How effective is machine learning in stock market predictions?

Ayyildiz, Nazif; Iskenderoglu, Omer

doi:10.1016/j.heliyon.2024.e24123

Cited by 4 publications

(2 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

Neural Network-Based Predictive Models for Stock Market Index Forecasting

Chahuán-Jiménez

2024

JRFM

View full text Add to dashboard Cite

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.

show abstract

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%

Neural Network-Based Predictive Models for Stock Market Index Forecasting

Chahuán-Jiménez

2024

JRFM

View full text Add to dashboard Cite

show abstract

A Performance Analysis of Stochastic Processes and Machine Learning Algorithms in Stock Market Prediction

Bouasabah

2024

Economies

View full text Add to dashboard Cite

In this study, we compare the performance of stochastic processes, namely, the Vasicek, Cox–Ingersoll–Ross (CIR), and geometric Brownian motion (GBM) models, with that of machine learning algorithms, such as Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN), for predicting the trends of stock indices XLF (financial sector), XLK (technology sector), and XLV (healthcare sector). The results showed that stochastic processes achieved remarkable prediction performance, especially the CIR model. Additionally, this study demonstrated that the metrics of machine learning algorithms are relatively lower. However, it is important to note that stochastic processes use the actual current index value to predict tomorrow’s value, which may overestimate their performance. In contrast, machine learning algorithms offer a more flexible approach and are not as dependent on the current index value. Therefore, optimizing the hyperparameters of machine learning algorithms is crucial for further improving their performance.

show abstract

Exploring the Future of Stock Market Prediction through Machine Learning: An Extensive Review and Outlook

Jain,

Saluja,

Pimplapure

et al. 2024

IJISME

View full text Add to dashboard Cite

A thorough analysis of trends and future directions reveals how machine learning is revolutionizing stock market forecasting. The most recent research on machine learning applications for stock market prediction during the previous 20 years is methodically reviewed in this article. Artificial neural networks, support vector machines, genetic algorithms in conjunction with other methodologies, and hybrid or alternative AI approaches were the categories used to group journal articles. Every category was examined to identify trends, distinct perspectives, constraints, and areas that needed more research. The results provide insightful analysis and suggestions for further study in this developing topic.

show abstract

How effective is machine learning in stock market predictions?

Cited by 4 publications

References 41 publications

Neural Network-Based Predictive Models for Stock Market Index Forecasting

Neural Network-Based Predictive Models for Stock Market Index Forecasting

A Performance Analysis of Stochastic Processes and Machine Learning Algorithms in Stock Market Prediction

Exploring the Future of Stock Market Prediction through Machine Learning: An Extensive Review and Outlook

Contact Info

Product

Resources

About