Artificial intelligence and hedge fund performance : An analysis of hedge fund trading styles - Niang, Joachim Amath Fabian (My University, 2021)
This study focuses on understanding the relationship between the level of automation employed by hedge funds on the level of performance that these funds are able to obtain. As technologies are constantly evolving and being used to further different fields, one could ask if the adaptation of the latest technological advancements in term of artificial intelligence could be used to fur- ther the trading performance of hedge funds. As hedge funds enjoy less restrictions for their trading processes, they are at a prime position to take advantage of every edge that can be obtained. Using data from the Preqin hedge fund database we can to uncover this level of automation by sorting funds based on their trading styles. The term AIML hedge funds refers to hedge funds using both artificial intelligence and machine learning. These AIML funds are taken as their own trading style and their performance is compared against systematic, discretionary and combined funds which utilize both the systematic and the discretionary methodologies in their trading processes. Using both the efficient market hypothesis and the behavioral finance frameworks, we are able to conduct a detailed analysis of both the motivation for the need of automation and for the existence of hedge funds. Past literature relating to hedge fund performance, artifi- cial intelligence and algorithmic trading, and hedge fund comparisons are also reviewed in de- tail. By only focusing on funds that trade U.S equities we are able to utilize common factor mod- els used for pricing U.S. equities. Performance is analyzed both in terms of the full sample period and by employing subsample analysis to uncover underlying performance persistence. Based on the results of our factor models we are able to see the statistically significant overper- formance shown by AIML funds. Moreover, our subsample analysis supports these findings and shows that the performance obtained by AIML funds is persistent. When the effects of serial correlation between the fund types is taken into account the outperformance of AIML is further established. Lastly, when comparing the alphas of AIML funds against the other hedge fund trad- ing style portfolios, AIML funds exhibit statistically significant outperformance even at a one percent level of significance. Thus, our results indicate that by using artificial intelligence hedge funds can improve their performance on a persistent basis and to stand out from their peers. Our results are not in breach of the efficient market hypothesis as the underlying reasons for AIML fund performance can be noted as their ability to adapt and their ability to take advantage of small market dislocations. Behavioral finance also shows how adaptability combined with an emotionless ability to execute strategies are key for AIML outperformance Our findings present interesting directions for future research and showcase the likely future trend of increased AI usage within the hedge fund industry.
Keywords: fi=Laskentatoimi ja rahoitus|en=Accounting and Finance|; Master's Degree Programme in Finance