We utilize ridge regression to create a novel set of characteristics-based "ridge factors". We propose Bayesian Average Stochastic Discount Factors (SDFs) based on these ridge factors, addressing model uncertainty in line with asset pricing theory. This approach shrinks the relative contribution of low-variance principal portfolios, avoiding model selection and presumption of a "true model". Our results demonstrate that ridge factor principal portfolios can achieve greater sparsity while maintaining prediction accuracy. Additionally, our Bayesian average SDF produces a higher Sharpe ratio for the tangency portfolio compared to other models.

This paper is the first study to systematically discuss the predictive power of crosssectional asset pricing anomalies on aggregate market excess return time series in the Chinese A-share market. The paper summarizes the anomalies and uses linear methods with different shrinkage techniques to extract predictive information from highdimensional long-short anomaly portfolio returns datasets. We find that long-short anomaly portfolio returns show highly significant out-of-sample predictive power of aggregate market excess returns, both statistically and economically. Unlike similar studies on U.S. stocks, the predictive power stems from stronger limits of arbitrage in the short-leg when using bid-ask spread as a proxy but from stronger limits of arbitrage in the long-leg when idiosyncratic volatility or market capitalization is used as proxies.

This paper examines the effects of economic shocks originating from China’s Five-Year Plans on firms’ shareholders and stakeholders in the U.S. Using establishment-level data, we show that the shocks were not preceded by low production or employment, nor were they anticipated by the U.S. stock market, but were followed by shrinkage of targeted sectors. Well-financed firms with adaptable sectorial and territorial layouts came out mostly unscathed due to within-firm adjustments, such as shifting production to upstream or downstream industries that benefited from the boost in the focal industries in China, or offshoring to encouraged industries in China. These adjustments extended limited benefits to employees and communities, measured by employment and opioid usage.

We empirically investigate the relation between anomaly portfolio returns and market return predictability in the Chinese stock market. Using 132 long-leg, short-leg, and long-short anomaly portfolio returns, we employ several shrinkage-based statistical learning methods to capture predictive signals of the anomalies in a high-dimensional setting. We find statistically and economically significant return predictability using long- and short-leg anomaly portfolio returns. Moreover, high arbitrage risk enhances forecasting performance, supporting that the predictability stems from mispricing correction persistence. Unlike the U.S. stock market, we find little evidence that the long-short anomaly portfolios can help predict market return due to the low persistence of asymmetric mispricing correction. We provide simulation evidence to sharpen our understanding of the differences found in the U.S. and Chinese stock markets.

Efficient portfolio is a portfolio that yields maximum expected return given a level of risk or has minimum level of risk given a level of expected return.However,the optimal portfolios seem not being as efficient as intended.Especially during financial crisis period.optimal portfolio is not an optimal investment as it does not yield maximum return given a specific level of risk,vice and versa.One possible explanion for an unimpressive performance of the seemingly efficient portfolio is incorrectness in parameter estimates called"estimation risk in parameter estimates".Five different estimating strategies are employed to explore ex post portfolio performance when estimation risk is incorporated.These strategies are traditional mean-variance(EV),Adjusted Beta(AB) approach,Capital Asset Pricing Model(CAPM),Single Index Model(SIM), and Single Index Model incorporating shrikage Bayesian factor namely Bayesian Single Index Model(BSIM).Among the five alternative strategies,shrinkage estimators incorporating the single index model outperforms other traditional portfolio selection strategies.Allowing for asset mispricing and applying Bayesian shrinkage adjusted factor to each asset's alpha,a single factor namely excess market return is adequate in alleviating estimation uncertainty. JEL：G320