Author: John    Kay

Major: Finance

Graduation Year: 2019

Thesis Advisor: Matthew Skaves

Description of Publication: This study is motivated by the theoretical framework that suggests market timing and other algorithmic trading strategies can add value in some aspects of the investment and portfolio management process. This study examines whether a moving average crossover strategy can outperform a buy and hold strategy across a set of stock portfolios. Although the algorithm used in this study is likely to underperform over the long run, the end-of-year selloff in 2018 revealed that moving average trading strategies add value during down markets. Consistent with Marshall et al. (2012) and Han et al. (2012), this study finds that the performance of the algorithm is amplified by the annual return, standard deviation, and downside deviation of the underlying portfolios of stocks. Where the preceding studies examine stock indices, this study examines exchange traded funds (ETFs) across six categories based on market capitalization: total market, mega cap, large cap, mid cap, small cap, and micro cap. The study finds that the algorithm outperforms the conventional buy and hold strategy across all ETFs during the observed down markets. Additionally, the study finds that the algorithm outperforms the most on the ETFs that experience the greatest selloffs. In conclusion, the study is optimistic about the use of trading algorithms to reduce the impact of market selloffs on investment returns.

Location of Publication:

URL to Thesis: https://digitalcommons.library.umaine.edu/honors/510/