When policymakers implement a disinflation program directed at high inflation, the real dollar value of their country’s stock market index experiences a cumulative abnormal 12-month return of 48 percent in anticipation of the event. In contrast, the average cumulative abnormal 12-month return associated with disinflations directed at moderate inflation is negative 18 percent. The 66-percentage point difference between cumulative abnormal returns, along with descriptive evidence and case studies, suggests that unlike the swift eradication of past high inflations documented by Sargent (1982), the US will not experience a quick, low-cost transition from moderate inflation to the Fed’s two-percent target.

Using confidential offer-level data on the US housing market, this paper examines the rounding-off heuristics in the bilateral bargaining process. We demonstrate that home sellers and home buyers follow different rounding-off heuristics. While sellers' list prices cluster more frequently around charm numbers (e.g. 349,999), buyers' offer prices and negotiated final sales prices cluster at salient round numbers.

Despite encouraging signs, India’s retail market remains largely off-limits to large international retailers like Wal-Mart and Carrefour. Opposition to liberalizing FDI in this sector raises concerns about employment losses, unfair competition resulting in the large-scale exit of incumbent domestic retailers, and infant industry arguments to protect the organized domestic retail sector that is at a nascent stage. Based on international evidence, we suggest that allowing entry by large international retailers into the Indian market may help tackle inflation, especially in food prices.

Factor analysis is a widely used tool to summarize high dimensional panel data via a small dimensional set of latent factors. Applications, particularly in finance, are often focused on observable factors with an economic interpretation. The objective of this paper is to provide a formal test for the question whether the factor spaces of latent and observable (economic) factors are equal.

Regulating short selling is difficult and controversial. We review the academic literature on short selling regulation and provide insights for future policymakers and academics. We organize the complex history of short selling regulation into three areas: disclosure requirements, securities lending restrictions, and trading restrictions. We identify, analyze and discuss 45 distinct regulations promulgated during the period 1896 through 2021, primarily by reviewing the academic literature associated with each regulation, including a discussion of the data sources employed. In so doing, we provide several insights regarding the effectiveness of regulatory approaches as well as the wider impact on markets.

In this paper, we develop new methods for analyzing high-dimensional tensor datasets. A tensor factor model describes a high-dimensional dataset as a sum of a low-rank component and an idiosyncratic noise, generalizing traditional factor models for panel data. We propose an estimation algorithm, called tensor principal component analysis (PCA), which generalizes the traditional PCA applicable to panel data.

We study competition and collaboration between a bank and a shadow bank that lend in the same market plagued by adverse selection. The bank has cheaper funding, whereas the shadow bank is endowed with a better screening technology. Our innovation is to allow the bank to lend to the shadow bank, i.e., to finance its competitors.

Global risk and risk aversion shocks have distinct distributional impacts on emerging market capital flows and returns. In particular, we find salient consequences of these different global shocks for tail risk in emerging markets. Open-end mutual fund trading provides a key mechanism linking shocks facing global investors to extreme capital flow and return realizations.

Reliably detecting insider trading is a major impediment to both research and regulatory practice. Using account-level transaction data, we propose a novel approach. Specifically, after extracting several key empirical features of typical insider trading cases from existing regulatory actions, we then employ a machine learning methodology to identify suspicious insiders across our full sample.