Abstract
In this research, I investigate the performance of three technical trading strategies—Moving Average Crossover, Bollinger Bands, and a combined Bollinger Bands with RSI ap- proach—applied to three major U.S. stocks: Apple (AAPL), Microsoft (MSFT) and Meta (META). By systematically tuning key parameters and conducting in-sample and out- of-sample testing, the study aims to evaluate the effectiveness and robustness of these strategies. The results show that it is possible to identify parameter sets that outperform the SPY benchmark during the in-sample period. In particular, the combined Bollinger Bands and RSI strategy achieved the highest returns on META. However, out-of-sample analysis reveals that Method 3 did not execute trades, highlighting limitations in signal adaptability. Meanwhile, Methods 1 and 2 maintained stronger performance on MSFT and META during the out-of-sample period but failed to beat the benchmark on AAPL. These findings emphasize the trade-offs between optimizing historical performance and ensuring future robustness. In general, this study underscores the importance of combining technical signals with adaptive parameter management to improve the resilience of strategies in real world markets.
Advisor
Long, Colby
Department
Statistical and Data Sciences
Recommended Citation
Sainbuyan, Bat-Orgil, "Exploring Trend-Following and Mean Reversion Strategies: Moving Averages, RSI, and Bollinger Bands on Apple, Microsoft, and Meta" (2025). Senior Independent Study Theses. Paper 11680.
https://openworks.wooster.edu/independentstudy/11680
Disciplines
Data Science
Publication Date
2025
Degree Granted
Bachelor of Arts
Document Type
Senior Independent Study Thesis
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