Fall 2016

Danny Ebanks, Federal Reserve: The Network of Large-Value Loans in the US: Concentration and Segregation

On this joint project with Anton Badev, we analyze the universe of large-value loans intermediated through Fedwire, the primary U.S. real-time, gross settlement service provided by the Federal Reserve System for the period from 2007 to 2015. We embed banks' bilateral lending relationships and interest rate quotes in a game on a graph following Badev (2013), for which we approximate the equilibrium play via a k-player dynamic. We document a series of fundamental changes in the topology of the bilateral loan network and propose a framework to study the evolution of the concentration of large...

Ryan Copus and Hannah Laqueur, UC Berkeley: Machines Learning Justice: A New Approach to the Problems of Inconsistency and Bias in Adjudication

Abstract: We offer a two-step algorithmic approach to the problems of inconsistency and bias in legal decision making. First, we propose a new tool for reducing inconsistency: Judgmental Bootstrapping Models (“JBMs”) built with machine learning methods. JBMs, by providing judges with recommendations generated from statistical models of themselves, can help those judges make better and more consistent decisions. To illustrate these advantages, we build a JBM of release decisions for the California Board of Parole Hearings. Second, we describe a means to address systematic biases that are...

Thomas Idzorek, CFA, Head of Investment Methodology and Economic Research at Morningstar: Popularity: A Unifying Asset Pricing Framework?

In a 2014 article, Thomas Idzorek and Roger Ibbotson introduced popularity as an asset pricing framework. Popularity seems to provide a transcendent principle or insight that explains return premiums that are consistent with equilibrium efficient market asset pricing explanations (traditional risk and return framework) as well as so-called anomalies that...

Jim Hawley & Hendrik Bartel, TruValue Labs: Big Data Analytics and ‘Non-Financial’ Sustainability Information—uses of and initial findings from TruValue Labs’ first years

We present an overview of the current state of ESG (environmental, social, and corporate governance) data in the context of the value of so-called non-financial information. TruValue Labs generates real-time ESG/sustainability data using natural language processing, machine learning, and elements of AI to quantify unstructured (text) information sources. We present an overview of how this quantification process works and follow on by examining a variety of techniques used to analyze this data output. We will present some of those techniques and initial results including TVL (TruValue Labs...

Farzad Pourbabaee, UC Berkeley: Portfolio selection: Capital at risk minimization under correlation constraint

We studied the portfolio optimization problem in the Black-Scholes setup, subject to certain constraints. Capital at Risk (CaR) has turned out to resolve many of the shortcomings of the Value at Risk, hence is taken in this presentation as the objective of the optimization problem. Then, the CaR minimizing portfolio is found under a general correlation constraint between the terminal value of the wealth and an arbitrary financial index. Results are derived from both incomplete and incomplete markets, and finally, simulations are performed to present the qualitative behavior of the optimal...

Robert M. Anderson, CDAR Co-Director: PCA with Model Misspecification

In this project with UC Berkeley Ph.D. Candidate Farzad Pourbabaee, Principal Component Analysis (PCA) relies on the assumption that the data being analyzed is IID over the estimation window. PCA is frequently applied to financial data, such as stock returns, despite the fact that these data exhibit obvious and substantial changes in volatility. We show that the IID assumption can be substantially weakened; we require only that the return data is generated by a single distribution with a possibly variable scale parameter. In other words, we assume that return is R t = v t φ t, where the...