学术论文

Using real-time data, we show that currency excess return predictability is in part due to mispricing. First, the risk-adjusted profitability of systematic trading strategies decreases after dissemination of the underlying academic research, suggesting that market participants learn about mispricing from publications. Moreover, the decline is greater for strategies with larger insample profits and lower arbitrage costs. Second, the effect of comprehensive risk adjustments on trading profits is limited, and signal ranks and alphas decay quickly. The finding that analysts’ forecasts are inconsistent with currency predictors implies that investors’ trading contributes to mispricing and suggests biased expectations as a possible explanation.

We use a novel experiment in China to examine the effects of having a quasi-official investor own a small number of shares on specific firm outcomes. We find that, relative to control firms, pilot firms experience an increase in dissenting votes from independent directors, a reduction in tunneling and earnings management activities, and an improvement in merger performance. Independent directors questioned by the quasi-official shareholder in activism events subsequently lose board seats in the director market. Overall, our results shed light on a new mechanism for enhancing the protection of minority shareholders.

Motivated by many applications such as online recommendations and individual choices, this article considers ranking inference of n items based on the observed data on the top choice among M randomly selected items at each trial. This is a useful modification of the Plackett-Luce model for M-way ranking with only the top choice observed and is an extension of the celebrated Bradley-Terry-Luce model that corresponds to M = 2. Under a uniform sampling scheme in which any M distinguished items are selected for comparisons with probability p and the selected M items are compared L times with multinomial outcomes, we establish the statistical rates of convergence for underlying n preference scores using both l2-norm and l∞-norm, under the minimum sampling complexity (smallest order of p). In addition, we establish the asymptotic normality of the maximum likelihood estimator that allows us to construct confidence intervals for the underlying scores. Furthermore, we propose a novel inference framework for ranking items through a sophisticated maximum pairwise difference statistic whose distribution is estimated via a valid Gaussian multiplier bootstrap. The estimated distribution is then used to construct simultaneous confidence intervals for the differences in the preference scores and the ranks of individual items. They also enable us to address various inference questions on the ranks of these items. Extensive simulation studies lend further support to our theoretical results. A real data application illustrates the usefulness of the proposed methods. Supplementary materials for this article are available online including a standardized description of the materials available for reproducing the work.

Boosting is one of the most significant developments in machine learning. This paper studies the rate of convergence of L-2-Boosting in a high-dimensional setting under early stopping. We close a gap in the literature and provide the rate of convergence of L-2-Boosting in a high-dimensional setting under approximate sparsity and without beta-min condition. We also show that the rate of convergence of the classical L-2-Boosting depends on the design matrix described by a sparse eigenvalue condition. To show the latter results, we derive new, improved approximation results for the pure greedy algorithm, based on analyzing the revisiting behavior of L-2-Boosting. These results might be of independent interest. Moreover, we introduce so-called "restricted L-2-Boosting". The restricted L-2-Boosting algorithm sticks to the set of the previously chosen variables, exploits the information contained in these variables first and then only occasionally allows to add new variables to this set. We derive the rate of convergence for restricted L-2-Boosting under early stopping which is close to the convergence rate of Lasso in an approximate sparse, high-dimensional setting without beta-min condition. We also introduce feasible rules for early stopping, which can be easily implemented and used in applied work. Finally, we present simulation studies to illustrate the relevance of our theoretical results and to provide insights into the practical aspects of boosting. In these simulation studies, L-2-Boosting clearly outperforms Lasso. An empirical illustration and the proofs are contained in the Appendix.

We explore the implications of ownership concentration for the recently concluded incentive auction that repurposed spectrum from broadcast TV to mobile broadband usage in the United States. We document significant multilicense ownership of TV stations. We show that in the reverse auction, in which TV stations bid to relinquish their licenses, multilicense owners have an incentive to withhold some TV stations to drive up prices for their remaining TV stations. Using a large-scale valuation and simulation exercise, we find that this strategic supply reduction increases payouts to TV stations by between 13.5 percent and 42.4 percent.

To balance the need for privacy and the benefits of big-data analytics, regulators around the world are giving consumers control over their data, allowing them to choose whether or not to voluntarily share their purchase history data with firms. Intuition suggests that voluntary data sharing only benefits consumers who can now choose to share their data only when it is profitable to do so. To investigate this argument, we build a model in which a monopolistic firm sells a repeatedly purchased product to consumers over two periods, and consumers decide whether or not to share their purchase history data with the firm, who can use it in the future to price discriminate against them. We find that, compared to when data collection is completely outlawed, voluntary data sharing can benefit the firm but at its consumers’ expense. Moreover, regulations that mandate firms to better protect consumer data against data breaches can backfire on consumers. Finally, we show that under voluntary data sharing, a firm’s ability to offer consumers a monetary incentive to share their data can improve profits without hurting consumers. Taken together, these findings underscore the surprising effects of voluntary data sharing and caution public policymakers of how certain data policies that, on the surface, seem purely beneficial can lead to unintended consequences.

We study voluntary cost disclosure by duopoly firms when they can invest in a cost-reduction technology, i.e., when their private cost is endogenously determined. We find that, contrary to most of the literature, firms disclose their endogenous cost information regardless of the type of competition. The underlying mechanisms and welfare implications, however, are different. Under Bertrand competition, cost disclosure helps a firm avoid aggressive investment in cost reduction to coordinate actions to the mutual advantage of the duopoly firms. Under Cournot competition, disclosing cost information enables a firm to show a hardened stance toward the competing firm. Although firms gain from their disclosure decisions under Bertrand competition, their disclosure decisions under Cournot competition place them in a prisoner’s dilemma, as both firms would be better off if they chose not to disclose their information. Consequently, consumers may lose under Bertrand competition but gain under Cournot competition.

This study examines the properties of innovation disclosures contained in new product announcements, a form of voluntary, nonfinancial disclosure. We analyze these properties using a novel, text-based measure of the extent of product innovation disclosed in new product announcements. We find that stock prices react more positively to announcements with more extensive innovation disclosure. In our main analyses, we first find that a higher level of innovation disclosure predicts a greater increase in future sales. We further find that this predictive ability falls when managers have stronger incentives to maximize their wealth and when the corporate governance structure and customers’ bargaining power weaken. Our research enhances the understanding of the properties of managerial voluntary, nonfinancial disclosures and contributes a text-based measure of innovation that captures managerial assessment of the extent of product innovation. This new measure is more generalizable and incrementally informative for firm value and future performance than conventional innovation measures that depend on the existence of patents or research and development expenses.

Information production associated with derivatives markets is not a sideshow; rather, it has significantly positive spillover effects on an array of corporate decisions of underlying firms. Using a regression-discontinuity design based on exogenous variation in options availability as an instrument for changes in the information environment, we show that options introductions have causal effects on corporate policies on both sides of the balance sheet. Through improved information efficiency, options availability reduces the need for debt and payout, increases efficient investment, and yields superior innovation. We conduct two independent experiments demonstrating that our instrument’s impact is not derived from alternative channels.