An investigational analysis on forecasting intraday values

Abstract

Purpose The algorithmic trading has advanced exponentially and necessitates the evaluation of intraday stock market forecasting on the grounds that any stock market series are foreseen to follow the random walk hypothesis. The purpose of this paper is to forecast the intraday values of stock indices using data mining techniques and compare the techniques’ performance in different markets to accomplish the best results. Design/methodology/approach This study investigates the intraday values (every 60th-minute closing value) of four different markets (namely, UK, Australia, India and China) spanning from April 1, 2017 to March 31, 2018. The forecasting performance of multivariate adaptive regression spline (MARSplines), support vector regression (SVR), backpropagation neural network (BPNN) and autoregression (1) are compared using statistical measures. Robustness evaluation is done to check the performance of the models on the relative ratios of the data. Findings MARSplines produces better results than the compared models in forecasting every 60th minute of selected stocks and stock indices. Next to MARSplines, SVR outperforms neural network and autoregression (1) models. The MARSplines proved to be more robust than the other models. Practical implications Forecasting provides a substantial benchmark for companies, which entails long-run operations. Significant profit can be earned by successfully predicting the stock’s future price. The traders have to outperform the market using techniques. Policy makers need to estimate the future prices/trends in the stock market to identify the link between the financial instruments and monetary policy which gives higher insights about the mechanism of existing policy and to know the role of financial assets in many channels. Thus, this study expects that the proposed model can create significant profits for traders by more precisely forecasting the stock market. Originality/value This study contributes to the high-frequency forecasting literature using MARSplines, SVR and BPNN. Finding the most effective way of forecasting the stock market is imperative for traders and portfolio managers for investment decisions. This study reveals the changing levels of trends in investing and expectation of significant gains in a short time through intraday trading.

An application on forecasting for stock market prices: hybrid of some metaheuristic algorithms with multivariate adaptive regression splines

“…Significant features were determined by feature selection methods and the prediction was carried out with DT, ANN and SVM methods. Manickavasagam and Visalakshmi (2019) used MARS, SVM, ANN and autoregression methods from data mining methods to predict the stock index values of four different countries (the UK, Australia, India and China) between 2017 and 2018. The best success was obtained with the MARS algorithm.…”

Implementation of ANN and GARCH for Stock Price Forecasting

“…He [22] proposes doubling the number of hidden neurons until the network's performance on the testing set deteriorates. For example, according to Manickavasagam [23], there should be at least five times the number of training facts as weights, which provides an upper limit on the total number of input and neurons. To test for these characteristics, this study applies multiple structures to all of the data, picking them at random, utilizing 2, 3, 4, 5, and 6 neurons in the hidden layer to discover the optimal structure based on the index, and then examines the outcomes.…”