Do Stocks Have a “Memory”? Evidence from the Indian Stock Market

Authors

  •   Archit Gupta MBA (Tech.) Student, NMIMS University, Vile Parle (West), Mumbai - 400 056
  •   Siba Panda Assistant Professor, Department of Data Science, NMIMS University, Vile Parle (West), Mumbai - 400 056

DOI:

https://doi.org/10.17010/ijrcm/2018/v5/i4/141544

Keywords:

Bayes' theorem

, conditional probability, Indian stock market, Nifty 50, Up day

C02

, C11, C12

Paper Submission Date

, August 20, 2018, Paper sent back for Revision, December 20, Paper Acceptance Date, December 26, 2018

Abstract

This paper predicted the price movement of Nifty 50 stocks on the Indian stock market using probability theory. It answered the question, what is the probability of a consecutive up day for a stock after a previous up day, where an up day refers to a trading day on which the closing price of a stock was greater than its previous day's closing price. It further found out the probability of a consecutive up day for a stock after two, three, four, and five previous up days, respectively. We used Bayes' theorem to calculate this. The period of study was from May 17, 2018 to August 10, 2018. The results showed that for some stocks, the probability of a consecutive up day after previous up days was not the same as having a “normal†up day (an up day not conditioned on any previous event whatsoever). This meant that some stocks had a “memory†and their previous up days could be used to predict their future up/down days. The findings of this paper would help both retail and institutional investors make better trading decisions.

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Published

2018-12-15

How to Cite

Gupta, A., & Panda, S. (2018). Do Stocks Have a “Memory”? Evidence from the Indian Stock Market. Indian Journal of Research in Capital Markets, 5(4), 18–26. https://doi.org/10.17010/ijrcm/2018/v5/i4/141544

Issue

Volume 5, Issue 4, October - December 2018

Section

Articles
Crossref
Scopus
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Europe PMC

References

Alexander, S. S. (1964). Price movement in speculative markets : Trend or random walks. In P. Cootner (ed.), The random character of stock market prices (pp. 338 - 372). Massachusetts: MIT Press.

Chang, P. H. K., & Osler, C. L. (1999). Methodical madness: Technical analysis and the irrationality of exchange rate forecasts. The Economic Journal, 109 (458), 636 - 661.

Hashimoto, Y., Ito, T., Ohnishi, T., Takayasu, M., Takayasu, H., & Watanabe, T. (2012). Random walk or a run: Market microstructure analysis of the foreign exchange rate movements based on conditional probability. Quantitative Finance, 12 (6), 893 - 905. doi: 10.3386/w14160

Inegbedion, H. E. (2012). Macroeconomic determinants of stock price changes: Empirical evidence from Nigeria. Indian Journal of Finance, 6 (2), 19 - 23.

Levich, R. M., & Thomas, L. R. (1993). The significance of technical trading-rule profits in the foreign exchange market: A bootstrap approach. Journal of International Money and Finance, 12(5), 451-474.

doi: https://doi.org/10.1016/0261-5606(93)90034-9

Levin, R. I., & Rubin, D. S., Rastogi, S., & Siddiqui, M. H. (1998). Statistics for management (7th ed.). India : Pearson.

Neely, C., Weller, P., & Dittmar, R. (1997). Is technical analysis in the foreign exchange market profitable? A genetic programming approach. Journal of Financial and Quantitative Analysis, 32 (4), 405 - 426.

doi: 10.2307/2331231

Niederhoffer, V., & Osborne, M. F. M. (1966). Market making and reversal on the stock exchange. Journal of the American Statistical Association, 61 (316), 897 - 916.

Ohira, T., Sazuka, N., Marumo, K., Shimizu, T., Takayasu, M., & Takayasu, H. (2002). Predictability of currency market exchange. Physica A: Statistical Mechanics and its Applications, 308 (1), 368 - 374. doi: 10.1016/S0378-4371(02)00561-7

Rechenthin, M. D. (2014). Machine - learning classification techniques for the analysis and prediction of high-frequency stock direction (Thesis). University of Iowa, Iowa.

Ryaly, V. R., Kumar, R. S. R. K. K., & Urlankula, B. (2014). A study on weak-form of market efficiency in selected Asian stock markets. Indian Journal of Finance, 8 (11), 34 - 43. doi: 10.17010/ijf/2014/v8i11/71842

Sazuka, N. (2006). Analysis of binarized high frequency financial data. The European Physical Journal B-Condensed Matter and Complex Systems, 50 (1-2), 129 - 131. doi: https://doi.org/10.1140/epjb/e2006-00139-4

Sweeney, R. J. (1988). Some new filter rule tests: Methods and results. The Journal of Financial and Quantitative Analysis, 23(3), 285-300. doi:10.2307/2331068

Tanaka - Yamawaki, M. (2003). Stability of Markovian structure observed in high frequency foreign exchange data. Annals of the Institute of Statistical Mathematics,55(2), 437 - 446. doi: https://doi.org/10.1007/BF02530510

Timmermann, A., & Granger, C. W. J. (2004). Efficient market hypothesis and forecasting. International Journal of Forecasting, 20(1), 15-27. doi: https://doi.org/10.1016/S0169-2070(03)00012-8