One-third of deals in the venture capital (VC) market involve a founder and investor from the same university. Venture capitalists are more likely to invest in and place larger bets on startups with founders from their alma mater. These deals are also more likely to lead to IPOs postfunding. Tests using VC partner turnover confirm a direct link between education ties and funding likelihood. Taken together, our results suggest that university connections facilitate improved deal-making and outcomes, rather than diverting funds toward lower-quality startups.

We examine the role of systematic mispricing and risk compensation in explaining cryptocurrency returns using instrumented principal component analysis. We demonstrate that both elements make meaningful contributions to the variation in returns through distinct economic mechanisms. Mispricing primarily operates through behavioral channels, capturing speculative demand and liquidity frictions. A pure-alpha strategy delivers large and significant Sharpe ratios, confirming the economic importance of mispricing. Risk compensation is driven by fundamental factors, including past performance and exposures to both cryptocurrency and equity market risk. Consistent with this equity exposure, we document increasing correlation between cryptocurrency and equity returns over time.

We exploit heterogeneity in decreasing returns to scale (DRS) parameters across mutual funds to analyze the importance of scalability for investors’ capital allocation decisions. We find strong evidence that steeper DRS attenuate flow sensitivity to performance. We calibrate a rational model of active fund management and show that a large fraction of cross-sectional variation in assets-under-management is due to investors anticipating the effects of scale on return performance. We conclude that DRS play a key role in achieving equilibrium in the intermediated investment management market.

This article studies the making of return predictability among economically linked firms. I characterize an asymmetric cross-firm tug-of-war: i) High peer overnight returns are followed by elevated overnight returns for focal stocks, which fully reverse during intraday, and ii) high peer intraday returns are followed by high intraday returns but minor overnight price reactions. This pattern aligns with the story that individuals’ persistent trading on salient information distorts opening prices, while slow-moving arbitrage by professional investors gradually corrects mispricing. Mutual fund and hedge fund flows exhibit distinct associations with the tug-of-war, supporting the hypothesis that heterogeneous demand drives the return predictability.

We introduce the implied value premium (IVP), the difference between the implied costs of capital of value and growth stocks, to predict time variation in the ex post value premium. During 1977–2023, IVP is the strongest predictor of the ex post value premium. It also predicts the investment premium, consistent with the Investment CAPM. However, IVP’s ability to predict the difference in cumulative abnormal returns around quarterly earnings announcements of value and growth stocks suggests that mispricing may also play a role. Overall, our results suggest that recent value underperformance reflects cyclical variation rather than a permanent shift.

We report evidence consistent with institutional investors using industry-level information that they obtain from their investments in venture capital (VC) funds to earn excess returns in publicly traded stocks. We use court rulings regarding the Freedom of Information Act as an exogenous shock affecting the information flow between VC funds and institutional investors to show that the excess returns are explained by information received via this channel. Thus, institutional investors serve as conduits of information from private to public markets. In the process, institutional investors earn higher returns from their VC investments than implied by the cash flows received therefrom.

Credit rating agencies (CRAs) are less likely and slower to downgrade firms with performance-sensitive debt (PSD) if these downgrades increase borrowing costs. This effect is stronger when CRAs rate their most profitable clients and is not driven by selection into PSD contracts, by borrowers adjusting their leverage, or by borrowers hiding information. Originating banks price the CRAs’ conflicts of interest and sell loans with more embedded conflicts more frequently. In contrast, secondary market participants do not price conflicts of interest to the same extent. The recent settlements between the major CRAs and the U.S. government do not prevent rating inflation.

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 examine how management disclosure affects media coverage by analyzing firms’ responses in earnings call Q&A sessions. Using a machine-learning-based “nonanswer” score, we find that evasive managerial responses reduce subsequent media attention, particularly from professional outlets generating original content. This supports a supply-driven view of media coverage: poor disclosure limits journalistic content creation. Reduced coverage, in turn, is associated with lower investor engagement and weaker stock performance. Our findings highlight the critical role of a firm’s information environment in shaping the level and nature of subsequent media coverage, with implications for broader financial market outcomes.

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.

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 study whether information design influences consumer behavior in a randomized field experiment with users of an online account aggregation app. Participants received a personalized index representing their net worth as a lifetime monthly cash flow. The presentation of this index varied across treatments in its framing and the salience of its display. Consumers exposed to a consumption-oriented frame and a salient comparison of the index with their past spending reduced discretionary spending. These findings show that minor variations in information presentation can significantly affect financial behavior, highlighting the power of design in promoting saving and informing policy and regulation.

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.

We examine whether intellectual property protection facilitates the greater incorporation of firm-specific information into the stock price. Employing the staggered, country-level adoption of the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS), we find that after adoption, stock prices become less synchronous, consistent with more firm-specific information being impounded into the stock price. We further show that this effect is more pronounced for more innovative firms, firms in countries with stronger law enforcement, and firms with more financial analyst coverage. Finally, we document that TRIPS induces a richer information environment characterized by more management forecasts and media coverage.

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.

While the Dodd–Frank Act (DFA) mandates board risk committees for large banks, we argue that such committees do not benefit all banks. Banks forced by the DFA to adopt a board risk committee do not experience a reduction in risk following adoption. In contrast, banks that voluntarily established risk committees before the DFA exhibit lower risk, especially when these committees possess greater risk expertise. Using unique interview data, we find that board risk committees serve as active monitors rather than merely rubber-stamping management proposals. However, regulatory-mandated tasks limit their monitoring role.