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APE-8: Aggregate Risk and Return Predictability
How Risky are the U.S. Corporate Assets?
1UW Madison, United States of America; 2University of Pennsylvania; 3Carnegie Mellon University
Utilizing market data on corporate bonds and equities, we measure the aggregate market value of U.S. corporate assets and their payouts to investors. In contrast to per share equity dividends, total corporate payouts are very volatile, turn negative when corporations raise capital, and are acyclical. This challenges the notion of risk and return since the risk premium on corporate assets is comparable to the standard equity premium. To reconcile this evidence, we show that aggregate net issuances, which are acyclical and highly volatile, mask a strong exposure of total payouts' cash components to low-frequency growth risks. We develop an asset-pricing framework to quantitatively assess this economic channel.
Expected returns and risk in the stock market
1Alliance Manchester Business School, United Kingdom; 2Anderson School, UCLA
We present new evidence on the predictability of aggregate stock market returns by developing two new prediction models, one risk-based, and the other purely statistical. Both models rely on extracting information from past returns of portfolios. The risk-based pricing kernel model expresses the expected excess return as the covariance of the market return with a pricing kernel that is a linear function of portfolio returns. The purely statistical discount rate model predicts the expected excess return directly as a function of weighted past portfolio returns. The two models provide independent evidence of predictable variation in returns, with R^2 of 6-9% for 1-quarter returns and 16-19% for 1-year returns. We show that innovations in the pricing kernel are not associated with discount rate news but with the cash flow component of the market return.
Capital Heterogeneity, Time-To-Build, and Return Predictability
City University of Hong Kong, Hong Kong S.A.R. (China)
I study how the two major types of business investment, equipment investment and structures investment, are differently linked to stock returns. I empirically show that equipment investment has a significantly stronger predictive power for stock returns than structures investment, both in-sample and out-of-sample, using US aggregate-, US asset-, US industry-, and UK aggregate-level data. To explain this empirical finding, I build a general equilibrium production model in which it takes a shorter time-to-build for equipment investment than for structures investment to transform into productive capital. In the model, equipment investment reacts to productivity shocks in a more timely manner, and thus it reflects more of the information contained in stock prices. In addition, the model provides theoretical support for previous empirical findings of return predictability uncovered from planned investment.
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