How effective is machine learning in stock market predictions?

Ayyildiz, Nazif; Iskenderoglu, Omer

doi:10.1016/j.heliyon.2024.e24123

Cited by 7 publications

(2 citation statements)

References 41 publications

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“…According to Chhajer et al (2022), ANNs are among the most widely used algorithms for stock market forecasting and analysis, and prove to be the best modelling technique for many datasets. According to Ayyildiz and Iskenderoglu (2024), ANNs were the best method for predicting the movement direction of stock market indices. They considered data from the period between 2012 and 2021 in this study of developed countries.…”

Section: Literature Reviewmentioning

confidence: 99%

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Neural Network-Based Predictive Models for Stock Market Index Forecasting

Chahuán-Jiménez

2024

JRFM

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The stock market, characterised by its complexity and dynamic nature, presents significant challenges for predictive analytics. This research compares the effectiveness of neural network models in predicting the S&P500 index, recognising that a critical component of financial decision making is market volatility. The research examines neural network models such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Recurrent Neural Network (RNN), and Gated Recurrent Unit (GRU), taking into account their individual characteristics of pattern recognition, sequential data processing, and handling of nonlinear relationships. These models are analysed using key performance indicators such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Directional Accuracy, a metric considered essential for prediction in both the training and testing phases of this research. The results show that although each model has its own advantages, the GRU and CNN models perform particularly well according to these metrics. GRU has the lowest error metrics, indicating its robustness in accurate prediction, while CNN has the highest directional accuracy in testing, indicating its efficiency in data processing. This study highlights the potential of combining metrics for neural network models for consideration when making decisions due to the changing dynamics of the stock market.

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Section: Literature Reviewmentioning

confidence: 99%

“…According to Ayyildiz and Iskenderoglu (2024), advance of forecasting in models began with (Markowitz 1952) whose investment portfolio gave rise to modern portfolio theory. In 1963, a single index model was developed to maximise returns on alternative equity investments.…”

Section: Introductionmentioning

confidence: 99%