Knowledge that Transforms

Using the macroeconomic forecasts of professional economists, we construct a comprehensive macro condition index that summarizes subjective expectations of output, inflation, and labor and housing market conditions. The index predicts stock returns and produces countercyclical equity premium forecasts, both in- and out-of-sample. Our results contrast with the procyclical subjective equity premia documented in recent literature. We show that the index reflects the true but unobserved macroeconomic condition that impacts the equity premium. Moreover, the predictability is not affected by belief biases and operates via a discount rate channel. The index’s predictability conforms to an explanation based on time-varying risk premia.

We proxy retail investor attention through Google Trends and find that fungible and non-fungible crypto tokens generate greater attention from high-gambling propensity regions. Crypto attention is higher during bubble-like episodes in the crypto market and for more lottery-like tokens. Moreover, retail crypto attention decreases after sports gambling is legalized. Higher token attention is associated with more contributors and higher fundraising. However, consumer credit default rates spike after periods of high crypto attention, but solely in the subprime segment. Overall, our findings suggest that gambling preferences strongly predict retail investor interest in the crypto market.

Firms falling short of earnings expectations are more likely to cite stakeholder-focused objectives in their public communications following earnings announcements. This behavior is consistent with managers preferring to be evaluated by subjective stakeholder-based performance criteria when falling short on objective shareholder-based measures. This increased use of stakeholder language is most evident among firms narrowly missing earnings estimates and appears unrelated to a firm’s actual environmental, social, and governance (ESG)-related activity. Stakeholder language appears to influence the evaluation of CEOs; turnover–performance sensitivity is lower for managers citing stakeholder value. Collectively, our findings are consistent with concerns that stakeholder objectives reduce managerial accountability for poor performance.

How should the lender of last resort provide liquidity to banks during periods of financial distress? During the 2008–2010 crisis, banks avoided borrowing from the Fed’s long-standing discount window but actively participated in its special monetary program, the Term Auction Facility, although both programs had the same borrowing requirements. Using an adverse selection model with endogenous borrowing decisions, we explain why the two programs suffer from different stigma costs and how the introduction of TAF incentivized banks’ borrowing. We discuss the empirical relevance of the model’s predictions. [Banks] deliberately did not ask for the liquidity they needed for fear of damaging their reputation—the ‘stigma’ problem… I do not think we were conscious of this before the crisis started and I do not think central banks have a convincing answer to it… This is, I think, still a challenge in how to manage the process of central bank provision of liquidity support. This is one of the big intellectual issues that has not been fully resolved. (Governor Mervyn King, Bank of England (2016)) For various reasons, including the competitive format of the auctions, [Term Auction Facility] has not suffered the stigma of conventional discount window lending and has proved effective for injecting liquidity into the financial system… Another possible reason that [Term Auction Facility] has not suffered from stigma is that auctions are not settled for several days, which signals to the market that auction participants do not face an immediate shortage of funds. (Ben Bernanke, testimony to U.S. House of Representatives (2010))

How does access to public equity markets affect the human capital of IPO filing firms? While IPO filing firms have high average wages and limited industrial diversification, a successful IPO increases departures of high-wage employees to startups and triggers industrial diversification through employment growth in non-core industries. Surprisingly, IPOs do not significantly affect the earnings growth of pre-IPO workers. Instead, post-IPO hires receive larger earnings increases upon joining. Overall, going public has a significant effect on a firm’s workforce and labor reallocation across firms.

An unintended consequence of recent governance reforms in the United States is firms’ greater reliance on older director candidates, resulting in noticeable board aging. We investigate this phenomenon’s implications for corporate governance. We document that older independent directors exhibit poorer board meeting attendance, are less likely to serve on or chair key board committees, and receive less shareholder support in annual elections. These directors are associated with weaker board oversight in acquisitions, CEO turnovers, executive compensation, and financial reporting. However, they can also provide particularly valuable advice when they have specialized experience or when firms have greater advisory needs.

This article studies how the rise of financial technology (Fintech) lending affects credit access, interest rates, and social welfare. We consider a lending competition model with two incumbent banks and a Fintech lender, which use different information and technologies to assess borrower creditworthiness. We show that Fintech lending could negatively affect high-quality borrowers’ access to credit when the Fintech lender’s screening accuracy is superior to that of the banks. Furthermore, Fintech lending may worsen the allocative efficiency of credit and reduce social welfare under some conditions. Analytical and numerical results suggest that Fintech lending mostly reduces the expected interest rates.

We show theoretically that the usual estimated investment strategies will not achieve the optimal Sharpe ratio when the dimensionality is high relative to sample size, and the $ 1/N $ rule is optimal in a 1-factor model with diversifiable risks as dimensionality increases, which explains why it is difficult to beat the $ 1/N $ rule in practice. We also explore conditions under which it can be beaten, and find that we can outperform it by combining it with the estimated rules when $ N $ is small, and by combining it with anomalies or machine learning portfolios, conditional on the profitability of the latter, when $ N $ is large.

Momentum profits depend mainly on the short leg and therefore on barriers to short sales. Our research indicates that the decline in momentum profitability in the past 2 decades is driven partly by a contemporaneous growth in stock options trading. Stock options offer an alternative to short selling, augmenting the stock lending market, and thereby contributing to improved pricing efficiency. The resulting reduction in barriers to short sales contributes to lower returns to momentum trading from the short leg. Our results persist after matching stocks with and without options based on different firm-level characteristics.

We demonstrate the benefits of merging traditional hypothesis-driven research with new methods from machine learning that enable high-dimensional inference. Because the literature on post-earnings announcement drift (PEAD) is characterized by a “zoo” of explanations, limited academic consensus on model design, and reliance on massive data, it will serve as a leading example to demonstrate the challenges of high-dimensional analysis. We identify a small set of variables associated with momentum, liquidity, and limited arbitrage that explain PEAD directly and consistently, and the framework can be applied broadly in finance.