Research
What Post-Crisis Changes Does the Economics Discipline Need?: Beware of Theory Envy!
What’s the Use of Economics?: Teaching the Dismal Science After the Crisis, London Publishing Partnership.
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This is a pre-conference essay prepared for 'What Post-Crisis Changes Does the Economics Discipline Need?', a conference organized by Diane Coyle and Enlightenment Economics, the Bank of England, and the U.K. Government Economic Service on 7 February 2012. In this essay, I trace the origins of 'theory envy' to Paul Samuelson and the mathematization of economics over the past half century, and consider its implications for how economics should be taught. Although this research program has produced many genuine breakthroughs in economics, any virtue can become a vice when taken to an extreme, and the recent financial crisis has given us an opportunity to reinvent our field. One innovation is to teach economics not from an axiomatic and technique-oriented perspective, but by posing challenges that can only be addressed through economic logic. Instead of starting microeconomics with the consumer’s problem of maximizing utility subject to a budget constraint, begin by challenging students to predict the impact of a gasoline tax on the price of gasoline, or asking them to explain why diamonds are so much more expensive than water, despite the fact that the latter is critical for survival unlike the former. Instead of starting macroeconomics with national income accounts, begin with the question of how to measure and manage the wealth of nations, or why inflation can be so disruptive to economic growth. Without the proper institutional, political, and historical context in which to interpret economic models, constrained optimization methods and fixed-point existence proofs have much less meaning and are more likely to give rise to theory envy. However, when students understand the “why” of their course of study, even the most complex mathematical tools can be mastered and are almost always applied more meaningfully.
This is a pre-conference essay prepared for 'What Post-Crisis Changes Does the Economics Discipline Need?', a conference organized by Diane Coyle and Enlightenment Economics, the Bank of England, and the U.K. Government Economic Service on 7 February 2012. In this essay, I trace the origins of 'theory envy' to Paul Samuelson and the mathematization of economics over the past half century, and consider its implications for how economics should be taught. Although this research program has produced many genuine breakthroughs in economics, any virtue can become a vice when taken to an extreme, and the recent financial crisis has given us an opportunity to reinvent our field. One innovation is to teach economics not from an axiomatic and technique-oriented perspective, but by posing challenges that can only be addressed through economic logic. Instead of starting microeconomics with the consumer’s problem of maximizing utility subject to a budget constraint, begin by challenging students to predict the impact of a gasoline tax on the price of gasoline, or asking them to explain why diamonds are so much more expensive than water, despite the fact that the latter is critical for survival unlike the former. Instead of starting macroeconomics with national income accounts, begin with the question of how to measure and manage the wealth of nations, or why inflation can be so disruptive to economic growth. Without the proper institutional, political, and historical context in which to interpret economic models, constrained optimization methods and fixed-point existence proofs have much less meaning and are more likely to give rise to theory envy. However, when students understand the “why” of their course of study, even the most complex mathematical tools can be mastered and are almost always applied more meaningfully.
Adaptive Markets and the New World Order
Financial Analysts Journal, 68 (2012), 18-29
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In the Adaptive Markets Hypothesis (AMH) intelligent but fallible investors learn from and adapt to changing economic environments. This implies that markets are not always efficient, but are usually competitive and adaptive, varying in their degree of efficiency as the environment and investor population change over time. The AMH has several implications including the possibility of negative risk premia, alpha converging to beta, and the importance of macro factors and risk budgeting in asset-allocation policies.
In the Adaptive Markets Hypothesis (AMH) intelligent but fallible investors learn from and adapt to changing economic environments. This implies that markets are not always efficient, but are usually competitive and adaptive, varying in their degree of efficiency as the environment and investor population change over time. The AMH has several implications including the possibility of negative risk premia, alpha converging to beta, and the importance of macro factors and risk budgeting in asset-allocation policies.
An Evolutionary Model of Bounded Rationality and Intelligence
with Thomas J. Brennan, PLOS One, 7:11 (2012)
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Most economic theories are based on the premise that individuals maximize their own self-interest and correctly incorporate the structure of their environment into all decisions, thanks to human intelligence. The influence of this paradigm goes far beyond academia–it underlies current macroeconomic and monetary policies, and is also an integral part of existing financial regulations. However, there is mounting empirical and experimental evidence, including the recent financial crisis, suggesting that humans do not always behave rationally, but often make seemingly random and suboptimal decisions.
Most economic theories are based on the premise that individuals maximize their own self-interest and correctly incorporate the structure of their environment into all decisions, thanks to human intelligence. The influence of this paradigm goes far beyond academia–it underlies current macroeconomic and monetary policies, and is also an integral part of existing financial regulations. However, there is mounting empirical and experimental evidence, including the recent financial crisis, suggesting that humans do not always behave rationally, but often make seemingly random and suboptimal decisions.
A Computational View of Market Efficiency
with Jasmina Hasanhodzic and Emanuele Viola, Quantitative Finance 7, 1043-1050
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We propose to study market efficiency from a computational viewpoint. Borrowing from theoretical computer science, we define a market to be efficient with respect to resources S (e.g., time, memory) if no strategy using resources S can make a profit. As a first step, we consider memory-m strategies whose action at time t depends only on the m previous observations at times t - m,...,t - 1. We introduce and study a simple model of market evolution, where strategies impact the market by their decision to buy or sell. We show that the effect of optimal strategies using memory m can lead to "market conditions" that were not present initially, such as (1) market bubbles and (2) the possibility for a strategy using memory m' > m to make a bigger profit than was initially possible. We suggest ours as a framework to rationalize the technological arms race of quantitative trading firms.
We propose to study market efficiency from a computational viewpoint. Borrowing from theoretical computer science, we define a market to be efficient with respect to resources S (e.g., time, memory) if no strategy using resources S can make a profit. As a first step, we consider memory-m strategies whose action at time t depends only on the m previous observations at times t - m,...,t - 1. We introduce and study a simple model of market evolution, where strategies impact the market by their decision to buy or sell. We show that the effect of optimal strategies using memory m can lead to "market conditions" that were not present initially, such as (1) market bubbles and (2) the possibility for a strategy using memory m' > m to make a bigger profit than was initially possible. We suggest ours as a framework to rationalize the technological arms race of quantitative trading firms.
Black’s Leverage Effect is Not Due to Leverage
with Jasmina Hasanhodzic
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One of the most enduring empirical regularities in equity markets is the inverse relationship between stock prices and volatility, first documented by Black (1976) who attributed it to the effects of financial leverage. As a company's stock price declines, it becomes more highly leveraged given a fixed level of debt outstanding, and this increase in leverage induces a higher equity-return volatility. In a sample of all-equity-financed companies from January 1972 to December 2008, we find that the leverage effect is just as strong if not stronger, implying that the inverse relationship between price and volatility is not driven by financial leverage.
One of the most enduring empirical regularities in equity markets is the inverse relationship between stock prices and volatility, first documented by Black (1976) who attributed it to the effects of financial leverage. As a company's stock price declines, it becomes more highly leveraged given a fixed level of debt outstanding, and this increase in leverage induces a higher equity-return volatility. In a sample of all-equity-financed companies from January 1972 to December 2008, we find that the leverage effect is just as strong if not stronger, implying that the inverse relationship between price and volatility is not driven by financial leverage.
The Origin of Behavior
with Thomas Brennan, Quarterly Journal of Finance 1 (2011), 55-108.
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We propose a single evolutionary explanation for the origin of several behaviors that have been observed in organisms ranging from ants to human subjects, including risk-sensitive foraging, risk aversion, loss aversion, probability matching, randomization, and diversification. Given an initial population of individuals, each assigned a purely arbitrary behavior with respect to a binary choice problem, and assuming that offspring behave identically to their parents, only those behaviors linked to reproductive success will survive, and less reproductively successful behaviors will disappear at exponential rates. This framework generates a surprisingly rich set of behaviors, and the simplicity and generality of our model suggest that these behaviors are primitive and universal.
We propose a single evolutionary explanation for the origin of several behaviors that have been observed in organisms ranging from ants to human subjects, including risk-sensitive foraging, risk aversion, loss aversion, probability matching, randomization, and diversification. Given an initial population of individuals, each assigned a purely arbitrary behavior with respect to a binary choice problem, and assuming that offspring behave identically to their parents, only those behaviors linked to reproductive success will survive, and less reproductively successful behaviors will disappear at exponential rates. This framework generates a surprisingly rich set of behaviors, and the simplicity and generality of our model suggest that these behaviors are primitive and universal.
Complexity, Concentration and Contagion: A Comment
Journal of Monetary Economics 58 (2011), 471-479
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Although the precise origins of the term "complex adaptive system" are unclear, nevertheless, the hackneyed phrase is now firmly ensconced in the lexicon of biologists, physicists, mathematicians, and, most recently, economics. However, as with many important ideas that become cliches, the original meaning is often obscured and diluted by popular usage. But thanks to the fascinating article by Gai, Haldane, and Kapadia, we have a concrete and practical instantiation of a complex adaptive system in economics, one that has real relevance to current policy debates regarding financial reform. Since there is very little to criticize in their compelling article, I will seek too amplify their results and place them in a broader context in my comments.
Although the precise origins of the term "complex adaptive system" are unclear, nevertheless, the hackneyed phrase is now firmly ensconced in the lexicon of biologists, physicists, mathematicians, and, most recently, economics. However, as with many important ideas that become cliches, the original meaning is often obscured and diluted by popular usage. But thanks to the fascinating article by Gai, Haldane, and Kapadia, we have a concrete and practical instantiation of a complex adaptive system in economics, one that has real relevance to current policy debates regarding financial reform. Since there is very little to criticize in their compelling article, I will seek too amplify their results and place them in a broader context in my comments.
The Financial Industry Needs its Own Crash Safety Board
The Financial Times
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MIT Sloan Prof. Andrew Lo authored this opinion piece supporting the creation of a “Capital Markets Safety Board’ (CMSB) patterned after the National Transportation Safety Board, dedicated to investigating, reporting, and archiving the ‘accidents’ of the financial industry.”
MIT Sloan Prof. Andrew Lo authored this opinion piece supporting the creation of a “Capital Markets Safety Board’ (CMSB) patterned after the National Transportation Safety Board, dedicated to investigating, reporting, and archiving the ‘accidents’ of the financial industry.”
Is It Real, or Is It Randomized?: A Financial Turing Test
with Jasmina Hasanhodzic and Emanuele Viola
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We construct a financial "Turing test" to determine whether human subjects can differentiate between actual vs. randomized financial returns. The experiment consists of an online videogame where players are challenged to distinguish actual financial market returns from random temporal permutations of those returns. We find overwhelming statistical evidence (p-values no greater than 0.5%) that subjects can consistently distinguish between the two types of time series, thereby refuting the widespread belief that financial markets "look random". A key feature of the experiment is that subjects are given immediate feedback regarding the validity of their choices, allowing them to learn and adapt. We suggest that such novel interfaces can harness human capabilities to process and extract information from financial data in ways that computers cannot
We construct a financial "Turing test" to determine whether human subjects can differentiate between actual vs. randomized financial returns. The experiment consists of an online videogame where players are challenged to distinguish actual financial market returns from random temporal permutations of those returns. We find overwhelming statistical evidence (p-values no greater than 0.5%) that subjects can consistently distinguish between the two types of time series, thereby refuting the widespread belief that financial markets "look random". A key feature of the experiment is that subjects are given immediate feedback regarding the validity of their choices, allowing them to learn and adapt. We suggest that such novel interfaces can harness human capabilities to process and extract information from financial data in ways that computers cannot
Lo: The Best Econ Book I’ve Read Recently
PBS Newshour, The Business Desk
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Andrew Lo, director of the MIT Laboratory for Financial Engineering, posted his commentary on a recent economics book in this online Q&A.
Andrew Lo, director of the MIT Laboratory for Financial Engineering, posted his commentary on a recent economics book in this online Q&A.
