Machine Learning on Stock Price Movement Forecast: The Sample of the Taiwan Stock Exchange

Chin-Sheng Huang, Yi-Sheng Liu

Abstract


This paper addresses problem of predicting direction of movement of stock price index for Taiwan stock markets. The study compares four prediction models, Artificial Neural Network (ANN), Support Vector Machine (SVM), random forest and naive-Bayes with two approaches for input to these models. The first data preprocess approach involves computation of ten technical parameters using stock trading data while the second approach focuses on representing these technical parameters as trend deterministic data. Accuracy of each of the prediction models for each of the two input approaches is evaluated. Evaluation is carried out on 19 years of historical data from 2000 to 2018 of Taiwan Stock Market Index. The experimental results suggest that for the first approach of input data where ten technical parameters are represented as continuous values, ANN outperforms other three prediction models on overall performance. Experimental results also show that the performance of all the prediction models improve when these technical parameters are represented as binary trend deterministic data.

Keywords: Naive-Bayes classification, Artificial neural networks, Support vector machine, Random forest, Machine learning, Forecast

JEL Classifications: C11; C15; C53; G17

DOI: https://doi.org/10.32479/ijefi.7560


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