A multi-stage machine learning approach for stock price prediction: Engineered and derivative indices
Year: 2024
Type: article
Abstract: In this paper, a multi-stage machine learning approach is proposed to predict the next day's stock prices. The proposed approach has three main stages, i.e., data collection, feature space generation, and prediction. In the first stage, 5283 records of daily historical series of open prices, close prices, the highest prices, the lowest prices, and the trading volume of assets of four well-known stocks are selected from FTSE 100. In the second stage, a novel set of engineered and derivative indices extracted from the original time series for stock price prediction are proposed. In the third stage, two Multi-Layer Perceptron (MLP) are proposed to predict the next day's stock prices using the engineered discrete and continuous indices. The case study uses the daily historical time series of stock prices between January 1, 2000, and December 31, 2020. The proposed machine learning approach presents suitable applicability and accuracy, respectively .
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,500
OpenAlex ID: W4403168311