Customer Reviews

Inside the Black Box: The Simple Truth About Quantitative Trading (Wiley Finance)

5 star:		(26)
4 star:		(1)
3 star:		(3)
2 star:		(3)
1 star:		(12)

 

 

This is an excellent book that fills an important need. It describes the nuts and bolts of quant trading without jargon or mystery. The most important point the book makes is there is no grand secret, no deep mystery. Quant traders make money using simple ideas anyone can understand, anyone can copy or come up with on their own; many of which are well-known and published. Too many books either muddy the waters that they may appear deep, or are so technical that people outside quant trading shops are unlikely to learn much from them.

The second major point, which the book makes indirectly throughout but only explicitly in the last chapter, is that simple does not mean easy. Successful quant trading requires extreme attention to details at every stage of the process. While it does not actually require great mathematical ability, people who do not think naturally in mathematical terms or who have not worked extensively in mathematical fields, are very rarely successful. Quants feel why some seemingly trivial things are vitally important, while other things can be safely ignored; without that feel you're flying blind.

The book does something important, it does it straightforwardly and well. Therefore there's not much to say about its good points. The rest of this review is criticisms, to correct the few major lapses. It's intended for people who have already read the book. If you haven't, and you have any interest in this field, buy it now and read the criticisms afterward.

I agree with Liberty4all that several reviews appear to be ballot-stuffing (this review seems to have been removed by Amazon, I don't think that's right, especially as it spawned a useful discussion with people weighing in from both sides). While I understand an author's temptation to ask a few friends to give five-star reviews, I don't approve of one-shot reviewers giving fluff. At least find some friends who review frequently and can say useful things.

The book makes a mess of the distinction between Alpha, which is earned from other active traders, and Beta, which is earned from buy-and-hold investors. What he calls "theory" in a strategy is no more than ad hoc marketing junk. Theory does not mean just saying you exploit a "documented behavioral bias" or "institutional rigidity." It means a real, sensible, testable theory of who is losing the money you're making. You need to know who those people are, why they are doing it and monitor that they keep doing it. Without a theory the only way you know your strategy stopped working is when you lose money, you never have warning, and you never know when it's safe to go back to it. Also, a theory tells you what to do when things stop working, the author seems to suggest that your only options are keep the strategy running, change it or shut it down. Professionals have several layers of backup plans. Theory is what separates a quant trader from a technical analyst.

Risk management is covered only in the portfolio management sense, in which risk a constraint or something to be minimized. Independent risk management is barely mentioned, and completely misdescribed. The author does not know what Value-at-Risk is, any paragraph with that term should be ignored. The first thing to ask any quant trader for is her VaR backtest. She should produce a number every day before trading starts such that she loses more than that amount 1 day in 20. The backtest should show the right number of break days, subject to statistical error, and those breaks should be independent in time and of the level of VaR.

Anyone who doesn't compute VaR or other periodic objective prediction is 20 years behind the time in risk management. Anyone who doesn't backtest isn't a quant (and the toy algorithms the author mentions never pass backtest). If you can't produce a good VaR, you don't understand your everyday risk, what happens 19 days out of 20 when markets are normal, so you can't possibly understand your tail risk. VaR is not a measure of risk, it tells you the range in which you can trust your models. You worry more when it is too small, when your models can only be validated in narrow circumstances, than when it is too big. It's not that you like losing money, but for two strategies with the same return and volatility track record, you trust the one that has survived significant adversity more than the one that has seen only mild days.

Leverage risk is treated only in the sense leverage multiplies your gains and losses. This is not what people mean by the term, they mean the risk leverage will disappear or the terms will change. Model risk is misdefined, it is not the risk of your trading model not working, it's the risk of pricing or hedging models giving bad results. Liquidity risk and redemption risk are not mentioned.

The author has a narrow idea of what quantitative methods can accomplish, and therefore gives quants a pass for losing money when unexpected events occur. Unexpected events are as much a part of modeling as expected events. There is less data about them, of course, and it may not be quantitative data. But if you leave them out of your model because you don't understand them or can't put a number on them, you're a number cruncher, not a quant. A quantitative theory has to account for everything that might happen. I don't mean quants never lose money in an unexpected event, I just mean that there are no excuses. If you lose money because of something, that means you bet it wouldn't happen. You might or might not have been paid a fair price for that bet, but you should have known you were making it.

The discussion of the credit crisis is superficial, it seems more a juxtaposition of phrases from editorials than a description. The account of the quant equity crisis of August 2007 is conventional but weak. The last few chapters appear to have been rushed, the writing style unravels a bit and the facts get shakier.

I think I've just told you everything bad about this book. Note that it's less than 1% of the length of the book. That pretty much sums up my judgment, this is a great book, 99% pure.

Rishi sent me an email in response to my earlier review in which I had stated "This book is written by someone who has never traded himself but has only allocated money to outside fund managers. I would not recommend this book to a serious quant trader."

While I wrote what I believed was true, Rishi informed me that I had made a false claim. From the website of Tradeworx, I understand that Rishi has set up a quant hedge fund with Manoj Narang, who I guess is his brother.

Therefore, here are my edited comments which I hope are more consructive:

Rishi's book is a good managerial overview of quant trading and the quant hedge fund business. However, unlike the book, Quantitative Trading Strategies: Harnessing the Power of Quantitative Techniques to Create a Winning Trading Program by Lars Kestner, Rishi's book lacks empirical analysis of how to build, test and deploy mathematical trading strategies. If the empirical side of quantitative trading could be emphasized in this book over the managerial side, this would be an awesome book.

This book is okay. It is a managerial overview of the quantitative trading field. Don't look for serious discussions of technical matters. There are some reviews on the flap which suggest this book can be useful to quants, but I think a seasoned quant will find more marketing material here than research material.

I am a professor of finance and I need to recommend some books to my students. I bought the book because of its high ratings, but now I agree with another reviewer most of them are hype. The author does know the subject but what he goes through is just too general to be useful. The table of contents tells almost everything.

This book is well written, but takes you nowhere. Almost all the concepts in this book are found in almost all trading books anywhere. The understandings you'll find in the most beginner style trading books are just "university-ified" to seem brilliant.
When you buy a book on what makes a car work you need it to include more than it's 5 or 6 major components, you want a breakdown of the parts that make it up and how they work. unfortunately this book gives little to no detail and as such is a waste of your money and more importantly your valuable time.

I don't know whether all 5-star reviews are faked or not, but those 1-star reviews on this book seem very unfair. Almost all those bad reviews have the same complaint: the book is not talking about technical details or actual trading strategies. It is true that this book only gives the basic framework of a block-box trading system, but this is the first book I read that gives the framework so clearly. Also, the contents covered by this book is already given very clearly in the Table of Contents --- nobody is cheating. So, what is this book's fault? Why does this book deserves 1-star rating?

I am currently a Computer Science/Statistics PhD student. My research is generally on statistical data analysis, with a dozen published paper on this topic. To gain some basic ideas of systematic trading and see how my knowledge/skills can be applied, I've read a couple of books on automatic/systematic trading (all are popular and high-rating ones from Amazon). By far, this book is the one gives me most insights about how my knowledge in statistical data analysis can be applied to the field of black-box trading.

In a word, this book gives very nice picture of automatic/systematic/black-box trading. It is general, not covering any technical details / specific trading strategies. But it should be good for graduate students in Engineering/Science to gain a clear idea about systematic trading and how their knowledge can be, generally, applied to the field.

After reading the book, whether you find out or not what is inside the black box, you will surely find out that this black book is pretty empty. It is a very amateurish shallow attempt on a topic which is, by definition, so interdisciplinary and complex. The book consists of just pages and pages of wordy superficial narratives occasionally with some basic statistical concepts expressed in words thrown in often along with comparisons and analogies of stock market with situations which are at the minimum laughable and at the maximum stupid (from someone with only few years in this business and barely with an undergraduate in economics, you could expect it and I will forgive the author for that) . There is a difference in being experienced to write a book and being ambitious to write a book. I would have no problem if the book was titled as "What quants do for living" or "A narrative to the life of a quant" or "Some well known pains in the rear of a quant's butt" but a book titled "Inside the black box..." is expected to at least, tell us exactly that -- what is exactly inside the black box and -- I believe it is algorithms and mathematical/statistical concepts that sit inside the black box. Too bad, the author was totally in the dark when he went inside that black box and hence failed to enlighten us.

There also seems to be conflict of interest in promoting this book. While in the books Acknowledgement, the author acknowledges some people (Steve Drobny, Mathew Rothman) to be hugely helpful while writing this book, but then on the back of the book jacket the same guys provide the so called presumably unbiased "praises" for the book. This along with numerous suspect and undeserving glorifying five star reviews fortunately questioned by many other Amazon reviewers do give you an uncomfortable feeling about the integrity of someone involved with this project. Apparently that immature someone does not even care about his own reputation. That aside, there is nothing in here for which I can recommend this book.

My final recommendation? A roll of toilet paper has more value and is more useful than this book.

Added Later: I could not get out of my mind the fact that lots of people have felt that 5 star reviews here for this book are bogus and the fact that the people acknolwledged by the author in helping him themselves went onto provide (undeserving) praises of the book on the back jacked of the book. So I did some investigating. They are very right. In general, you would expect that good and bad reviews are randomly spaced over time. This is more or less true for bad reviews but most five star reviews are clustered between late August 2009 to early Nov. 2009, with often several reviews written on the same day (August 25, August 26, Sept. 1, Oct. 4...) and many written just a day apart. How likely is that? It is clear that these were written by buddies who are possibly asked by author to write five star reviews. The Author pretends to know statistics but did not realize that with simple statistical ideas, others can figure out that he is trying to trick the system. Can he explain why only those who read his book in Aug-Nov. 2009 likes it very much with 5 star awards (I thought markets had seasonality, but not the liking for the books). Can he explain, how come good reviews dried up later? How come suddenly people started disliking his book? I guess, if such statistical patterns are to be erased, infinite number of friends will have to write in future infinite number of reviews at random occasions for the rest of the life of the book. What a desparation for fame! I hope Amazon will notice it and do something about it.

Do not recommand this book to any one who have some basic knowledge and would like to know more of Quant Trading. My knowledge in quantitative trading is limited and I really learned nothing from this book.

It is silly for people to complain that this book didn't improve their stat arb strategies by 2 bps within 20 minutes of picking it up!

Obviously, no real quant is going to reveal the inner workings of their strategies to the public, nor even to a colleague.
Any practitioner is best served by reading professional journals and PhD theses (see SSRN for the best collection) for incremental improvements and ideas.

What Rishi has written is a terrifically readable, concise yet sufficiently detailed overview of the process of quantitative trading.
Anyone can dump hundreds of pages of redundant and irrelevant content into an impressive tome - whereas this is a few hours of reading for a truly solid introduction.

With this information, someone new to the industry will know if this is a potential specialization for them, and anyone tangentially involved, such as investors, fund-of-funds allocators or risk managers, will get an idea about what their quants are dealing with.

This book provides a very superficial overview of (some) quantitative trading strategies -- superficial enough that I don't see how the book can be useful to a professional -- no real discussion of backtesting methodology or the pitfalls of overfitting is given. Indeed, not many examples of any kind are given. On the other hand, the book assumes enough knowledge (and uses enough jargon) that I do not see how it can be useful to a "lay" reader.

The information density of the book is low, and copy editing is quite bad (so bad, that it is surprising that the book is published by Wiley -- a major publisher).