collation of indian stock markets versus global … · 2021. 8. 23. · collation of indian stock...

Tianjin Daxue Xuebao (Ziran Kexue yu Gongcheng Jishu Ban)/ Journal of Tianjin University Science and Technology ISSN (Online): 0493-2137 E-Publication: Online Open Access Vol:54 Issue:08:2021 DOI 10.17605/OSF.IO/AK5DF

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COLLATION OF INDIAN STOCK MARKETS VERSUS GLOBAL MARKETS

Cheenu Goel Chitkara Business School, Chitkara University, Punjab, India

Abstract

The stock market's task and globally activity, has developed. These tasks are starting to be more and more vital on an international scale. As the Indian stock market continues to be associated with the world's top stock markets, this particular article was composed with the goal of capturing worldwide fashion along with patterns together with the simultaneous.

The stock markets as well as switches included in this specific analysis include those of India, the United States, South Korea, Hong Kong, Japan, Taiwan, China, and the United Kingdom while in the post global fiscal crisis phase. Each of those stock switches has the own history of its, trends, and social and political context. Depending on everyday closing costs of stock market indexes for every one of the selected nations, the study's time period is actually from three 1st January 2014 to 31st January 2020.

Keywords: Stock Market, Long Run Relationship, Global Stock Markets, National Stock Exchange, Bombay Stock and post-global financial.

Introduction

The Indian Stock Market and global monetary business sectors have seen changes that are indeed impressive since the LPG changes of 1990 90 one. India received LPG measures to kill trade snags with worldwide stock commercial centers. It's widened the skylines of the. The LPG changes have left transformed the worldwide market into a homegrown market for India. [1] [2] [3]

The fundamental inspirations for doing this examination are entirely follows: Localized emergencies, for example, for example the breakdown of the Russian security market, the Asian monetary emergency, and the most current U.S. monetary issue set off the world monetary issue of the most recent 100 years [6] [4] [5].

Nevertheless, the market's integration is actually driven by the players. They have to all make use of exactly the same guidelines for valuing monetary instruments. In the industry, all players must be treated equally. Tariffs, taxes, and restrictions on offering in assets that are international are just several of the hurdles that are present in the international stock market. The price of expertise obstructs the free movement of capital from one industry to yet another. As a consequence of market integration, international investors might now take part in the domestic market, and domestic investors may now take part in international markets. [7] [8] [9].


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Indian Stock Market

To satisfy the protocols of the Worldwide Markets, the Indian Markets, for instance NSE, BSE, have attained the overall standards. The presentation of the electronic exchanging framework, moving settlement in the spot of the record time frame settlement, expansion in exchanging hours, dematerialization of offers, substitution of the convey forward exchanging framework by list based and script-based prospects and alternatives, selection of hazard the board through inspiration at the clearing organization and so on has all around the world normalized the Indian Stock Market. [10] [11] [12]. The headway of innovation has expanded the entrance of financial backers to the market and furthermore the straightforwardness has expanded [13] [14].

Progression allowing port folio adventure from new institutional monetary supporters (FIIs) as advantages saves, shared resources, hypothesis trusts in etc. since LPG re designs [15] [16]. Additionally, Indian financial backers have likewise been permitted to tap the unfamiliar market with worldwide safe receipts (GDRs), America and depository receipts (ADRs) and unfamiliar cash convertible bonds (FCBs). Thus, the joining of the business sectors has happened. analyzing the blend business sectors at the overall level [16] [17].

Review of Literature

Sarat Dhal (2009) directed a multivariate co combination examination of stock recorded lists from, just as and India, from April 1993 to July 2009.The discoveries show that these business sectors have a solitary co joining association, with the Indian Stock Market assuming a pivotal part [18] [19].Srinivasa Rao Gangadharan and C.A Yoonus (2011) used regular outcomes from March 2005 to November 2010 to evaluate the consequences of the worldwide monetary emergency on the degree of monetary reconciliation between the US and the Indian stock market. There's no co joining between the 2 lists, as a investigation [19] [20] Amalendu Bhunia and Amitabh Das (2011) looked at the interconnectedness among stock markets in some South East Asian countries and the Indian stock market by August one, 1991 to July thirty one, 2011.Following the global financial problem, the data shows a growth in interconnectedness amongst Southeast Asian markets [21].

In their study, Kanakarajammal and Paulraj (2014) taken a gander at the short and long-haul harmony connections between the Indian financial exchange alongside the financial exchanges of the United States, China, Singapore, and Germany. The investigation's information was gathered from January 2003 to December 2013.The examination inferred that there is co reconciliation with the chose files, and that the Indian financial exchange includes a bigger effect on the US securities exchange and that there is bidirectional causation between the Singapore along with Indian financial exchanges [22].

In their study, Kanakarajammal and Paulraj (2015) observationally investigated the post change period co development connect between the US, China, and Indian securities exchanges. The examination included data from January 1991 through


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December 2013.According to the report, the Indian securities exchange isn't associated with the business sectors in China or perhaps the United States. impacts the Chinese market [23].Aman Srivastava (2015) utilized time series globally lists info coming from the US, Japan, Singapore, Hong Kong, and India from January 1992 to April 2014, too, discovered that there's rather high long haul mix however nominal transient incorporation [24].Shivi Khanna (2016) explored value conduct of the stock market in India corresponding to the UK and Japanese stock markets before & after the worldwide monetary emergency, closing that both the Japanese and UK stock markets were coordinated with the Indian market preceding the emergency, while post-emergency impacted by the UK by the Japanese stock market. [25] Z.Li et al. (2018) produced 2 bid ask spread assessors determined by each day very high as well as costs that are minimal and also exhibited the exactness of theirs in assessing exchange bills across a wide range of marketplaces as well as time periods. Ekinci et al. (2019) discovered that developing market are very fragile to the declarations made by produced economies.Singhal et al. (2019) Discoveries of the examination suggest that globally gold bills emphatically effect the inventory price of Mexico while oil great influences them contrarily.Ramos and Righi (2020) showed Market-wide volatility has a favorable impact on stock liquidity. Anagnostidis and Fontaine (2020) discovered which trading automation has improved the usage of algorithmic trading, which is actually based mostly on common and complicated techniques which have been discovered to have a negative influence on trading during difficult periods.

Need for Study

The Global Market has today turned into a typical stage. Different players from different foundations as well as nations are currently in a regular store. The Global Market is impacted by diverse marketplaces of various nations. The effect of one market more than various assumes an essential component. The 9/11 assaults on Exchange Pinnacles influenced the American exchange. Nevertheless, the impact was on the worldwide level [26] [28] [29]. The nearby marketplaces of various nations had been also affected straightforwardly as well as by implication. Thusly, right here we're evaluating the outcome of different markets as well as the near investigation of the different market segments. Therefore, the investigation of worldwide stock market prospect lists as well as the connection which is present between the markets is actually examined [30] [31] [32].

Scope of Study

The accompanying exploration is actually contemplating the stock markets as well as trades of India, U S A, China, Taiwan, Japan,Hon g Kong, South Korea and U K .

Purpose of the Study

To study the collation of Indian stock market with global market.


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Research Methodology

The technique for gathering the information utilized here is optional. The above recorded and the accompanying information is accessible on the different stock trades of various nations. The approach of the investigation fundamentally incorporates examining pattern developments [51] [59] [60]. To deal with the US stock market, the basic files P and S hundred, BSE SENSEX and Clever for India,. [52] [52] [53].

Process for data analysis includes:-

1. Descriptive Analysis of the Value of Stocks.

2. Graphical examination of value records.

3. Correlation between {the different the distinctive the many} stock files

Analysis and Findings

Information: World's Major Stock Exchanges (Asia, America) just as Europe


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1. Descriptive Analysis

Table 1. The Value of Stocks of World's Major Stock Exchange Markets

In all probability the most incredibly regarded stocks on earth are indeed on the Trade, followed by the numerous other American Stock Trade perceived as NASDAQ, which obviously legitimizes the predominant circumstance of America in the Global Stock Trade along with Public Stock Trade lie in 10th situation in addition to eighth independently [60] [61] [62]. The Korean Trade is inside the 10th situation just as the past work is really of the Taiwan Trade [33] [34] [35].


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Table 2: The Indices of the Major Stock Exchange Markets

The speed increase of improvement Records of the Trade is actually significantly more contrasted with any excess Stock Trade Files [36] [37] [38]. The data of the SZSE in 2016 were 2,202 and presently in 2020 it is 11,160 [39] [40] [41]. Be that as it might, in the present time frame, the Bombay Stock Trade is actually operating the world's best trades for the circumstance of Files followed by the Hang Seng Trade of Blare Kong. The Stock Trade KRX 100 is in the last situation on account of Lists [48] [49] [50].


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Table 3: Relative Central Tendency of all of the Stock Exchanges

The focal propensity of the various Stock Trades' Lists is actually believed as Mean, Standard and middle Deviation. It's once again driven by the Bombay Stock Trade, trailed. Probably the most decreased mean worth is actually of the Trade of China [42] [43] [44].

2. Graphical Analysis

To have the option to show the improvements of the arrangements of the picked countries' securities exchanges, the going with graphical show have been made

Figure 1: The Value of the Various Stock Exchanges

The close to assessment likely the most helpful jobs are truth be told kept up with through the BSE, Hang Nikkie alongside Seng. The speed increase of adjustments of the SZSE is really displayed here, along with that is higher contrasted with others. The current least position is really of the SSE, FTSE (UK Stock Exchange) & KRX 100 [45] [46] [47].


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Table 4: In order to show the Correlation between India's Stock Price with alternative Countries’

Stock Prices.

A strong association with Japan, China and Taiwan Stock Markets. Regardless, no closure can be drawn dependent on examination of association and subsequently, high colossal relationship warrants a point by guide concentrate toward find whether the marvel is temporary or is considerable as time goes on moreover. Granger causality and co compromise strategies ought to be used to examine the relationship results for examination of short run and since a long time back run impacts independently.

Discussion and conclusions

The eventual outcomes of this particular assessment unequivocally assist with supporting the view that there is a sizable joining among Indian and furthermore


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worldwide money related market sections. The BSE Sensex has seen more significant change that has been set up by an uncommonly tall Co capable of assortment stood out from numerous other select records. The Sensex, the Indian seat mark record, has demonstrated a decent connection. The appraisal uncovers that there is a frail trade off. The Indian financial exchange showed a superb incredible connection and extraordinary printer interface with around the world stock commercial centers. In the Asia Pacific locale, when appeared differently in relation to Hang Kong, the association of the Indian market with normal business sectors isn't as beguiling. The Chinese stock once-over, i.e., SSE Composite overview has found a decent connection with the combined. The Japanese stock record, i.e., Nikkei225, has solid getting along with the world's incredible securities exchanges. It is an extraordinary connection with every one of. The European once-over, i.e., FTSE100 has demonstrated a solid connection with all the US securities exchange archives, the Japanese financial exchange record. .

The study concludes that the Indian stock market, which has connected with stock markets of the relative multitude of nations, is worldwide coordinated over thenext1 0 to1 5 years, necessities to move towards a far more prominent incorporated worldwide monetary world and policymakers in India need to reth ink the edge.

References

1. Adebiyi AA, Ayo CK, Adebiyi MO, Otokiti SO (2012) Stock price prediction using neural network with hybridized market indicators. J Emerg Trends Comput Inf Sci 3(1):1–9 2. Agrawal JG, Chourasia VS, Mittra AK (2013) State-of-the-art in stock prediction techniques. Int J Adv Res Electr Electron Instrum Eng 2(4):1360–1366. 3. Aiken, M.: Using a Neural Network to Forecast Inflation. Industrial Management & Data Systems 99(7), 296–301 (1999)CrossRefGoogle Scholar 4. Athanasoglou, P.P., Brissimis, S.N., Delis, M.D.: Bank-specific, Industry-specific and Macroeconomic Determinants of Bank Profitability. International Financial Markets, Institutions & Money 18, 121–136 (2008)CrossRefGoogle Scholar 5. Atsalakis S, Valavanis KP (2009) Surveying stock market forecasting techniques—part II: soft computing methods. Expert Syst Appl 36(3):5932–5941 6. Bone R, Crucianu M (2002) Multi-step-ahead prediction with neural networks: a review. 9emes rencontres internationales: Approches Connexionnistes en Sciences 2:97–106. 7. Border J, Evan JH (2014) Stock market for beginners. CreateSpace Independent Publishing Platform, Scotts Valley 8. Boritz, J.E., Kennedy, D.B.: Effectiveness of Neural Network Types for Prediction of Business Failure. Expert Systems with Applications 9(4), 503–512 (1995)CrossRefGoogle Scholar 9. Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. Wiley, New York. 10. Brockwell PJ, Davis RA, Calder MV (2002) Introduction to time series and forecasting. Springer, Berlin.

https://doi.org/10.1108/02635579910291984

https://doi.org/10.1108/02635579910291984

https://doi.org/10.1016/j.intfin.2006.07.001

https://doi.org/10.1016/j.intfin.2006.07.001

https://doi.org/10.1016/0957-4174(95)00020-8

https://doi.org/10.1016/0957-4174(95)00020-8

http://scholar.google.com/scholar_lookup?title=Effectiveness%20of%20Neural%20Network%20Types%20for%20Prediction%20of%20Business%20Failure&author=J.E..%20Boritz&author=D.B..%20Kennedy&journal=Expert%20Systems%20with%20Applications&volume=9&issue=4&pages=503-512&publication_year=1995


August 2021 | 314

11. Brunell, P.R., Folarin, B.O.: Impact of Neural Networks in Finance. Neural Computation & Application 6, 193–200 (1997)CrossRefGoogle Scholar 12. Cavalcante RC, Brasileiro RC, Souza VL, Nobrega JP, Oliveira AL (2016) Computational intelligence and financial markets: a survey and future directions. Expert Syst Appl 55:194–211 13. Chandra A, Thenmozhi M (2015) On asymmetric relationship of India volatility index (India VIX) with stock market return and risk management. Decision 42(1):33–55. 14. Chokmani, K.T., Quarda, J.V., Hamilton, S., Hosni, G.M., Hugo, G.: Comparison of Ice-Affected Streamflow Estimates Computed Using Artificial Neural Networks and Multiple Regression Techniques. Journal of Hydrology 349, 383–396 (2008)CrossRefGoogle Scholar 15. Chopra S, Yadav D, Chopra AN (2019) Artificial neural networks based indian stock market price prediction: before and after demonetization. Int J Swarm Intell Evol Comput 8(174):1–7. 16. Choudhary, S., Singhal, S. International linkages of Indian equity market: evidence from panel co-integration approach. J Asset Manag 21, 333–341 (2020). https://doi.org/10.1057/s41260-020-00165-2. 17. Cichocki A, Unbehauen R (1993) Neural networks for optimization and signal processing. Wiley, Hoboken. 18. Coley DA (2003) An introduction to genetic algorithms for scientists and engineers. World Scientific Publishing Co, Singapore. 19. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297. 20. Dote Y, Hoft RG (1998) Intelligent control: power electronic systems. Oxford University Press, Oxford. 21. Dutta, A. 2018. Implied volatility linkages between the US and emerging equity markets: A note. Global Finance Journal 35: 138–146. 22. Ekinci, C., Akyildirim, E., Corbet, S. (2019). Analysing the dynamic influence of US macroeconomic news releases on Turkish stock markets. Finance Research Letters, 31, 155–164. 23. Gately, E.: Neural Networks for Financial Forecasting. Wiley, New York (1996)Google Scholar 24. Goh AT (1995) Back-propagation neural networks for modeling complex systems. Artif Intell Eng 9(3):143–151. 25. Haleh H, Moghaddam BA, Ebrahimijam S (2011) A new approach to forecasting stock price with EKF data fusion. Int J Trade Econ Finance 2(2):109–114 26. Han, J., Lu, H., Feng, L.: Stock Movement Prediction and N dimensional Inter-Transaction Association Rules. In: Proc. of 1998 SIGMOD 1996 Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD 1998), Seattle, Washington, June 1998, pp. 12:1-12:7 (1998)Google Scholar 27. Haron, S.: Determinants of Islamic Bank Profitability. Global Journal of Finance & Economics 1(1), 11–33 (2005)MathSciNetGoogle Scholar 28. Haykin S (1999) Neural network: a comprehensive foundation. Prentice Hall, Upper Saddle River. 29. Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, Cambridge. 30. Ibrahim D (2016) An overview of soft computing. Procedia Comput Sci 102:34–38. 31. Karray O, Silva CD (2004) Soft computing and intelligent systems design: theory, tools, and applications. Pearson Education, London.

https://doi.org/10.1007/BF01501506

https://doi.org/10.1007/BF01501506

https://doi.org/10.1016/j.jhydrol.2007.11.024

https://doi.org/10.1016/j.jhydrol.2007.11.024

https://doi.org/10.1057/s41260-020-00165-2

http://scholar.google.com/scholar_lookup?title=Neural%20Networks%20for%20Financial%20Forecasting&author=E..%20Gately&publication_year=1996

https://scholar.google.com/scholar?q=Han%2C%20J.%2C%20Lu%2C%20H.%2C%20Feng%2C%20L.%3A%20Stock%20Movement%20Prediction%20and%20N%20dimensional%20Inter-Transaction%20Association%20Rules.%20In%3A%20Proc.%20of%201998%20SIGMOD%201996%20Workshop%20on%20Research%20Issues%20on%20Data%20Mining%20and%20Knowledge%20Discovery%20%28DMKD%201998%29%2C%20Seattle%2C%20Washington%2C%20June%201998%2C%20pp.%2012%3A1-12%3A7%20%281998%29

http://www.ams.org/mathscinet-getitem?mr=2208864

http://www.ams.org/mathscinet-getitem?mr=2208864


August 2021 | 315

32. Komori YA (1992) Neural fuzzy training approach for continue speech recognition improvement. In: Proceeding IEEE international conference on acoustics, speech, and signal processing, vol 1, pp 405–408. 33. Kumar BS, Ravi V (2016) A survey of the applications of text mining in financial domain. Knowl Based Syst 114:128–147. 34. Kumar D, Murugan S (2013) Performance analysis of Indian stock market index using neural network time series model. In: International conference on pattern recognition, informatics and mobile engineering. IEEE, pp 72–78. 35. Lam M (2004) Neural network techniques for financial performance prediction: integrating fundamental and technical analysis. Decis Support Syst 37(4):567–581 36. Leigh W, Purvis R, Ragusa JM (2002) Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support. Decis Support Syst 32(4):361–377. 37. Leshno, M., Spector, Y.: Neural Network Prediction Analysis: The Bankruptcy Case. Neurocomputing 10, 125–147 (1996)CrossRefGoogle Scholar 38. Li Y, Ma W (2010) Applications of artificial neural networks in financial economics: a survey. In: International symposium on computational intelligence and design. IEEE, pp 211–214. 39. Li, Z., Lambe, B., Adegbite, E. (2018). New bid-ask spread estimators from daily high and low prices. International Review of Financial Analysis, 60, 69–86. 40. Lin CS, Chiu SH, Lin TY (2012) Empirical mode decomposition-based least squares support vector regression for foreign exchange rate forecasting. Econ Model 29(6):2583–2590 41. Mender, B.: Introduction to Probability and Statistics, 9th edn. International Thomson Publishing (1994)Google Scholar 42. Metcalfe AV, Cowpertwait PS (2009) Introductory time series with R. Springer, Berlin, pp 2–5. 43. Miao K, Chen F, Zhao ZG (2007) Stock price forecast based on bacterial colony RBF neural network. J Qingdao Univ (Nat Sci Ed) 2(11):2–11 44. Morgan, S.: Neural Networks and Speech Processing. Kluwer Academic Publishers, Dordrecht (1991)CrossRefzbMATHGoogle Scholar 45. Murphy JJ (1999) Technical analysis of the financial markets. New York Institute of Finance, New York 46. Murphy, J.J.: Technical Analysis of Financial Markets – A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance (1999)Google Scholar 47. Nagnostidis, P., Fontaine, P. (2020). Liquidity commonality and high frequency trading: Evidence from the French stock market. International Review of Financial Analysis, 69, 101428 48. Nassirtoussi AK, Wah TY, Ling DNC (2011) A novel FOREX prediction methodology based on fundamental data. Afr J Bus Manag 5(20):8322–8330 49. Nguyen, N., Cripps, A.: Predicting Housing Value: A Comparison of Multiple Linear Regression Analysis and Artificial Neural Networks. Journal of Real Estate Research 22(3), 313–336 (2001)Google Scholar. 50. Nikfarjam A, Emadzadeh E, Muthaiyah S (2010) Text mining approaches for stock market prediction. In: International conference on computer and automation engineering. IEEE, pp 256–260. 51. Pathak A, Shetty NP (2019) Indian stock market prediction using machine learning and sentiment analysis. Comput Intell Data Min 711:595–603

https://doi.org/10.1016/0925-2312(94)00060-3

https://doi.org/10.1016/0925-2312(94)00060-3

https://scholar.google.com/scholar?q=Mender%2C%20B.%3A%20Introduction%20to%20Probability%20and%20Statistics%2C%209th%20edn.%20International%20Thomson%20Publishing%20%281994%29

https://doi.org/10.1007/978-1-4615-3950-6

https://doi.org/10.1007/978-1-4615-3950-6

http://scholar.google.com/scholar_lookup?title=Neural%20Networks%20and%20Speech%20Processing&author=S..%20Morgan&publication_year=1991

https://scholar.google.com/scholar?q=Murphy%2C%20J.J.%3A%20Technical%20Analysis%20of%20Financial%20Markets%20%E2%80%93%20A%20Comprehensive%20Guide%20to%20Trading%20Methods%20and%20Applications.%20New%20York%20Institute%20of%20Finance%20%281999%29

http://scholar.google.com/scholar_lookup?title=Predicting%20Housing%20Value%3A%20A%20Comparison%20of%20Multiple%20Linear%20Regression%20Analysis%20and%20Artificial%20Neural%20Networks&author=N..%20Nguyen&author=A..%20Cripps&journal=Journal%20of%20Real%20Estate%20Research&volume=22&issue=3&pages=313-336&publication_year=2001


August 2021 | 316

52. Pedrycz W (1990) Fuzzy sets in pattern recognition: methodology and methods. Pattern Recognit 23(1):121–146. 53. Ramos, H. P., Righi, M. B. (2020). Liquidity, implied volatility and tail risk: A comparison of liquidity measures. International Review of Financial Analysis, 69, 101463. 54. Refenes, A.N., Zapranis, A., Francis, G.: Stock Performance Modeling Using Neural Networks: A Comparative Study with Regression Models. Neural Networks 7(2), 375–388 (1994)CrossRefGoogle Scholar 55. Rivera RA, Rendón MV, Ortiz JR (2015) Genetic algorithms and Darwinian approaches in financial applications: a survey. Expert Syst Appl 42(21):7684–7697. 56. Roman, J., Jameel, A.: Backpropagation and Recurrent Neural networks in Financial Analysis of MultipleStock Market Returns. In: Proceedings of the 29th Annual Hawaii International Conference on System Sciences (1996)Google Scholar 57. Roy R, Furuhashi T, Chawdhry PK (1998) Advances in soft computing: engineering design and manufacture. Springer, Berlin. 58. Rumelhart, D.E., McClelland, J.L.: PDP Research @OUP: Parallel Distributed Processing Volume: Foundations, The Massachusetts Institute of Technology (1988) Google Scholar 59. Saerens M (2002) Neural controller based on back-propagation algorithm. IEE Proc Radar Signal Process 138(1):55–62. 60. Sarwar G (2012) Is VIX an investor fear gauge in BRIC equity markets? J Multinatl Finance Manag 22(3):55–65. 61. Setty DV, Rangaswamy TM, Subramanya KN (2010) A review on data mining applications to the performance of stock marketing. Int J Comput Appl 1(3):24–34. 62. Si YW, Yin J (2013) OBST-based segmentation approach to financial time series. Eng Appl Artif Intell 26(10):2581–2596. 63. Singhal, Shelly & Choudhary, Sangita & Biswal, Pratap Chandra, 2019. "Return and volatility linkages among International crude oil price, gold price, exchange rate and stock markets: Evidence from Mexico," Resources Policy, Elsevier, vol. 60(C), pages 255-261. 64. Soni S (2011) Applications of ANNs in stock market prediction: a survey. Int J Comput Sci Eng Technol 2(3):71–83. 65. Tang, Z., Almeida, C., Fishwick, P.A.: Simulation: Time series forecasting using neural networks vs. Box-Jenkins methodology, pp. 303–310 (1991)Google Scholar 66. Tay FE, Cao L (2001) Application of support vector machines in financial time series forecasting. Omega 29(4):309–317 67. Tsinaslanidis PE, Kugiumtzis D (2014) A prediction scheme using perceptually important points and dynamic time warping. Expert Syst Appl 41(6):848–860. 68. Wang JZ, Wang JJ, Zhang ZG, Guo SP (2011) Forecasting stock indices with back propagation neural network. Expert Syst Appl 38(11):14346–14355. 69. Wicirow, Rumelhart: L&R: Journal of Communications of the ACM. Neural Networks: Applications in Industry, Business and Science 37(3), 93–105 (1994)Google Scholar 70. Yuhas B, Ansari N (2012) Neural networks in telecommunications. Springer, Berlin. 71. Zadeh LA (1994) Fuzzy logic, neural networks, and soft computing. Commun ACM 37(3):77–84.

https://doi.org/10.1016/0893-6080(94)90030-2

https://doi.org/10.1016/0893-6080(94)90030-2

https://scholar.google.com/scholar?q=Roman%2C%20J.%2C%20Jameel%2C%20A.%3A%20Backpropagation%20and%20Recurrent%20Neural%20networks%20in%20Financial%20Analysis%20of%20MultipleStock%20Market%20Returns.%20In%3A%20Proceedings%20of%20the%2029th%20Annual%20Hawaii%20International%20Conference%20on%20System%20Sciences%20%281996%29

https://scholar.google.com/scholar?q=Rumelhart%2C%20D.E.%2C%20McClelland%2C%20J.L.%3A%20PDP%20Research%20%40OUP%3A%20Parallel%20Distributed%20Processing%20Volume%3A%20Foundations%2C%20The%20Massachusetts%20Institute%20of%20Technology%20%281988%29%20

https://scholar.google.com/scholar?q=Rumelhart%2C%20D.E.%2C%20McClelland%2C%20J.L.%3A%20PDP%20Research%20%40OUP%3A%20Parallel%20Distributed%20Processing%20Volume%3A%20Foundations%2C%20The%20Massachusetts%20Institute%20of%20Technology%20%281988%29%20

https://ideas.repec.org/a/eee/jrpoli/v60y2019icp255-261.html



https://ideas.repec.org/s/eee/jrpoli.html

https://scholar.google.com/scholar?q=Tang%2C%20Z.%2C%20Almeida%2C%20C.%2C%20Fishwick%2C%20P.A.%3A%20Simulation%3A%20Time%20series%20forecasting%20using%20neural%20networks%20vs.%20Box-Jenkins%20methodology%2C%20pp.%20303%E2%80%93310%20%281991%29

http://scholar.google.com/scholar_lookup?title=L%26R%3A%20Journal%20of%20Communications%20of%20the%20ACM&author=.%20Wicirow&author=.%20Rumelhart&journal=Neural%20Networks%3A%20Applications%20in%20Industry%2C%20Business%20and%20Science&volume=37&issue=3&pages=93-105&publication_year=1994

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