We consider the optimal intertemporal consumption and investment policy of a constant absolute risk aversion (CARA) investor who faces fixed and proportional transaction costs when trading multiple risky assets. We show that when asset returns are uncorrelated, the optimal investment policy is to keep the dollar amount invested in each risky asset between two constant levels and upon reaching either of these thresholds, to trade to the corresponding optimal targets. An extensive analysis suggests that transaction cost is an important factor in affecting trading volume and that it can significantly diminish the importance of stock return predictability as reported in the literature.
Wavelet analysis is a new mathematical method developed as a unified field of science over the last decade or so. As a spatially adaptive analytic tool, wavelets are useful for capturing serial correlation where the spectrum has peaks or kinks, as can arise from persistent dependence, seasonality, and other kinds of periodicity. This paper proposes a new class of generally applicable wavelet-based tests for serial correlation of unknown form in the estimated residuals of a panel regression model, where error components can be one-way or two-way, individual and time effects can be fixed or random, and regressors may contain lagged dependent variables or deterministic/stochastic trending variables. Our tests are applicable to unbalanced heterogenous panel data. They have a convenient null limit N(0,1) distribution. No formulation of an alternative model is required, and our tests are consistent against serial correlation of unknown form even in the presence of substantial inhomogeneity in serial correlation across individuals. This is in contrast to existing serial correlation tests for panel models, which ignore inhomogeneity in serial correlation across individuals by assuming a common alternative, and thus have no power against the alternatives where the average of serial correlations among individuals is close to zero. We propose and justify a data-driven method to choose the smoothing parameter—the finest scale in wavelet spectral estimation, making the tests completely operational in practice. The data-driven finest scale automatically converges to zero under the null hypothesis of no serial correlation and diverges to infinity as the sample size increases under the alternative, ensuring the consistency of our tests. Simulation shows that our tests perform well in small and finite samples relative to some existing tests.
This paper examines how the stocks' investability affects the cross-sectional behavior of stock return volatility in emerging markets. We find that the highly investable stocks experienced higher volatility even after controlling for the country, industry, size, and turnover. We show that the highly investable emerging market portfolio is more correlated with the world market, and the non-investable portfolio is less correlated. The volatility of highly investable stocks increases substantially around the 1998 financial crisis, while the volatility of non-investable does not jump as much.
We study inference in structural models with a jump in the conditional density, where location and size of the jump are described by regression curves. Two prominent examples are auction models, where the bid density jumps from zero to a positive value at the lowest cost, and equilibrium job-search models, where the wage density jumps from one positive level to another at the reservation wage. General inference in such models remained a long-standing, unresolved problem, primarily due to nonregularities and computational difficulties caused by discontinuous likelihood functions. This paper develops likelihood-based estimation and inference methods for these models, focusing on optimal (Bayes) and maximum likelihood procedures. We derive convergence rates and distribution theory, and develop Bayes and Wald inference. We show that Bayes estimators and confidence intervals are attractive both theoretically and computationally, and that Bayes confidence intervals, based on posterior quantiles, provide a valid large sample inference method.
This paper computes the effective duration of callable corporate bonds, using a contingent-claims model that incorporates both default risk and call risk. The model generates empirical implications regarding the cross-sectional variation and the firm-specific determinants of duration, and demonstrates that the effect of the call feature is to shorten duration (except for low-grade bonds). The effective duration is also estimated empirically for a large sample of long-term corporate bonds, using monthly bond price and interest rate data. Cross-sectional regression analysis is used to test the empirical implications of the model regarding the determinants of effective duration, and the empirical results are quite supportive of the model’s predictions.
Journal of Accounting and Economics200438, 297-331
When cumulative net operating income (accounting value-added) outstrips cumulative free cash flow (cash value-added), subsequent earnings growth is weak. If investors with limited attention focus on accounting profitability, and neglect information about cash profitability, then net operating assets, the cumulative difference between operating income and free cash flow, measures the extent to which reporting outcomes provoke over-optimism. During the 1964–2002 sample period, net operating assets scaled by total assets is a strong negative predictor of long-run stock returns. Predictability is robust with respect to an extensive set of controls and testing methods.
Unlike previous studies that examine how emerging market return volatility changes subsequent to stock market liberalization, this paper investigates the impact of investibility, or the degree to which a stock can be foreign-owned, on emerging market volatility. We find a positive relation between return volatility and the investibility of individual stocks, even after controlling for country, industry, firm size, and turnover. We also find that a highly investible emerging market portfolio is subject to larger world market exposure than a non-investible portfolio, suggesting that highly investible stocks are more integrated with the world and therefore more vulnerable to world market risk.
We propose that stock‐market participation is influenced by social interaction. In our model, any given “social” investor finds the market more attractive when more of his peers participate. We test this theory using data from the Health and Retirement Study, and find that social households—those who interact with their neighbors, or attend church—are substantially more likely to invest in the market than non‐social households, controlling for wealth, race, education, and risk tolerance. Moreover, consistent with a peer‐effects story, the impact of sociability is stronger in states where stock‐market participation rates are higher.
It has been alleged that firms and analysts engage in an "earnings‐guidance game" where analysts first issue optimistic earnings forecasts and then "walk down" their estimates to a level that firms can beat at the official earnings announcement. We examine whether the walk‐down to beatable targets is associated with managerial incentives to sell stock after earnings announcements on the firm's behalf (through new equity issuance) or from their personal accounts (through option exercises and stock sales). Consistent with these hypotheses, we find that the walk‐down to beatable targets is most pronounced when firms or insiders are net sellers of stock after an earnings announcement. These findings provide new insights on the impact of capital‐market incentives on communications between managers and analysts.