Artificial intelligence and hedge fund performance : An analysis of hedge fund trading styles

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

An investigation of the behaviour of financial markets using agent-based computational models

PhD ThesisThis thesis aims to investigate the behaviour of financial markets by using agent-based computational models. By using a special adaptive form of the Strongly Typed Genetic Programming (STGP)- based learning algorithm and real historical data of stocks, indices and currency pairs I analysed various stylized facts of financial returns, market efficiency and stock market forecasts. This thesis also sought to discuss the following: 1) The appearance of herding in financial markets and the behavioural foundations of stylised facts of financial returns; 2) The implications of trader cognitive abilities for stock market properties; 3) The relationship between market efficiency and market adaptability; 4) The development of profitable stock market forecasts and the price-volume relationship; 5) High frequency trading, technical analysis and market efficiency. The main findings and contributions suggest that: 1) The magnitude of herding behaviour does not contribute to the mispricing of assets in the long run; 2) Individual rationality and market structure are equally important in market performance; 3) Stock market dynamics are better explained by the evolutionary process associated with the Adaptive Market Hypothesis; 4) The STGP technique significantly outperforms traditional forecasting methods such as Box-Jenkins and Holt-Winters; 5) The dynamic relationship between price and volume revealed inconclusive forecasting picture; 6) There is no definite answers as to whether high frequency trading is harmful or beneficial to market efficiency

An Empirical Investigation On The Post-Earnings Announcement Drift And AlgorithmicTrading

Motivated by the widespread adoption of AT in financial markets, this dissertation investigates whether algorithmic trading (AT) reduces the Post-Earnings Announcement Drift (PEAD), the financial anomaly where investors under-react to earnings information. Studies suggest AT is associated with sophisticated trading and lower transaction costs and these two factors contribute to lowering PEAD. I conjecture algorithmic traders have an incentive to profit from (and therefore reduce the presence of) PEAD; however the evidence presented in this thesis fails to show that AT attenuates this anomaly. This thesis is composed of three essays. The first essay (Chapter 2) identifies the factors that explain PEAD and asks two questions: 1) does PEAD still exist; and 2) if so, has it been fully explained. I find PEAD remains a statistically and economically significant anomaly and that low investor sophistication, arbitrage risk and transaction costs are robust but nevertheless incomplete explanations. In other words, one, albeit incomplete, explanation for PEAD is that investors with low sophistication systematically under-react to earnings information and sophisticated traders cannot fully arbitrage the mispricing due to unhedgeable idiosyncratic risks and transaction costs. The second essay (Chapter 3) considers whether AT’s association with lower transaction costs and sophisticated trading implies AT attenuates PEAD. I further conjecture that if sophisticated algorithmic traders are better at extracting trading signals from earnings information AT should also improve price discovery around earnings announcements. After controlling for other explanatory factors, however, my findings show that AT does not contribute to the attenuation of PEAD, but that it is associated with improved price discovery. The third and final essay (Chapter 4) provides an explanation for why the relation between AT and PEAD may be insignificant. I suggest order-splitting can result in the under-estimation of transaction costs (measured by effective spreads) and I argue one predominant function of AT is to execute large orders via sequences of small transactions. I therefore adjust for a potential bias in the measure of effective spreads by treating sequences of consecutive buy or sell orders as a single transaction. I then revisit a popular study which documents the market impact of AT but show that a structural increase in AT is associated with insignificant improvements in effective spreads