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
safwan@umt.edu.my ; 
safwan.mohdnor@live.vu.edu.au
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
nurhaiza@umt.edu.my
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

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Received 1.03.2020
Received in revised form 20.03.2020
Accepted 26.03.2020
Available online 15.04.2020
Updated version of the paper as of 30.07.2020