Job Market Paper
Trading Complex Risks
This paper studies how complexity impacts markets’ ability to aggregate information and distribute risks. I amend fundamental asset pricing theory to reflect agents’ imperfect knowledge about complex dividend distributions and test its clear-cut predictions in the laboratory. Market equilibria corroborate complexity-averse trading behavior. Despite being overpriced, markets efficiently share complex risks between buyers and sellers. While complexity induces noise in individual trading decisions, market outcomes remain theory-consistent. This striking feature reconciles with a random choice model, where bounds on rationality are reinforced by complexity. By adjusting for estimation biases, traders reduce the variation in market-clearing prices of complex risks.
Winner of the NASDAQ Award for the Best Paper on Asset Pricing, WFA 2019, Huntington Beach
AFA 2018 (PhD Poster Session), RBFC 2018, FIRN 2018, Paris December Finance Meeting 2018, MPI Experimental Finance Workshop 2019, EF 2019, WFA 2019, Econometric Society Australasian Meeting 2019, EFA 2019
Asset Pricing under Computational Complexity
With Peter Bossaerts, Elizabeth Bowman, Harvey Huang, Carsten Murawski, Anirud Suthakar, Shireen Tang and Nitin Yadav
We investigate how markets solve the standard but computationally hard problem of maximizing utility subject to a budget constraint with indivisibilities. In a first experiment with complete markets, we show that the theory of computation sheds light on which problems are hard for individuals to solve, and whether computational difficulty is reflected in price quality. Under computational complexity, complete markets are fairly ineffective in revealing important information, resulting in noisy prices. In a second experiment, inspired by the theory of oracles in computer science, we show that a reduction to only one traded asset can actually improve information dispersion. Our findings demonstrate how a market design that solely focuses on the transmission of incomplete but crucial information can lead to a more efficient spreading of knowledge in society (Hayek, 1945).
Barcelona GSE Summer Forum 2019, EF 2019, Econometric Society Australasian Meeting 2019, CEPR European Summer Symposium in Financial Markets (morning session), Helsinki Finance Summit 2019, FIRN 2019, Paris December Finance Meeting 2019, 30th Annual Utah Winter Finance Conference
Structured Finance Meets Investor Bias: The demand and supply effects from misestimating and shrouding correlation risk
With Marc Chesney and Jonathan Krakow
We study the relation between the inherent complexity of structured products and their endogenous issuer margins. First, using a sample of 4,460 yield enhancement products (YEP), we document a shift towards more complex payoff structures. Margins for more complex products are twice as high relative to their less complex counterparts, while the former’s realized investor returns are lower and negative on average. We identify uncompensated correlation risk as the main mechanism behind this discrepancy. Second, we conduct a laboratory experiment to measure individuals’ willingness-to-pay for YEPs with varying levels of complexity. Our experimental findings provide a micro-foundation for our field results. We find that subjects systematically underestimate the embedded correlation risk of more complex products. The resulting relative overpricing is increasing in the underlying volatility and in subjects' overconfidence. Moreover, the willingness to invest in structured products is higher if the risk-free rate is lower. In the face of unprecedented low interest rates and a rising popularity of YEPs, we argue that our findings are of direct policy relevance.
Risk and Return around the Clock
With Alexandre Ziegler
We investigate price discovery over the 24-hour trading day for equities, currencies, bonds, and commodities. Sizable price discovery occurs around the clock for most assets. For a given asset, intraday risk and return distributions are fairly similar, indicating a broadly constant risk-return-relationship during the day. Although the amount of price discovery varies significantly during the day and differs across assets, price discovery is generally efficient around the clock. Most assets do not exhibit the U-shaped intraday volatility pattern that has been documented for US equities, even if only main trading hours are considered. Intraday spikes in volatility are driven by the open or close of the market for the respective asset or other assets and by macroeconomic announcements. Both diffusion and jump risk are important drivers of intraday volatility patterns, and US macroeconomic news account for a sizable fraction of jump-driven volatility. For some – but not all – assets, the relationship between volume and volatility that can generally be observed during the trading day does not hold at the time of jumps, suggesting that traders anticipate large price moves at the time of scheduled announcements and market depth falls accordingly.
Asset Pricing In a World of Imperfect Foresight
With Peter Bossaerts and Wenhao Yang
We consider one of the canonical models of asset pricing, where agents with quadratic preferences are allowed to re-trade a limited set of securities for a number of periods, after which these securities expire, and agents consume their liquidation values. A key assumption in this model is that agents have perfect foresight: they correctly predict prices in all future contingencies. We show that, under myopia, prices generically are as if agents had perfect foresight. Yet their choices are ``wrong,'' because agents incorrectly believe that they will never re-trade. In an experiment, we confirm that prices and choices are indeed as predicted by the myopic equilibrium. We conclude that modern asset pricing theory can provide guidance for valuation and policy even if agents are (necessarily) boundedly rational.
Draft available upon request.
EF 2019, Econometric Society Australasian Meeting 2019, FIRN 2019
Emotional Engagement and Trading Performance: An Experimental Approach
With Peter Bossaerts, Kristian Rotaru and Kaitong Xu
In a series of laboratory market experiments, we investigate the relationship between subjects’ trading activity and changes in emotional states. Over the course of a repeated trading game, we measure individual psychophysiological responses, i.e., heart rate variability (HRV) and skin conductance response (SCR), while simultaneously collecting real-time trading data. We find that earnings tend to be higher for subjects whose SCR response is correlated with asset mispricing or their individual wealth. Furthermore, subjects whose HRV predicts market volatility tend to perform better on average, whereas the inverse relation holds for those with below-average earnings. Our results contribute to the existing literature in the field of decision neuroscience, showing that not only are subjects emotionally engaged during trading, but there also exists a complementary relationship between emotions and effective decision-making in financial markets.
Draft available upon request.
9th Behavioural Finance and Capital Markets Conference