Do daily price extremes influence short-term investment decisions? Evidence from the Indian equity market
-
DOIhttp://dx.doi.org/10.21511/imfi.19(4).2022.10
-
Article InfoVolume 19 2022, Issue #4, pp. 122-131
- Cited by
- 475 Views
-
83 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
For short-term investments in equity markets, investors use price points, candlestick patterns, moving averages, support and resistance levels, trendlines, price patterns, relative strength index, and moving average convergence-divergence as reference(s) for making decisions. This study investigates whether investors use daily price extremes (highest and lowest prices for the day) for making short-term investments or trading decisions in the context of the Indian equity market. Using 6,902 observations of daily data of the NIFTY 50 index since its launch, it is observed that daily price extremes (high or low) have no impact on opening returns of the next trading day. Based on the dummy regression analysis, next-day opening returns were found to be statistically significant, which implies the presence of momentum behavior. However, insignificant coefficients for high or low-price extremes of the day mean that investors do not use them as an anchor or reference point for decisions. Results are consistent over time and robust to the rising or falling markets. Further, opening returns were seen to be more volatile than closing returns in the first half of the sample, and they are less volatile in the second half, implying that markets have become more efficient in the last few years.
- Keywords
-
JEL Classification (Paper profile tab)G10, G11, G14, G41
-
References50
-
Tables4
-
Figures0
-
- Table 1. Descriptive statistics
- Table 2. The setup of extreme price event and returns on day t+1
- Table 3. Momentum in opening price returns
- Table 4. Regression results for the full sample and sub-samples
-
- Abu-Mostafa, Y. S., & Atiya, A. F. (1996). Introduction to financial forecasting. Applied Intelligence, 6(3), 205-213.
- Ansari, V. A., & Khan, S. (2012). Momentum anomaly: Evidence from India. Managerial Finance, 38(2), 206-223.
- Arévalo, R., García, J., Guijarro, F., & Peris, A. (2017). A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting. Expert Systems with Applications, 81, 177-192.
- Bahadar, S., Mahmood, H., & Zaman, R. (2019). The Herds of Bulls and Bears in Leveraged ETF Market. Journal of Behavioral Finance, 20(4), 408-423.
- Baker, H. K., Kumar, S., Goyal, N., & Gaur, V. (2018). How financial literacy and demographic variables relate to behavioral biases. Managerial Finance, 45(1), 124-146.
- Bessembinder, H. (2021). Wealth Creation in the US Public Stock Markets 1926–2019. The Journal of Investing, 30(3), 47-61.
- Bremer, M., & Sweeney, R. J. (1991). The Reversal of Large Stock-Price Decreases. The Journal of Finance, 46(2), 747-754.
- Cao, L., & Tay, F. E. H. (2001). Financial Forecasting Using Support Vector Machines. Neural Computing & Applications, 10(2), 184-192.
- Cen, L., Hilary, G., & Wei, K. C. J. (2013). The Role of Anchoring Bias in the Equity Market: Evidence from Analysts’ Earnings Forecasts and Stock Returns. Journal of Financial and Quantitative Analysis, 48(1), 47-76.
- Chan, K., Hameed, A., & Tong, W. (2000). Profitability of Momentum Strategies in the International Equity Markets. The Journal of Financial and Quantitative Analysis, 35(2), 153-172.
- Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83, 187-205.
- Choudhary, K., & Sethi, N. (2014). A Study of Overreaction Hypothesis in the Indian Equity Market. Asia-Pacific Journal of Management Research and Innovation, 10(4), 355-366.
- Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor Psychology and Security Market Under- and Overreactions. The Journal of Finance, 53(6), 1839-1885.
- De Bondt, W. F. M., & Thaler, R. (1985). Does the Stock Market Overreact? The Journal of Finance, 40(3), 793-805.
- Eugster, P., & Uhl, M. W. (2022). Technical analysis: Novel insights on contrarian trading. European Financial Management, n/a(n/a).
- Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work*. The Journal of Finance, 25(2), 383-417.
- Fama, E. F. (1995). Random Walks in Stock Market Prices. Financial Analysts Journal, 51(1), 75-80.
- Friesen, G. C., Weller, P. A., & Dunham, L. M. (2009). Price trends and patterns in technical analysis: A theoretical and empirical examination. Journal of Banking & Finance, 33(6), 1089-1100.
- Garman, M. B., & Klass, M. J. (1980). On the Estimation of Security Price Volatilities from Historical Data. The Journal of Business, 53(1), 67-78.
- George, T. J., & Hwang, C.-Y. (2004). The 52-Week High and Momentum Investing. The Journal of Finance, 59(5), 2145-2176.
- Guresen, E., Kayakutlu, G., & Daim, T. U. (2011). Using artificial neural network models in stock market index prediction. Expert Systems with Applications, 38(8), 10389-10397.
- Hao, Y., Chou, R. K., Ko, K.-C., & Yang, N.-T. (2018). The 52-week high, momentum, and investor sentiment. International Review of Financial Analysis, 57, 167-183.
- Hirshleifer, D. (2001). Investor Psychology and Asset Pricing. The Journal of Finance, 56(4), 1533-1597.
- Hong, H., & Wang, J. (2000). Trading and Returns under Periodic Market Closures. The Journal of Finance, 55(1), 297-354.
- Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
- Jegadeesh, N., & Titman, S. (2011). Momentum. Annual Review of Financial Economics, 3(1), 493-509.
- Lee, C. M. C., Myers, J., & Swaminathan, B. (1999). What is the Intrinsic Value of the Dow? The Journal of Finance, 54(5), 1693-1741.
- Li, J., & Yu, J. (2012). Investor attention, psychological anchors, and stock return predictability. Journal of Financial Economics, 104(2), 401-419.
- Lo, A. W. (2004). The Adaptive Markets Hypothesis. The Journal of Portfolio Management, 30(5), 15-29.
- Lu, T.-H., Shiu, Y.-M., & Liu, T.-C. (2012). Profitable candlestick trading strategies – The evidence from a new perspective. Review of Financial Economics, 21(2), 63-68.
- Lyle, M. R., & Yohn, T. L. (2021). Fundamental Analysis and Mean-Variance Optimal Portfolios. The Accounting Review, 96(6), 303-327.
- Malkiel, B. G. (2003). A random walk down Wall Street: The time-tested strategy for successful investing (Completely rev. and updated). W. W. Norton.
- Metghalchi, M., Hayes, L. A., & Niroomand, F. (2019). A technical approach to equity investing in emerging markets. Review of Financial Economics, 37(3), 389-403.
- Moshirian, F., Nguyen, H. G. (Lily), & Pham, P. K. (2012). Overnight public information, order placement, and price discovery during the pre-opening period. Journal of Banking & Finance, 36(10), 2837-2851.
- Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. Penguin.
- O’Connell, D., Hickerson, K., & Pillutla, A. (2011). Organizational Visioning: An Integrative Review. Group & Organization Management, 36(1), 103-125.
- Odean, T. (1998). Are Investors Reluctant to Realize Their Losses? The Journal of Finance, 53(5), 1775-1798.
- Park, A., & Sabourian, H. (2011). Herding and Contrarian Behavior in Financial Markets. Econometrica, 79(4), 973-1026.
- Park, C.-H., & Irwin, S. H. (2007). What Do We Know About the Profitability of Technical Analysis? Journal of Economic Surveys, 21(4), 786-826.
- Parkinson, M. (1980). The Extreme Value Method for Estimating the Variance of the Rate of Return. The Journal of Business, 53(1), 61-65.
- Pring, M. J. (2002). Technical analysis explained: The successful investor’s guide to spotting investment trends and turning points. McGraw-Hill Professional.
- Raut, R. K., Das, N., & Mishra, R. (2020). Behaviour of Individual Investors in Stock Market Trading: Evidence from India. Global Business Review, 21(3), 818-833.
- Shah, D., Isah, H., & Zulkernine, F. (2019). Stock Market Analysis: A Review and Taxonomy of Prediction Techniques. International Journal of Financial Studies, 7(2), 26.
- Shiller, R. J. (2015). Irrational Exuberance: Revised and Expanded Third Edition. In Irrational Exuberance. Princeton University Press.
- Sturm, R. R. (2008). The 52-Week High Strategy: Momentum and Overreaction in Large Firm Stocks. The Journal of Investing, 17(2), 55-67.
- Sturm, R. R. (2013). Market Efficiency and Technical Analysis Can they Coexist? Research in Applied Economics, 5(3), 1-16.
- Sturm, R. R. (2021). The Influence of Daily Price Extremes on Short-Term Stock Returns. Journal of Behavioral Finance, 22(3), 254-264.
- Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases. Science, 185(4157), 1124-1131.
- Zhong, X., & Enke, D. (2017). Forecasting daily stock market return using dimensionality reduction. Expert Systems with Applications, 67, 126-139.
- Zielonka, P. (2004). Technical analysis as the representation of typical cognitive biases. International Review of Financial Analysis, 13(2), 217-225.