Knowledge that Transforms

We analyze how corporate direct investments in fintech startups affect startup performance and that of investing firms. Corporate investment in fintech startups is associated with a greater likelihood of successful exit, more and higher-quality innovation, and a greater inflow of high-quality inventors. A stacked difference-in-differences analysis shows that direct investments enhance the operating performance and equity-market valuation of corporate investors in the financial services sector, but not those in the nonfinancial sector. We establish two channels that drive fintech startups’ performance improvements: strategic alliance formation between investors and startups, and enhanced startup monitoring by corporate investors.

In 2017, the European Union Emissions Trading System underwent a policy intervention that resulted in a surge in carbon prices. Using this setting as a quasi-natural experiment, we focus on employment, productivity, and emission outcomes among covered enterprises. Results show that emission-intensive private firms, particularly those with financial constraints, are more likely to downsize by divesting production assets, reducing both workforce and emissions. Smaller, cash-strapped listed firms are also prone to downsize by decreasing their operating leverage while maintaining emission output and asset levels. Positive productivity outcomes indicate that both private and listed firms become leaner postintervention.

This article provides the first comprehensive evidence that the return extrapolation behavior of investors leads to biases in the expectations of volatility. Lower past returns are associated with higher expectations of volatility when using the physical, risk-neutral, and survey measures to estimate volatility expectations. Consistent with the return extrapolation framework, recent past returns have a larger impact than distant past returns on volatility expectations. Biases in volatility expectations are i) distinct from extrapolating past realized volatility, ii) asymmetrically induced by recent past negative returns, and iii) lead investors to pay more to insure against the perceived higher expected volatility.

We investigate whether chief financial officers (CFOs) serving on U.S. corporate boards benefit shareholders in M&A transactions. We find that acquisitions made by firms with CFOs on boards have significantly better acquirer announcement returns. This is due to the CFO director’s ability to select targets with better strategic and financial fit. CFO board membership can create shareholder value if there are effective governance regimes restraining managerial entrenchment and CFOs’ interests are closely aligned with those of shareholders through equity ownership. Furthermore, sitting on boards enables CFOs to secure more and cheaper financing for their acquisitions.

This study utilizes U.S. Patent Office data to explore potential improvements in the patent examination process through machine learning. It shows that integrating machine learning with human expertise can increase patent citations by up to 26%. Using machine learning predictions as benchmarks, I find that the early expiration rate of granted patents positively correlates with examiners’ false acceptance rates. These errors negatively impact public companies’ operational performance and reduce successful IPO or M&A exits for private firms. Overall, this study highlights significant social and economic benefits of incorporating machine learning as a robo-advisor in patent screening.

We show that investors price short-term stock market outcomes very different from outcomes that occur further into the future. To this end, we introduce the expected forward pricing kernel and decompose long-term pricing kernels into short-term and expected forward pricing kernels. Using index options, we find that kernels with maturities of up to 12 months are U-shaped and show that this results from the shape of the 1-month pricing kernel. Once we remove the impact of the 1-month kernel, the expected forward kernels are in line with standard long-run risk models in terms of their shape, level, and time-series variation.

We introduce a method to determine the investor’s optimal portfolio size that maximizes the expected out-of-sample utility under parameter uncertainty. This portfolio size trades off between accessing investment opportunities and limiting the number of estimated parameters. Unlike sparse methods such as lasso, which exclude assets during the optimization step, our approach fixes the optimal number of assets before optimizing the portfolio weights, which improves robustness and provides greater flexibility in practical implementations. Empirically, our size-optimized portfolios outperform their counterparts applied to all available assets. Our methodology renders portfolio theory valuable even when the data-set dimension and sample size are comparable.

We use negative durability shocks from vehicle discontinuations to study asset-backed lending and income-based lending (IBL) in auto finance. Discontinuations lead to increased down payments, higher loan-to-value ratios, and larger post-default personal recoveries. These results all indicate that economically disadvantaged consumers are relatively more reliant on unsecured IBL, in stark contrast to corporate financing patterns. Vehicle recoveries on discontinued cars are lower for borrowers who purchase after discontinuations, implying that depreciation is partially borrower-dependent. Our findings suggest that lower-income borrowers, in particular, benefit from technologies that facilitate IBL, such as income monitoring.

The growing popularity of home-sharing platforms such as Airbnb, partly fueled by hosts’ ability to evade local taxes and regulations, has been shown to elevate housing costs by reallocating long-term housing units to the short-term rental market. This study assesses whether enhanced tax enforcement can mitigate this trend. We analyze staggered tax collection agreements between Airbnb and Florida counties, wherein Airbnb collects taxes from the hosts directly. Using a difference-in-differences methodology, we find these agreements significantly slow the growth of housing costs, highlighting the importance of tax policy in addressing the sharing economy’s influence on housing affordability.

International equity funds attain superior subsequent performance by actively changing their country asset allocations, which we capture through a new measure of active country rotation intensity. Across funds, those that rotate country allocations with the greatest intensity on average have the highest value added. We offer evidence that a fund’s change of holdings in a country is associated with future outperformance in those specific holdings. Outperformance is concentrated on the downside when funds sell down country holdings before subsequent poor country market returns. Overall, our findings affirm that active international mutual funds have country market timing abilities.