Knowledge that Transforms

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.

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.

This article offers two novel backtests to evaluate the adequacy of well-known systemic risk measures such as CoVaR, MES, SES, and SRISK. Both the new backtests are robust to estimation risk (i.e., their null distributions remain invariant in the presence of estimation risk). While existing backtest is consistent against divergence from the null hypothesis up to a finite order, the article shows that the new backtests are fully consistent. The real-world implications brought by the new backtests are economically significant as they reveal significantly more cases of inadequate systemic risk modeling among the major financial institutions.

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.

Using manually compiled cost of equity (COE) estimates disclosed in takeover regulatory filings, we provide novel evidence on how investment bankers estimate discount rates. COE estimates are related to several risk proxies, such as beta and size. Other firm characteristics are unrelated to COE estimates or provide relations contradicting academic evidence. We also explore the role of incentives. For example, banks use significantly higher COEs in management buyouts, which potentially underestimates target value, making the bid more attractive for target shareholder approval.