant
improvement Few odd cases: improved R-square
FF portfolio using Daub4, soft, universal and heuristic
Technical Analysis
Charting, pattern watching Common practice among traders Not well studied in academia Our work modeled after seminal paper
by Lo et al
Goal
Determine if Technical Patterns have information content
Distribution of conditional returns (post-pattern) is different from distribution of unconditional returns
Replace Lo’s Kernel regression based smoothing algorithm (for pattern recognition) with wavelet denoising
Common Technical Patterns
Pattern Recognition
Parameterize patterns Characterize patterns by geometry of
local extrema Need denoised price path for
securities
Defining Patterns
Defined in terms of sequences of local extrema e.g. head and shoulders
e1 is a max e3 > e1, e3 > e5 e1 and e5 within 4% of their average e2 and e4 within 4% of their average
Wavelet Smoothing
Smooth out noise for pattern recognition
Mimics human cognition in extracting regularity from noisy data
Information Content
Measure 1 day conditional return after completion of pattern continuously compounded lagged by 3 days to allow for reaction time to
pattern Measure 1 day unconditional return
Random sample, periodic sample Check if both return series are from the
same distribution
Data and Testing
Data Stocks from Nasdaq 100 index 19950101 to 19991231 Daily price
Goodness-of-fit Normalize returns from each stock Combine all conditional returns to increase
strength of test Kolmogorov-Smirnov goodness-of-fit test
Example Detected Pattern
Results
About 300 Head&Shoulders pattern detected in 5 year data per denoising technique
Distribution of conditional returns found significantly different from the distribution of unconditional returns
Patterns have information content!
Conclusion
Wavelet analysis seems to add little value in asset pricing paradigm
Wavelet smoothing might prove useful in cognitive/behavioral finance studies in its ability to mimic human cognition
The End
Questions?