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Against the Gods: The Remarkable Story of Risk Paperback – Unabridged, August 31, 1998
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With the stock market breaking records almost daily, leaving longtime market analysts shaking their heads and revising their forecasts, a study of the concept of risk seems quite timely. Peter Bernstein has written a comprehensive history of man's efforts to understand risk and probability, beginning with early gamblers in ancient Greece, continuing through the 17th-century French mathematicians Pascal and Fermat and up to modern chaos theory. Along the way he demonstrates that understanding risk underlies everything from game theory to bridge-building to winemaking. --This text refers to the Hardcover edition.
From Publishers Weekly
Risk management, which assumes that future risks can be understood, measured and to some extent predicted, is the focus of this solid, thoroughgoing history. Probability theory, pioneered by 17th-century French mathematicians Blaise Pascal and Pierre de Fermat, has made possible the design of great bridges, electric power utilities and insurance policies. The statistical sampling methods invented by dour Swiss scientist Jacob Bernoulli undergird diverse activities such as the testing of new drugs, stock-picking and wine tasting. Bernstein (Capital Ideas) animates his narrative with a colorful cast of risk-analyzers, including gambling addict Girolamo Cardano, 16th-century Italian physician to the Pope; and John Maynard Keynes, whose concerns over economic uncertainty compelled him to recommend an active, interventionist role for government. Bernstein also traces the development of business forecasting, game theory, insurance and derivatives, and surveys recent advances in risk forecasting made possible through chaos theory and by the development of neural networks.
Copyright 1996 Reed Business Information, Inc. --This text refers to the Hardcover edition.
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Top customer reviews
I was trained on statistics, SPC, Project Management etc etc, but I felt that something was missed in the classical approaches to risk management.
So I went for a book that could give me a real insight to the matter.
This is definitivelly the book. Don't even think to start study Statisitcs without reading it. Don't even think to approach risk management without reading it.
You will here discover the two faces of risk: stat and Gut.
And only then you will start manage risk.
And by the way is an amazing well written book, read it even if you just want a great book to read under the sun on a beach, instead of those wastingtimers briks...
On a serious note, the book's main value to me is sharing it with colleagues and friends who shy away from probability and other quantitative methods. It has broken the "I-hate-math" barrier for more than one person with whom I have shared a copy (or purchased one as a gift), and I dare say it has helped in their professional advancement. This is particularly true for project managers who get tossed into the profession by chance (yes, that was intended as an oblique pun), and quickly discover that their success depends upon a good understanding of probability in order to correctly estimate work as well as manage project risk. Most of these folks are great at relationship management, but fall into the "I-hate-math" category. This book leads them to the math part via a great story with gentle introductory examples of the underlying numbers. In that respect it not only sparks interest in the subject, but provides the self-confidence to wade into the "dreaded subject".
Before I go off into a rambling and confusing review of an excellent book, let me end this with two points: (1) it is great reading, regardless of your interest in probability or risk - view it as an historical novel and enjoy it, and (2) it is a gentle introduction to [admittedly] dry material that you will have to get from technical books. I loved it - your mileage may vary.
The author however seems not to be aware of the notion of 'model risk' that is embedded in modern approaches to risk management and financial engineering. This is apparent when he speaks of the inability of risk analysts to input concepts into computing machines that they themselves cannot conceive. The issue for risk management is not whether these concepts are exact representations or reality, but rather the cost or risk associated with their inaccuracy. In addition, risk analysts do not need to conceptualize on a level that is extremely far from current paradigms. They need not think the 'unthinkable" as the author believes that they do. Instead, their goal is to invent concepts, models or even new paradigms that allow risk managers to make estimates based on these concepts. But these managers do not view these models as sacrosanct, or as "oracles" as the author puts it. In fact there is typically a large amount of skepticism exhibited towards the models, and the managers at time do resort to personal intuitions and hunches.
The author though is correct in his opinion of the huge role of machines in risk management and in finance in general. With each passing day these machines are given more responsibility for doing financial analysis, forecasting, trading, and even model building. And more importantly, they are beginning to actually construct concepts and theories about how markets work, with the guidance for the time being of human experts. This trend will continue, and with faster and faster machines on the horizon, and with more trust placed in these machines, one can expect even more volatility in the financial markets. This volatility will require even smarter machines to deal with the huge risk trade-offs that will be involved, and it is likely that the machines will compete fiercely with each other as the strive to optimize the financial health of the firms that deploy them.
Thus there are very challenging times ahead for risk management, and therefore it is important to keep its role in proper context. It is not done for the sake of it, and it depends on conceptions and theories that were developed centuries ago, as the author of this book shows in great detail. It is wise to keep in mind these historical origins to the same degree that risk algorithms depend on historical data. Risk in the twenty-first century will dwarf anything that has come before, and new political ideologies. legal and regulatory frameworks, and systems of ethics will arise just to deal with its complexity. The degree to which humans are overwhelmed by this risk will be inversely proportional to their willingness to learn from history as well as depart from it, and to interact with the most complex technology ever constructed.
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Also, outright wrong in some parts - for instance, that the most volatile stocks generate...Read more