A Non-Random Walk Down Wall Street

by Andrew W. Lo and A. Craig MacKinlay

Book Overview

For over half a century, financial experts have regarded the movements of markets as a random walk--unpredictable meanderings akin to a drunkard's unsteady gait--and this hypothesis has become a cornerstone of modern financial economics and many investment strategies. Here Andrew W. Lo and A. Craig MacKinlay put the Random Walk Hypothesis to the test. In this volume, which elegantly integrates their most important articles, Lo and MacKinlay find...

• Format: Paperback
• Language: English
• ISBN: 0691092567
• ISBN13: 9780691092560
• Release Date: January 2002
• Publisher: Princeton University Press
• Length: 448 Pages
• Weight: 1.35 lbs.
• Dimensions: 1.1" x 6.1" x 9.4"

Customer Reviews

not for those of limited intellect

There is no indication anywhere that this book was intendedeither as a follow on to Burton Malkiel's A Random Walk DownWall Street or as a primer for day traders. Hence it israther disappointing to read the reviews of those whosomehow managed to reach one of the preceding conclusions.Several statistical studies have made it clear that themarkets are not completely random as asserted by much of theacademic economics community. It is impossible to prove orrefute the Efficient Markets Hypothesis, because, as Farmerputs it, the EMH, by itself, is not a well-posed andempirically refutable hypothesis. This book tries torigorously analyze the markets as they are. The average investor could easily reach the same conclusionsas Burton Malkiel strives for, namely that he is best offinvesting in an index fund. However, they do it in a moreinteresting fashion than simply asserting that, on average,one cannot beat the average.

Excellent Econometric Analysis for the Right Audience

This is a book about financial economics, not day-trading. The techniques used is advanced econometric analysis, not technical charting. The purpose is to clarify some common myth about efficient market theory and the random walk hypothesis, not to show one how to pick stocks. Just like the authors' other book ("Econometrics of Financial Markets"), this one is of the highest high quality, and does a superb job on what it set out to be.Some readers seem to be disappointed at this book by naively assuming what the title implies, as shown by some of the reviews here. They really can't blame anyone but themselves. Just because Burton Malkiel's classic didn't show us how to day trade doesn't mean a book with the opposite title will do so, nor did the authors ever claim that, either.

A non-random challenge to the random walk hypothesis

The random walk hypothesis, considered the bedrock of financial theory and modeling, is challenged in this collection of eleven papers by the authors. They attempt in these papers to show that the financial markets do contain a certain degree of predictability, and they illustrate this by both analyzing empirical data and with the development of various mathematical formalisms. It is always interesting when a given paradigm which is entrenched in the minds of a field's practicioners, is challenged and shown to be either inconsistent or not supporting the real facts. The authors make a strong case in this book against the inherent randomness of the financial markets, and they do so in a way that is very understandable. Also, after a consideration of their results, one can construct practical trading software packages that are based on financial models not using the random walk hypothesis. Thus their study is very useful from a practical, everyday trading point of view. After a brief overview of the efficient markets hypothesis, in the next chapter the authors go right into the analysis of the efficient markets hypothesis by using a specification test based on variance estimators. They conclude from their results that the random walk model is not consistent with the behavior of weekly returns. Interestingly, they find large (negative) autocorrelations in security prices. They do not conclude though that all financial models based on the random walk hypothesis are invalid, but rather they use the specification test to study various stochastic price processes. Since volatilities do change over time, the authors are careful not to reject the random walk hypothesis because of heteroskedasticity; the test they do employ takes into account changing variances. They also discuss the possible role that non-trading practices may have on their conclusions. For the purely mathematical reader, they include in an Appendix to the chapter proofs of the theorems they used in the chapter. In Chapter 3, the authors employ Monte Carlo simulations to study the variance ratio, Dickey-Fuller, and Box-Pierce tests under Gaussian null and heteroskedastic null hypotheses. They also consider the power of the variance ratio test against an AR(1) process, AR(1) + random walk, and an integrated AR(1) process models of asset price behavior. The discussion is very thorough, and they conclude that the variance ratio test is a viable tool to use for inference in financial modeling. Since they do inform the reader the particular packages they use to perform the Monte Carlo simulations, their results, which they report in tables in the chapter, can be straightforwardly checked. A somewhat esoteric but very readable account of what has been called nonsynchronous trading is given in the next chapter. They begin the discussion by employing an interesting and elementary argument that explains very well the consequences of ignoring nonsynchronicity in the sampling of multi

not a primer for day traders

The other reviews are right...this book is definitely not a how-to guide for personal investors looking to "beat the market." It's essentially an academic tome, so its theme is tightly circumscribed (so they do not and should ask about all asset markets that might possibly be relevant to investors -- only the stock market over certain periods). The exposition is extremely sophisticated and makes use of cutting edge mathematical and especially statistical modeling to make the case. The punch line has two important parts: (i) the "random walk" hypothesis is false -- day to day movements in stock prices are not random bouncing that many extant models claim they should be; and (ii) most of us will never have the capabilities to employ these modeling techniques to put the rubber to the road and find out WHICH way stock X is going on December 13.So it's fascinating in regard to the mechanics of asset pricing, but totally useless as a practical investment guide. But that doesn't mean it's a *bad* book or that it warrants a 3-star rating (the average at the time of this review). Blame _Business Week_ if you expected something else. The book is exceptional and does no more and no less than what it claims to do.

Interesting Book

This is a very interesting book and would have been useful in the empirical finance Ph.D. class I took at Berkeley several years ago. Note that this is a difficult read and is orientated towards Ph.D's and their doctoral students.