The Forecasting Efficiency of Monthly Stock Indices between Macroeconomic Factors and Technical Indicators by Using Augmented Genetic Algorithm and Artificial Neural Network Model

Yeong-Jia Goo,Chia-Chen Chen

doi:10.4236/me.2020.117094

Abstract

The purpose of this study is to compare the forecasting efficiency of stock indices between macroeconomics and technical analysis by using augmented Genetic Algorithm and Artificial Neural Network model. Monthly data of Taiwan stock index, electronic index, and financial index, from Jan. 2001 to Dec. 2019 are collected. Eight influential macroeconomic factors and seven commonly watched technical indicators are used as determinants. Three models are adopted for comparison. The models include the ARMA(p, q) model as the benchmark, GA_ANN with macroeconomic factors, and GA_ANN with technical indicators. The sliding window method with 24-, 30-, 36-, 42- and 48-month training base periods is simulated. Linear unit root tests of ADF, PP, and KPSS, and nonlinear unit root test of KSS are examined. Internal validity index of hit ratio and external validity indices of MAPE, HR, ARV and Theil U coefficients are compared. The empirical findings are summarized as follows. 1) The overall forecasting performance between MACRO and TECH models shows little difference. The electronic and financial stock indices have the out-of-sample hit ratios of 77.78% and 68.89%, respectively. Thus, these two stock indices may be suitable for making meaningful investment decisions. 2) The best training base observed from the market stock index is between 30 to 48 months. The best base observed from the electronic stock index is between 42 to 48 months. The best base observed from the financial stock index is between 42 to 48 months. Thus, the training base from 42 to 48 months exhibits better forecasting performance. 3) The optimal transformation parameter under ANN may range from 0.50 to 0.99 and may not be a constant parameter.

Highlights

Stock index forecasting has been empirically investigated over the past decades
Eight influential macroeconomic factors and seven commonly watched technical indicators are used as determinants
The electronic and financial stock indices have the out-of-sample hit ratios of 77.78% and 68.89%, respectively. These two stock indices may be suitable for making meaningful investment decisions

Summary

Introduction

Stock index forecasting has been empirically investigated over the past decades. The importance of stock index forecasting in making speculation, hedge, and arbitrage investment decisions is addressed by many practitioners, financial engineers, and academic researchers. Due to the stochastic and much like a random walk phenomenon nature of stock index movement, the task of making efficient forecast is challenging and requires innovative thinking in investment theory, model settings, and variable selection. While ARIMA models could be used to build a stock market index forecasting model, the results are usually unsatisfactory (Khandelwal et al, 2015; Ariyo et al, 2014; Zhang, 2003). Some of researchers utilized the technical indicators in forecasting the stock returns (Paluch & Jackowska-Strumiłło, 2018; Paluch & Jackowska-Strumiłło, 2012; Sutheebanjard & Premchaiswadi, 2010; Tilakaratne, Morris, Mammadov & Hurst, 2007)

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Conclusion