Knowledge that Transforms

Does the recent proliferation of technology in lending process have an impact on business loan market competition? Using a theoretical model that assumes heterogeneity in lenders’ screening abilities and borrowers’ investment horizons, we show that FinTech (Traditional) lenders primarily supply unsecured (asset-backed) loans to borrowers with short-term (long-term) projects. The model builds on the interplay between screening ability and collateral requirements to characterize the competition between two ex-ante symmetric lenders. Lenders use screening technology and collateral requirements to mitigate competition and restrict the supply of credit through an endogenous segmentation of the loan market. As information technology improves, the effect on credit supply and equilibrium interest rates becomes more nuanced and depends on the market segment. The results offer a supply-side explanation for the growth of unsecured lending.

Conventional linear benchmarked contracts tend to cause excessive pegging to the benchmark and thus price distortion of stocks in the benchmark. This paper studies the optimal delegation contract when there is principal-agent friction. Specifically, it explores the impacts of incorporating the risk of invested portfolio in the contract on optimal strategies of the principal and the agent as well as on equilibrium asset prices. When agency friction is severe, the optimal contract provides rewards for portfolio risk to improve risk sharing and grants compensation for index return to propel the agent to deviate from pegging to index. In equilibrium, the principal conducts index investment while the agent invests only in individual risky assets, and price distortion caused by agency friction is mitigated.

Using a unique proprietary dataset of daily mutual fund trading records and the COVID-19 pandemic-triggered lockdown in Wuhan (China) as a natural experiment, we find that individual mutual fund investors in Wuhan significantly reduced their daily trading frequency, total investment of their portfolios, and risk level of their invested funds during the lockdown period as compared to investors in other cities. The results suggest that the elimination of face-to-face interaction among individual investors during the lockdown reduced their information sharing, which led to more conservatism in their financial trading. We rule out alternative explanations of salience bias due to limited investor attention and temporary changes in personal circumstances such as depression and/or income reduction, during the lockdown period. Finally, consistent with the theory of naïve investor trading, we also find that investors received higher trading returns during the lockdown as they reduced trading aggressively in the absence of face-to-face interactions.

In a theoretical context where innovators borrow loans or settle for state-owned enterprise (SOE) sponsorship for their projects, we examine the effects of banking prudential regulations and their interaction with corporate leverage on the patenting partnership choice in China using a unique matched patent-firm-bank loan dataset for 15,623 observations in the 2013–17 period. We use a unique instrumental variable (IV) strategy to identify idiosyncratic bank prudential reform shocks associated with the post-2012 Basel III regulation and find prudential metrics (corporate leverage) of the financiers (firms) to positively (negatively) influence SOE patenting partnership choice, though prudential regulation mitigates the latter. Prudential reforms therefore come at a cost of further SOE dominance. However, conditional on an innovation project being SOE sponsored, we find positive spillover effect from the SOE’s employment mandate to loan productivity. Our results are robust across different IV strategies, alternative measures, sub-sample and mechanism analyses.

This study examines how options trading influences brand development strategies by encouraging managerial risk-taking. We find that firms with higher levels of options trading tend to introduce more new trademarks, which exhibit lower citation rates from subsequent trademarks. These firms favor brand creation over extension, leading to increased brand riskiness, as evidenced by greater trademark diversity. Potential channels for these effects include increased institutional ownership by transient investors and enhanced managerial hedging opportunities. These effects are more pronounced in firms with weaker governance, managers with higher pay-risk sensitivity, younger managerial teams, and intense competition. Additionally, we observe a negative relation between unrelated brand diversification, driven by options trading, and firm value. Our findings support the notion that active options markets incentivize managers to pursue riskier brand strategies.

This article studies the impact of fractional integration on volatility modelling and option pricing. We propose a general discrete-time pricing framework based on affine multi-component volatility models that admit ARCH( ∞ ) representations. This not only nests a large variety of option pricing models from the literature, but also allows for the introduction of novel covariance-stationary long-memory affine GARCH pricing models. Using an infinite sum characterization of the log-asset price’s cumulant generating function, we derive semi-explicit expressions for the valuation of European-style derivatives under a general variance-dependent stochastic discount factor. Moreover, we carry out an extensive empirical analysis using returns and S&P 500 options over the period 1996–2019. Overall, we find that once the informational content from options is incorporated into the parameter estimation process, the inclusion of fractionally integrated dynamics in volatility is beneficial for improving the out-of-sample option pricing performance. The largest improvements in the implied volatility root-mean-square errors occur for options with maturities longer than one year, reaching 28% and 18% when compared to standard one- and two-component short-memory models, respectively.

We present a methodology for detecting flash crashes by identifying short-term V-shaped price reversals. Our approach, based on drift burst test statistics, aligns with the SEC’s forensic definition of market access rule violations, highlighting its potential as a market surveillance tool. Flash crashes have become more frequent over the past decade and are typically accompanied by high volumes, high volatility, and an increase in odd-lot trades. They are more likely to occur following periods of high volumes, elevated price impact, low volatility, and heightened algorithmic activity.

This paper examines the impact of intraday periodicity on forecasting realized volatility using a heterogeneous autoregressive model (HAR) framework. We show that periodicity inflates the variance of the realized volatility and biases jump estimators. This combined effect adversely affects forecasting. To account for this, we propose a periodicity-adjusted HAR model, HARP, where predictors are constructed from the periodicity-filtered data. We demonstrate empirically (using 30 stocks from various business sectors and the SPY for the period 2000–2020) and via Monte Carlo simulations that the HARP models produce significantly better forecasts across all forecasting horizons. We also show that adjusting for periodicity when estimating the variance risk premium improves return predictability.

We examine how cybercrime impacts victims’ risk-taking and returns. The results from our difference-in-differences analysis of a sample of victim and matched non-victim investors on the Ethereum blockchain are in line with prospect theory and suggests that victims increase their long-term total risk-taking after losing part of their wealth, leading to lower risk-adjusted returns in the post-cybercrime period. Victims’ long-term total risk-taking increases because they increase diversifiable risk due to victims’ post-cybercrime withdrawal from altcoins. At the same time, the reduction in risk-adjusted returns correlates with increased trading activity and churn, due plausibly to managing cybercrime exposure. In the cross-section of Ethereum addresses, we show that the most affluent victims take a systematic approach to restore their pre-cybercrime wealth level, while the least affluent victims turn into gamblers. Finally, a parsimonious forensic model explains a good part of the addresses’ probability of being involved in cybercrime, on both the victim and the cybercriminal side.

This study examines the ability of the linear-rational square-root model to simultaneously capture cross-sectional and time-series dynamics of bond yields and their variances. The preferred model specification comprises five factors, two of which are not spanned by the yield curve, introducing unspanned stochastic volatility (USV). This specification provides a close in-sample fit to yields and yield variances, emphasizing the need for USV. Out-of-sample testing demonstrates low variance forecast errors. The specification provides evidence of USV in conditional yield variance and bond risk premia, linked to macroeconomic uncertainty.