Comparative analysis of machine learning and deep learning techniques for prediction of the stock market
Comparative study of machine learning and deep learning techniques for stock market prediction, highlighting CNN's superior performance.
What it examines
This paper compares machine learning and deep learning techniques for stock market forecasting, introducing data visualization for preprocessing. It evaluates logistic regression, support vector machine, multi-layer perceptron, and convolutional neural networks to determine the most effective model for predicting stock prices.
What it concludes
The research suggests that CNNs are highly effective for stock market prediction, outperforming traditional ML models. Future research should focus on complex models and ensemble learning to improve accuracy. Potential applications include better investment decision-making and enhanced financial market analysis.
Evidence objects
The research suggests that CNNs are highly effective for stock market prediction, outperforming traditional ML models. Future research should focus on complex models and ensemble learning to improve accuracy. Potential applications include better investment decision-making and enhanced financial market analysis.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
In recent years, there has been a continuing search for reliable instruments that can predict trends in financial markets and activities related to investments. In the past, academics have used traditional methods to forecast the investment worth of equities by analyzing metrics such as the financial records of companies from both a fundamental and technical point of view. The effectiveness of these strategies could decrease as market information asymmetry continues to rise and high-frequency trading becomes increasingly prevalent. Researchers have developed novel methodologies as a result of the progress that has been made in the field of artificial intelligence technology. One of these methodologies is the application of neural networks for forecasting. In the meantime, data visualization is becoming increasingly common, which could make it easier to conduct an in-depth analysis of the advantages and disadvantages presented by various models. The purpose of this research is to evaluate the performance of machine learning and deep learning strategies, including logistic regression, support vector machine, multi-layer perceptron and convolution neural networks, in forecasting stock market prices where various data visualization techniques are utilized for investigation. The findings from error analysis demonstrate that convolutional neural networks operate superbly.
Source row: 429 · abstract type: unknown