Cheng F. Lee, Chunchi Wu, K. C. John Wei, The Heterogeneous Investment Horizon and the Capital Asset Pricing Model: Theory and Implications, The Journal of Financial and Quantitative Analysis, Vol. 25, No. 3 (Sep., 1990), pp. 361-376

Based on the theory of the pricing of capital assets developed by Sharpe [12], Lintner [9] and Mossin [11], Professor Jensen formulated a return-generating model to measure portfolio performance [5]. In a subsequent paper, Professor Jensen [6] investigated the impact of the investment horizon on the functional form of the model. Lee [8] has proposed a generalized specification of the model to resolve this problem. Alternative estimation methods for testing the linearity of the model in terms of time-series data have also been suggested by Lee. Moreover, the stability of the beta coefficient over time and the impact of the market's condition on both the alpha (or, Jensen's measure of performance [5]) and beta of the model have come under scrutiny in financial research.

We construct a measure ( fLMA ) of the extent to which neighboring firms hire similar types of workers, based on the similarity between the labor profile of a firm and that of its locality. We show that a firm’s innovation is positively related to fLMA. The enhanced labor mobility induced by higher fLMA is an important channel for this positive relation. This relation is stronger when firms have increased outside job opportunities for employees, increased knowledge spillovers via coworkership, and more employee stock options. Innovation is higher when intellectual property ownership is with employers, not employees. This effect increases in fLMA.

Using high-frequency data on over 7 million import transactions, we study the disruptions to U.S. firms’ trade patterns and growth immediately following the initial COVID-19 trade shock. While large firms were not direct recipients of government fiscal support, they experienced fewer disruptions when located in counties where small businesses (SMEs) received government stimulus loans under the Paycheck Protection Program. These effects were largest in counties with greater share of SMEs and stronger input–output linkages between large firms and SMEs. Our results point to local spillovers between SMEs and large firms as being an important determinant of firm resiliency during crises.

We study how derivatives (with nonlinear payoffs) affect the underlying asset’s liquidity. In a rational expectations equilibrium, informed investors expect low conditional volatility and sell derivatives to the others. These derivative trades affect different investors’ utility differently, possibly amplifying liquidity risk. As investors delta hedge their derivative positions, price impact in the underlying drops, suggesting improved liquidity, because informed trading is diluted. In contrast, effects on price reversal are ambiguous, depending on investors’ relative delta hedging sensitivity (i.e., the gamma of the derivatives). The model cautions of potential disconnections between illiquidity measures and liquidity risk premium due to derivatives trading.

Using a large hand-collected database of chief executive officer (CEO) bonus structures, we find that when a CEO’s bonus is directly tied to earnings per share (EPS), his company is more likely to conduct a buyback. This effect is especially pronounced when a company’s EPS is right below the threshold for a bonus award. Share repurchasing increases the probability the CEO receives a bonus and the magnitude of that bonus, but only when bonus pay is EPS based. Bonus-driven repurchasing firms do not exhibit positive long-run abnormal returns.

Price declines over the previous quarter lead to stronger reversals across the subsequent 2 months. We explain this finding based on the dual notions that liquidity provision can influence reversals and that agents who act as de facto liquidity providers may be less active in past losers. Supporting these observations, we find that active institutions participate less in losing stocks and that the magnitude of monthly return reversals fluctuates with changes in the number of active institutional investors. Thus, we argue that fluctuations in liquidity provision with past return performance account for the link between return reversals and past returns.