Models Applied in Stock Market Prediction: A Literature Survey

Dassanayake, Wajira; Jayawardena, Chandimal; Ardekani, Iman Tabatabaei; Sharifzadeh, Hamid

doi:10.34074/ocds.12019

Cited by 3 publications

(1 citation statement)

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“…Accurate prediction of the stock market is still a big challenge because of the complexity and stochastic nature of the market data [16][17][18][19][20][21]. Specifically, the Malaysian stock market is a growing emerging market characterised by asymmetrical dynamic behaviour and weak market efficiency [22,23].…”

Section: Introductionmentioning

confidence: 99%

Empirical Analysis for Stock Price Prediction Using NARX Model with Exogenous Technical Indicators

Dhafer

Nor

Alkawsi

et al. 2022

Computational Intelligence and Neuroscience

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Stock price prediction is one of the major challenges for investors who participate in the stock markets. Therefore, different methods have been explored by practitioners and academicians to predict stock price movement. Artificial intelligence models are one of the methods that attracted many researchers in the field of financial prediction in the stock market. This study investigates the prediction of the daily stock prices for Commerce International Merchant Bankers (CIMB) using technical indicators in a NARX neural network model. The methodology employs comprehensive parameter trails for different combinations of input variables and different neural network designs. The study seeks to investigate the optimal artificial neural networks (ANN) parameters and settings that enhance the performance of the NARX model. Therefore, extensive parameter trails were studied for various combinations of input variables and NARX neural network configurations. The proposed model is further enhanced by preprocessing and optimising the NARX model’s input and output parameers. The prediction performance is assessed based on the mean squared error (MSE), R-squared, and hit rate. The performance of the proposed model is compared with other models, and it is shown that the utilisation of technical indicators with the NARX neural network improves the accuracy of one-step-ahead prediction for CIMB stock in Malaysia. The performance of the proposed model is further improved by optimising the input data and neural network parameters. The improved prediction of stock prices could help investors increase their returns from investment in stock markets.

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