AIML funds

So far, we have been able to observe all the main different trading style available within the Preqin (2021a) hedge fund database, but in terms of the topic of this thesis the most essential trading style remains, AI funds. These funds are listed in the database as sys-tematic funds using AI, discretionary funds using AI and as combined funds using AI, but as AI and ML can be considered as a revolutionary new approach altogether that changes the overall style of a fund regardless of its underlying trading style, all hedge fund trading styles using AI are grouped into one. The term AIML, artificial intelligence machine learn-ing, refers to AI and ML being used in the funds and this is the same notation as used in Preqin (2020, p. 36-37) report, hence its usage in this thesis.

The choice to combine funds into AIML funds is also due to the themes of this thesis where AI usage in general is to be compared against more conventional hedge funds.

Additionally, the findings by Harvey et al. (2017) have motivated this decision, as they

found that discretionary funds using AI and ML frameworks and systematic funds doing the same are more common than generally believed, and as such the differences amongst these trading styles will be negligible in the future.

AIML funds can be therefore seen as combined funds in this thesis, in the regard that they also combine approaches used by both systematic and discretionary funds. Still as discussed before AIML funds are to be considered as their own trading style due to the large fundamental differences when compared to the other conventional fund styles dis-cussed before.

As we have seen in the literature review part of this thesis, AI is very different from tra-ditional algorithms in multiple different aspects. In terms of AIML funds the same con-clusions can be drawn. As noted by both Sun et al. (2012) and Stein (2009), the unique-ness of trading strategies is especially important for performance, as common strategies will have their abnormal returns disappear due to increased competition surrounding the same anomaly. Matias and Reboredo (2012) on the other hand were able to uncover that AI models are more suited for dealing with nonlinear market data as opposed to traditional algorithms and Gerlein et al. (2016) note the general consensus that AI mod-els are better for financial forecasting. Mullainathan and Spiess (2017) on the other hand detail how AI models do not require specific programming as they are simply given an input along with a desired output, and the model itself finds the best course of action in terms of a function.

As such AIML funds can be noted as being inherently better than systematic funds and as being notable better at uncovering unique trading strategies that were seen as a key source of outperformance. If the costly process of creating a trading strategy isn’t needed and an AI model is able to find it automatically, one can hypothesize that an AI model will be able to find near infinite combinations of possible strategies as the param-eters and the complexities of these models are adjusted.

Therefore, at least in theory AIML funds should not have similar issues with commonality in trading strategies and when compared against the conventional hedge fund trading styles seen so far, it can be noted that the nearest fund style is the combined approach.

As AI models are able to learn and are not static trading algorithms as was the case with systematic funds, AI models are also able to adapt. Combined funds were seen as taking this combined approach to especially benefit from the advantages of having more hu-man involvement, which can also be noted as their ability to adapt. Conversely to com-bined funds AIML funds do not need to try to find a balance between this level of human involvement which is necessary, and involvement caused by behavioral biases and as such the AIML approach can already be considered superior on paper.

AIML funds are therefore able to operate without emotion like systematic funds, and follow their own set strategies to perfection, have the ability to adapt and learn that discretionary human-driven funds are able to leverage and to have all the before men-tioned aspects without fundamental issues, such as the difficulty of combined funds to decide when human involvement is appropriate.

Chen et al. (2004) note that while unique strategies are difficult to obtain in the first place, these are also difficult to scale up. As discussed, AI models are able to learn, and these models can also be retrained so that new strategies can be obtained. The difficulty in scaling up strategies has been noted by other research papers so far and in terms of AIML funds this issue is naturally also present. The Preqin (2021, p. 19-20) report notes that especially managers of small funds are able to pursue more risky and exotic trading strategies, and one could say that AI trading can still be categorized in this group. Hence, AIML funds can be theorized to be small in terms of AUM and further confirmation for this can be obtained by observing the algorithm aversion shown by investors detailed by Dietvorst et al. (2015).

Gerlein et al. (2016) are able to show in their research paper that AI models have shown great successes in forecasting asset prices and that they are also able to uncover hidden

patterns in data that can lead to the creation of completely different strategies than what human traders could imagine or even comprehend. As such the understanding of why AI models operate the way they do might become blurred and this is also noted by Harvey et al. (2017). Gerlein et al. (2016) also detail that while all the before mentioned ad-vantages are present, AI models and hence AIML funds still need to focus on periodic retraining of models to keep them relevant due to the dynamics of the markets.

Thus, concludes our analysis of the different hedge fund trading styles in the scope of this research, organized similarly as is seen in the Preqin (2021a) hedge fund data in the following chapter. It can be seen that all funds of conventional trading styles are rather similar, with differences coming mostly from the data that is being used and the way trading strategies are executed. Additionally, it can be noted that AIML funds are funda-mentally different from conventional style funds in various aspects and now it remains to be seen if this difference is reflected as outperformance against the other hedge fund trading styles.