"A movement is over when the news is out," so goes the Wall Street maxim. For thousands of years, technical analysis—marred with common misconceptions likening it to gambling or magic and dismissed by many as "voodoo finance"—has sought methods for spotting trends in what the market's done and what it's going to do. After all, if you don't learn from history, how can you profit from it?

In The Evolution of Technical Analysis, the director of MIT's Laboratory for Financial Engineering, Andrew Lo, and coauthor Jasmina Hasanhodzic present an engaging account of the origins and development of this mysterious "black art," tracing its evolution from ancient Babylon to the rise of Wall Street as the world's financial center. Along the way, the practices of Eastern technical analysts like Munehisa Homma ("the god of the markets") are compared and contrasted with those of their Western counterparts, such as Humphrey Neill, William Gann, and Charles Dow ("the father of technical analysis").

With deep roots in antiquity, technical analysis is part art and part science, seeking to divine trends, reversals, cycles, and other predictable patterns in historical market prices. While the techniques for capturing such regularities have evolved considerably over the centuries, the all-too-human predilection to extrapolate into the future using the past has been a constant driving force throughout history.

The authors chronicle the fascinating and unexpected path of charting that likely began with simple superstitions and coincidences, and has developed into widespread practices in many markets and instruments, involving sophisticated computational algorithms and visualization techniques. The Evolution of Technical Analysis is the story of how some early technicians failed miserably, how others succeeded beyond their wildest dreams, and what it means for traders today.

Trading volume is an important aspect of the economic interactions in financial markets among various investors. Both volume and prices are driven by underlying economic forces, and thus convey important information about the workings of the market. This chapter focuses on the empirical characteristics of prices and volume in stock markets. The interactions between prices and quantities in an equilibrium yield a rich set of implications for any asset pricing model, when an explicit link between economic fundamentals and the dynamic properties of asset returns and volume are derived. By exploiting the relation between prices and volume in the dynamic equilibrium model, one can identify and construct the hedging portfolio, which can be used by all investors to hedge against changes in market conditions. This hedging portfolio has considerable forecast power in predicting future returns of the market portfolio and its abilities to explain cross-sectional variation in expected returns is comparable to other popular risk factors such as market betas, the Fama and French SMB factor, and optimal forecast portfolios. The presence of market frictions, such as transactions costs, can influence the level of trading volume and serve as a bridge between the market microstructure literature and the broader equilibrium asset pricing literature.

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.”

Andrew Lo, director of the MIT Laboratory for Financial Engineering, posted his commentary on a recent economics book in this online Q&A.

The quantitative aspirations of economists and financial analysts have for many years been based on the belief that it should be possible to build models of economic systems—and financial markets in particular—that are as predictive as those in physics. While this perspective has led to a number of important breakthroughs in economics, "physics envy" has also created a false sense of mathematical precision in some cases. We speculate on the origins of physics envy, and then describe an alternate perspective of economic behavior based on a new taxonomy of uncertainty. We illustrated the relevance of this taxonomy with two concrete examples: the classical harmonic oscillator with some new twists that make physics look more like economics, and a quantitative equity market-neutral strategy. We conclude by offering a new interpretation of tail events, proposing an 'uncertainty checklist' with which our taxonomy can be implemented, and considering the role that quants played in the current financial crisis.

We apply machine-learning techniques to construct nonlinear nonparametric forecasting models of consumer credit risk. By combining customer transactions and credit bureau data from January 2005 to April 2009 for a sample of a major commercial bank's customers, we are able to construct out-of-sample forecasts that significantly improve the classification rates of credit-card-holder delinquencies and defaults, with linear regression R-squared's of forecasted/realized delinquencies of 85%. Using conservative assumptions for the costs and benefits of cutting credit lines based on machine-learning forecasts, we estimate the cost savings to range from 6% to 25% of total losses. Moreover, the time-series patterns of estimated delinquency rates from this model over the course of the recent financial crisis suggests that aggregated consumer-credit risk analytics may have important applications in forecasting systemic risk.

A key result of the Capital Asset Pricing Model (CAPM) is that the market portfolio—the portfolio of all assets in which each asset's weight is proportional to its total market capitalization—lies on the mean-variance-efficient frontier, the set of portfolios having mean-variance characteristics that cannot be improved upon. Therefore, the CAPM cannot be consistent with efficient frontiers for which every frontier portfolio has at least one negative weight or short position. We call such efficient frontiers 'impossible', and show that impossible frontiers are difficult to avoid. In particular, as the number of assets, n, grows, we prove that the probability that a generically chosen frontier is impossible tends to one at a geometric rate. In fact, for one natural class of distributions, nearly one-eighth of all assets on a frontier is expected to have negative weights for every portfolio on the frontier. We also show that the expected minimum amount of shortselling across frontier portfolios grows linearly with n, and even when shortsales are constrained to some finite level, an impossible frontier remains impossible. Using daily and monthly U.S. stock returns, we document the impossibility of efficient frontiers in the data.

The Heretics of Finance provides extraordinary insight into both the art of technical analysis and the character of the successful trader. Distinguished MIT professor Andrew W. Lo and researcher Jasmina Hasanhodzic interviewed thirteen highly successful, award-winning market professionals who credit their substantial achievements to technical analysis. The result is the story of technical analysis in the words of the people who know it best; the lively and candid interviews with these gurus of technical analysis.

The first half of the book focuses on the technicians' careers:

• How and why they learned technical analysis
• What market conditions increase their chances of making mistakes
• What their average workday is like
• To what extent trading controls their lives
• Whether they work on their own or with a team
• How their style of technical analysis is unique

The second half concentrates on technical analysis and addresses questions such as these:

• Did the lack of validation by academics ever cause you to doubt technical analysis?
• Can technical analysis be applied to other disciplines?
• How do you prove the validity of the method?
• How has computer software influenced the craft?
• What is the role of luck in technical analysis?
• Are there laws that underlie market action?
• What traits characterize a highly successful trader?
• How you test patterns before you start using them with real money?

Interviewees include:
Ralph J. Acampora, Laszlo Birinyi, Walter Deemer, Paul Desmond, Gail Dudack, Robert J. Farrell, Ian McAvity, John Murphy, Robert Prechter, Linda Raschke, Alan R. Shaw, Anthony Tabell, Stan Weinstein.

Some people are blaming the economic crisis on financial engineering and business school education. Similar to how the 1986 space shuttle disaster cannot be blamed on aerospace engineering, it is inaccurate to blame the crisis on technical know-how. Rather, the misuse of technology and poor judgment are to blame. Initial evidence about the current crisis suggests that executives at financial institutions did not deem risk assessments to be important. This suggests a lack of judgment, understanding, and training. As financial markets become more complex, it is becoming harder for conventional, two-year MBA programs to sufficiently train MBA candidates. But education beyond this level typically gets no support from the federal government, unlike other engineering fields. The Sloan School of Management at the Massachusetts of Institute of Technology awarded only four PhDs in finance in 2007, similar to other top business schools. To foster greater expertise, it is important to offer scholarships in financial engineering that could be paid for by a small

This document is the revised written testimony titled "Hedge Funds, Systemic Risk, and the Financial Crisis of 2007–2008" submitted to the US House of Representatives Oversight Committee for its hearing on hedge funds and the financial market, held November 13, 2008, and is not a formal academic research paper, but is intended for a broader audience of policymakers and regulators. Academic readers may be alarmed by the lack of comprehensive citations and literature review, the imprecise and qualitative nature of certain arguments, and the abundance of illustrative examples, analogies, and metaphors. Accordingly, such readers are hereby forewarned—this paper is not research but is instead a summary of the policy implications that I have drawn from my interpretation of that research.

I begin with a proposal to measure systemic risk and argue that this is the natural starting point for regulatory reform since it is impossible to manage something that cannot be measured. Then I review the relation between systemic risk and hedge funds, and show that early warning signs of the current crisis did exist in the hedge-fund industry as far back as 2004. However, I argue that financial crises may be an unavoidable aspect of human behavior, and the best we can do is acknowledge this tendency and be properly prepared. This behavioral pattern, as well as traditional economic motives for regulation—public goods, externalities, and incomplete markets—are relevant for systemic risk or its converse, 'systemic safety', and I suggest applying these concepts to the functions of the financial system to yield a rational process for regulatory reform. Also, I propose the formation of a new investigative office patterned after the National Transportation Safety Board (NTSB) to provide the kind of information aggregation and transparency that is called for in the previous sections. Another aspect of transparency involves fair-value accounting, and I review some of the recent arguments for its suspension and propose developing a new branch of accounting focusing exclusively on risk. I conclude with a discussion of the role of financial technology and education in the current crisis, and argue that more finance training is needed, not less.

This document is the written testimony submitted to the US House of Representatives Financial Services Committee for its hearing on systemic risk regulation, held October 29, 2009, and it is not a formal academic research paper, but is intended for a broader audience of policymakers and regulators. Academic readers may be alarmed by the lack of comprehensive citations and literature review, the imprecise and qualitative nature of certain arguments, and the abundance of illustrative examples, analogies, and metaphors. Accordingly, such readers are hereby forewarned—this paper is not research, but is instead a summary of the policy implications that I have drawn from my interpretation of that research. This testimony focuses on three themes: (1) Establishing the means to measure and monitor systemic risk on an ongoing basis is the single-highest priority for financial regulation reform; (2) Systemic risk measurement and regulation will likely require new legislation compelling systemically important entities to provide more transparency on a confidential basis to regulators, e.g., information regarding their assets, liabilities, holdings, leverage, collateral, liquidity, counterparties, and aggregate exposures to key financial variables and other risks; and (3) Because systemic risk cuts across multiple regulatory bodies that do not necessarily share the same objectives and constraints, it may be more efficient to create an independent agency patterned after the National Transportation Safety Board (NTSB), solely devoted to measuring, tracking, and investigating systemic risk events in support of—not in competition with—all regulatory agencies.

In this guest post, MIT Sloan Prof. Andrew Lo provides an insightful look at how "extended periods of prosperity act as an anesthetic in the human brain," lulling everyone involved into "a drug-induced stupor that causes us to take risks that we know we should avoid."

The push for financial regulatory reform has highlighted an important debate surrounding the Efficient Markets Hypothesis, the idea that market prices are rationally determined and fully reflect all available information. If true, the EMH implies that regulation is largely unnecessary because markets allocate resources and risks efficiently via the "invisible hand". However, critics of the EMH argue that human behaviour is hardly rational but is driven by "animal spirits" that generate market bubbles and busts, and regulation is essential for reining in misbehavior.

In this opinion piece, MIT Sloan Prof. Andrew Lo writes, “The world has become more complex over the past 20 years, and we need to update our investment paradigm to incorporate these new complexities... To achieve true diversification, investors must now have a broader set of asset classes and risk exposures, long and short, in their portfolios.”