A Non-Random Walk Down Wall Street

I picked up this book to try to get some background on arguments for and against random walk. Maybe this book has that. I don't know. I have a B.S. in Math and I don't want to go back to school for two years to understand this book.The book is a bunch of math, that's it. If you're looking for a higher level, "semi-technical" discussion this isn't it.If you want to sit down with a nice cup of tea and enjoy some graduate level statistics, then go ahead and get it.

After reading A Random Walk, I was expecting another easy, entertaining read. This turned out to be much more technical. Even with a fairly strong statistics background, I still got lost. The style is much more dry. It's not written for the general public like A Random Walk is.

This guy is making me so much money in the markets right now it is ridiculous.

Great book, very detailed.

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 multiple time series. The authors list ten consequences of the presence of nonsynchronous trading and then study the empirical evidence for nontrading effects. Also, they give a brief summary of the implications of employing Markov chains to build dependence into the nontrading process, motivating readers to perform the necessary calculations on their own. The next chapter focuses on contrarian investment strategies; namely one that takes advantage of negative serial dependence in asset returns. The authors summarize the data on autocorrelation properties and also present a formal model of a particular contrarian strategy. They conclude, interestingly, that a large portion of contrarian profits cannot be attributed to overreaction. The most interesting chapter in the book is the next one on long-range dependence in stock market prices, for it is here that many alternative statistical techniques have been devised to study this dependence. The R/S statistic is modified and then used by the authors to test for long-range dependence in daily and monthly stock return indices. Surprisingly, they find that after correcting for short-range dependencies, there is no evidence of long-range dependence in this data. The authors switch gears somewhat in Chapter 7, where they discuss deviations from the capital asset pricing model. They discuss effectively the two models which attempt to explain these differences, based on risk-based and nonrisk-based alternatives. These two models are proposed as alternatives to the multifactor asset pricing models that have been employed to explain deviations from CAPM. In chapter 8, data-snooping biases are investigated using the theory of induced order statistics and tested with Monte Carlo simulations. The authors effectively convince the reader of the impact of data-snooping biases in asset pricing models, and how these biases arise from tendencies to focus on anomalous data. Even more practical considerations are considered in Chapter 9, where the authors show how to maximize predictability in asset returns. They use a model of time-varying premiums to estimate what they call the maximally predictable portfolio, with this model using an out-of-sample rolling estimation technique to avoid data snooping problems. Monte Carlo simulations are again used to validate the results of the models. They emphasize in their conclusions that predictability does not imply market inefficiency. Emphasizing the discreteness of real price data, the irregular timing of transaction prices, and the conditional nature of price changes, the authors develop in Chapter 10 a model that addresses these issues using what they call an ordered probit model. They conclude, using some interesting technical analysis with their model and its comparison with empirical data, that discreteness is important in financial modeling. Chapter 11 is very empirical, wherein the authors study transaction data on the S&P 500 futures contracts with the goal of studying price behavior in relation to arbitrageur strategies. They conclude that on the average, mispricing increases with time to maturity and is path-dependent. The last chapter of the book discusses the October 1987 stock market crash, with the goal of analyzing order imbalances and stock returns. They conclude that there are notable differences in the returns realized by stocks in the S&P index and those that are not, interestingly.

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.

This book is a collection of papers that seek to disprove the Efficient Market Hypothesis (EMH). The book is very mathematical and statistical. I have not checked the validity of the proofs so you should take my rating with a grain of salt and study the book for yourself. My rating of this book is based on my personal belief that EMH is an oversimplified and incorrect theory cooked up by academics with little market-experience and I found the argumentation of this book compelling overall.This book is available for download free of charge on Princeton's webpage.If you like a less demanding 'counter-proof' of EMH I can recommend Warren Buffett's essay entitled 'The Superinvestors of Graham-and-Doddsville' which is also available on the internet.

Andy Lo is one of the best minds in quant investing out there right now, and I should have expected this to be a challenging read. But it was a little heavy on the calculus even so. Thankfully it's well written enough that you can take the math for granted and still get a lot out.

Perfect, thank you. No Issues to report

Was dieses Buch nicht ist:Dieses Buch ist keine(zumindestens keine direkte)Anleitung, wie man den Markt schlagen kann. Es ist auch relativ theoretisch (ordentliche Portion Formalismus)...also nur für Fortgeschrittene eine gute Nacht LektüreWas diese Buch ist:Eine Aufklährung für all die Menschen (insbesondere trader), die immer wieder die Random Walk Theorie anführen, um die quantitative Finanztheorie als weltfremden, theoretischen Schwachsinn zu betiteln.Andrew Lo ist Direktor am MIT Laboratory for Financial Engineering. Mehr Akademie geht nicht!!!Das Buch ist von 1999....die Paper, auf denen das Buch basiert, sind teilweise noch wesentlich älter.Also schließen wir daraus, dass1.) einige (meiner Meinung nach fast alle) Akademiker und/oder Quants schon lange NICHT mehr wirklich von der Random Walk Theorie ausgehen ...2.) und diese dennoch nutzen, wenn sie aus modelltechnischen Gründen notwendig, sinnvoll oder wenigstens vertretbar ist.Wer sich über den Sinn und Zweck eines Modells mal Gedanken macht, wird erkennen, dass 1) und 2) kein Widerspruch ist (IMHO)....Das Buch ist ehrlich gesagt eine Ansammlung von Papern und Artikeln und dafür ist es nicht gerade billig. Viele der Paper gibts als working paper online für umme (mal ein Blick auf die home page des Autors werfen)...Dennoch kann ich das Buch nur empfehlen. Andrew Lo ist ein begnadeter Empiriker. Er bietet definitiv eine Menge Information und Diskussionsstoff für viele Finanzmarktökonometrie-Klassen. Es so kompakt auf dem Tisch liegen zu haben, ist gut investiertes Geld. Die Graphiken sind aussagekräftig, genauso wie die vielen Tabellen, auch wenn diese teilweise etwas anstrengend zu lesen sind.Klassiker!!!Für interessierte ein Pflichtkauf!!!