In this paper, we use a Fama-French model and Markov regime-switching model to capture time series behavior of many financial variable. Alternatively, classification by cluster analysis help to learn the different characteristics of the sample between stock returns and risk factors. This empirical result shows that the excess return in the low volatility state tends to be greater than that in the high volatility state. The stock returns in each regime have a higher probability of remaining in their original state, especilly in low volatility state. This article also found the influence of risk factors affecting the stock returns is not symmetrical. In the state of low volatility, market factors and momentum effect have a significant influence in stock returns, and in the high volatility state, except the size effect, market and behavior factors have a significant influence in stock returns. Markov-switching models have proved to be useful for modeling a range of economic time series in the stock market. The regime-switching model has a superior performance in capturing the risk sensitivities of the stock return beyond the findings based on the Fama-French models. At last, we find the cluster analysis is feasible for the multi-factor model. The returns of mature companies have a primarily impact of market risk premium, while the major factor affecting returns with characteristics of growth companies is a investor sentiment. In addition, it is found that small companies¡¦ returns are vulnerable to investors sentiment. In this case, investors will invest based on stock's past performance, so the momentum effect significantly affect the stock returns.

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This paper explores the relationship between lagged stock returns and export growth in a panel of worldwide markets. Previous studies have focused on analyzing the effect of future economic output growth on stock returns. This study finds that annualized changes in a foreign country’s exports to the United States five to seven years in the future, defined as long-term, positively predict the annual stock market returns while the nation’s total export changes are already priced-in. An additional percentage point increase in long-term exports to the United States growth results in a 0.1 to 3.5 percentage point rise in annual stock returns. However, both growth in total exports and those to the United States do not predict equity returns over the short-term, defined as average annual growth from year 0 to year 4. Thus, establishing a foothold and cracking the highly competitive and homogeneous United States market is not guaranteed and unpredictable, requiring 5 years of investments before successful foreign firms are able convert it into earnings. Alternatively, investors may be shortsighted, uninformed, and pay limited attention about a foreign country’s exports to the United States beyond their forecast horizon, for example, five years. Moreover, the analysis finds that GDP growth at both the foreign country and United States level does not affect lagged foreign stock returns and could be priced-in, unlike long-term growth in the nation’s exports to United States.

The earnings game and myopic performance focus induce managers to use judgment and influence to alter the reported earnings. Earnings management is the umbrella term for such manipulative actions, by accruals management or real activates management. The implicit market reactions by the stock returns indicate the effect of EM and if the behaviors are opportunistic or informative for the stakeholders. Accounting variables explain less of the stock return variation and speculative short-term news drives the variation of stock return. Research Question: Can earnings management indicators improve the forecasting of stock returns? The main purpose of the study is to investigate whether EM can be utilized to forecast returns from improving the forecasting of earnings. The authors will include both AM and RAM measures to investigate the different inherent forecasting abilities, adding to the asset pricing research and valuation area. The authors aim to enhance the explanation of cross-sectional variation of stock returns from accounting variables. The authors aim to develop a model more specified to explain the future stock returns from the accounting relationships. An additional purpose is to include transactions with the firm (stock repurchases) to potentially increase the signaling value of the manipulation behaviors. The theoretical framework consists of a discussion of theories and empirical findings regarding the accounting characteristic and relationship with stock returns. Earnings management is explained in-depth along with the empirical findings related to the concept. The capital market perspective is explained by the efficient market and behavioral finance. The chapter is concluded by concepts explaining the relationship and explanations for earnings management and the impact of information. The sample consists of 3545 firms from NASDAQ and NYSE for the years 1992-2012, which equates to around 40 000 observations. We utilize 11 different EM indicators, constructed to capture abnormal components which indicate manipulative actions. The EM indicators’ association with future stock returns is tested by yearly and industry-yearly firm characteristics framework regressions. The firm characteristic framework is developed to control for firm characteristics and evaluate the standalone effect of EM. The result is expanded by investigating earnings persistence, correlations, robust regression and portfolio sorts. The results suggest that total accruals, discretionary accruals, unexpected core earnings, production cost and stock returns are associated with subsequent stock returns. Abnormal SG&A expenses, Abnormal R&D expenses and abnormal cash flows from operations are not associated with stock returns. Earnings are downward manipulated prior and during stock repurchases. The change in ATO and PM diagnostic captures AM but not RAM. The concluding remarks are that EM indicators are associated with future stock returns and improve the forecasting of stock returns via a more accurate forecast of earnings.

[pt] O Equity Premium Puzzle tem sido muito estudado no mundo desde 1985, ano da publicação do trabalho de Mehra e Prescott. O intuito desta dissertação foi fazer uma análise e avaliação do Equity Premium Puzzle utilizando diferentes contextos vividos na economia brasileira no período de 1990 até 2005. O modelo utilizado foi o do agente representativo com utilidade separável no tempo desenvolvido por Mehra e Prescott (1985). A fim de realizar comparações de resultados foi utilizado também o modelo revisado por Mehra (2003) e um modelo com utilidade tipo Kreps - Porteus com processo de dotação seguindo a cadeia de Markov. / [en] The Equity Premium Puzzle has been very studied in the world since 1985, year of the publication of the work of Mehra and Prescott. The intention of this dissertation was to make an analysis and valuation of the Equity Premium Puzzle being used different contexts lived in the Brazilian economy in the period of 1990 up to 2005. It was used the representative agent model with separable utility in the time developed for Mehra and the Prescott (1985). In order to carry through comparisons of results was used also the model revised for Mehra (2003) and a model with utility type Kreps - Porteus with endowment process having followed the Markov´s chain.

[pt] A teoria clássica de finanças considera que os investidores racionais são capazes de arbitrar ou desfazer distorções provocadas por investidores menos racionais ou providos de sentimento. Entretanto, distorções persistentes nos preços de ativos de mercado levam a crer que há limites à arbitragem. Novos modelos visam a entender como fatores comportamentais influenciam o retorno futuro das ações, de modo a explicar as flutuações de mercado e indicar a formação de bolhas especulativas. Sendo assim, o presente trabalho tem como objetivo construir um índice de sentimento do mercado brasileiro de janeiro de 2002 a dezembro de 2009, verificando as relações entre o sentimento do mercado e a precificação das ações em períodos subseqüentes. Dentre as variáveis para a construção do índice de sentimento, lança-se mão de indicadores relacionados a transações na Bolsa de Valores de São Paulo e na Comissão de Valores Mobiliários brasileira, além do índice de confiança do consumidor. Os resultados deste estudo empírico indicam que há uma relação negativa entre o sentimento dos investidores e a precificação dos ativos no trimestre subseqüente, sendo esta relação mais forte em empresas que apresentam maior risco e menor valor de mercado. / [en] The classical finance theory considers that rational investors are capable of arbitrate or offset distortions provoked by less rational investors. However, persistent distortions in asset pricing suggest that there are limits to arbitrage. New models intend to understand how behavioral factors influence the stock return in subsequent periods, in order to explain market fluctuation and the formation of speculative bubbles. The main objective of the present work is to build a sentiment index of the Brazilian market, from January 2002 until December 2009, verifying the relations between market sentiment and the value of some stocks in the subsequent period. Among the variables used in the sentiment index, there are indicators related to transactions in the São Paulo Stock Exchange and the Brazilian Securities and Exchange Commission and the consumer confidence index. The results of this empirical study indicate that there is a negative relation between the investors’ sentiment and the asset pricing in the following quarter. This relation is stronger in companies that are riskier and have smaller market value.

It is important to know if earnings variables influence stock returns. This is important not just for investors who want to know what drives stock returns, but also for the overall economy as stock returns and stock markets are also considered to be significant indicators of its performance. Many studies were conducted in the past but with inconclusive results. The aim of the thesis is to examine the relationship between net income and stock returns using two approaches, namely panel data model and multiple linear regression. We utilize a dataset of companies selected from the S&P500 Index. We also analyse possible heterogeneity in cross section and time. Moreover, we incorporate additional factors which have been proven to have significant explanation power for stock returns. Our findings from the panel data estimation suggest that there is no relationship between scaled net income and stock returns. We find there are random effects present between the companies and three structural breaks in time. Furthermore, we explore the significance of the consumer sentiment index and the percentage change in the book value per share variables in the panel estimation. We do not confirm the debt to equity ratio and the GDP growth news factors in the panel estimation as significant. Results concerning the...

My dissertation investigated business cycle effects on US sectoral stock returns. The first chapter examined the relationship between the business cycle and sectoral stock returns. First, I calculated constant correlation coefficients between the business cycle and sectoral stock returns. Then, I employed the DCC GARCH model to estimate time-varying correlation coefficients for each pair of the business cycle and sectoral stock returns. Finally, I ran regression of sectoral returns on dummy variables designed to capture the four stages of the business cycle. I found that though sectoral stock returns were closely related to the business cycle, they did not share some of its main characteristics. The second chapter developed two models in order to discuss possible asymmetric business cycle effects on US sectoral stock returns. One was a GARCH model with asymmetric explanatory variables and the other one was an ARCH-M model with asymmetric external regressors. In the second model, square root of conditional variance of the business cycle proxy was characterized as positive or negative risk, depending on the algebraic sign of past innovations driving the business cycle proxy. I found that some sectors changed their cyclicities from expansions to recessions. Negative shocks to business cycles had most power to influence sectoral volatilities. Positive and negative parts of business cycle risk had same effects on some sectors but had opposite effects on other sectors. A general conclusion of both models was that business cycle had stronger effects than own sectoral effects in driving sectoral returns. The third chapter discussed Chinese business cycle effects on US sectoral stock returns at two horizons. At a monthly horizon, the third lag of Chinese IP growth rate had positive effects on most sectors. The second lag of US IP growth rate had positive effects on almost all sectors. At a quarterly horizon, besides the extensive positive effects of the first lag of Chinese IP growth rate, the third and fourth lags also had effects on some sectors. The US IP growth rate had the same pattern, namely positive first and fourth lag effects and negative third lag effects. Using a 5-year rolling fixed window, I found that these business cycle effects were time-varying. The major changes in parameters resulted from the elimination of quota on textiles by WTO, the terrorist attacks on the US, and the 2007 financial crisis.

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This paper proposes the basic predictive regression and Markov Regime-Switching regression to predict the excess stock returns in both US and Sweden stock markets. The analysis shows that the Markov Regime-Switching regression models out perform the linear ones in out-of-sample forecasting, which is due to the fact that the regime-switching models capture the economic expansion and recession better.