Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Management Research and Economic Development
Series Vol. 86 , 28 May 2024
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Exploring Efficient Quantitative Trading Strategies: A Comprehensive Comparison of Momentum, SMAs and Machine Learning
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Abstract
To provide an objective analysis, this study examines three quantitative trading strategies: Momentum, Moving Average Crossover, and Machine Learning individually but in a common methodological setting. In order to achieve higher returns at lower levels of risk due to the advent of algorithmic trading, such strategies must be explored. The two strategies that we analyze include the Momentum strategy that capitalizes on the persistence in price trends and the Moving Average Crossover strategy that relies on average price movements as trading signals. In addition, in this study, Machine Learning methods are applied which implement predictive algorithms to predict the price movements in the future based on their historical patterns. In order to assess the performance of each strategy, this investigation relies on one data set and uses a series of financial metrics to see how well each strategy performs with the objective of identifying both strengths and weaknesses that these strategies exhibit within different market situations.
Keywords
Quantitative Trading, Momentum Strategy, Moving Average Crossover, Machine Learning, Strategy Comparison
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Management Research and Economic Development
- ISBN (Print)
- 978-1-83558-439-2
- ISBN (Online)
- 978-1-83558-440-8
- Published Date
- 28 May 2024
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/86/20240940
- Copyright
- 28 May 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited