by Riccardo Rebonato
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Product Description
Today's top financial-risk professionals have come to rely on ever-more sophisticated mathematics in their attempts to come to grips with financial risk. But this excessive reliance on quantitative precision is misleading--and it puts us all at risk. This is the case that Riccardo Rebonato makes in Plight of the Fortune Tellers--and coming from someone who is both an experienced market professional and an academic, this heresy is worth listening to. Rebonato forcefully argues that we must restore genuine decision making to our financial planning, and he shows us how to do it using probability, experimental psychology, and decision theory. This is the only way to effectively manage financial risk in a manner congruent with how human beings actually react to chance. Rebonato challenges us to rethink the standard wisdom about probability in financial-risk management. Risk managers have become obsessed with measuring risk and believe that these quantitative results by themselves can guide sound financial choices--but they can't. In this book, Rebonato offers a radical yet surprisingly commonsense solution, one that seeks to remind us that managing risk comes down to real people making decisions under uncertainty. Plight of the Fortune Tellers is not only a book for the decision makers of Wall Street, it's a must-read for anyone concerned about how today's financial markets are run. The stakes have never been higher--can you risk it?
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Average Customer Review:
0 of 1 people found the following review helpful:
 The smarter cousin of "Black swan", 2008-06-16
I hate to give less than five stars to a book by the author of 'Volatility and correlation', but I cannot honestly say that I am happy with it. This is a highly intelligent and amusing book - it would make a great 20-page paper, with an exciting reference list - but one that just didn't tell me much new. I don't think that risk-management practitioners (good ones, anyway) will be enlightened either. It's in this sense - what are the new insights? - that the book invites an ambivalent association with Taleb's bestseller. Still, it's better by any measure.
0 of 1 people found the following review helpful:
The future repeats the past., 2008-05-29
I have long like the insight of Bernstein, doctor turned author. If you like understanding how world business evolves on a very long time scale, then you will like this book. The adage that history repeats in cycles is driven home. Globalization, trade imbalances, plagues, and power struggles all occurred many times in the past. Some groups benefited; some didn't. Read and heed!
2 of 2 people found the following review helpful:
 Risk management is about making decisions under uncertainty, 2008-03-29
The main theme is that risk management is not about measuring risk, or assessing probabilities, but it is about making decisions under uncertainty. The author says that the existing framework of risk management, which is heavily based on "frequentist" approach to probabilities (i.e. repeatability under identical conditions, weak prior beliefs, etc.) does not necessarily serve for decision-usefulness associated with managing risks; "subjective" (Bayesian) probabilities tend to be better suited to the purposes. Focusing on the outcome of decisions relieves us from dogmatic probabilists and allows us eclectically to arrive at the best prediction we can, using whatever tool we have at our disposal. While the author's argument appears to make a lot of sense, the Bayesian probabilities brings in subjectivity such as prior information/knowledge, which in itself seems helpful, I wonder what if we are not confident of such prior information, as we cannot know what we cannot anticipate (i.e. an "unknown unknown": an uncertainty that is unanticipated)? Or put it differently, if we already have had good, reliable prior information about whatever the risk we attempt to assess, then, we would not have much to worry about to begin with, I presume..... Well, we probably should not try to rely on statistical approach to such an extremely high percentile to be considered effectively meaningless (I hasten to throw in my disclaimer here that I am not proficient enough in statistics to discuss the matter in detail!)
Those who have found Nassim Nicholas Taleb's "The Black Swan" and "Fooled by Randomness" fascinating would be intrigued by this timely, engaging , and highly accessible account, which provides not only professional risk managers but also amateur investors like me with numerous insights.
9 of 9 people found the following review helpful:
Timely and insightful; best of its kind!, 2007-12-05
In my opinion this is the most valuable book on investment risk management of the past few years. Yet, no equations! However, with cogent arguments and literate prose, Rebonato lays out a case against the unfortunately prevalent misuse of statistical models in risk management.
Second edition should fix the minor annoyances, like "manger" for "manager" (appearing several times) and "form" for "from" (ditto), but the content should be read by everyone with interest in the area.
Especially welcomed are his arguments. Rather than setting up straw swans and knocking them down, or simply labeling alternative views as offensive or idiotic, he carefully sets out deep background for thinking about risk, and for thinking about probabilities, then shows how and why the well-meaning (and useful in the right context) VaR ideas are on a trajectory that is likely to go horribly wrong.
What to do? Unfortunately, the problem is hard and there are likely no easy solutions. But thinking correctly (my word) about the problem lets us roll up our sleeves and work on the right parts of the problem.
Investment management is all about risk management. We want to understand the risks in front of us, accept the risks we think we can get paid properly for, and avoid the ones where the bet is not in our favor. The Rumsfeldian "unknown unknowns" are the ones that are likely to cause the most damage. Those are what should keep us up at night trying to imagine. If they become "known unknowns", e.g. liquidity and linkage risks which showed up July/Aug 2007, we can get to work understanding and managing them.
Best (financial/investment) book of the year.
14 of 17 people found the following review helpful:
Very interesting and important, 2007-11-08
This book is one of the many that have come out in the last few years that has addressed the virtues and vices of financial modeling. Many of these books are devoted to the proposition that modeling has caused deep problems in the financial markets, but the evidence they present for this assertion is typically very weak. Considering the scale of modeling in financial institutions throughout the world, it would be naïve to assume that modeling has not influenced the markets, but it would also be unjustified from an empirical standpoint to say that modeling has been the predominant influence in market degradation. But if one believes that modeling has played the major role in this regard, then there will be a strong temptation to seek alternative methodologies for optimizing the risk/return trade-off.
The author is one of these, as can be ascertained early on in the book where he refers to data as giving "power to actions and decisions." However, the author is aware of the problems with the misguided imputation of power to concepts or ideas that are applied to contexts that are extremely rare in human experience. Thus he devotes several pages of the book to the "frequentist" interpretation of probability, and offers the Bayesian alternative. This is not to say that the frequentist approach should be completely discarded, for he discusses contexts where it is appropriate. One of these concerns the need for say a 99.9 percentile in some implementations of the Basel II accords. Such a level of confidence will be very problematic from the standpoint of validation given the paucity of real historical data. The author also offers suggestions for how risk managers are to clean up their act in the final chapter of the book.
He also discusses the possible use of belief theory in risk management, but apparently he is not aware that this approach has been tried in some contexts. In fact, this reviewer has applied some of the concepts from belief-theory to the problem of mortgage-broker scoring. Belief theory even has a "belief calculus" that has been applied to the modeling of financial portfolios, with the goal of learning how the returns change as new information is obtained on the factors that impact the portfolio. The belief calculus is similar to what is done using Bayesian networks, but with belief functions used to model the dependencies in the factors. Belief theory abandons the additive principle of probability theory, in that the 'belief mass', or "degree of belief" that is assigned to certain sets does not have to sum to one. However, belief functions is that they can be expressed as a probability using the so-called 'pignistic transformation', but one will obtain a loss in information if this is done. The author asserts that belief theory is a viable methodology, in that one's "confidence" that a certain event or number of events is about to occur is expressed by the willingness to "bet on" that event or events. But "credo" is Latin for "I believe" and "pignus" is Latin for a wage or bet, and certainly in everyday conversation one frequently hears "it is my belief that this will happen....I would bet a month's wages."
There is no arguing that decision making is the real essence of risk management, but does this have to involve subjective judgments, as the author seems to imply, or can it be done by a suitable collection of algorithms that are sophisticated enough to deal with most contingencies? If so, could this be taken one step further and allow the decision-making process to be automated, possibly using intelligent machines? Given the advances in artificial intelligence, this scenario is getting more plausible. But in all approaches to risk management, whether automated or not, one must still answer whether the human or machine estimation of probabilities of events is meaningful and how to assess if this is the case. Will this involve the use of traditional statistics or will some other approach be used?
Prospect theory, also discussed in the book, has certainly been a useful paradigm in risk management, to the degree that it has been utilized. But indeed how can one really know what concepts or methodologies are actually being employed by senior risk managers? In many cases, the analyst or modeler makes assumption that the management is using the results of the modeling efforts, but instead the management is relying on intuition or guesswork to make risk decisions, and completely ignoring the data from the models. In addition, distinguishing the impact of decisions based on modeling versus those based on intuition is more difficult than is realized at first glance.
Another important point to make here is that the Bayesian approach to the calculation of probabilities may not be part of the model itself, but frequently plays a major role in the validation and empirical support for the model. A similar situation occurs in other fields, such as physics, where Bayesian calculations permeate experimental confirmation of theories, but where the theories are stated in a frequentist framework.
Given the extreme events in the fixed-income sector at the present time, it is difficult to argue with the author's claim that financial risk management must be done in a different way. In fact, just this month a popular technology journal referred to a meeting of a couple of hundred of the more well-known financial modelers, who declared the summer of 2007 to be the worse ever for financial modeling. So the author is not alone in his opinions. But due to bureaucratic inertia and resistance from the status quo, finding the right time to implement these changes can be problematic, even when there is unanimous agreement that these changes are necessary.
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