A neural network approach for fundamental investment analysis: a case of Athens Stock Exchange

Economic Annals-ХХI: Volume 182, Issue 3-4, Pages: 56-63

Citation information:
Nor, S. M., & Zawawi, N. H. M. (2020). A neural network approach for fundamental investment analysis: a case of Athens Stock Exchange. Economic Annals-XXI, 182(3-4), 56-63. doi: https://doi.org/10.21003/ea.V182-07

Safwan Mohd Nor
PhD (Finance),
RHB Islamic Endowed Scholar in Finance,
Associate Professor of Finance,
Faculty of Business,
Economics and Social Development,
University of Malaysia Terengganu
Kuala Nerus, Terengganu, 21030, Malaysia;
Research Associate,
Victoria Institute of Strategic Economic Studies,
Victoria University 
Melbourne, Victoria, 3000, Australia
ORCID ID: https://orcid.org/0000-0003-0791-2363

Nur Haiza Muhammad Zawawi
PhD (Accounting),
Senior Lecturer,
Faculty of Business,
Economics and Social Development,
University of Malaysia Terengganu
Kuala Nerus, Terengganu, 21030, Malaysia
ORCID ID: https://orcid.org/0000-0002-9894-643X

A neural network approach for fundamental investment analysis: a case of Athens Stock Exchange

Abstract. This paper explores investment profitability in an emerging European stock market using fundamental analysis enhanced by artificial neural networks. Using a set of accounting-based financial ratios from publicly available data source, we find that these ratios possess useful information in forecasting future stock returns of Athens Stock Exchange (ATHEX) constituent firms. By combining long and short rules, the neurally reinforced fundamental strategy surpasses the unconditional buy-and-hold rule in the holdout subperiod in terms of returns (total and annualized) and risk (volatility, downside volatility and drawdown) measures. Overall results remain consistent even in the presence of trading costs. Our findings suggest that stock prices in Greece do not fully incorporate financial statement information and thus inconsistent with the principle of market efficiency at the semi-strong form.

Keywords: Fundamental Analysis; Financial Ratios; Neural Networks; Out-of-Sample; Athens Stock Exchange

JEL Classifications: C45; G14; G17

Acknowledgement and Funding: The main author is the RHB Islamic Endowed Scholar in Finance and Associate Professor of Finance at University of Malaysia Terengganu. The authors thank RHB Islamic Bank Berhad for the financial support in publishing this paper (Grant Number: 53276).

Contribution: Both authors contributed significantly to this work.

DOI: https://doi.org/10.21003/ea.V182-07


  1. World Bank. (2020). Stocks traded, total value (current US$). Washington: The World Bank Group.
    Retrieved from https://data.worldbank.org/indicator/CM.MKT.TRAD.CD
  2. Rotella, R. P. (1992). The elements of successful trading: developing your comprehensive strategy through psychology, money management, and trading methods. Engelwood Cliffs, NJ: New York Institute of Finance.
  3. Sloan, R. G. (2019). Fundamental analysis redux. The Accounting Review, 94(2), 363-377.
    doi: https://doi.org/10.2308/accr-10652
  4. Aby, C. D., Briscoe, N. R., Elliott, R. S., & Bacadayan, A. (2001). Value stocks: a look at benchmark fundamentals and company priorities. Journal of Deferred Compensation, 7(1), 20-31.
  5. Graham, B., & Zweig, J. (2003). The intelligent investor: the definitive book on value investing. Revised ed. New York: Harper Business Essentials.
  6. Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). A systematic review of fundamental and technical analysis of stock market predictions. Artificial Intelligence Review, 53, 3007-3057.
    doi: https://doi.org/10.1007/s10462-019-09754-z
  7. Gepp, A., Harris, G., & Vanstone, B. (2020). Financial applications of semidefinite programming: a review and call for interdisciplinary research. Accounting & Finance.
    doi: https://doi.org/10.1111/acfi.12543
  8. Yao, J., & Tan, C. L. (2002). Neural networks for technical forecasting of foreign exchange rates. In K. Smith, & J. Gupta (Eds.). Neural networks in business: techniques and applications (pp. 189-204). Hershey PA: Idea Group Publishing.
  9. Eakins, S. G., & Stansell, S. R. (2003). Can value-based stock selection criteria yield superior risk-adjusted returns: an application of neural networks. International Review of Financial Analysis, 12(1), 83-97.
    doi: https://doi.org/10.1016/S1057-5219(02)00124-2
  10. Quah, T-S. (2008). DJIA stock selection assisted by neural network. Expert Systems with Applications, 35(1-2), 50-58.
    doi: https://doi.org/10.1016/j.eswa.2007.06.039
  11. Fama, E. F. (1965). Random walks in stock market prices. Financial Analysts Journal, 21(5), 55-59. doi: https://doi.org/10.2469/faj.v21.n5.55
  12. Fama, E. F. (1970). Efficient capital markets: a review of theory and empirical work. Journal of Finance, 25(2), 383-417.
    doi: https://doi.org/10.2307/2325486
  13. Alexakis, C., Patra, T., & Poshakwale, S. (2010). Predictability of stock returns using financial statement information: evidence on semi-strong efficiency of emerging Greek stock market. Applied Financial Economics, 20(16), 1321-1326.
    doi: https://doi.org/10.1080/09603107.2010.482517
  14. Thawornwong, S., & Enke, D. (2003). Forecasting stock returns with artificial neural networks. In G. P. Zhang (Ed.). Neural Networks in Business Forecasting (pp. 47-79). New York: Information Science Publishing.
  15. Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 5(2), 359-366.
    doi: https://doi.org/10.1016/0893-6080(89)90020-8
  16. Azoff, E. M. (1994). Neural network time series forecasting of financial markets. West Sussex: John Wiley & Sons.