科技论文的标准格式图(Application of Machine Learning in Predicting Stock Prices A Literature Review - Machine Learning for Stock Price Prediction A Review)

科技 2023年10月02日 07:15 90 琥珀街作者

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Introduction

In recent years, machine learning techniques have gained immense popularity due to their successful application in various fields. One such field which has seen tremendous growth in the application of machine learning techniques is the stock market. Stock market prediction has always been a fascination for investors as it allows them to make informed decisions about buying and selling stocks. Machine learning techniques have shown promising results in predicting stock prices. In this paper, we will provide a literature review of the application of machine learning in predicting stock prices.

Previous Research

A lot of research has been done in the field of stock market prediction using machine learning techniques. One such research by Brown and Cliff (2003) used a combination of neural networks and technical analysis to predict stock prices. The study showed that neural networks were better at predicting short term stock prices while technical analysis was better at predicting long term stock prices. Another study by Tsai and Hung (2010) used an adaptive neural network with fuzzy time series to predict stock prices. The results showed that their model outperformed traditional time series models in predicting stock prices.

Types of Machine Learning Techniques Used

There are several types of machine learning techniques used in predicting stock prices. One such technique is regression analysis which is used to predict the linear relationship between a dependent variable and independent variable. Another technique is decision tree which is used to predict the outcome based on a set of conditions. Neural networks are also widely used in predicting stock prices where the network learns from past data to make predictions. Support vector machines (SVMs) are another popular machine learning technique used to predict stock prices. SVMs are used to find the best separation boundary between different classes of data.

Challenges and Limitations

Despite the promising results of using machine learning techniques in predicting stock prices, there are several challenges and limitations. One of the main challenges is the availability of data. Stock prices often fluctuate rapidly and it is difficult to capture and interpret all the relevant data. Another challenge is the complexity of the stock market which involves numerous factors such as market trends, economic indicators, and geopolitical events. These factors make it difficult to accurately predict stock prices using machine learning techniques.

Conclusion

In conclusion, the application of machine learning techniques in predicting stock prices has gained immense popularity due to their successful results. Previous research has shown that various machine learning techniques can be used to predict stock prices with varying degrees of accuracy. However, there are several challenges and limitations in using these techniques which need to be addressed. Further research needs to be done to improve the accuracy of these techniques and overcome the limitations and challenges that exist in the field of stock market prediction.