上市公司综合盈利水平与股票收益

本文基于2000-2017年上市公司的财务及股票交易数据,研究了上市公司综合盈利水平与股票收益之间的关系。我们使用目前资产定价文献中较新的偏最小二乘法和组合预测法,从12个衡量公司盈利能力的指标中提取了一个测度上市公司综合盈利水平的指标。研究结果显示,上市公司综合盈利水平能够显著预测未来股票收益。使用单因子偏最小二乘法、取12个月斜率的平均值构造的综合盈利水平最有效,以其构建的多空对冲投资组合能产生15%的年平均收益,夏普比率达到0.75。与此对应,组合预测法提取的上市公司综合盈利水平的预测能力稍低,但依然显著。在控制了其他公司特征变量后,综合盈利水平对于股票收益的解释能力依然稳健。本文还从经济机制的角度出发,探讨了综合盈利水平对收益的预测来源。我们发现,上市公司综合盈利水平与股票预期回报的正向关系在投资摩擦更低的组中更高,而在错误定价程度更高的组通常更低。这些结果支持了基于投资摩擦的Q理论,而与行为金融的错误定价理论相悖。     

Idiosyncratic momentum and the cross‐section of stock returns: Further evidence

In this article, we evaluate the profitability and economic source of the predictive power of the idiosyncratic momentum effect, by using five popular asset pricing models to construct the idiosyncratic momentum. We show that all five idiosyncratic momentum strategies produce similar return predictability and consistently outperform the conventional momentum strategy in the cross‐sectional pricing of equity portfolios and individual stocks. This positive effect of idiosyncratic momentum on returns is consistent with the investment capital asset pricing model (CAPM). Further analysis reveals that the firm‐level idiosyncratic momentum effect cannot extend to the aggregate stock market.     

Cash, investments and asset returns

We use an investment-based asset pricing model to examine the effect of firms’ investments relative to cash holdings on stock returns, assuming holding cash lowers transaction costs. We find that mimicking portfolios based on investments relative to non-cash capital and based on investments relative to cash capital are priced for various testing portfolios. On average, momentum stocks and growth stocks are more sensitive to the factor constructed using investment relative to cash.     

Investor Sentiment and REIT Returns

Recent advances in the field of behavioral finance have given a fillip to the use of behavioral factors in asset pricing models. This study adds to the understanding of the REIT return generating process by exploring the behavioral impact of investor sentiment on REIT returns. The results show that when investors are optimistic (pessimistic), REIT returns become higher (lower). These findings are robust when conventional control variables are considered. Empirical analysis indicates steady erosion in the importance of the default and term structure interest rate variables previously considered as important determinants of REIT returns. Previous noise trading papers that consider the impact of institutional traders conclude that institutional investors cannot arbitrage away noise trader risk. The results of this paper find an exception in the case of small REITs. Examination of REITs based on size reveals that the return generating process of small REITs differs from that of mid-size and large REITs. Analysis of the return generating process by performance shows high performance REITs are more sensitive to the independent variables in the model as compared to the low and mid performance REITs.     

Firm profitability and expected stock returns: Evidence from Latin America

Despite their higher valuation ratios, larger size, and higher investment needs, profitable firms outperform, in both raw and risk-adjusted returns, unprofitable firms in Latin America. The positive effect of firm profitability on stock returns is pervasive in univariate and bivariate sorts, panel regressions, across sub-regional markets, and among small and large stocks. A five-factor model that includes market, size, distress, profitability, and investment factors prices profitability portfolios better than other popular factor models. Five-factor alphas of profitability portfolios tend to be lower and less statistically significant, both individually and collectively, than alphas from other three widely-used pricing models.     

Profitability and investment‐based factor pricing models

Ex ante predictors of stock returns must exhibit explanatory power across the feasible set of investments. But empirical results of factor pricing models that incorporate firm investment and profitability cannot explain the apparently high returns of US small stocks with very high investment levels and very low profitability. Whilst these stocks comprise only a small fraction of US data sets, this is not the case across global markets. Using a data set that is concentrated with stocks that exhibit high investment despite low profitability, we demonstrate that such factor models are limited in their explanatory power over these stocks.     

Discovering REIT Price Discovery: A New Data Setting

This study decomposes real estate investment trust (REIT) returns into two components: (1) real returns, and (2) public returns. The real returns are based on the changes in the private, appraisal-based net asset values of REITs, whereas the public returns are measured by the variations in REITs’ premiums/discounts. This study then investigates the price discovery of REIT prices. The results indicate that lagged public returns are useful in predicting real returns. In addition, the study documents concurrent factor exposures for public returns and lagged factor exposures for private returns under a variety of asset pricing models. Overall, the results are consistent with the notion that public markets are more efficient in processing information.

Investment and the Cross‐Section of Equity Returns

The data show that, upon being hit by adverse profitability shocks, large public firms have ample latitude to divest their least productive assets, reducing the risk faced by shareholders and the returns that they are likely to demand. In the one‐factor production‐based asset pricing model, when the frictions to capital adjustment are shaped to respect the evidence on investment, the model‐generated cross‐sectional dispersion of returns is only a small fraction of that documented in the data. Our conclusions hold even when operating or labor leverage is modeled in ways shown to be promising in the extant literature.     

The Variation of Economic Risk Premiums in Real Estate Returns

We examine the predictable components of returns on stocks, bonds, and real estate investment trusts (REITs). We employ a multiple-beta asset pricing model and find that there are varying degrees of predictability among stocks, bonds, and REITs. Furthermore, we find that most of the predictability of returns is associated with the economic variables employed in the asset pricing model. The stock market risk premium is highly important in capturing the predictable variation in stock portfolios, and the bond market risk premiums (term and risk structure of interest rates) are important in capturing the predictable variation in bond portfolios. For REITs, however, both the stock and bond market risk premiums capture the predictable variation in returns. REITs have comparable return predictability to stock portfolios. We conclude that there is an important economic risk premium for REITs that are not captured by traditional multiple-beta asset pricing models.     

Assessing the Real Estate Pricing Puzzle: A Diagnostic Application of the Stochastic Discounting Factor to the Distribution of REIT Returns

This article applies a general asset-pricing framework and the volatility bounds methodology of Hansen and Jagannathan (1991) to REIT returns. The state of real estate asset pricing remains somewhat of a puzzle relative to the identification of state variables and the structural form of models. This article offers a framework whereby real estate asset-pricing models and data can be diagnosed to answer questions about the shortcomings. In addition, several nominated discount processes are investigated for success in pricing real estate securities. Although the nominated specifications demonstrate some success in satisfying the restrictions on the first and second moments of the real estate returns distribution, they do not successfully price the securities under a no-arbitrage condition. This result calls into question previous real estate performance studies that employ these risk-adjustment processes.     

Corporate financing decisions, managerial market timing, and real investment

Both market timing and investment-based theories of corporate financing predict under-performance after firms raise capital, but only market timing predicts that the composition of financing (equity compared with debt) should also forecast returns. In cross-sectional tests, we find that the amount of net financing is more important than its composition in explaining future stock returns. In the time series, investment-based factor models explain abnormal stock performance following a variety of corporate financing events that previous studies link to market timing. At the aggregate level, the amount of new financing is also more important for future market returns than its composition. Overall, our joint tests reveal that measures of real investment are correlated with future returns and measures of managerial market timing are not.     

Can Linear Predictability Models Time Bull and Bear Real Estate Markets? Out-of-Sample Evidence from REIT Portfolios

A recent literature has shown that REIT returns contain strong evidence of bull and bear dynamic regimes that may be best captured using nonlinear econometric models of the Markov switching type. In fact, REIT returns would display regime shifts that are more abrupt and persistent than in the case of other asset classes. In this paper we ask whether and how simple linear predictability models of the vector autoregressive (VAR) type may be extended to capture the bull and bear patterns typical of many asset classes, including REITs. We find that nonlinearities are so deep that it is impossibile for a large family of VAR models to either produce similar portfolio weights or to yield realized, ex-post out-of-sample long-horizon portfolio performances that may compete with those typical of bull and bear models. A typical investor with intermediate risk aversion and a 5-year horizon ought to be ready to pay an annual fee of up to 5.7 % to have access to forecasts of REIT returns that take their bull and bear dynamics into account instead of simpler, linear forecast.     

A latent factor model for the Chinese stock market

We propose a new latent factor model for the Chinese stock market based on an instrumented principal component analysis (IPCA). Compared with other common asset pricing models, the new latent factor model explains a larger proportion of individual and portfolio return variation and shows significant out-of-sample predictability. The long-short investment strategy formed by the IPCA factor also presents the highest average return and Sharpe ratio. Subsample and different horizon results are robust. Market beta, profitability and momentum emerge as the most important characteristics in driving the latent factors. We also provide evidence on the economic grounds of the new latent factor model.     

Does Inflation Illusion Explain the Relation between REITs and Inflation?

We examine whether the observed negative relations between real estate investment trust (REIT) returns and inflation can be explained by the inflation illusion. We identify the mispricing component in REIT prices based on present value models, both linear and loglinear, and then we investigate whether inflation can explain the mispricing component. When we allow for time-varying interest rates, inflation no longer explains the REIT mispricing component. Instead, we find that behavioral factors such as consumer sentiments contribute to the mispricing of REIT prices.     

The information content of aggregate profitability

Recently, there has been significant interest in the information content of aggregate accounting profitability. I collect evidence on whether aggregate profitability captures information about changes in the cost of capital as predicted by classical investment theory. Consistent with these predictions, I find that the stock market return is negatively related to future accounting profitability for several years into the future. I provide evidence that this relation is most likely due to a positive association between changes in expected returns which exert a negative impact on stock returns and future profitability. These findings indicate that aggregate accounting profitability reflects significant economic content related to the cost of capital. This study is the first to link changes in accounting profitability to the market cost of capital under an investment-based mechanism.     

Tests of the Relations Among Marketwide Factors,Firm-Specific Variables,and Stock Returns Using a Conditional Asset Pricing Model

In this article we generalize Harvey's (1989) empirical specification of conditional asset pricing models to allow for both time-varying covariances between stock returns and marketwide factors and time-varying reward-to-covariabilities. The model is then applied to examine the effects of firm size and book-to-market equity ratios. We find that the traditional asset pricing model with commonly used factors can only explain a small portion of the stock returns predicted by firm size and book-to-market equity ratios. The results indicate that allowing time-varying covariances and time-varying reward-to-covariabilities does little to salvage the traditional asset pricing models.     

Liquidity and asset pricing: Evidence from the Hong Kong stock market

This study investigates the role of liquidity in pricing stock returns in the Hong Kong stock market. Our results show that liquidity is an important factor for pricing returns in Hong Kong after taking well-documented asset pricing factors into consideration. The results are robust to adding portfolio residuals and higher moment factor in the factor models. The results are also robust to seasonality, and conditional-market tests. We also compare alternative factor models and find that the liquidity four-factor model (market excess return, size, book-to-market ratio, and liquidity) is the best model to explain stock returns in the Hong Kong stock market, while the momentum factor is not found to be priced.     

What explains the investment growth anomaly?

We examine if an existing asset pricing model in an unconditional or conditional setting can explain the investment growth anomaly, as represented by higher returns on stocks of the firms with lower growth in capital expenditures. Our results indicate that the conditional Fama–French 3-factor model that allows factor loadings to be time-varying and further linked to firm-level characteristics and the business cycle can explain the anomaly.     

The investment manifesto

A deep-ingrained doctrine in asset pricing says that if an empirical characteristic-return relation is consistent with investor “rationality,” the relation must be “explained” by a risk (factor) model. The investment approach questions the doctrine. Factors formed on characteristics are not necessarily risk factors; characteristics-based factor models are linear approximations of firm-level investment returns. The evidence that characteristics dominate covariances in horse races does not necessarily mean mispricing; measurement errors in covariances are likely to blame. Most important, risks do not “determine” expected returns; the investment approach is no more and no less “causal” than the consumption approach in “explaining” anomalies.