Research
Moore’s Law versus Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrei A., and Andrew W. Lo (2013), Moore’s Law versus Murphy’s Law: Algorithmic Trading and Its Discontents, Journal of Economic Perspectives 27 (2), 51–72.
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Financial markets have undergone a remarkable transformation over the past two decades due to advances in technology. These advances include faster and cheaper computers, greater connectivity among market participants, and perhaps most important of all, more sophisticated trading algorithms. The benefits of such financial technology are evident: lower transactions costs, faster executions, and greater volume of trades. However, like any technology, trading technology has unintended consequences. In this paper, we review key innovations in trading technology starting with portfolio optimization in the 1950s and ending with high-frequency trading in the late 2000s, as well as opportunities, challenges, and economic incentives that accompanied these developments. We also discuss potential threats to financial stability created or facilitated by algorithmic trading and propose “Financial Regulation 2.0,” a set of design principles for bringing the current financial regulatory framework into the Digital Age.
Using Algorithmic Attribution Techniques To Determine Authorship In Unsigned Judicial Opinions
Li, William, Pablo D. Azar, David Larochelle, Phil Hill, James Cox, Robert C. Berwick, and Andrew W. Lo (2013), Using Algorithmic Attribution Techniques to Determine Authorship in Unsigned Judicial Opinions, Stanford Technology Law Review 16 (3), 503–534.
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This article proposes a novel and provocative analysis of judicial opinions that are published without indicating individual authorship. Our approach provides an unbiased, quantitative, and computer scientific answer to a problem that has long plagued legal commentators. Our work uses natural language processing to predict authorship of judicial opinions that are unsigned or whose attribution is disputed. Using a dataset of Supreme Court opinions with known authorship, we identify key words and phrases that can, to a high degree of accuracy, predict authorship. Thus, our method makes accessible an important class of cases heretofore inaccessible. For illustrative purposes, we explain our process as applied to the Obamacare decision, in which the authorship of a joint dissent was subject to significant popular speculation. We conclude with a chart predicting the author of every unsigned per curiam opinion during the Roberts Court.
Kaminski, Kathryn M., and Andrew W. Lo (2014), When Do Stop-Loss Rules Stop Losses?, Journal of Financial Markets 18 (1), 234–254.
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We propose a simple analytical framework to measure the value added or subtracted by stoploss rules—predetermined policies that reduce a portfolio’s exposure after reaching a certain threshold of cumulative losses—on the expected return and volatility of an arbitrary portfolio strategy. Using daily futures price data, we provide an empirical analysis of stop-loss policies applied to a buy-and-hold strategy using index futures contracts. At longer sampling frequencies, certain stop-loss policies can increase expected return while substantially reducing volatility, consistent with their objectives in practical applications.
Fagnan, David E., Jose-Maria Fernandez, Andrew W. Lo, and Roger M. Stein (2013), Can Financial Engineering Cure Cancer?, American Economic Review 103 (3), 406–411.
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In this paper, we describe a new approach to financing biomedical innovation that we first proposed in Fernandez, Stein, and Lo (2012) and extend in several ways here: using portfolio theory and securitization to reduce the risk of translational medicine. By combining a large number of drug-development projects within a single portfolio, a "megafund," it becomes possible to reduce the investment risk to such an extent that issuing bonds backed by these projects becomes feasible. Debt financing is a key innovation because the cost of each drug-development project can be several hundred million dollars; hence, a sufficiently diversified portfolio may require tens of billions of dollars of investment capital, and debt markets have much greater capacity than either private or public equity markets. If these bonds are structured to have different priorities, the most senior class or “tranche” may be rated by credit-rating agencies, opening up a much larger pool of institutional investors who can purchase such instruments, e.g., pension funds, sovereign wealth funds, endowments, and foundations. Open-source software available via the link above.
On a New Approach for Analyzing and Managing Macrofinancial Risks
Merton, Robert C., Monica Billio, Mila Getmansky, Dale Gray, Andrew W. Lo, and Loriana Pelizzon (2013), On a New Approach for Analyzing and Managing Macrofinancial Risks, Financial Analysts Journal 69 (2), 22–33.
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At the fifth annual CFA Institute European Investment Conference on 19 October 2012 in Prague, Robert C. Merton gave a presentation on analyzing and managing macrofinancial risk. This article is based on his talk and on research he carried out with his coauthors.
with Jasmina Hasanhodzic and Emanuele Viola, Hasanhodzic, Jasmina, Andrew W. Lo, and Emanuele Viola (2019), What Do Humans Perceive in Asset Returns?, Journal of Portfolio Management 45 (4), 49–60.
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In this article, the authors run experiments to test if and how human subjects can differentiate time series of actual asset returns from time series that are generated synthetically via various processes, including AR1. In contrast with previous anecdotal evidence, they find that subjects can distinguish between the two. These results show that temporal charts of asset prices convey to investors information that cannot be reproduced by summary statistics. They also provide a first refutation based on human perception of a strong form of the efficient-market hypothesis. Their experiments are implemented via an online video game (http://arora.ccs.neu.edu). The authors also link the subjects’ performance to statistical properties of the data and investigate whether subjects improve performance while playing.
Abbe, Emmanuel A., Amir E. Khandani, and Andrew W. Lo (2012), Privacy-Preserving Methods for Sharing Financial Risk Exposures, American Economic Review 102 (3), 65–70.
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Unlike other industries in which intellectual property is patentable, the financial industry relies on trade secrecy to protect its business processes and methods, which can obscure critical financial risk exposures from regulators and the public. We develop methods for sharing and aggregating such risk exposures that protect the privacy of all parties involved and without the need for a trusted third party. Our approach employs secure multi-party computation techniques from cryptography in which multiple parties are able to compute joint functions without revealing their individual inputs. In our framework, individual financial institutions evaluate a protocol on their proprietary data which cannot be inverted, leading to secure computations of real-valued statistics such as concentration indexes, pairwise correlations, and other single- and multi-point statistics. The proposed protocols are computationally tractable on realistic sample sizes. Potential financial applications include: the construction of privacy-preserving real-time indexes of bank capital and leverage ratios; the monitoring of delegated portfolio investments; financial audits, and the publication of new indexes of proprietary trading strategies.
Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors
Billio, Monica, Mila Getmansky, Andrew W. Lo, and Loriana Pelizzon (2012), Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors, Journal of Financial Economics 104 (3), 535–559.
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A significant contributing factor to the Financial Crisis of 2007–2009 was the apparent interconnectedness among hedge funds, banks, brokers, and insurance companies, which amplified shocks into systemic events. In this paper, we propose five measures of systemic risk based on statistical relations among the market returns of these four types of financial institutions. Using correlations, cross-autocorrelations, principal components analysis, regime-switching models, and Granger causality tests, we find that all four sectors have become highly interrelated and less liquid over the past decade, increasing the level of systemic risk in the finance and insurance industries. These measures can also identify and quantify financial crisis periods. Our results suggest that while hedge funds can provide early indications of market dislocation, their contributions to systemic risk may not be as significant as those of banks, insurance companies, and brokers who take on risks more appropriate for hedge funds.
Lo, Andrew W. (2012), Reading about the Financial Crisis: A Twenty-One-Book Review, Journal of Economic Literature 50 (1), 151–178.
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The recent financial crisis has generated many distinct perspectives from various quarters. In this article, I review a diverse set of 21 books on the crisis, 11 written by academics, and 10 written by journalists and one former Treasury Secretary. No single narrative emerges from this broad and often contradictory collection of interpretations, but the sheer variety of conclusions is informative, and underscores the desperate need for the economics profession to establish a single set of facts from which more accurate inferences and narratives can be constructed.
Chan, Nicholas, Mila Getmansky, Shane M. Haas, and Andrew W. Lo (2006), Do Hedge Funds Increase Systemic Risks?, Federal Reserve Bank of Atlanta Economic Review 91 (4), 49–80.
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In this article, we attempt to quantify the potential impact of hedge funds on systemic risk by developing a number of new risk measures for hedge funds and applying them to individual and aggregate hedge-fund returns data. These measures include: illiquidity risk exposure, nonlinear factor models for hedge-fund and banking-sector indexes, logistic regression analysis of hedge-fund liquidation probabilities, and aggregate measures of volatility and distress based on regime-switching models. Our preliminary findings suggest that the hedge-fund industry may be heading into a challenging period of lower expected returns, and that systemic risk is currently on the rise. This is a redacted version of our paper "Systemic Risk and Hedge Funds".