Research articles for the 2019-07-14
SSRN
We develop an agent-based simulation of the catastrophe insurance and reinsurance industry and use it to study the problem of risk model homogeneity. The model simulates the balance sheets of insurance firms, who collect premiums from clients in return for ensuring them against intermittent, heavy-tailed risks. Firms manage their capital and pay dividends to their investors, and use either reinsurance contracts or cat bonds to hedge their tail risk. The model generates plausible time series of profits and losses and recovers stylized facts, such as the insurance cycle and the emergence of asymmetric, long tailed firm size distributions. We use the model to investigate the problem of risk model homogeneity. Under Solvency II, insurance companies are required to use only certified risk models. This has led to a situation in which only a few firms provide risk models, creating a systemic fragility to the errors in these models. We demonstrate that using too few models increases the risk of nonpayment and default while lowering profits for the industry as a whole. The presence of the reinsurance industry ameliorates the problem but does not remove it. Our results suggest that it would be valuable for regulators to incentivize model diversity. The framework we develop here provides a first step toward a simulation model of the insurance industry for testing policies and strategies for better capital management.
SSRN
Empirical literature on reallocation of resources during business cycles provides an evidence of increased reallocation of labor across firms during downturns. In this paper I build a theoretical model with search frictions in labor market, that is consistent with this observation, and study implications of search and match frictions for the cross section of stock returns. In the model firms having more growth opportunities benefit from recessions due to more slack in the labor market which allows them to expand quicker and convert higher share of their growth opportunities into profitable projects. This feature generates a return spread between value and growth firms. In the model sorts of stocks based on different growth indicators yield patterns documented empirically in previous studies.
arXiv
We study the effectiveness of a community-level information intervention aimed at improving sanitation using a cluster-randomized controlled trial (RCT) in Nigerian communities. The intervention, Community-Led Total Sanitation (CLTS), is currently part of national sanitation policy in more than 25 countries. While average impacts are exiguous almost three years after implementation at scale, the results hide important heterogeneity: the intervention has strong and lasting effects on sanitation practices in poorer communities. These are realized through increased sanitation investments. We show that community wealth, widely available in secondary data, is a key statistic for effective intervention targeting. Using data from five other similar randomized interventions in various contexts, we find that community-level wealth heterogeneity can rationalize the wide range of impact estimates in the literature. This exercise provides plausible external validity to our findings, with implications for intervention scale-up. JEL Codes: O12, I12, I15, I18.
SSRN
We show that firms with a skillful management team tend to disclose more accounting information than other comparable firms. We explore several channels to explain this finding. First, we find that investors treat the disclosure of skillful management team as more credible. In particular, investors react more (less) significantly to earning announcements of skillful (other) management teams. Second, we find that a higher disclosure quality helps skillful management teams earn higher total compensation. In addition, this effect also reduces the corporate’s crash risk and becomes stronger in the sample of higher takeover probability or information asymmetry. Together, our study provides a very first evidence on how managerial skill determines a firm’s disclosure policy.
arXiv
We develop a new topological structure for the construction of a reinforcement learning model in the framework of financial markets. It is based on Lipschitz type extension of reward functions defined in metric spaces. Using some known states of a dynamical system that represents the evolution of a financial market, we use our technique to simulate new states, that we call ``dreams". These new states are used to feed a learning algorithm designed to improve the investment strategy.
SSRN
We examine how the state of the economy affects the extent to which investors value management earnings forecasts. We find that stock price reactions to news conveyed in management earnings forecasts are larger during economic downturns, as measured by quarters of negative U.S. GDP growth, NBER recession periods, and periods following negative market-wide stock returns. We also find that analysts make larger revisions to their forecasts in response to news in management earnings forecasts during downturns. Supporting the notion that the higher value investors and analysts place on management forecasts in downturns is justified, we show that management forecasts become even more accurate relative to analyst forecasts during these periods. Overall, these results are consistent with the hypothesis that because adverse economic conditions lower the precision of market participants’ beliefs about a firm’s value, investors and analysts value management-provided information more in downturns.
arXiv
We study the most famous example of a large financial market: the Arbitrage Pricing Model, where investors can trade in a one-period setting with countably many assets admitting a factor structure. We consider the problem of maximising expected utility in this setting. Besides establishing the existence of optimizers under weaker assumptions than previous papers, we go on studying the relationship between optimal investments in finite market segments and those in the whole market. We show that certain natural (but nontrivial) continuity rules hold: maximal satisfaction, reservation prices and (convex combinations of) optimizers computed in small markets converge to their respective counterparts in the big market.
arXiv
We study dynamic allocation problems for discrete time multi-armed bandits under uncertainty, based on the the theory of nonlinear expectations. We show that, under strong independence of the bandits and with some relaxation in the definition of optimality, a Gittins allocation index gives optimal choices. This involves studying the interaction of our uncertainty with controls which determine the filtration. We also run a simple numerical example which illustrates the interaction between the willingness to explore and uncertainty aversion of the agent when making decisions.
arXiv
Economic theory is a mathematically rich field in which there are opportunities for the formal analysis of singularities and catastrophes. This article looks at the historical context of singularities through the work of two eminent Frenchmen around the late 1960s and 1970s. Ren\'e Thom (1923-2002) was an acclaimed mathematician having received the Fields Medal in 1958, whereas G\'erard Debreu (1921-2004) would receive the Nobel Prize in economics in 1983. Both were highly influential within their fields and given the fundamental nature of their work, the potential for cross-fertilisation would seem to be quite promising. This was not to be the case: Debreu knew of Thom's work and cited it in the analysis of his own work, but despite this and other applied mathematicians taking catastrophe theory to economics, the theory never achieved a lasting following and relatively few results were published. This article reviews Debreu's analysis of the so called ${\it regular}$ and ${\it crtitical}$ economies in order to draw some insights into the economic perspective of singularities before moving to how singularities arise naturally in the Nash equilibria of game theory. Finally a modern treatment of stochastic game theory is covered through recent work on the quantal response equilibrium. In this view the Nash equilibrium is to the quantal response equilibrium what deterministic catastrophe theory is to stochastic catastrophe theory, with some caveats regarding when this analogy breaks down discussed at the end.
arXiv
We develop a novel methodology based on the marriage between the Bhattacharyya distance, a measure of similarity across distributions of random variables, and the Johnson-Lindenstrauss Lemma, a technique for dimension reduction. The resulting technique is a simple yet powerful tool that allows comparisons between data-sets representing any two distributions. The degree to which different entities, (markets, universities, hospitals, cities, groups of securities, etc.), have different distance measures of their corresponding distributions tells us the extent to which they are different, aiding participants looking for diversification or looking for more of the same thing. We demonstrate a relationship between covariance and distance measures based on a generic extension of Stein's Lemma. We consider an asset pricing application and then briefly discuss how this methodology lends itself to numerous market-structure studies and even applications outside the realm of finance / social sciences by illustrating a biological application. We provide numerical illustrations using security prices, volumes and volatilities of both these variables from six different countries.
