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SSRN Research Paper Series
The Social Science Research Network’s Research Paper Series includes working papers produced by Stanford GSB the Rock Center.
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Persuasion, Science, and Miracles
As early as St Augustine and Aquinas, philosophers have studied the epistemology of contradictions to natural laws (aka, miracles). In this article, we develop a game-theoretic framework to analyze the concept of a miracle as understood by…
The Race Between Tax Enforcement and Tax Planning: Evidence From a Natural Experiment in Chile
Profit shifting by multinational corporations is thought to reduce tax revenue around the world. We analyze the introduction of standard regulations aimed at limiting profit shifting. Using administrative tax and customs data from Chile in…
Rank-and-File Accounting Employee Incentives and Financial Reporting Quality
An extensive literature examines whether senior executives’ contractual incentives influence their financial reporting decisions. However, little is known about whether — and how — the incentives of lower-level (or “rank-and-file”)…
Equity Book-to-Market Ratios Above One and Macroeconomic Risk
Equity book-to-market ratios (BTM) should not exceed one if a firm’s return on equity exceeds its cost of capital or it employs conservative accounting. Yet, BTM is above one for many firms, particularly in recession years. We address whether…
SystemMatch: Optimizing Preclinical Drug Models to Human Clinical Outcomes via Generative Latent-Space Matching
Translating the relevance of preclinical models (in vitro, animal models, or organoids) to their relevance in humans presents an important challenge during drug development. The rising abundance of single-cell genomic data from human tumors and…
Taxing Property in Developing Countries: Theory and Evidence from Mexico
The property tax is the most under-utilized tax in developing countries. We evaluate the revenue and welfare effects of the main policy instruments used to raise property tax revenue: tax rate changes and enforcement. Using administrative data…
Testing for Outliers with Conformal p-values
This paper studies the construction of p-values for nonparametric outlier detection, taking a multiple-testing perspective. The goal is to test whether new independent samples belong to the same distribution as a reference data set or are…
Exorbitant Privilege Gained and Lost: Fiscal Implications
We study three centuries of UK fiscal history. Before World War I, when the UK dominated global bond markets, the UK’s government debt was not always fully backed by its future surpluses. As predicted by theories of safe asset determination,…
Unstable Inference from Banks' Stable Net Interest Margins
This paper shows that stable net-interest margins of banks are uninformative about banks’ interest rate exposure. We show that neither deposits nor market power are essential for achieving stable net-interest margins (NIM) in long-short fixed…
Bargaining and International Reference Pricing in the Pharmaceutical Industry
The United States spends twice as much per person on pharmaceuticals as European countries, in large part because prices are much higher in the US. This fact has led policymakers to consider legislation for price controls. This paper assesses the…
Conformalized Survival Analysis
Existing survival analysis techniques heavily rely on strong modelling assumptions and are, therefore, prone to model misspecification errors. In this paper, we develop an inferential method based on ideas from conformal prediction, which can…
Firm-specific Information and Proprietary Costs: Evidence from Mandated Integrated Reports
Our study addresses whether integrated report quality, IRQ, is negatively associated with stock price synchronicity, an inverse measure of firm-specific information, and the extent to which the relation between IRQ and synchronicity is attenuated…
Internationalizing Like China
We empirically characterize how China is internationalizing the Renminbi by selectively opening up its domestic bond market and propose a dynamic reputation model to explain China’s internationalization strategy. While previously closed to…
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control
We introduce a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees. Our calibration algorithm works with any underlying model and (unknown) data-generating…
Voluntary Disclosure and Personalized Pricing
Firms have ever increasing access to consumer data, which they use to personalize their advertising and to price discriminate. This raises privacy concerns. Policymakers have argued in response that consumers should be given control over…
Measuring U.S. Fiscal Capacity using Discounted Cash Flow Analysis
We use discounted cash flow analysis to measure a country’s fiscal capacity. Crucially, the discount rate applied to projected cash flows includes a GDP risk premium. We apply our valuation method to the CBO’s projections for the U.S. federal…
Public Firm Disclosures and the Market for Innovation
We examine the spillover effect of public firm innovation disclosures on the patent trading market. Relative to equity markets, the patent market is decentralized and rife with information frictions, yet it serves as an important mechanism…
A General Theory of the Stochastic Linear Bandit and Its Applications
Recent growing adoption of experimentation in practice has led to a surge of attention to multiarmed bandits as a technique to reduce the opportunity cost of online experiments. In this setting, a decision-maker sequentially chooses among a set…
How do Private Equity Fees Vary across Public Pensions?
We study how investment fees vary within private-capital funds. Net-of-fee return clustering suggests that most funds have two tiers of fees, and we decompose differences across tiers into both management and performance-based fees. Managers of…
Patient-Level Clinical Expertise Enhances Prostate Cancer Recurrence Predictions with Machine Learning
With rising access to electronic health record data, application of artificial intelligence to create clinical risk prediction models has grown. A key component in designing these models is feature generation. Methods used to generate features…