Indian Journal of Industrial Relations: Economics & Social Dev.

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Does Big Data Influence the Efficiency of the Capital Markets?


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1 Indian Institute of Management, Raipur, India
     

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This paper examines the adaptation of the ‘big data’ strategies in the developed capital markets and its effect on the efficiency of the capital markets. The big data strategy and algorithms use the power of high capacity computing to affect the high frequency trading which improves the efficiency in the market. However, high frequency trading also poses many regulatory challenges for the Security and Exchange Commission. Social media and microblogs affect the risk appetite of the investors. The sentiment and decision-making pattern of the investors are influenced by the continuous flows of the information through the social media which affects the capital markets.
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  • Aldridge, I. (2013), “Market Microstructure and the Risks of High-frequency Trading”, http://dx.doi.org/10.2139/ssrn.2294526

  • Aït-Sahalia, Y. & Saglam, M. (2013), High Frequency Traders: Taking Advantage of Speed (No. w19531), National Bureau of Economic Research

  • Angel, J. J. & McCabe, D. (2013), “Fairness in Financial Markets: The Case of High Frequency Trading”, Journal of Business Ethics, 112(4): 585-95

  • Alanyali, M., Moat, H. S. & Preis, T. (2013), “Quantifying the Relationship between Financial News and the Stock Market”, Scientific Reports, 3: 3578

  • Blocher, J., Cooper, R., Seddon, J. & Vliet, B. V. (2016), “Phantom Liquidity and High-Frequency Quoting”, The Journal of Trading, 11(3): 6-15

  • Bollen, J., Mao, H. & Zeng, X. (2011), “Twitter Mood Predicts the Stock Market”, Journal of Computational Science, 2(1): 1-8.

  • Bartov, E., Faurel, L., & Mohanram, P. (2015), “Can Twitter Help Predict Firm-Level Earnings and Stock Returns?” The Accounting Review (In-Press), http://aaajournals.org/doi/10.2308/accr-51865 accessed on February 24, 2018

  • Easley, D., Lopez de Prado, M. M. & O’Hara, M. (2012), “The Volume Clock: Insights into the High-Frequency Paradigm”, The Journal of Portfolio Management, 39(1): 1929.

  • Funk, R. J. & Hirschman, D. (2014), “Derivatives and Deregulation: Financial Innovation and the Demise of Glass–Steagall”, Administrative Science Quarterly, 59(4): 669-704.

  • Fama, E. F. (1991), “Efficient Capital Markets: II”, The Journal of Finance, 46(5): 15751617.

  • Foster, G. (1979), “Briloff and the Capital Market”, Journal of Accounting Research, 26274.

  • Gilbert, E., & Karahalios, K. (2010, May), “Widespread Worry and the Stock Market”, in The International AAAI Conference on Web and Social Media (ICWSM):. 59-65), http://www.icwsm.org/2010/papers.shtml accessed on February 24, 2018.

  • Jin, X., Shen, D. & Zhang, W. (2016), “Has Microblogging Changed Stock Market Behavior? Evidence from China”, Physica A: Statistical Mechanics and its Applications, 452: 151-56.

  • Moat, H. S., Preis, T., Olivola, C. Y., Liu, C. & Chater, N. (2014), “Using big Data to Predict Collective Behavior in the Real World”, Behavioral and Brain Sciences, 37(1): 9293.

  • Nagata, S. & Inui, K. (2014), “Does High-speed Trading Enhance Market Efficiency? Empirical Analysis on “Arrowhead” of the Tokyo Stock Exchange”, The Journal of Trading, 9(4): 37-47.

  • Snijders, C., Matzat, U. & Reips, U. D. (2012), “ Big Data: Big Gaps of Knowledge in the Field of Internet Science”, International Journal of Internet Science, 7(1): 1-5.

  • Seth, T., & Chaudhary, V. (2015), Big Data: Algorithms, Analytics, and Applications. Sound Parkway, Roca Raton, FL, CRC Press

  • Seddon, J. Jonathan. & Currie, W. L. (2017), “A Model for Unpacking Big Data Analytics in High-frequency Trading”, Journal of Business Research, 70: 300-07.

  • Sharpe, W. F. (1964), “Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk”, The Journal of Finance, 19(3): 425-42.

  • Seddon, J. J. & Currie, W. L. (2017), “A Model for Unpacking Big Data Analytics in HighFrequency Trading”, Journal of Business Research, 70: 300-07.

  • Singh, A. P. (2016), “R&D Spillovers & Productivity Growth: Evidence from Indian Manufacturing”, The Indian Journal of Industrial Relations, 51(4): 563-79.

  • Singh, A. P. (2016), “Do Technology Spillovers Accelerate Performance of Firms? Unravelling a Puzzle from Indian Manufacturing Industry”, Annals of the University Dunarea de Jos of Galati: Fascicle: XVII, Medicine, 22(3).

  • Shorter, G. W. & Miller, R. S. (2014), High-frequency trading: background, concerns, and regulatory developments (Vol. 29). Washington, DC: Congressional Research Service.

  • Tian, X., Han, R., Wang, L., Lu, G. & Zhan, J. (2015), “Latency Critical Big Data Computing in Finance”, The Journal of Finance and Data Science, 1(1): 33-41.

  • Ye, M. & Li, G. (2017), “Internet Big Data and Capital Markets: a Literature Review”, Financial Innovation, 3(1): 6.

  • Zhang, X., Fuehres, H. & Gloor, P. A. (2011), “Predicting Stock Market Indicators Through Twitter ‘I hope it is not as bad as I fear”’, Procedia-Social and Behavioral Sciences, 26: 55-62.

  • Zhang, W. & Skiena, S. (2010), “Trading Strategies to Exploit Blog and News Sentiment. In The International AAAI Conference on Web and Social Media (ICWSM): 2010, http://www.icwsm.org/2010/papers.shtml accessed on February 24, 2018


Abstract Views: 197

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  • Does Big Data Influence the Efficiency of the Capital Markets?

Abstract Views: 197  |  PDF Views: 2

Authors

Rajesh Kumar Singh
Indian Institute of Management, Raipur, India
S. K. Mitra
Indian Institute of Management, Raipur, India
Sumeet Gupta
Indian Institute of Management, Raipur, India

Abstract


This paper examines the adaptation of the ‘big data’ strategies in the developed capital markets and its effect on the efficiency of the capital markets. The big data strategy and algorithms use the power of high capacity computing to affect the high frequency trading which improves the efficiency in the market. However, high frequency trading also poses many regulatory challenges for the Security and Exchange Commission. Social media and microblogs affect the risk appetite of the investors. The sentiment and decision-making pattern of the investors are influenced by the continuous flows of the information through the social media which affects the capital markets.

References