Design and Implementation of Machine Learning Based Multi Factor Quantitative Trading Strategy

Yu Zhang ()
Additional contact information
Yu Zhang: Beijing University of Posts and Telecommunications, School of Computer Science

A chapter in Proceedings of the 2022 International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2022), 2022, pp 977-984 from Springer

Abstract: Abstract Quantitative trading is a trading method that combines finance, mathematics, and computer science to achieve a goal. This method can help investors to filter out negative emotional influences effectively so that it is becoming more and more widely used in the Chinese stock market. Traditional quantitative trading strategies predict the trend of stock prices by analyzing fundamental indicators or technical indicators and building formulas quantitatively. However, this paper will use the emerging machine learning technologies to analyze the influence of multiple factors which impacts the stock price, then predict the return of particular stocks and stress the trading strategy. This research’s main work consists of obtaining financial data from third-party platforms and defining indicators; using Support Vector Machine, Random Forest, and XGBoost machine learning algorithms to build prediction models to predict which stock can bring excess return; generating the stock holding list; and designing the trading strategy accordingly. The outcomes are a multiple factors quantitative trading strategy based on machine learning which brings a steady excess return ratio while bearing low risk. The research achievement solves some problems in the current quantitative trading strategy: the selection of indicators is biased, a single machine learning model is not effective through a long period of time, the process of strategy research is not convenient enough.

Keywords: Quantitative trading; Machine learning; Stock indicators; Strategy optimization (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-052-7_111

Ordering information: This item can be ordered from
http://www.springer.com/9789464630527

DOI: 10.2991/978-94-6463-052-7_111

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().