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Application of Machine Learning in Financial Markets

Author(s)

Jundi Jing, Bingqi Li*, Youtai Wang

Affiliation(s)

Xi’an Jiaotong-liverpool University, Suzhou, China *Corresponding Author.

Abstract

It investigates the applicability of machine learning in financial markets, especially the specific practices of supervised, unsupervised, and reinforcement learning in market forecasting, data analysis, and trading strategy optimization. In the paper, this random forest-based model for stock price prediction is set up and evaluated by collecting and processing historical market data of a financial institution, thus indicating how standardization and hyperparameter optimization can significantly improve model performance. These researchers will focus, in addition to that, on the efficiency of unsupervised learning applied to the domains of pattern recognition in markets and anomaly detection, and also pay attention to reinforcement learning for the optimization of trading strategies in dynamic market conditions. The results of empirical research prove that, although financial markets are hugely uncertain and complex, by rational data processing, constructing models for optimization, machine learning technology can greatly enhance the accuracy of the market forecast and substantially provide very strong support to trading decisions. This paper provides a theoretical foundation and an empirical basis for further extending the application scope of machine learning in more diversified financial scenarios.

Keywords

Machine Learning; Financial Market; Market Forecasting; Data Analysis; Trading Strategy Optimization

References

[1]Nandi, B., Jana, S., Das, K. P.: Machine learning-based approaches for financial market prediction: A comprehensive review. Journal of AppliedMath, 2023. [2]El Hajj, M., Hammoud, J.: Unveiling the Influence of Artificial Intelligence and Machine Learning on Financial Markets: A Comprehensive Analysis of AI Applications in Trading, Risk Management, and Financial Operations. Journal of Risk and Financial Management, 2023. [3]Nandi, B., Jana, S., Das, K. P.: Machine learning-based approaches for financial market prediction: A comprehensive review. Journal of AppliedMath, 2023. [4]Medvedev, A.: Forecasting financial markets using advanced machine learning algorithms. E3S Web of Conferences, 2023. [5]Antulov-Fantulin, N., Kolm, P. N.: Advances of Machine Learning Approaches for Financial Decision Making and Time-Series Analysis: A Panel Discussion. Journal of Financial Data Science, 2023. [6]Parmar, I., Agarwal, N., Saxena, S., Arora, R., Gupta, S., Dhiman, H., Chouhan, L.: Stock Market Prediction Using Machine Learning. 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), 2018. [7]Rundo, F., Trenta, F., Di Stallo, A. L., Battiato, S.: Machine Learning for Quantitative Finance Applications: A Survey. Applied Sciences, 9(24), 5574(2019). [8]Manogna, R. L., Anand, A.: A bibliometric analysis on the application of deep learning in finance: status, development and future directions. Kybernetes, (2023). [9]Ghoddusi, H., Creamer, G. G., Rafizadeh, N.: Machine Learning in Energy Economics and Finance: A Review. Energy Economics, 83, 104793(2019). Henrique, B., Sobreiro, V. A., Kimura, H.: Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications, 124, 226-251(2019).