Abstract
Prediction of financial time series such as stock and stock indexes has remained the main focus of researchers because of its composite nature and instability in almost all of the developing and advanced countries. The main objective of this research work is to predict the direction movement of the daily stock prices index using the artificial neural network (ANN) and support vector machine (SVM). The datasets utilized in this study are the KSE-100 index of the Pakistan stock exchange, Korea composite stock price index (KOSPI), Nikkei 225 index of the Tokyo stock exchange, and Shenzhen stock exchange (SZSE) composite index for the last ten years that is from 2011 to 2020. To build the architect of a single layer ANN and SVM model with linear, radial basis function (RBF), and polynomial kernels, different technical indicators derived from the daily stock trading, such as closing, opening, daily high, and daily low prices and used as input layers. Since both the ANN and SVM models were used as classifiers; therefore, accuracy and F-score were used as performance metrics calculated from the confusion matrix. It can be concluded from the results that ANN performs better than SVM model in terms of accuracy and F-score to predict the direction movement of the KSE-100 index, KOSPI index, Nikkei 225 index, and SZSE composite index daily closing price movement.
Highlights
Prediction of the stock market index and its direction has remained the most interesting and difficult task for researchers, data scientists, and econometricians because of its volatile nature
The autoregressive conditional heteroskedasticity (ARCH) and generalized autoregressive conditional heteroskedasticity (GARCH) models proposed by Engle [4] and Bollerslev [5] have been used by numerous data scientists to forecast the financial time series data. e well-known ARMA proposed in [6] is the hybrid form of moving average (MA) and autoregressive (AR) terms and is used under
For further details of the applicability of the supporting machine for the prediction of the stock markets, the readers are suggested to consult the review paper by [23]. It is evident from the literature review that forecasting the direction movement of the stock price index is of paramount importance, but there are very limited studies conducted on the prediction of the KSE-100 index using support vector machine and artificial neural network with numerous technical indicators as input layers. erefore, the main objective of this research work is to model the Pakistan KSE-100 index along with other well-established stock prices using state-of-the-art machine learning models with a higher degree of forecasting accuracy
Summary
Prediction of the stock market index and its direction has remained the most interesting and difficult task for researchers, data scientists, and econometricians because of its volatile nature. Complexity stationary conditions to forecast the time series data In contrast to this method, autoregressive integrated moving average (ARIMA) models were proposed for nonstationary time-series datasets. With the expansion of machine learning techniques, recently, many scientists have been using these models for the prediction of the nonlinear and complex nature of financial time series data. E novelty in this paper is that we have used this idea to build the architect for FFNN that predict the direction movement of the four well-known stock price indexes daily closing price movement by extracting various (15) technical indicators from the historical data and used them as input layers to ANN and SVM e rest of the paper is categorized into the following sections.
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