Search

We document significant persistence in the market timing performance of active individual investors, suggesting that some investors are skilled at timing. Using data on all trades by active Finnish individual investors over almost 15 years, we also show that the net purchases of skilled versus unskilled investors predict monthly market returns. Our results lend credibility to the view that market returns are predictable, without having to specify which variables active investors use to successfully time the market. (JEL G10, G11, G12, G14, G15).

As organizations are increasingly engaged in the digital world with greater dependency on data, crime and activism have shifted from the streets to the internet. In this paper, we study the impact of activist hacking campaigns on financial institutions. We look into how target institutions’ deep web and dark web exposure in terms of different risk categories is affected by hacking campaigns, and the interactions of the risk categories during the campaigns. On average, hacking campaigns raise the target institutions’ deep web and dark web exposure by 62 percent per year during the first two years after the campaigns’ start date. Small financial institutions are more vulnerable to the campaigns than large institutions. Further, leaked employee passwords amplify the campaign effect substantially, which allows us to forecast the institutions’ cyber exposure changes during and after the campaigns.

We study incentive contracts in asset management business under dynamic actions and relationships between an investor, a partner of an investment company, and a fund manager of the company. The investor cannot perfectly observe the partner and manager’s actions, and similarly, the partner cannot perfectly observe the manager’s actions. We show how the actions of the participants and the costs of their actions interact. For instance, the optimal effort of the manager falls in the partner’s fundraising cost. We extend the model to a case with an investor, a partner, and multiple managers. In this case, each manager’s effort rises in the effectiveness of the managers’ cooperation and falls in their other managers’ effort cost.

Many innovations today are data-driven, such as self-driving cars. To improve the algorithms of these products, firms make substantial investments in data collection. However, the data is limited for an individual firm, which caps the benefits of the algorithms. Therefore, companies and policymakers ponder whether data collected by individual competing firms should be shared. More specifically, we ask the following three questions. First, when do firms voluntarily share their data? Second, if governments were to regulate data sharing, when would they mandate or prohibit data sharing to promote innovation? Third, when are firms and governments (mis)aligned in their data sharing decisions? Using a game-theoretic model, we identify two key factors that determine the answers to the questions: (i) the degree of complementarity or substitutability among firms' datasets and (ii) the performance uncertainty of the innovation. Overall, our analysis offers guidance to governments on when and how to regulate data sharing. Specifically, governments should mandate (respectively, prohibit) data sharing when firms' datasets are complements (respectively, substitutes) and the performance uncertainty is moderate (respectively, high). Our findings also shed light on several recent developments, such as the new antitrust law proposals by governments to regulate data markets.

In 2019/2020, the emergence of coronavirus disease 2019 (COVID‐19) resulted in rapid increases in infection rates as well as patient mortality. Treatment options addressing COVID‐19 included drug repurposing, investigational therapies such as remdesivir, and vaccine development. Combination therapy based on drug repurposing is among the most widely pursued of these efforts. Multi‐drug regimens are traditionally designed by selecting drugs based on their mechanism of action. This is followed by dose‐finding to achieve drug synergy. This approach is widely‐used for drug development and repurposing. Realizing synergistic combinations, however, is a substantially different outcome compared to globally optimizing combination therapy, which realizes the best possible treatment outcome by a set of candidate therapies and doses toward a disease indication. To address this challenge, the results of Project IDentif.AI (Identifying Infectious Disease Combination Therapy with Artificial Intelligence) are reported. An AI‐based platform is used to interrogate a massive 12 drug/dose parameter space, rapidly identifying actionable combination therapies that optimally inhibit A549 lung cell infection by vesicular stomatitis virus within three days of project start. Importantly, a sevenfold difference in efficacy is observed between the top‐ranked combination being optimally and sub‐optimally dosed, demonstrating the critical importance of ideal drug and dose identification. This platform is disease indication and disease mechanism‐agnostic, and potentially applicable to the systematic N‐of‐1 and population‐wide design of highly efficacious and tolerable clinical regimens. This work also discusses key factors ranging from healthcare economics to global health policy that may serve to drive the broader deployment of this platform to address COVID‐19 and future pandemics., When highly contagious pathogens with unpredictable clinical courses emerge, rapid therapeutic responses are required. To address this challenge, the application of an artificial intelligence platform is reported to rapidly identify globally optimized infectious disease combination therapy regimens by simultaneously reconciling the best drugs and doses from a large parameter space.

This article analyzes multiple agents who forecast an underlying dynamic state based on streams of (partially overlapping) information. Each agent minimizes a convex combination of their forecasting error and deviation from the other agents' forecasts. As a result, the agents exhibit herding behavior - a bias that has been well-recognized in the economics and psychology literature. Our first contribution is a general framework for analyzing agents' forecasts under different levels of herding. The underlying state dynamics can be non-linear with seasonality, trends, shocks, or other time-series components. Our second contribution describes how models within our framework can be estimated from data. We apply our estimation procedure to surveys of equity price forecasts and find that the agents concentrate on average 37% of their efforts on making similar forecasts. However, there is substantial variation in the level of herding over time; even though herding falls substantially during the 2007-2008 financial crisis, it rises after the crisis.

We study how crowdfunding, as a source of public information, affects competing venture capital (VC) firms' investment decisions in terms of the timing, likelihood, and expected amount of VC investment in a startup. Our economy consists of one startup, one crowdfunding platform, and two VC firms with differing prior beliefs about the startup's probability of success. The startup first approaches both VC firms for funding, which is modeled as a second-price auction between the two firms, and then turns to the crowdfunding platform if rejected by both. After the crowdfunding, the VC firms update their assessments of the startup's success based on the information collected from the crowdfunding platform, and the startup seeks funding from them again. We find that crowdfunding and the competition between the VC firms raise the expected amount of VC investment. Moreover, crowdfunding increases the chance of a startup receiving VC funding only for those startups deemed unfavorable by both VC firms; for debatable startups about which VC firms' views diverge, crowdfunding may reduce their chance of receiving VC investment. We then examine how the relationship between crowdfunding and VC investment is shaped by the characteristics of the startup and the crowdfunding platform. For instance, when the more optimistic VC firm has a moderate expectation for the startup and the investment risk is high (resp., low), that VC firm tends to invest before the startup seeks crowdfunding if the crowdfunding information is sufficiently accurate (resp., noisy). Finally, we observe more nuanced crowdfunding effects in several model extensions, for example, if the startup is allowed to strategically decide on the timing to receive a VC investment, and if VC firms can observe private signals on the startup.

Insider misconduct, such as financial misconduct and privacy breaches, has received an increasing amount of public scrutiny in recent years. Empirical studies have argued that employee seniority plays a role in insider misconduct. Our study provides intuitions for these empirical findings and insights into how regulations can be designed to improve social welfare. We model the interactions between lawmakers, organizations, and their employees under the risk of insider misconduct. Employees are the source of insider threats. Firms interact with employees and enforce a certain level of monitoring. Lawmakers influence other parties’ incentives by imposing legal penalties. We study the interaction among the three parties in a two-stage game model. In the first stage, we analyze an optimization problem where the lawmakers decide the penalties to employees for engaging in misconduct and firms for overlooking this behavior. In the second stage, we analyze a simultaneous game where the firms decide the monitoring effort and employees decide whether to engage in misconduct and study the comparative statics. We derive closed-form solutions to the equilibriums in the second stage. We show how monitoring and misconduct change with respect to financial incentives and employee seniority. Under regularity conditions, we formulate an optimization problem for the lawmakers as a linear optimization problem. Our model helps explain empirical evidence that middle management and top management typically cause large-scale misconduct. From a welfare point of view, a seniority-based penalty for misconduct is better than a uniform penalty. In addition, stiff penalties are not necessarily more helpful than a milder regulation.