Return Predictability of a Corporate Governance Neural Networks Trading System

21 Pages Posted: 25 Aug 2011

See all articles by Safwan Mohd Nor

Safwan Mohd Nor

Victoria University - Center for Strategic Economic Studies (CSES); Universiti Malaysia Terengganu (UMT)

Sardar M. N. Islam

Victoria University of Technology - Centre for Strategic Economic Studies

Date Written: August 25, 2011

Abstract

This article examines whether a corporate governance investment strategy, within the context of a full-fledged trading system, can generate economically significant returns. Using artificial neural networks to design buy/sell rules, we address the limitation in corporate governance literature by incorporating money management and risk control strategies in the trading system. In addition, the simulation considers realistic constraints which we observed also lacking in the literature, namely capital restriction, round lot, short selling and transaction costs. The trading system is developed in sample for out of sample forecasting, and we measure its performance using several performance metrics including the Sharpe and Sortino ratios. The overall results indicate superior performance of the corporate governance trading system compared to the benchmark buy-and-hold strategy, with significantly better returns and much greater Sharpe and Sortino ratios. This suggests that the market is inefficient at the semi-strong form. Consequently, this study has implications for public policy and the accuracy of previous studies.

Keywords: Corporate governance, Trading strategy, Fundamental analysis, Neural networks, Stock returns, Market efficiency, Public policy

JEL Classification: G12, G14, G19

Suggested Citation

Nor, Safwan Mohd and Islam, Sardar M. N., Return Predictability of a Corporate Governance Neural Networks Trading System (August 25, 2011). Available at SSRN: https://ssrn.com/abstract=1916643 or http://dx.doi.org/10.2139/ssrn.1916643

Safwan Mohd Nor (Contact Author)

Victoria University - Center for Strategic Economic Studies (CSES) ( email )

Level 13, 300 Flinders Street
Melbourne, Victoria 3000
Australia

Universiti Malaysia Terengganu (UMT) ( email )

Kuala Terengganu, Terengganu 21030
Malaysia

Sardar M. N. Islam

Victoria University of Technology - Centre for Strategic Economic Studies ( email )

P.O. Box 14428
Melbourne City, Victoria 8001
Australia

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