Knowledge that Transforms

We examine the impact on a firm when it is forced to switch its bank relationship from one branch to another branch of the same bank, and how the firm’s information opacity (as proxied by the frequency with which the firm provides financial statements to the bank) moderates the consequences of relationship loss. We find the effect depends on the relative balance between the hard accounting information provided to the bank and the soft information about the firm due to its prior branch relationship. We show the loss of soft information provided to loan officers at the new branch, as a result of the forced branch switch, has a significant effect on the cost, maturity, and availability of loans from the new branch. Furthermore, we document the moderating effect of accounting information opacity on loan conditions upon relationship loss.

This paper investigates the information content of aggregate hedge fund flow and its predictive power with respect to bond yields. Using a sample of 9725 hedge funds from 1994 to 2012, we find that fund flow is negatively related to the changes in 10-year Treasury and Moody’s Baa bond yields one month ahead. This relation is still pronounced after controlling for other determinants of yield changes, including the amount of arbitrage capital available in the economy, suggesting a non-trivial effect of flow-induced hedge fund trading on bond yields. Flow impact on corporate bonds is further amplified during periods of decreasing market liquidity, consistent with a fire-sale hypothesis. Hedge fund flow also predicts convergence between constant maturity swap rate and constant maturity Treasury rate, as well as between the TIPS and Treasury bond yields, suggesting that hedge funds exploit arbitrage opportunities in these fixed-income markets.

We examine intra-market return comovement within each of 33 economies’ stock exchanges from 1995 through 2013 using a model-free comovement gauge. We find that the stability of international macroeconomic trilemma policies, the number of crises, and the extent of turnover overshadow the empirical relevance of many variables previously thought to be important for intra-market comovement, including country risk, corruption, and investor protections. We also use a much longer historical sample of U.S. firms to examine compositional explanations of the well-known U.S. comovement decline and to decompose the comovement into trend and cycle. Our findings challenge the compositional explanations of the decline; additionally, they suggest that the most recent uptick reflects short-term conditions, rather than a trend reversal.

We explore how the joint modeling of financial assets, especially dependent credit spreads, can utilize methodologies from geostatistical modeling. The considered approach is essentially based on modeling data as realizations of a (Gaussian) random field. This allows for a parsimonious representation of the dependence structure by means of a covariance function taken to be a function of the distance between observations. A key benefit of this ansatz is the possibility to include new data points, i.e. to consider new companies in existing financial applications. Consequently, geostatistical modeling has appealing benefits in the context of covariance matrix estimation and missing data imputation. We thoroughly discuss the necessary adjustments when applying geostatistical methods to the high-dimensional framework that entails the modeling of financial data, instead of the 2D/3D coordinate space encountered in original applications of the method. We illustrate the two use cases of covariance matrix estimation and missing data imputation on a data set of CDS spreads of constituents of the iTraxx universe, and sketch how the presented techniques could be exploited for market risk modeling.

We embed human capital as an innate, illiquid asset in Markowitz’ one-period mean-variance framework. By solving the Markowitz problem for different values of the ratio of human capital to financial wealth, we emulate life-cycle effects in household portfolio decisions. The portfolio derived with this simple approach matches the optimal portfolio from the much more complicated dynamic life-cycle models. An application illustrates that young households may optimally refrain from stock investments because a house investment combined with a mortgage is more attractive from a pure investment perspective. Another application examines the theoretical support for the observed growth/value tilts in households’ portfolios.

We develop a top-down macro stress test that assesses a community bank's ability to withstand a severe and prolonged period of high credit losses. The model groups banks by geography and subjects them to the 90th percentile chargeoff rates that banks experienced between 2008 and 2012. Because of local data limitations, our historical loss approach better reflects patterns of community bank stress than a linear econometric approach that estimates the relationship between macroeconomic conditions and bank performance. We put all U.S. community banks at year-end 2017 through the test and highlight two results. First, banks are much better prepared to withstand an adverse shock than they were on the verge of the financial crisis because banks have shifted away from the riskiest loan types. Second, the Tax Cuts and Jobs Act of 2017 has increased bank insolvency risk from an adverse shock in 2018 because the higher bank capital is more than offset by the weaker automatic stabilizer effect from operating losses.

This study shows that a group of individual investors in the financial markets displays symptoms of compulsive gambling, or an addiction to trading, based on a standard diagnostic checklist from the American Psychiatric Association. In a representative sample of Dutch retail investors, we find that 4.4% of the investors meet the criteria for compulsive gambling in the financial markets. Another 3.6% meet the criteria for problem gambling, which is a less severe form of gambling disorder. Investors with symptoms of compulsive gambling problems tend to follow a more active and speculative trading style, indicated by a higher frequency of stock trading, day-trading and investing in derivatives and leveraged products.

Citing consumer protection concerns, several states have recently enacted interest rate caps on small loans. After cataloguing the history of such legislation, we test whether these laws caused a decrease in the number of payday-lending establishments and subsequently prompted variation on incidence of bankruptcy filings. To motivate a causal interpretation of our estimates, we create a synthetic control that serves as a counterfactual from which we estimate the aggregate treatment effect of these interest rate ceilings. Importantly, we estimate the treatment effect for each period after the imposition of the cap, yielding novel insights about the dynamic heterogeneity in the relationship between payday-loan access and bankruptcy. Our results show payday-lending establishments drop by approximately 100%–a banishment of the industry. We find no short-run or long-run effects of these bans on bankruptcy. The range of our estimates allows us to rule out magnitudes that were documented in several previous studies.

We examine the impact of the 2016 U.S. SEC Tick Size Pilot Program on transaction costs in both the equity and options markets. We find that an increase in the tick size from one-cent to five-cents increases percent bid-ask spreads for test stocks vis-à-vis control stocks; however, this increase is substantially reduced when the test stocks have actively traded options. We also find a spillover effect in transaction costs from the underlying stock market to the options market, as both percent bid-ask spreads and implied volatility spreads widen in options for test stocks versus control stocks. Lastly, we find reversal effects at the conclusion of the pilot program, as percent spreads in both the equity and options markets narrow when the tick size is reduced.

Compared to the past literature on prediction markets that uses small-scale observational field data or experiments, this present research examines the efficiency of such markets by studying catastrophe (CAT) bonds. We collect actual catastrophe loss data, match them with the defined trigger events of each CAT bond contract, and then employ an empirical pricing framework to obtain the excess CAT premiums in order to represent the market-based forecasts. Our results indeed show that market-based forecasts have more significant predictive content for future CAT losses than professional forecasts that use natural catastrophe risk models. Although the predictive information for CAT events is specialized and complex, our evidence supports that CAT bond markets are successful prediction markets that efficiently aggregate information about future CAT losses. Our resultsalso highlight that actual CAT losses in future periods can explain the excess CAT bond spreads in the primary market and provide support for market efficiency when pricing CAT risk.