Knowledge that Transforms

When making investment decisions, people rely heavily on price charts displaying the past performance of an asset. Price charts can come with any time frame, which the provider might strategically choose. We analyze the impact of the time frame on retail investors’ behavior, particularly trading activity and risk-taking, in a controlled experiment with 1041 retail investors. We find that shorter time frames are associated with more trading activity, resulting in higher transaction fees and investor welfare losses. However, the time frame does not affect average risk-taking.

Stochastic optimization models have been extensively applied to financial portfolios and have proven their effectiveness in asset and asset-liability management. Occasionally, however, they have been applied to dynamic portfolio problems including not only assets traded in secondary markets but also derivative contracts such as options or futures with their dedicated payoff functions. Such extension allows the construction of asymmetric payoffs for hedging or speculative purposes but also leads to several mathematical issues. Derivatives-based nonlinear portfolios in a discrete multistage stochastic programming (MSP) framework can be potentially very beneficial to shape dynamically a portfolio return distribution and attain superior performance. In this article we present a portfolio model with equity options, which extends significantly previous efforts in this area, and analyse the potential of such extension from a modeling and methodological viewpoints. We consider an asset universe and model portfolio set-up including equity, bonds, money market, a volatility-based exchange-traded-fund (ETF) and over-the-counter (OTC) option contracts on the equity. Relying on this market structure we formulate and analyse, to the best of our knowledge, for the first time, a comprehensive set of optimal option strategies in a discrete framework, including canonical protective puts, covered calls and straddles, as well as more advanced combined strategies based on equity options and the volatility index. The problem formulation relies on a data-driven scenario generation method for asset returns and option prices consistent with arbitrage-free conditions and incomplete market assumptions. The joint inclusion of option contracts and the VIX as asset class in a dynamic portfolio problem extends previous efforts in the domain of volatility-driven optimal policies. By introducing an optimal trade-off problem based on expected wealth and Conditional Value-at-Risk (CVaR), we formulate the problem as a stochastic linear program and present an extended set of numerical results across different market phases, to discuss the interplay among asset classes and options, relevant to financial engineers and fund managers. We find that options’ portfolios and trading in options strengthen an effective tail risk control, and help shaping portfolios returns’ distributions, consistently with an investor’s risk attitude. Furthermore the introduction of a volatility index in the asset universe, jointly with equity options, leads to superior risk-adjusted returns, both in- and out-of-sample, as shown in the final case-study.

In 2015 the Tokyo Stock Exchange (TSE) implemented Arrowhead Renewal improvements (ARI) that reduced latency from about one millisecond to less than 0.5 ms. Simultaneously, the ARI introduced new risk management functions to improve market fairness by reducing manipulative trading strategies. We find a dramatic improvement in market fairness as proxied by marking-the-close incidents, which declined by 61.19%. We find a much smaller improvement in market efficiency. Specifically, there was a reduction in the effective (quoted) spread of 6.45% (5.79%). The most dramatic improvement in market quality (fairness and efficiency) was for low-tick-size and high-market-capitalization stocks.

Natural catastrophe risk is increasingly being covered through alternative capital instead of reinsurance. Since most such instruments do not trade in an active market, their ongoing valuation is a challenge. As a solution, we propose to exploit pricing information embedded secondary market catastrophe bond quotes. Specifically, we use a reduced form model to extract implied Poisson intensities from regularly observed prices. Next, we show that the intensities can be explained by time to maturity and modeled probability of first loss. Along these two dimensions, we estimate smooth intensity surfaces that allow investors to mark illiquid catastrophe risk positions to market.

This paper investigates the impact of loan renegotiations on firms’ credit risk using the CDS market as a measure of credit risk. Using a sample of public US firms for 2010–2017, we document a significant decrease in CDS spreads and returns that we interpret as evidence of a certification effect. The finding suggests that the loan renegotiations are on average beneficial for the firm. The strongest reactions are for material amendments such as line of credit amount or tranche amount. Additionally, we find negative stock market returns, although barely statistically significant. Moreover, we identify an anticipation effect of up to 30 days before the announcement date on the CDS market, possibly due to informed trading by CDS banks of their speculative-rated borrowers’ CDS contracts. Finally, we show that firm-specific CDS returns lead idiosyncratic stock returns, especially around the announcement date and for speculative-rated firms.

Previous studies document statistically significant evidence of crude oil return predictability by several forecasting variables. We suggest that this evidence is misleading and follows from the common use of within-month averages of daily oil prices in calculating returns used in predictive regressions. Averaging introduces a bias in the estimates of the first-order autocorrelation coefficient and variance of returns. Consequently, estimates of regression coefficients are inefficient and associated t-statistics are overstated, leading to false inference about the true extent of in-sample and out-of-sample return predictability. On the contrary, using end-of-month data, we do not find convincing evidence for the predictability of oil returns. Our results highlight and provide a cautionary tale on how the choice of data could influence hypothesis testing for return predictability.

We study whether management practices determine merger and acquisition (M&A) success. We model management as an unobserved (latent) variable in a standard microeconomic model of the firm and derive firm-year management estimates. We validate these estimates against benchmark survey data on management practices and by using Monte Carlo simulation. We show that our measure is among the most important determinants of value creation in M&A deals, substantially increasing the predictive power of models that explain cumulative abnormal returns. Thus, we offer a measure of management practices that identifies the best-performing M&As. Our results are robust to the inclusion of acquirer fixed effects and many control variables, and to several other sensitivity tests. We identify the Q-theory as the key mechanism driving our results.

We show that hedge fund managers who more actively and astutely adjust the political sensitivity of their portfolios, in line with the dynamic U.S. political landscape, improve their investment performance. Funds that tilt their portfolios toward market segments expected to perform better during the new political regime, specifically around U.S. Presidential elections, generate significantly higher alphas. Further, hedge fund families with greater responsiveness to political changes exhibit persistently superior performance and are more likely to survive. Hedge fund investors reward more responsive fund managers with greater inflows.

We examine whether the relationship between managerial risk-taking incentives and bank risk is sensitive to the underlying macroeconomic conditions. We find that risk-taking incentives provided to bank executives are associated with higher bank riskiness during economic downturns. We attribute this finding to the increase in moral hazard during macroeconomic downturns when the perceived probability of future bailouts and government guarantees rises. This association is particularly strong for larger banks, banks that maintain lower capital ratios and banks that are managed by more powerful Chief Executive Officers (CEOs). Our findings highlight the importance of the interaction between managerial incentives and the macroeconomic environment. Boards and regulators may find it useful to consider the countercyclical nature of the relationship between risk-taking incentives and bank riskiness when designing managerial compensation.

Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how performance can be increased further by using open/high/low/close (OHLC) price data instead of simply using daily returns. A key innovation, for the improved modeling of not only dynamic variances but also of dynamic correlations, is the concept of a regularized return, obtained from a volatility proxy in conjunction with a smoothed sign of the observed return.