Automated prediction of stock markets returns, based on wide spectrum of financial statements
Abstract
Historical stock markets data receive attention by researchers since the early days of modern econometrics. Many believe that historical data contains important models, which can be used for tracking of future movements in stock prices. But in many cases, this information offer only limited (and very often biased) overview of some of the indicators for public companies and offer very little context for the long term internal processes. For this reason, models based solely on historical returns in general performs poorly and can be useful only for short term modeling of returns. After entering in the digital era, significant part of the management processes of companies became computer-driven. This made access to internal accounting data much easier and offered unique possibility to include various indicators from companies balance sheets for improvement of existing forecasting models. In this paper will be reviewed innovative ML-based system for generation of predicting models, proposed in recent paper by Bogdanova et. al. (2021), based on combined input from historic price data and set of variables from accounting reports.
References
2. Bogdanova, B., 2021. “Applied AI in Support of Investment Decision Making”
3. Roberts, H., 1959. “Stock-markets “Patterns” and Financial Analysis: Methodological suggestions”, The Journal of Finance, https://doi.org/10.2307/2976094
3. Fama, E., 1991. “Efficient capital markets: II”, Journal of Finance, 46(5), pp. 1575-1617, https://doi.org/10.1111/j.1540-6261.1991.tb04636.x
4. Fama, E., 1970. Efficient capital markets: a review of theory and empirical work. Journal of Finance, 25(2), pp. 383-417, https://doi.org/10.2307/2325486
5. Basu, S., 1977. “Investment Performance of Common Stocks in Relation to Their Price-Earnings Rations: A test of Efficient Market Hypothesis.”, The Journal of Finance, 32(3), pp. 663-682, https://doi.org/10.1111/j.1540-6261.1977.tb01979.x
6. Kirkpatrick, Charles D. and Dahlquist, Julie R., 2006). “Technical Analysis: The Complete Resource for Financial Market Technicians”, Financial Times Press, ISBN: 9780133093506
7. Ou, J. and Penman, S., 1989 “Financial statement analysis and the prediction of stock returns”, Journal of Accounting and Economics, vol. 11, no. 4, pp. 295-329, https://doi.org/10.1016/0165-4101(89)90017-7
8. M. Noma, 2010. "Value investing and financial statement analysis", Hitotsubashi Journal of Commerce and Management, vol. 44, no. 1, pp. 29-46, https://doi.org/10.15057/18701
9. Nissim, D., 2022, “Big Data, accounting information, and valuation”, Journal of Finance and Data Science, vol 8, pp. 69-85, https://doi.org/10.1016/j.jfds.2022.04.003
10. Tibshirani, R., 1996. “Regression Shrinkage and Selection via the Lasso”, Journal of the Royal Statistical Society B, vol. 58, pp. 267-288, https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
11. Zou, H. and Hastie, T., 2005. “Regularization and Variable Selection via the Elastic Net”, Journal of Royal Society B, vol. 67(2), pp. 301-320, https://doi.org/10.1111/j.1467-9868.2005.00503.x
12. Masri, S.F and Bekey G.A, 1980. “A global optimization algorithm using adaptive random search”, Journal of Applied Mathematics and Computation, vol. 7 (4), pp. 353-375, https://doi.org/10.1016/0096-3003(80)90027-2
13. Ball, R. and Brown, P., 1968. “An empirical evaluation of accounting income numbers”, Journal of Accounting Research, vol. 6 (2), pp. 159-178, https://doi.org/10.2307/2490232
14. La Porta, R., 1996. “Expectations and cross-section of stock returns”, Journal of Finance, vol. 51 (5), pp. 1715-1742, https://doi.org/10.1111/j.1540-6261.1996.tb05223.x
15. Lee, W. and Jiang, C. and Indro, D., 2002. “Stock Market Volatility, Excess Returns, and the Role of Investor Sentiment.” Journal of Banking & Finance, vol. 26, pp. 2277–2299, https://doi.org/10.1016/S0378-4266(01)00202-3
16. Baker, M. and Wurgler J. and Yuan Y., 2012. “Global, local, and contagious investor sentiment”, Journal of Financial Economics, vol. 104(2), pp. 272-287, https://doi.org/10.1016/j.jfineco.2011.11.002
17. Koppel, C., 2021. “Does Social Media Sentiment Matter in the Pricing of U.S. Stocks?”, University of St. Gallen, https://dx.doi.org/10.2139/ssrn.3771788
18. Dash, S. and Maitra, D., 2022. “The COVID-19 pandemic uncertainty, investor sentiment, and global equity markets: Evidence from the time-frequency co-movements“, The North American Journal of Economics and Finance, vol. 62, 101712, https://doi.org/10.1016/j.najef.2022.101712
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
By submitting a paper for publishing the authors hereby comply with the following provisions: 1. The authors retain the copyrights and only give the journal the right for first publication while licensing the work under Creative Commons Attribution License, which grants permissions to others to share the contribution citing this journal as first publication of the text. 2. The authors may enter separate, additional contractual relations for non-exclusive distribution of the published version of the work in this journal (e.g. to upload it in an institutional depository, or to be published in a book), given that they cite the first publication in this journal. 3. The authors are allowed and are encouraged to publish their works online (e.g. to upload it in an institutional depository, personal websites, social networks, etc.) before, during, and after the submission of the paper here, because this may lead to productive exchange, as well as earlier and larger referencing of the published works (see The Effect of Open Access).
