Certain market variables, such as the close-end fund discount rate, have been used to construct investor sentiment indices. However, besides investor sentiment, those market variables are also affected by economic fundamentals and market friction.Cognitive factors in learning psychology the existing studies on invertor sentiment indices adjust the effect of fundamental economy partially while leave the effect of market friction unadjusted.Cognitive factors in learning psychology we propose more robust investor sentiment indices with both of these two effects to be adjusted and show that those alternative sentiment indices performs better than the existing sentiment indices.Cognitive factors in learning psychology

While economic variables have been used extensively to forecast the U.S. Bond risk premia, little attention has been paid to the use of technical indicators which are widely employed by practitioners.Cognitive factors in learning psychology in this paper, we fill this gap by studying the predictive ability of using a variety of technical indicators vis-a-vis the economic variables.Cognitive factors in learning psychology we find that the technical indicators have statistically and economically significant in- and out-of-sample forecasting power. Moreover, we find that utilizing information from both technical indicators and economic variables substantially increases the forecasting performances relative to using just economic variables.Cognitive factors in learning psychology

We propose a new investor sentiment index that is aligned with the purpose of predicting the aggregate stock market. By eliminating a common noise component in sentiment proxies, the new index has much greater predictive power than existing sentiment indices both in- and out-of-sample, and the predictability becomes both statistically and economically significant.Cognitive factors in learning psychology in addition, it outperforms well recognized macroeconomic variables and can also predict cross-sectional stock returns sorted by industry, size, value, and momentum.Cognitive factors in learning psychology the driving force of the predictive power appears stemming from investors’ biased belief about future cash flows.

The modern portfolio theory pioneered by markowitz (1952) is widely used in practice and extensively taught to mbas.Cognitive factors in learning psychology however, the estimated markowitz's portfolio rule and most of its extensions not only underperform the naive 1/N rule (that invests equally across N assets) in simulations, but also lose money on a risk-adjusted basis in many real data sets.Cognitive factors in learning psychology in this paper, we propose an optimal combination of the naive 1/N rule with one of the four sophisticated strategies--- the markowitz rule, the jorion (1986) rule, the mackinlay and pastor (2000) rule, and the kan and zhou (2007) rule--- as a way to improve performance.Cognitive factors in learning psychology we find that the combined rules not only have a significant impact in improving the sophisticated strategies, but also outperform the 1/N rule in most scenarios.Cognitive factors in learning psychology since the combinations are theory-based, our study may be interpreted as reaffirming the usefulness of the markowitz theory in practice.

Economic objectives are often ignored when estimating parameters, though the loss of doing so can be substantial.Cognitive factors in learning psychology this paper proposes a way to allow bayesian priors to reflect the objectives. Using monthly returns of the fama-french 25 size and book-to-market portfolios and their three factors from january 1965 to december 2004, we find that investment performance under the objective-based priors can be significantly different from that under alternative priors, with differences in terms of annual certainty-equivalent returns greater than 10% in many cases.Cognitive factors in learning psychology in terms of out-of-sample performance, the bayesian rules under the objective-based priors can outperform substantially some of the best rules developed in the classical framework.Cognitive factors in learning psychology

In this paper, we provide a model-free test for asymmetric correlations in which stocks move more often with the market when the market goes down than when it goes up.Cognitive factors in learning psychology we also provide such tests for asymmetric betas and covariances. In addition, we evaluate the economic significance of incorporating asymmetries into investment decisions.Cognitive factors in learning psychology when stocks are sorted by size, book-to-market and momentum, we find strong evidence of asymmetry for both the size and momentum portfolios, but no evidence for the book-to-market portfolios.Cognitive factors in learning psychology moreover, the asymmetries can be of substantial economic importance for an investor with a disappointment aversion preference of ang, bekaert and liu (2005).Cognitive factors in learning psychology if the investors's felicity function is of the power utility form and if his coefficient of disappointment aversion is between 0.55 and 0.25, he can achieve over 2% annual certainty-equivalent gains when he switches from a belief in symmetric stock returns into a belief in asymmetric ones.Cognitive factors in learning psychology

As the usual normality assumption is firmly rejected by the data, investors encounter a data-generating process (DGP) uncertainty in making investment decisions.Cognitive factors in learning psychology in this paper, we propose a novel way to incorporate uncertainty about the DGP into portfolio analysis. We find that accounting for fat tails leads to nontrivial changes in both parameter estimates and optimal portfolio weights, but the certainty–equivalent losses associated with ignoring fat tails are small.Cognitive factors in learning psychology this suggests that the normality assumption works well in evaluating portfolio performance for a mean-variance investor.

This paper investigates the predictive ability of international volatility risks for the daily chinese stock market returns.Cognitive factors in learning psychology we employ the innovations in implied volatility indexes of seven major international markets as our international volatility risk proxies. We find that international volatility risks are negatively associated with contemporaneous chinese daily overnight stock returns, while positively forecast next-day chinese daytime stock returns.Cognitive factors in learning psychology the US volatility risk (ΔVIX) is particularly powerful in forecasting chinese stock returns, and plays a dominant role relative to the other six international volatility measures.Cognitive factors in learning psychology ΔVIX's forecasting power remains strong after controlling for chinese domestic volatility and is robust in- and out-of-sample. Economically, high ΔVIX forecasts high chinese domestic market volatility,

cognitive factors in learning psychology

Little attention has been paid to that of the chinese stock market. In this paper, we investigate the dynamic asset allocation strategies in chinese stock market to explore the chinese stock market return predictability.Cognitive factors in learning psychology we find significant out-of-sample forecasting power of several return predictors, and the downside risk measure performs particularly well. We then examine the performance of dynamic portfolio strategies in the chinese stock market such as aggregate market timing strategy, and industry, size, and value rotation strategies.Cognitive factors in learning psychology we provide strong evidence that the dynamic portfolio strategies deliver superior outperformance relative to the passive buy-and-hold portfolio strategies.Cognitive factors in learning psychology specifically, the dynamic portfolio strategies can beat the buy-and-hold benchmarks by about 600 basis points per annum, and almost double the sharpe ratios.Cognitive factors in learning psychology

We analyze return predictability for the chinese stock market, including the aggregate market portfolio and the components of the aggregate market, such as portfolios sorted on industry, size, book-to-market and ownership concentration.Cognitive factors in learning psychology considering a variety of economic variables as predictors, both in-sample and out-of-sample tests highlight significant predictability in the aggregate market portfolio of the chinese stock market and substantial differences in return predictability across components.Cognitive factors in learning psychology among industry portfolios, finance and insurance, real estate, and service exhibit the most predictability, while portfolios of small-cap, low book-to-market ratio and low ownership concentration firms also display considerable predictability.Cognitive factors in learning psychology two key findings provide economic explanations for component predictability: (i) based on a novel out-of-sample decomposition, time-varying systematic risk premiums captured by the conditional CAPM model largely account for component predictability; (ii) industry concentration significantly explain differences in return predictability across industries, consistent with the information-flow frictions emphasized by hong, torous, and valkanov (2007).Cognitive factors in learning psychology