Abstract
Stock return forecasting is of utmost importance in the business world. This has been a major topic of research for many academicians for decades. Recently, regularization techniques have reported significant increase in the forecast accuracy of the simple regression model. Still, more robust techniques are desired which can further help improve stock prices predictions. Furthermore, it is important to recommend top stocks rather than predicting exact stock returns. The technique should be scalable to very large datasets. The present paper proposes a normalization technique which results in a form of regression that is more suitable for ranking the stocks by expected returns. The ranking is done out of the comparison between the stocks in the previous quarter over a big set of important fundamental, technical and general indicators. Two large datasets consisting of altogether 946 unique companies listed at Indian exchanges were used for experimentation. Stochastic Gradient Descent technique is used in this work to train the parameters, which allows scalability to even larger datasets. Five different metrics were used for evaluating the different models. Results were also analysed subjectively through plots. The returns obtained were higher than other popular models and benchmark indices.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Arnott, R. D., & Lovell, R. D. (1993). Rebalancing. The Journal of Investing, 2(1), 5–10. https://doi.org/10.3905/joi.2.1.5
Bhar, R., & Nikolova, B. (2009). Oil prices and equity returns in the BRIC countries. World Economy, 32(7), 1036–1054. https://doi.org/10.1111/j.1467-9701.2009.01194.x
Campbell, J. Y., & Thompson, S. B. (2008). Predicting excess stock returns out of sample: Can anything beat the historical average? Review of Financial Studies, 21(4), 1509–1531. https://doi.org/10.1093/rfs/hhm055
Ciulla, G., & D’Amico, A. (2019). Building energy performance forecasting: A multiple linear regression approach. Applied Energy, 253. https://doi.org/10.1016/j.apenergy.2019.113500
Dangl, T., & Halling, M. (2012). Predictive regressions with time-varying coefficients. Journal of Financial Economics, 106(1), 157–181
Dichtl, H., Drobetz, W., & Wambach, M. (2016). Testing rebalancing strategies for stock-bond portfolios across different asset allocations. Applied Economics, 48(9), 772–788. https://doi.org/10.1080/00036846.2015.1088139
Dutta, A., Bandopadhyay, G., & Sengupta, S. (2012). Prediction of stock performance in the indian stock market using logistic regression. International Journal of Business and Information, 7(1), 105–136
Elliott, G., & Gargano, A. (2013). Complete subset regressions. Journal of Econometrics, 177(2), 357–373. https://doi.org/10.1016/J.JECONOM.2013.04.017
Firmino, P. R. A., de Mattos Neto, P. S. G., & Ferreira, T. A. E. (2015). Error modeling approach to improve time series forecasters. Neurocomputing, 153, 242–254. https://doi.org/10.1016/j.neucom.2014.11.030
Fuhrmann, R. (2019). Stock exchanges around the World. Investopedia. https://www.investopedia.com/financial-edge/1212/stock-exchanges-around-the-world.aspx
Giebel, S., & Rainer, M. (2013). Neural network calibrated stochastic processes: Forecasting financial assets. Central European Journal of Operations Research, 21(2), 277–293. https://doi.org/10.1007/s10100-011-0234-3
Graybill, F. A. (1976). Theory and application of the linear model (Vol. 183). North Scituate, MA: Duxbury Press
Hagedorn, R., Doblas-Reyes, F. J., & Palmer, T. N. (2005). The rationale behind the success of multi-model ensembles in seasonal forecasting — I. Basic concept. Tellus a: Dynamic Meteorology and Oceanography, 57(3), 219–233. https://doi.org/10.3402/tellusa.v57i3.14657
Hainaut, D., & Moraux, F. (2019). A switching self-exciting jump diffusion process for stock prices. Annals of Finance, 15(2), 267–306. https://doi.org/10.1007/s10436-018-0340-5
Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Applications to nonorthogonal problems. Technometrics, 12(1), 69–82. https://doi.org/10.1080/00401706.1970.10488635
Hosseini, S. M., Saifoddin, A., Shirmohammadi, R., & Aslani, A. (2019). Forecasting of CO2 emissions in Iran based on time series and regression analysis. Energy Reports, 5, 619–631. https://doi.org/10.1016/j.egyr.2019.05.004
Hribar, R., Potočnik, P., Šilc, J., & Papa, G. (2019). A comparison of models for forecasting the residential natural gas demand of an urban area. Energy, 167, 511–522. https://doi.org/10.1016/j.energy.2018.10.175
Imran, K. (2018). Prediction of stock performance by using logistic regression model: evidence from Pakistan Stock Exchange (PSX). Asian Journal of Empirical Research, 8(7), 247–258.
Ioffe, S., & Szegedy, C. (2015). Batch normalization: accelerating deep network training by reducing internal covariate shift. http://proceedings.mlr.press/v37/ioffe15.pdf
Jaconetti, C. M., Kinniry, F. M., & Zilbering, Y. (2010). Best practices for portfolio rebalancing. Vanguard Research, 2010, 1–17.
Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing, 11(2), 2664–2675. https://doi.org/10.1016/j.asoc.2010.10.015
Li, J., & Tsiakas, I. (2017). Equity premium prediction: The role of economic and statistical constraints. Journal of Financial Markets, 36, 56–75. https://doi.org/10.1016/j.finmar.2016.09.001
Li, J., Tsiakas, I., & Wang, W. (2014). Predicting exchange rates out of sample: Can economic fundamentals beat the random walk? E Journal of Financial Econometrics
Ma, J. (2009). A stochastic correlation model with mean reversion for pricing multi-asset options. Asia-Pacific Financial Markets, 16(2), 97–109. https://doi.org/10.1007/s10690-009-9088-2
Neely, C. J., Rapach, D. E., Tu, J., & Zhou, G. (2014). Forecasting the equity risk premium: The role of technical indicators. Management Science, 60(7), 1772–1791. https://doi.org/10.1287/mnsc.2013.1838
Purwar, A. & Singh, S. K. (2014). Empirical evaluation of algorithms to impute missing values for financial dataset, International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), pp. 652-656. https://doi.org/10.1109/ICICICT.2014.6781356
Rabinovitch, R. (1989). Pricing stock and bond options when the default-free rate is stochastic. The Journal of Financial and Quantitative Analysis, 24(4), 447. https://doi.org/10.2307/2330978
Rapach, D. E., Strauss, J. K., & Zhou, G. (2010). Out-of-sample equity premium prediction: Combination forecasts and links to the real economy. Review of Financial Studies, 23(2), 821–862. https://doi.org/10.1093/rfs/hhp063
Sangvinatsos, A. (2017). A random walk down wall street: The time-tested strategy for successful investing. Quantitative Finance, 17(3), 327–330. https://doi.org/10.1080/14697688.2016.1256598
Tokat, Y., & Wicas, N. W. (2007). Portfolio rebalancing in theory and practice. The Journal of Investing, 16(2), 52–59. https://doi.org/10.3905/joi.2007.686411
Tripathi, V. (2008). Company fundamentals and equity returns in India. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1134651
Welch, I., & Goyal, A. (2008). A Comprehensive look at the empirical performance of equity premium prediction. Review of Financial Studies, 21(4), 1455–1508. https://doi.org/10.1093/rfs/hhm014
Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175. https://doi.org/10.1016/S0925-2312(01)00702-0
Zhang, Y., Wei, Y., Ma, F., & Yi, Y. (2019). Economic constraints and stock return predictability: A new approach. International Review of Financial Analysis, 63, 1–9. https://doi.org/10.1016/j.irfa.2019.02.007
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Kartikay Gupta: formerly afffiliated with Mathematics Department, IIT Delhi.
Appendix
Appendix
See Fig. 3; Tables 3, 4 and 5.
Cumulative Mean Returns, Mean return of companies ranked according to expected return as per each of the models. Each subfigure denotes different dataset or period, i.e., (i) Q4 2017–18, BSE dataset, (ii) Q4 2017–18, NSE dataset, (iii) Q3 2017–18, BSE dataset, (iv) Q3 2017–18, NSE dataset
Rights and permissions
About this article
Cite this article
Gupta, K., Chatterjee, N. Stocks Recommendation from Large Datasets Using Important Company and Economic Indicators. Asia-Pac Financ Markets 28, 667–689 (2021). https://doi.org/10.1007/s10690-021-09341-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10690-021-09341-9