Evidências empíricas: arbitragem no mercado brasileiro com fundos ETFs

According to risk management literature, diversification helps mitigate risk. Index funds, known as exchange-traded funds (ETFs), which were recently introduced into the Brazilian market, make diversification straightforward to accomplish. This paper investigates the efficiency of the valuation process of the Ibovespa iShares with respect to the fair value of the shares. For this purpose, a high-frequency time series analysis of ETFs and Ibovespa was used, followed by strategy simulations that included goodwill and negative goodwill between asset sets with and without transaction costs. To avoid data-snooping effects on the transaction outcomes, a time series bootstrap was applied. The results initially indicated share-pricing inefficiency because the inclusion of goodwill and negative goodwill in the strategy resulted in returns of 172.5% above the fund's index. Additionally, it became apparent that even with the introduction of operating costs, the gains continued to exhibit inefficiency. However, after applying the bootstrap technique, the results did not suggest excess returns, which could be attributed to data snooping. Therefore, the results demonstrate the impossibility of agents earning abnormal returns from the differences between the values of the ETF share and its corresponding index, thereby indicating that the Ibovespa iShare fund pricing is efficient.De acordo com a literatura de gestão de risco, a diversificação contribui para a mitigação do risco. Neste sentido, os fundos de índice Exchange Traded Funds (ETF), recém-introduzidos no mercado brasileiro, permitem sua fácil execução. Dentro deste contexto, o presente artigo investiga a eficiência do processo de valuation das cotas do fundo iShare Ibovespa com relação ao seu valor justo. Para isto, primeiramente é empregada uma análise das séries temporais de alta frequência do ETF e Ibovespa, seguido de simulações de estratégias que contemplem ágios/deságios entre as séries dos ativos, sem e com custos de transação. A fim de evitar efeitos de Data-Snooping nos resultados das operações, foi aplicado um Bootstrap para séries temporais. No primeiro momento os resultados apontam para uma ineficiência do apreçamento das cotas, visto que a incorporação de ágios/deságios na estratégia produziu retornos de 172,5% acima de seu índice. No segundo, verifica-se que mesmo com a introdução dos custos operacionais, os ganhos ainda assim apresentam ineficiência. Entretanto, a partir da técnica de Bootstrap, os resultados não apontaram para retornos excedentes, o que pode ser atribuído meramente ao fenômeno de Data-Snooping. Os resultados evidenciam, portanto, a inviabilidade dos agentes em auferir rendimentos anormais a partir de divergências entre os valores da cota do ETF e seu respectivo índice, o que indica uma eficiência nas precificações das cotas do fundo iShare Ibovespa

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