Uncertainty affects business cycles and asset prices. We estimate firm-level productivity and decompose total uncertainty risk measured as cross-sectional productivity dispersion into macro uncertainty (an aggregate component) and micro uncertainty (an idiosyncratic component). We find that macro uncertainty is strongly counter-cyclical and priced among stocks, but micro uncertainty is acyclical and not priced. Moreover, we show that the expected investment growth factor proposed in Hou et al. (2020) captures macro uncertainty risk, which helps us understand the success of the q5-model.
This paper explores how longevity shocks transmit to corporate debt markets. We show that changes in life expectancy propagate to corporate debt via life insurers through their adjustment of the duration of their corporate bond holdings to match the duration of their liabilities. Life insurers demand more long-term bonds when longevity increases unexpectedly. Their demand of bonds of specific maturities affects corporate term spreads. Corporations exploit the predictable variation in term spreads by adjusting new debt maturities in response to longevity shocks. The debt response is concentrated among insurer-dependent firms and those with investment-grade ratings, which life insurers prefer.
We develop a statistical framework to learn the high-dimensional stochastic discount factor (SDF) from a large set of characteristic-based portfolios. Specifically, we build on the maximum-Sharpe ratio estimated and sparse regression method proposed in Ao et al. (2019) to construct the SDF portfolio, and develop a statistical inference theory to test the SDF loadings. Applying our approach to 194 characteristic-based portfolios, we find that the SDF constructed by about 20 of them performs well in explaining stock returns.
We test whether the diversification of marginal investor affects the underlying firm’s cost of equity. We use institutional investor holdings data to identify the marginal investor. We measure institutional investor diversification as the goodness of fit of a benchmark asset pricing model with respect to the investor portfolio returns. We find that firms with less diversified investors have a higher cost of equity and lower real investment. These findings are not driven by firm size, idiosyncratic volatility, institutional ownership, liquidity, investor stock selectivity, or behavioral biases. Collective evidence leans toward the market incompleteness explanation (Merton, 1987).
We study the duration-hedging trades of duration-sensitive strategic investors, i.e., pensions and life insurers. We use longevity shocks to identify their duration-hedging trades. Longevity shocks affect these investors' liability duration and induce them to adjust their asset duration. When longevity shocks are low (high), they buy more short- (long-) duration stocks and sell more long- (short-) duration stocks. Because prior winners (losers) have shorter (longer) duration, they behave like momentum (contrarian) traders when longevity shocks are low (high). We further verify this channel using capital flows and cross-state longevity variations.
Motivated by production-based asset pricing models, we study the pricing power of fundamental risks to understand the prevailing pricing factors. We find that six aggregate productivity components trace 13 of 15 prevailing pricing factors, including all factors proposed in Fama-French six-factor model (Fama and French, 2018), q-factor model (Hou et al., 2020), and the mispricing models (Stambaugh and Yuan, 2017; Daniel et al., 2020), except for the expected investment growth factor (Hou et al., 2020) and the post-earnings-announcement drift (Daniel et al., 2020). However, the first productivity component is not captured by these factor models, which represents the labor risk.
This paper investigates how disagreement, asset returns and liquidity are affected by three types of heterogeneity in information environment: asymmetric information (AI), idiosyncratic noises (IN), and different opinion (DO). Using a market microstructure model, we incorporate analyst forecasts into endogenous informed trading. This framework allows us to empirically interpret the level of AI, IN, and DO decomposed from analyst disagreement. Our model shows that AI increases both illiquidity and pricing error; IN reduces illiquidity but increases pricing error; DO reduces both illiquidity and pricing error. Using data over 1987–2016, the empirical results support the implications of theoretical model. Moreover, we find that stocks with high AI or high IN tend to be overpriced, and stocks with low DO tend to be underpriced.
We present a theory of endogenous coalition formation in financial markets, which highlights the information sharing and market competition features of coalitions. Allied members enjoy benefits of information advantage and monopolistic power in trading, but forming coalitions incurs direct costs of setting up coalitions and indirect costs from market liquidity dry-ups. Such a trade-off determines the coalition structure of the economy. As allied members behave more monopolistically, coalitions have negative effects on price informativeness and market liquidity. From the information perspective, financial intermediaries (e.g., asset management companies in the mutual fund industry) can be viewed as coalitions of of market players (e.g., fund managers). Our theory provides novel insights about the structure of this industry.