Zhongtian Chen

This paper explores the role of memory in shaping belief formation of financial market participants. We estimate a structural machine learning model of memory-based belief formation applied to consensus earnings forecasts of sell-side stock analysts. The estimated model reveals significant recall distortions compared to a benchmark model trained to fit realized earnings revisions. Specifically, analysts over-recall distant historical episodes most of the time, when recent events are more useful for forming forecasts than those in the distant past, but under-recall them during crisis times, when history helps to interpret unusual events. We document two potential driving forces behind these distortions. First, analyst memory overweights the importance of past earnings and forecasts. Second, analysts are more likely to selectively forget past positive events. Our model of analyst recalls strongly predicts their earnings forecast revisions and errors, as well as stock returns, which suggests that distorted recalls might contribute to mispricing of assets in financial markets.

Are there risk factors that are pervasive across major classes of corporate securities: stocks, bonds, and options? We employ a novel econometric procedure that relies on asset characteristics to estimate a conditional latent factor model. A common risk factor structure prominently emerges across asset classes. Several common factors explain a substantial amount of time-series variation of individual asset returns across all three asset classes, and have sizable Sharpe ratios. Several of our factors are highly correlated with some of asset-class-specific factors as well as macroeconomic and financial variables. While a small set of common factors does not fully capture the cross-section of average returns, imposing the factor structure is useful in practice, especially in out-of-sample analysis. A mean-variance efficient portfolio that utilizes asset characteristics achieves a high Sharpe ratio as different asset classes hedge each other’s exposures to the common factors.

By surveying a large sample of investors active on social media, we study how social media affects investors’ narratives about the stock market. When describing reasons for market movements in free-text questions, we find that investors tend to use words more similar to social media than to traditional outlets. The optimism contained in social media affects investor memory and beliefs, resulting in both more positive recalls about past stock market returns and more optimistic expectations about future market performance.