Jim Chen's SSRN abstracts

Welcome to Jim Chen's SSRN abstracts. This is a human-friendly display of the RSS feed  for Mr. Chen's official SSRN page (http://ssrn.com/author=68651), reorganized by reverse chronological order rather than number of downloads. To receive updates as Mr. Chen posts new papers or updates old papers, please use the following form:

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REVISION: Principles of Political Economy and the Taxation of Nations: Econometric and Machine-Learning Evaluation of Tariffs, https://privwww.ssrn.com/abstract=3791744 (September 8, 2021)

Demography affects the ability of countries to manage their debt levels and to make macroeconomic policy. By the same token, the demographic attributes of labor influence political decisions among nations, including international trade policy. In particular, the free movement of labor is a bedrock principle of the European Union. That legal guarantee has prompted one country to leave the Union, even as it inspires other countries to join.

This study investigates the influence of (labor) demographics on tariffs in 45 OECD and non-OECD countries. A series of econometric models reveals evidence that the population and labor force may influence tariff levels. By contrast, migration does not. Income per capita and consumption affect tariff rates. Machine-learning methods confirm conclusions reached through conventional econometrics and shed further light on the relationship between tariff levels and their hypothesized predictors. The absence of a significant relationship between ...
REVISION: Split Decisions: Practical Machine Learning for Empirical Legal Scholarship, https://privwww.ssrn.com/abstract=3731307 (September 8, 2021)

Multivariable regression may be the most prevalent and useful task in social science. Empirical legal studies rely heavily on the ordinary least squares method. Conventional regression methods have attained credibility in court, but by no means do they dictate legal outcomes. Using the iconic Boston housing study as a source of price data, this Article introduces machine-learning regression methods. Although decision trees and forest ensembles lack the overt interpretability of linear regression, these methods reduce the opacity of black-box techniques by scoring the relative importance of dataset features. This Article will also address the theoretical tradeoff between bias and variance, as well as the importance of training, cross-validation, and reserving a holdout dataset for testing.
REVISION: The Capital Asset Pricing Model, https://privwww.ssrn.com/abstract=3844183 (September 8, 2021)

The capital asset pricing model (CAPM) is an influential paradigm in financial risk management. It formalizes mean-variance optimization of a risky portfolio given the presence of a risk-free investment such as short-term government bonds. The CAPM defines the price of financial assets according to the premium demanded by investors for bearing excess risk.
New: A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities, https://privwww.ssrn.com/abstract=3901479 (September 4, 2021)

A suite of clustering methods, applied to the matrix of conditional volatility by trading days and individual assets or asset classes, can identify critical periods in markets for crude oil, refined fuels, and other commodities. Unsupervised machine learning provides a viable alternative to rules-based and subjective definitions of crises in financial markets and the broader economy. Five clustering methods—affinity propagation, mean-shift, spectral, k-means, and hierarchical agglomerative clustering—can identify anomalous periods in commodities trading. These methods identified the financial crisis of 2008–09 and the initial stages of the Covid-19 pandemic. Applied to four energy-related markets—Brent, West Texas intermediate, gasoil, and gasoline—the same methods identified additional periods connected to events such as the September 11 terrorist attacks and the 2003 Persian Gulf war. t-distributed stochastic neighbor embedding facilities the visualization of commodity trading ...
New: This Is the Way the World Ends, Not with a Bang but Bonds and Bullets, https://privwww.ssrn.com/abstract=3904934 (September 1, 2021)

This article explores instinctive frames of human decision-making in environmental and resource economics. Higher-moment asset pricing combines rational, mathematically informed economic reasoning with psychological and biological insights. Leptokurtic blindness and skewness preference combine in particularly challenging, even perverse, ways for carbon mitigation. Information uncertainty and the innate preference for bonds-and-bullets portfolios may impair responses to catastrophic climate change.
REVISION: Fear in a Handful of Dust: The Epidemiological, Environmental, and Economic Drivers of Death by PM2.5 Pollution, https://privwww.ssrn.com/abstract=3879073 (August 25, 2021)

This study evaluates a wide range of epidemiological, environmental, and economic factors affecting morbidity and mortality from PM2.5 exposure in the 27 member-states of the European Union. This form of air pollution inflicts considerable social and economic damage in addition to loss of life and well-being. This study creates and deploys a comprehensive data pipeline. The first step consists of conventional linear models and supervised machine-learning alternatives. Critically, these regression methods do more than predict health outcomes in the EU-27 and relate those predictions to independent variables. Linear regression and its machine-learning equivalents also inform unsupervised machine learning methods such as clustering and manifold learning. Lower-dimension manifolds of this dataset’s feature space reveal the relationship among EU-27 countries and their success (or failure) in managing PM2.5 morbidity and mortality. Principal component analysis informs further ...
REVISION: Agriculture, End to End, https://privwww.ssrn.com/abstract=3218894 (August 25, 2021)

Agriculture consists of a process for converting energy and biological information into physical products for human consumption. Increasingly sophisticated biotechnology in agricultural inputs makes manifest this definition of agriculture as information flow. The architectural ideal in information science is the end-to-end principle. All intelligence within an information platform arises from its ends. The corollary of the end-to-end principle, however, is that intervening layers facilitating the transmission of intelligence become “dumb pipe,” whose sole contribution consists of efficient transport of information. Within its own domain, agriculture has become dumb pipe. The rise of bioengineered inputs has rendered obsolete the evolutionary contribution of farmers. At the other end of the value chain, consumer-driven preferences in food restrict the inputs that farmers may deploy. “Intelligence” propelled by preferences and tastes among affluent consumers constrain choices ...
New: Clustering Commodity Markets in Space and Time: Clarifying Returns, Volatility, and Trading Regimes Through Unsupervised Machine Learning, https://privwww.ssrn.com/abstract=3791138 (March 31, 2021)

Unsupervised machine learning can interpret logarithmic returns and conditional volatility in commodity markets. k-means and hierarchical clustering can generate a financial ontology of markets for fuels, precious and base metals, and agricultural commodities. Manifold learning methods such as multidimensional scaling (MDS) and t-distributed stochastic neighbor embedding (t-SNE) enable the visualization of comovement and other financial relationships in three dimensions.

Different methods of unsupervised learning excel at different tasks. k-means clustering based on logarithmic returns works well with MDS to classify commodities and to create a spatial ontology of commodities trading, A strikingly different application involves k-means clustering of the matrix transpose, such that conditional volatility is evaluated by trading date rather than by commodity. This approach can isolate the two most calamitous temporal regimes of the past two decades: the global financial crisis ...
New: Interpreting Linear Beta Coefficients Alongside Feature Importances in Machine Learning, https://privwww.ssrn.com/abstract=3795099 (March 29, 2021)

Machine-learning regression models lack the interpretability of their conventional linear counterparts. Tree- and forest-based models offer feature importances, a vector of probabilities indicating the impact of each predictive variable on a model’s results. This brief note describes how to interpret the beta coefficients of the corresponding linear model so that they may be compared directly to feature importances in machine learning.
REVISION: Models for Predicting Business Bankruptcies and Their Application to Banking and Financial Regulation, https://privwww.ssrn.com/abstract=3329147 (February 26, 2021)

Models for predicting business bankruptcies have evolved rapidly. Machine learning is displacing traditional statistical methodologies. Three distinct techniques for approaching the classification problem in bankruptcy prediction have emerged: single classification, hybrid classifiers, and classifier ensembles. Methodological heterogeneity through the introduction and integration of machine-learning algorithms (especially support vector machines, decision trees, and genetic algorithms) has improved the accuracy of bankruptcy prediction models. Improved natural language processing has enabled machine learning to combine textual analysis of corporate filings with evaluation of numerical data. Greater accuracy promotes external processes of banks by minimizing credit risk and by facilitating regulatory compliance.
REVISION: An Introduction to Machine Learning for Panel Data, https://privwww.ssrn.com/abstract=3717879 (January 28, 2021)

Machine learning has dramatically expanded the range of tools for evaluating economic panel data. This paper applies a variety of machine-learning methods to the Boston housing dataset, an iconic proving ground for machine learning. Though machine learning often lacks the overt interpretability of linear regression, methods based on decision trees score the relative importance of dataset features. In addition to addressing the theoretical tradeoff between bias and variance, this paper discusses practices rarely followed in traditional economics: the splitting of data into training, validation, and test sets; the scaling of data; and the preference for retaining all data. The choice between traditional and machine-learning methods hinges on practical rather than mathematical considerations. In settings emphasizing interpretative clarity through the scale and sign of regression coefficients, machine learning may best play an ancillary role. Wherever predictive accuracy is paramount, ...
REVISION: Split Decisions: Decision Tree-Based Machine Learning for Empirical Legal Scholarship, https://privwww.ssrn.com/abstract=3731307 (December 11, 2020)

The most prevalent and useful task in social science may be multivariable regression. Empirical legal studies rely heavily on the ordinary least squares method. Using the iconic Boston housing study as a source of price data, this article introduces machine-learning methods based on decision trees and their ensembles as additional methods for regression. Although trees and forests lack the overt interpretability of linear regression, these methods reduce the opacity of black-box techniques by scoring the relative importance of dataset features. This paper will also address the theoretical tradeoff between bias and variance, as well as the importance of training, cross-validation, and reserving a holdout dataset for testing.
REVISION: An Introduction to Machine Learning for Panel Data: Decision Trees, Random Forests, and Other Dendrological Methods, https://privwww.ssrn.com/abstract=3717879 (December 11, 2020)

Perhaps no task is more prevalent, and more useful, in economic analysis than the prediction of a numerical value through its relationship with other variables. By far the most popular tool for regression is the multivariable generalization of ordinary least squares.

Machine learning and artificial intelligence have dramatically expanded the range of tools available in economics. Open-source software and a burgeoning coding community have made these methods more accessible to a broader audience.

"Dendrological" machine-learning methods use decision trees to divide data, variable by variable. Ensembles of decision trees harness the Delphic wisdom of potentially thousands of miniature regressors.

Trees and forests admittedly lack the overt interpretability of linear regression. Machine-learning often offset the opacity of these "black-box" techniques by scoring the relative importance of dataset features. This paper will also address the theoretical tradeoff ...


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