In Chapter 1, I investigate whether returns of strategies based on asset pricing anomalies exhibit time series persistence which can be attributed to flow-induced trading by mutual funds. I find persistence for thirteen characteristics, which is statistically significant for five including size, corporate investment, and bankruptcy likelihood. The persistence is not explained by individual stock momentum and is not limited to certain calendar months. The return predictability can be used to construct new trading strategies, which on average earn 4.5% annually. A price pressure measure of mutual fund flow-driven trading explains a substantial part of the strategy performance persistence. In Chapter 2, we propose a new approach for estimating expected returns on individual stocks from firm characteristics. We treat expected returns as latent variables and develop a procedure that filters them out using the characteristics as signals and imposing restrictions implied by a one factor asset pricing model. The estimates of expected returns obtained by applying our method to thirteen asset pricing anomalies generate a wide cross-sectional dispersion of realized returns. Our results provide evidence of strong commonality in the anomalies. The use of portfolios based on the filtered expectations as test assets increases the power of asset pricing tests. In Chapter 3, we examine the sensitivity of fourteen asset pricing anomalies to extreme observations using robust regression methods. We find that although all anomalies except size are strong and robust for stocks with presumably low returns, most of them are sensitive to individual influential observations for stocks with presumably high returns. For some anomalies, extreme observations distort regression results for all stocks and even portfolio returns. When the impact of such observations is mitigated, eight anomalies become positively related to expected returns for stocks with low characteristics meaning that these anomalies have an inverted J-shaped form. Chapter 4 concludes by summarizing the main contributions of three chapters and their implications. / text

In this study, I examine whether the pricing of book-tax differences reflects mispricing or a priced risk factor. I provide new evidence that temporary book-tax differences are mispriced by developing portfolios that trade on the information in book-tax differences for future accruals and cash flows. I develop and test predictions on whether book-tax difference mispricing is the value-glamour anomaly in disguise. Both signals of mispricing relate to firm growth and, thus, both may capture mispricing due to over-extrapolation of realized growth to future growth. I find that the book-tax difference pricing anomaly is subsumed by the value-glamour anomaly. Specifically, trading on the information in book-tax differences does not yield incremental returns relative to a value-glamour trading strategy. Hence, mispricing associated with book-tax differences relates more generally to the mispricing of expected growth as extrapolated from past growth.

Recent studies suggest that the conditional CAPM might hold, period-by-period, and that time-varying betas can explain the failures of the simple, unconditional CAPM. We argue, however, that significant departures from the unconditional CAPM would require implausibly large time-variation in betas and expected returns. Thus, the conditional CAPM is unlikely to explain asset-pricing anomalies like book-to-market and momentum. We test this conjecture empirically by directly estimating conditional alphas and betas from short-window regressions (avoiding the need to specify conditioning information). The tests show, consistent with our analytical results, that the conditional CAPM performs nearly as poorly as the unconditional CAP

We examine the predictability of expected stock returns across horizons using machine learning. We use neural networks, and gradient boosted regression trees on the U.S. and international equity datasets. We find that predictabil- ity of returns using neural networks models decreases with longer forecasting horizon. We also document the profitability of long-short portfolios, which were created using predictions of cumulative returns at various horizons, be- fore and after accounting for transaction costs. There is a trade-off between higher transaction costs connected to frequent rebalancing and greater returns on shorter horizons. However, we show that increasing the forecasting hori- zon while matching the rebalancing period increases risk-adjusted returns after transaction cost for the U.S. We combine predictions of expected returns at multiple horizons using double-sorting and buy/hold spread, a turnover reduc- ing strategy. Using double sorts significantly increases profitability on the U.S. sample. Buy/hold spread portfolios have better risk-adjusted profitability in the U.S. JEL Classification G11, G12, G15, C55 Keywords Machine learning, asset pricing, horizon pre- dictability, anomalies Title Multi-horizon equity returns predictability via machine learning

Este trabalho procura aprofundar o estudo de anomalias ao CAPM no mercado acionário brasileiro e explorar as relações destas anomalias com a característica dificuldade financeira, a qual é representada pela classificação de risco das empresas, usando estratégias de compra e venda a descoberto baseadas nas anomalias. As anomalias estudadas serão o efeito de momento, momento nos lucros, a volatilidade idiossincrática, o crescimento dos ativos, o investimento em capital e o efeito contrário. Nosso objetivo é examinar o impacto da característica dificuldade financeira sobre o retorno esperado das ações de empresas do grupo de menor classificação de risco. Para cumprir nosso objetivo, inicialmente usamos todas as ações da Bolsa de Valores de São Paulo (Bovespa) para comparar estas com a amostra de empresas que possuem classificação de crédito de longo prazo. O período estudado é de Janeiro de 2000 a Dezembro de 2012. Os métodos usados foram baseados em ordenação de carteiras e regressões univariadas e multivariadas de corte transversal. Encontramos algumas evidências de que empresas com classificação de crédito sugerem retornos anormais diferentes daqueles da amostra de todas as empresas. Este resultado foi significante, negativo e persistente em todas as especificações. Evidenciamos também que para empresas do menor tercil de classificação de crédito, o efeito contrário está presente e com retornos anormais positivos e significantes de 2,02% a.m. Isto nos dá alguma evidência de que a deterioração de crédito poderia ter um impacto no retorno ajustado exigido pelos investidores. / In this paper, we extend the CAPM anomalies study field in the Brazilian stock market and we explore the relationship between these anomalies and financial distress, represented by a credit rating classification, using anomaly-based trading strategies. The anomalies selected for this study are: momentum effect, earnings momentum, idiosyncratic volatility, asset growth, capital investment and the reversal effect. Our main goal is to investigate the impact of financial distress on the expected return of companies in lowest credit rating group. To fulfill this goal, first we use all the stocks in the São Paulo Stock Exchange (Bovespa), to compare these with the subset of companies which have a long-term credit rating. We studied the period from January 2000 through December 2012. The procedures carried in this study are based on portfolio sorts and cross-sectional univariate and multivariate regressions. We find evidence that the subsample of companies with a credit rating have abnormal returns different from that of the whole sample. These results are statistically significant, negative and persistent across all specifications. We also find some evidence that for companies in the lowest tercile of credit rating, the reversal effect is present and with positive and statistically significant abnormal returns in the magnitude of 2.02% per month. This gives some evidence that credit deterioration could have an impact on the risk-adjusted return required by investors.

Este trabalho procura aprofundar o estudo de anomalias ao CAPM no mercado acionário brasileiro e explorar as relações destas anomalias com a característica dificuldade financeira, a qual é representada pela classificação de risco das empresas, usando estratégias de compra e venda a descoberto baseadas nas anomalias. As anomalias estudadas serão o efeito de momento, momento nos lucros, a volatilidade idiossincrática, o crescimento dos ativos, o investimento em capital e o efeito contrário. Nosso objetivo é examinar o impacto da característica dificuldade financeira sobre o retorno esperado das ações de empresas do grupo de menor classificação de risco. Para cumprir nosso objetivo, inicialmente usamos todas as ações da Bolsa de Valores de São Paulo (Bovespa) para comparar estas com a amostra de empresas que possuem classificação de crédito de longo prazo. O período estudado é de Janeiro de 2000 a Dezembro de 2012. Os métodos usados foram baseados em ordenação de carteiras e regressões univariadas e multivariadas de corte transversal. Encontramos algumas evidências de que empresas com classificação de crédito sugerem retornos anormais diferentes daqueles da amostra de todas as empresas. Este resultado foi significante, negativo e persistente em todas as especificações. Evidenciamos também que para empresas do menor tercil de classificação de crédito, o efeito contrário está presente e com retornos anormais positivos e significantes de 2,02% a.m. Isto nos dá alguma evidência de que a deterioração de crédito poderia ter um impacto no retorno ajustado exigido pelos investidores. / In this paper, we extend the CAPM anomalies study field in the Brazilian stock market and we explore the relationship between these anomalies and financial distress, represented by a credit rating classification, using anomaly-based trading strategies. The anomalies selected for this study are: momentum effect, earnings momentum, idiosyncratic volatility, asset growth, capital investment and the reversal effect. Our main goal is to investigate the impact of financial distress on the expected return of companies in lowest credit rating group. To fulfill this goal, first we use all the stocks in the São Paulo Stock Exchange (Bovespa), to compare these with the subset of companies which have a long-term credit rating. We studied the period from January 2000 through December 2012. The procedures carried in this study are based on portfolio sorts and cross-sectional univariate and multivariate regressions. We find evidence that the subsample of companies with a credit rating have abnormal returns different from that of the whole sample. These results are statistically significant, negative and persistent across all specifications. We also find some evidence that for companies in the lowest tercile of credit rating, the reversal effect is present and with positive and statistically significant abnormal returns in the magnitude of 2.02% per month. This gives some evidence that credit deterioration could have an impact on the risk-adjusted return required by investors.