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 have both in and out of sample, and the predictability becomes both statistically and economically significant. In addition, it outperforms well-recognized macroeconomic variables and can also predict cross-sectional stock returns sorted by industry, size, value, and momentum. The driving force of the predictive power appears to stem from investors' biased beliefs about future cash flows.

Using various empirical measures, we find that, in China, firms with high profitability generate substantially higher future stock returns than those with low profitability. This positive effect of profitability on expected returns is robust to controlling for other firm characteristics and risks. We show that the profitability premium is stronger among firms with low investment friction, which is consistent with the implications of investment-based q-theory asset pricing models. However, the premium is not stronger among firms with high limits to arbitrage, contradicting behavioral mispricing explanations.

ABSTRACT Traditional negotiation trading dominates the electronic limit order book (LOB) of the X-Bond platform in the Chinese government bond market. Using a unique dataset, we conduct the first systematic study of the X-Bond’s role. We find that: (1) trades via LOB are notably more cost effective than via negotiation, with cost differences influenced by factors such as trader groups, bond types, trade sizes, and on-/off-the-run status; (2) electronic trading reduces the costs of negotiation trades through increased liquidity, an information channel, and a liquidity’s externality; and (3) due to the absence of an interdealer market, the X-Bond platform primarily serves as a liquidity source of last resort for managing inventory risk.

This paper constructs a manager sentiment index based on the aggregated textual tone of corporate financial disclosures. We find that manager sentiment is a strong negative predictor of future aggregate stock market returns, with monthly in-sample and out-of-sample R2s of 9.75% and 8.38%, respectively, which is far greater than the predictive power of other previously studied macroeconomic variables. Its predictive power is economically comparable and is informationally complementary to existing measures of investor sentiment. Higher manager sentiment precedes lower aggregate earnings surprises and greater aggregate investment growth. Moreover, manager sentiment negatively predicts cross-sectional stock returns, particularly for firms that are difficult to value and costly to arbitrage.

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 have both in and out of sample, and the predictability becomes both statistically and economically significant. In addition, it outperforms well-recognized macroeconomic variables and can also predict cross-sectional stock returns sorted by industry, size, value, and momentum. The driving force of the predictive power appears to stem from investors' biased beliefs about future cash flows.