The Forecasting Efficiency of Monthly Stock Indices between Macroeconomic Factors and Technical Indicators by Using Augmented Genetic Algorithm and Artificial Neural Network Model ()
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.
Share and Cite:
Goo, Y. and Chen, C. (2020) The Forecasting Efficiency of Monthly Stock Indices between Macroeconomic Factors and Technical Indicators by Using Augmented Genetic Algorithm and Artificial Neural Network Model.
Modern Economy,
11, 1329-1341. doi:
10.4236/me.2020.117094.
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