Assume data on Nj stock (asset) returns are available for p stocks, allowing us to construct approximate density functions f(xj) for (j=1, 2, …, p) from p empirical cumulative distribution functions (ECDFs). Our portfolio choice is designed to rank ECDF-induced, ill-behaved f(xj) densities subject to multiple modes, asymmetric fat tails, dips, turns, and numerous overlaps. Older portfolio theory assumes that parameters like the mean, variance, and percentiles fully describe f(xj). All six of our algorithms avoid (expected) utility theory. The only available algorithm by Anderson for order-k Stochastic Dominance (SDk) needs a trapezoidal approximation. Our new exact algorithm for SDk is based on ECDFs and overcomes pairwise comparisons. We include algorithms for statistical inference using the bootstrap and one for “pandemic proof” out-of-sample portfolio performance comparisons from our R package ‘generalCorr’. We suggest a test for “zero cost profitable arbitrage” and illustrate our algorithms in action by using two sets of recent 169-month stock returns. We do not claim to suggest new optimal portfolios.

In August 2020, the Federal Open Market Committee adopted a far-reaching Revised Statement on Longer-Run Goals and Monetary Policy Strategy. The framework contains two major changes from the original 2012 statement. First, policy decisions will attempt to mitigate shortfalls, rather than deviations, of employment from its maximum level. Second, the FOMC will implement Flexible Average Inflation Targeting. We show how to modify the rules in the Fed’s Monetary Policy Report to be consistent with the revised statement, how the pattern of falling behind the curve, pivot, and getting back on track in Fed policy during 2021 and 2022 could have been avoided by following inertial rules consistent with either the original or the revised statements, and how current and projected Fed policy for 2023 – 2026 is in accord with the prescriptions from inertial rules.

We identify a comprehensive list of thirty-eight characteristics for predicting cross-sectional FX options returns. We find that three factors—long-term straddle momentum, implied volatility, and illiquidity—can generate economically and statistically significant risk premia not explained by other return predictors. Meanwhile, the predictability of the other characteristics becomes insignificant after accounting for the FX option three-factor model. The significance of the three factors is confirmed through a series of robustness tests covering different data sources, alternative options strategies, diversification effects, bootstrapping, and omitting crisis years.

Glaeser and Lang (2024; GL) reviews the accounting literature on innovation, which has increased substantially in recent years. GL makes an important contribution to accounting research by bringing into the literature the implications of Romer's Nobel Prize winning endogenous growth theory and by explaining how accounting research addresses questions related to innovation. We contribute to accounting research by building on GL's foundation to suggest three main paths forward for future innovation research. First, focus on innovation's three defining attributes: novelty, nonrivalry, and partial excludability. Second, determine the needs of various users of information about a firm's innovation activities and how to meet those needs; we focus on the needs of investors. Third, address questions our discussion highlights as potentially important for future research on financial reporting and innovation, including the crucial question of an innovation's identifiability.

Internal and external parties meaningfully shape corporate tax outcomes. However, we lack a holistic understanding of the major parties involved and their comparative effects. Using proprietary IRS data for public and private firms, we identify the top executives, corporate accountants, external accounting firms, and individual tax preparers and examine the comparative importance of these parties on corporate tax outcomes. We find that external individual tax preparers matter much more than the accounting firms that employ them. Internal actors (top accountants and executives) explain more of the variation in corporate tax outcomes than external actors (individual tax preparers and accounting firms). We also find some evidence that individuals’ characteristics are associated with the tax behavior of the corporations they serve. Overall, we conclude that some of the actors who are unobservable in public data play a greater role in corporate tax outcomes than parties that are a focus of prior research.

The patent system grants inventors temporary monopoly rights in exchange for a public disclosure detailing their innovation. These disclosures are meant to allow others to recreate and build on the patented innovation. We examine how the quality of these disclosures affects follow-on innovation. We use the plausibly exogenous assignment to patent applications of examiners who differ in their enforcement of disclosure requirements as a source of variation in disclosure quality. We find that some examiners are significantly more lenient with respect to patent disclosure quality requirements, and that patents granted by these examiners include significantly lower-quality disclosures and generate significantly less follow-on innovation. Overall, our evidence suggests that high-quality patent disclosures create knowledge spillovers that spur follow-on innovation.

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.

We examine the structural stability of Gaussian shadow rate term structure models in a sample of Treasury yields that includes the “effective lower bound” (ELB) period from 2008 to 2015. After highlighting the challenges of testing for structural breaks in a latent-factor model, we proceed to document various pieces of empirical evidence for a structural break. As one of several practical implications, the expected policy rate paths during ELB years are notably shallower in our model that accommodates a structural break compared with a model that imposes structurally stability.

We examine bankruptcy within business groups. Groups have incentives to support financially distressed subsidiaries, as the bankruptcy of a subsidiary may impose severe costs on the group as a whole. This is in part because, in several countries, bankruptcy courts often “pierce the corporate veil” and hold groups liable for their distressed subsidiaries’ obligations as if they were their own. Using a large cross-country sample of group-affiliated firms, we show that, by reallocating resources within the corporate structure, business groups actively manage intra-group credit risk to prevent costly within-group insolvencies. Moreover, we document that recent regulatory changes in the approval and disclosure of related party transactions are costly for business groups in that they constrain their ability to shield their subsidiaries from credit-risk shocks. Our study informs the current regulatory debate on related party transactions by highlighting an important cost of anti-self-dealing regulation.