Stock Market Index Prediction Using Artificial Neural Network

Abstract

Often, nonlinearity exists in the financial markets while Artificial Neural Network (ANN) could be used to expect equity market returns for the next years. ANN has been improved its ability to forecast the daily stock exchange rate and to investigate several feeds using the back propagation algorithm. The proposed research utilized five neural network models, Elman network, Multilayer Perceptron (MLP) network, Elman network with Self-Optimizing Map (SOM), MLP with SOM filter and simple linear regression, for estimating new values. Results were examined to investigate the predicting ability and to provide an effective feeds for future values. The result of the proposed simulation showed that SOM could greatly improve the convergence of the neuron networks; whereas Elman network did a better performance to capture the temporal pattern of the symbolic streams generated by SOM.A benchmark of linear regression model was also employed to show the ability of neural network models to generate higher accuracy in forecasting financial market index.

Predicting Stock Market Movements Using Neural Networks: A Review and Application Study

“…This approach not only aids in demystifying the decision-making process of ANNs but also fosters trust in their use for stock market predictions. Al-akashi (2022) demonstrates the application of ANNs in forecasting stock market indices, utilizing various neural network models to predict future values. This research underscores the potential of ANNs to capture the nonlinearity in financial markets, offering a more nuanced understanding of market dynamics compared to traditional linear models.…”

A Machine Learning Application for Medical Image Analysis Using Deep Convolutional Neural Networks (CNNs) and Transfer Learning Models for Pneumonia Detection

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“…By integrating a new decision-support mechanism with the VGG16 model, this model enhances the ability to differentiate between viral and bacterial pneumonia in children's chest X-ray images.Data analysis and prediction are at the heart of machine learning, a subfield of artificial intelligence. To summarise, the goal of machine learning is to understand and use humancomprehensible models by deciphering data structures [1].Although it falls within the umbrella of computer science, machine learning stands apart from traditional approaches to computing. If we talk about computers doing computations or solving problems by following established sets of instructions, we're talking about conventional algorithmic computing.…”

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The Impact of Foreign Stock Market Indices on Predictions Volatility of the WIG20 Index Rates of Return Using Neural Networks

“…In finance neural networks are used in the study of financial series (Krollner et al, 2010;Zhang et al, 2023), stock prices (Bodart & Candelon, 2009;Niu et al, 2023), stock market indices (Radomska, 2021;Alkhoshi & Belkasim, 2018;Kumar & Murugan, 2013;Moghaddam et al, 2016;Song & Choi, 2023;Bhandari et al, 2022;Al-Akashi, 2022), forecasting volatility of many financial variables (Donaldson & Kamstra, 1996a, b;Salchenberger et al, 1992;Kristjanpoller et al, 2014;Ramos-Pérez et al, 2019;Liu et al, 2017;Hamid & Iqbal, 2004;Sahiner et al, 2021). For example, volatility analysis of the S&P 500 index using the LTSM40 network was conducted (Xiong et al, 2016).…”