Knowledge that Transforms

We find that common ownership among acquirers enhances rather than hinders competition in the firm sale process. One common owner raises the likelihood that target firms are sold through auction (vs. negotiation with one buyer) by 21.5%. The effect is causal according to identifications based on mergers between financial institutions. Exploring economic channels, we observe selling firms respond to common ownership among acquirers by avoiding cross-owned acquirers, bargaining hard, and inviting more buyers when cross-owned acquirers initiate the deal but not by terminating the deal. Consistent with enhanced competition, common ownership among acquirers is positively associated with deal quality.

What are the benefits of access to the bond market for unlisted firms, and how does it affect their bank lending conditions? Using a regulatory reform that allowed unlisted firms to issue minibonds, we address these questions comparing new bank loans to issuers with concurrent loans to matched non-issuers. After the first minibond issuance, issuers obtain lower interest rates on bank loans of similar maturity, largely reflecting a shift in the seniority structure of corporate debt, and reduce the use of bank loans while increasing their total financial debt. They also increase turnover, total and fixed assets, particularly intangible assets.

We develop a real options model in which an entrepreneur facing ambiguity makes optimal investment and financing decisions for an innovation project. We introduce jumps in innovation returns and model investors’ aversion to ambiguity in both diffusion and jump risks. Debt accelerates investment by lowering the threshold and shortening expected waiting time, thereby increasing project value. This effect strengthens under greater ambiguity, offering a novel rationale for why debt—not equity—fosters innovation. Our results provide a coherent explanation for recent empirical findings on debt’s role in innovation and contribute to the broader literature on investment under uncertainty.

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.