Knowledge that Transforms

To enforce insider trading laws, financial regulators require top executives to make their own-company trades public. One implication of this regulatory focus is that executives below the top fly under the radar. We use administrative register data from Norway to examine whether executives below the top in listed companies earn abnormal returns on purchases in own-company stock. We find evidence of abnormal returns on such trades, about 50 to 100 basis points at the 1-month horizon. The abnormal returns on purchases in other stocks are negative, making high investor ability an unlikely explanation.

We analyze 18 quadrillion models for the joint pricing of corporate bond and stock returns. Strikingly, we find that equity and nontradable factors alone suffice to explain corporate bond risk premia once their Treasury term structure risk is accounted for, rendering the extensive bond factor literature largely redundant for this purpose. While only a handful of factors, behavioral and nontradable, are likely robust sources of priced risk, the true latent stochastic discount factor is dense in the space of observable factors. Consequently, a Bayesian Model Averaging Stochastic Discount Factor explains risk premia better than all low-dimensional models, in- and out-of-sample, by optimally aggregating dozens of factors that serve as noisy proxies for common underlying risks, yielding an out-of-sample Sharpe ratio of 1.5 to 1.8. This SDF, as well as its conditional mean and volatility, are persistent, track the business cycle and times of heightened economic uncertainty, and predict future asset returns.

We surveyed all Chinese public firms in 2019 and 2022 to examine the real effects of financial markets. Over 90% of firms say they actively monitor the stock market, and the most common reasons they provide are that they learn new information from the price and that they depend on the price for financing. Focusing on the learning channel, we examine how the responses relate to firm characteristics and actions. Firms that indicate learning have characteristics that suggest greater benefit from market information. They also exhibit higher investment-to-price sensitivity. We provide results on what dimensions of information firms learn about.

We test whether forecast bias affects household stock trading by combining measures of bias elicited in laboratory experiments with administrative trade-level data. On average, subjects exhibit positive forecast bias (i.e., extrapolators), while a large minority exhibit negative forecast bias (i.e., contrarians). Forecast bias is positively associated with past excess returns of stocks that are purchased: Extrapolators (contrarians) purchase past winners (losers). Forecast bias is negatively associated with the capital gains of stocks that are sold. Furthermore, forecast bias explains investor heterogeneity in the relation between market returns and net flows to stocks. Overall, our study provides evidence of a common mechanism – forecast bias – that links past returns to trading decisions for purchases, sales, and net flows.