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Institute
The rise of shale gas and tight oil development has triggered a major debate about hydraulic fracturing (HF). In an effort to bring light to HF practices and their potential risks to water quality, many U.S. states have mandated disclosure for HF wells and the fluids used. We employ this setting to study whether targeting corporate activities that have dispersed externalities with transparency reduces their environmental impact. Examining salt concentrations that are considered signatures for HF impact, we find significant and lasting improvements in surface water quality between 9-14% after the mandates. Most of the improvement comes from the intensive margin. We document that operators pollute less per unit of production, cause fewer spills of HF fluids and wastewater and use fewer hazardous chemicals. Turning to how transparency regulation works, we show that it increases public pressure and enables social movements, which facilitates internalization.
This study compares the performance of various machine learning methods in predicting the outcomes of mergers and acquisitions (M&A), with application in merger arbitrage. Merger arbitrage capitalizes on price inefficiencies around merger announcements, empirically offering consistent, near-market-neutral returns with Sharpe ratios around 1.20 and a beta of 0.14. Leveraging logistic regression, random forest, gradient boosting machine, and neural network, I analyse 21,020 M&A deals with up to 522 predictors from 1999 to 2023. I examine two datasets: one with all features available prior to deal resolution, serving as an upper bound for predictability, and another with only features available on the announcement. Among the applied methods, XGBoost outperforms in predicting deal closure probabilities, with pseudo-out-of-sample receiver operating characteristic area under the curve (ROC-AUC) scores of 0.99 and 0.81 for the full-feature and announcement-date-only sets, respectively.
I apply these predictions to cash-only merger arbitrage from 2021 to 2023, using a classification method and testing a promising fair value investment criterion. I find that equal-weighted portfolios perform best, driven by diversification and small-size premia, achieving annualized alphas of 10 to 20% against the Fama-French five-factor model. XGBoost’s superior predictive power translates into the best merger arbitrage performance, delivering Sharpe ratios of up to 1.57 for long-only portfolios and 0.60 for zero-net-investment long-short strategies, with the latter maintaining market neutrality. I confirm these results during a second trading period from 2018 to 2020, revealing different market dynamics and similar or better model performance, with Sharpe ratios as high as 2.15.
These findings establish new benchmarks for M&A deal closure prediction, highlight the value of machine learning-driven strategies in enhancing merger arbitrage performance, and offer valuable insights for both researchers and practitioners.
Does political conflict with another country influence domestic consumers' daily consumption choices? We exploit the volatile US-China relations in 2018 and 2019 to analyze whether US consumers reduce their visits to Chinese restaurants when bilateral relations deteriorate. We measure the degree of political conflict through negativity in media reports and rely on smartphone location data to measure daily visits to over 190,000 US restaurants. A deterioration in US-China relations induces a significant decline in visits not only to Chinese but also to other foreign ethnic restaurants, while visits to typical American restaurants increase. We identify consumers' age, race, and cultural openness to moderate the strength of this ethnocentric effect.
The hierarchical feature regression (HFR) is a novel graph-based regularized regression estimator, which mobilizes insights from the domains of machine learning and graph theory to estimate robust parameters for a linear regression. The estimator constructs a supervised feature graph that decomposes parameters along its edges, adjusting first for common variation and successively incorporating idiosyncratic patterns into the fitting process. The graph structure has the effect of shrinking parameters towards group targets, where the extent of shrinkage is governed by a hyperparameter, and group compositions as well as shrinkage targets are determined endogenously. The method offers rich resources for the visual exploration of the latent effect structure in the data, and demonstrates good predictive accuracy and versatility when compared to a panel of commonly used regularization techniques across a range of empirical and simulated regression tasks.