Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets
Sujin Pyo; Jaewook Lee; Mincheol Cha; Huisu Jang · 2017 · PLoS ONE
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied to forecast financial markets. This study predicts the trends of the Korea Composite Stock Price Index 200 (KOSPI 200) prices using nonparametric machine learning models: artificial neural network, support vector machines with polynomial and radial basis function kernels. In addition, this study states controversial issues and tests hypotheses abou
Abstract by Sujin Pyo; Jaewook Lee; Mincheol Cha; Huisu Jang, PLoS ONE (2017) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1371/journal.pone.0188107