Assessing the Efficiency of the Autoregressive Market Model in Predicting Asset Prices
DOI:
https://doi.org/10.59640/cbr.v15i2.29-41Keywords:
Asset pricing models, Forecasting return, Autoregressive model, Investment decision, Rational decisionAbstract
This study introduces the Autoregressive Market Model (ARMM) aimed at enhancing the accuracy and predictability of stock returns within the rapidly evolving Indian market, which is attracting increasing global investment interest. Noting the inadequacies of existing models like the Capital Asset Pricing Model (CAPM) in capturing the complexities of emerging markets, the ARMM incorporates historical return data to forecast future trends more accurately. The model was rigorously tested using daily log returns from a diverse set of 20 companies listed on the NSE 500, spanning from April 2017 to March 2022. The findings demonstrate that the ARMM is highly effective for asset pricing, significantly outperforming traditional models in accuracy and reliability. The robustness of the ARMM is further supported by its freedom from common statistical issues such as autocorrelation, multicollinearity, and heteroskedasticity. The major implication of this study is its potential to influence investment strategies in emerging markets by providing investors with a more precise asset pricing tool. This enhancement in asset pricing accuracy can lead to improved decision-making, potentially fostering greater market stability and efficiency in developing economies.
References
Banerjee, S., & Kremer, I. (2010). Disagreement and learning: Dynamic patterns of trade. The Journal of Finance, 65(4), 1269–1302. https://doi.org/10.1111/j.1540-6261.2010.01570.x
Barber, B. M., & Odean, T. (2001). The internet and the investor. The Journal of Economic Perspectives, 15(1), 41–54. https://doi.org/10.1257/jep.15.1.41
Barber, B. M., Lee, Y. T., Liu, Y. J., & Odean, T. (2009). Just how much do individual investors lose by trading? Review of Financial Studies, 22(2), 609–632. https://doi.org/10.1093/rfs/hhn046
Basu, D., & Chawla, D. (2010). An empirical test of CAPM—the case of Indian stock market. Global Business Review, 11(2), 209–220. https://doi.org/10.1177/097215091001100206
Basu, D., & Chawla, D. (2012). An empirical test of the arbitrage pricing theory—the case of Indian stock market. Global Business Review, 13(3), 421–432. https://doi.org/10.1177/097215091201300305
Biagini, S., & ?erný, A. (2020). Convex duality and Orlicz spaces in expected utility maximization. Mathematical Finance, 30(1), 1–35. https://doi.org/10.1111/mafi.12209
Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57–82. https://doi.org/10.1111/j.1540-6261.1997.tb03808.x
Cederburg, S. (2019). Pricing intertemporal risk when investment opportunities are unobservable. Journal of Financial and Quantitative Analysis, 54(4), 1759–1789. https://doi.org/10.1017/S0022109018000972
Chakraborty, N., Elgammal, M. M., & McMillan, D. (2019). Rational functions: An alternative approach to asset pricing. Applied Economics, 51(20), 2091–2119. https://doi.org/10.1080/00036846.2018.1540848
Chen, J. M. (2021). The capital asset pricing model. SSRN Electronic Journal, 18(3), 3–24. https://doi.org/10.2139/ssrn.3844183
Fama, E. F., & French, K. R. (1992). The cross-section of expected stock returns. The Journal of Finance, 47(2), 427–465. https://doi.org/10.1111/j.1540-6261.1992.tb04398.x
Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1–22. https://doi.org/10.1016/j.jfineco.2014.10.010
Gayawan, E., & Ipinyomi, R. A. (2009). A comparison of Akaike, Schwarz and R Square criteria for model selection using some fertility models. Australian Journal of Basic and Applied Sciences, 3(4), 3524–3530.
Hara, K., Ok, E. A., & Riella, G. (2019). Coalitional expected multi-utility theory. Econometrica, 87(3), 841–870. https://doi.org/10.3982/ecta14156
Hua, N., Dalbor, M. C., Lee, S., & Guchait, P. (2016). An empirical framework to predict idiosyncratic risk in a time of crisis: Evidence from the restaurant industry. International Journal of Contemporary Hospitality Management, 28(1), 156–176. https://doi.org/10.1108/IJCHM-03-2014-0134
Ji, Z. Y., Chang, V., Lan, H., Hsu, C. H. R., & Valverde, R. (2020). Empirical research on the Fama-French three-factor model and a sentiment-related four-factor model in the Chinese blockchain industry. Sustainability, 12(12), 5170. https://doi.org/10.3390/su12125170
Jindal, D., & Singla, R. (2016). A study of the impact of the Indian stock market crash of 2008 on IPOs listed on the National Stock Exchange. Journal of Academic Research in Economics, 8(3), 359–374. https://EconPapers.repec.org/PEc:shc:jaresh:v:8:y:2016:i:3:p:359-374
John, E. I. (2019). Effect of macroeconomic variables on stock market performance in Nigeria. Journal of Economics, Management and Trade, 22(6), 1–14. https://doi.org/10.9734/jemt/2019/v22i630110
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185
Keswani, S., & Wadhwa, B. (2021). Association among the selected macroeconomic factors and Indian stock returns. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2021.01.841
Kumagai, N. (2003). An empirical analysis on Medicaid. Iryo To Shakai, 12(3), 39–59. https://doi.org/10.4091/iken1991.12.3_39
Kumar, J., & Prince, N. (2022). Overconfidence bias in the Indian stock market in diverse market situations: An empirical study. International Journal of System Assurance Engineering and Management, 13(6), 1062–1072. https://doi.org/10.1007/s13198-022-01792-1
Lekovi?, M., & Staniši?, T. (2018). Capital asset pricing model versus arbitrage pricing theory. In Contemporary issues in economics, business and management (pp. 64-78). https://scidar.kg.ac.rs/handle/123456789/16484
Lintner, J. (1965). Security prices, risk, and maximal gains from diversification. The Journal of Finance, 20(4), 587–615. https://doi.org/10.1111/j.1540-6261.1965.tb02930.x
Mahendra, A., Mohanty, S. P., & Sudalaimuthu, S. (2021). Financial astrology and behavioral bias: Evidence from India. Asia-Pacific Financial Markets, 28(1), 3–17. https://doi.org/10.1007/s10690-020-09310-8
Malladi, R. K., & Dheeriya, P. L. (2021). Time series analysis of cryptocurrency returns and volatilities. Journal of Economics and Finance, 45(1), 75–94. https://doi.org/10.1007/s12197-020-09526-4
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
Miglietti, C., Kubosova, Z., & Skulanova, N. (2020). Bitcoin, Litecoin, and the Euro: An annualized volatility analysis. Studies in Economics and Finance, 37(2), 229–242. https://doi.org/10.1108/SEF-02-2019-0050
Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica, 34(4), 768. https://doi.org/10.2307/1910098
Mushinada, V. N. C. (2020). Are individual investors irrational or adaptive to market dynamics? Journal of Behavioral and Experimental Finance, 25, 100243. https://doi.org/10.1016/j.jbef.2019.100243
Mushinada, V. N. C., & Veluri, V. S. S. (2019). Elucidating investors rationality and behavioural biases in Indian stock market. Review of Behavioral Finance, 11(2), 201–219. https://doi.org/10.1108/rbf-04-2018-0034
Nagy, R. A., & Obenberger, R. W. (1994). Factors influencing individual investor behavior. Financial Analysts Journal, 50(4), 63–68. https://doi.org/10.2469/faj.v50.n4.63
Paramanik, R. N., & Singhal, V. (2020). Sentiment analysis of Indian stock market volatility. Procedia Computer Science, 176, 330–338. https://doi.org/10.1016/j.procs.2020.08.035
Peng, K. L., Wu, C. H., Lin, P. M. C., & Kou, I. T. E. (2023). Investor sentiment in the tourism stock market. Journal of Behavioral and Experimental Finance, 37, 100732. https://doi.org/10.1016/j.jbef.2022.100732
Rabin, M., & Schrag, J. L. (1999). First impressions matter: A model of confirmatory bias. The Quarterly Journal of Economics, 114(1), 37–82. https://doi.org/10.1162/003355399555945
Robson, A. J., & Samuelson, L. (2011). The evolutionary foundations of preferences. In Handbook of Social Economics (pp. 221–310). https://doi.org/10.1016/b978-0-444-53187-2.00007-3
Romansco, A. U., & Kindleberger, C. P. (1979). Manias, panics, and crashes: A history of financial crises. The American Historical Review, 84(2), 416. https://doi.org/10.2307/1855147
Ross, S. A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13(3), 341–360. https://doi.org/10.1016/0022-0531(76)90046-6
Sehrawat, N., Kumar, A., Nigam, N. K., Singh, K., & Goyal, K. (2020). Test of capital market integration using Fama-French three-factor model: Empirical evidence from India. Investment Management and Financial Innovations, 17(2), 113–127. https://doi.org/10.21511/imfi.17(2).2020.10
Shalaei, S. K. (2017). Studying the impact of accruals quality and market risk premium on stock return excess using Fama-French three factor model. Journal of Politics and Law, 10(2), 114. https://doi.org/10.5539/jpl.v10n2p114
Sharma, M., & Jain, A. (2020). Role of size and risk effects in value anomaly: Evidence from the Indian stock market. Cogent Economics and Finance, 8(1), 1838694. https://doi.org/10.1080/23322039.2020.1838694
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442. https://doi.org/10.1111/j.1540-6261.1964.tb02865.x
Smith, A. R., & Marshall, L. D. (2016). Confidently biased: Comparisons with anchors bias estimates and increase confidence. Journal of Behavioral Decision Making, 30(3), 731–743. https://doi.org/10.1002/bdm.1996
Sureiman, O., & Mangera, C. M. (2020). regression analysis simplified. Journal of the Practice of Cardiovascular Sciences, 6(2), 116–120. https://doi.org/10.4103/jpcs.jpcs_18_20
Taneja, Y. P. (2010). Revisiting Fama French three-factor model in Indian stock market. Vision, 14(4), 267–274. https://doi.org/10.1177/097226291001400403
Werner, R., Valev, D., & Danov, D. (2009). A measure for the linear relationships between time series. Statistical Methods for Time Series Analysis, 92–96.
Wilms, R., Mäthner, E., Winnen, L., & Lanwehr, R. (2021). Omitted variable bias: A threat to estimating causal relationships. Methods in Psychology, 5, 100075. https://doi.org/10.1016/j.metip.2021.100075
Won, D. C., & Chay, J. B. (2006). Market equilibrium in the capital asset pricing model: Heterogeneous expectations. Asia-Pacific Journal of Financial Studies, 35(1), 41–67. https://scholarx.skku.edu/handle/2021.sw.skku/87723
