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The Money Formula: Dodgy Finance, Pseudo Science, and How Mathematicians Took Over the Markets Paperback – June 12, 2017
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"Irrevent and fun, it is at the same time very rigorous in explaining how soem fairly basic mathematics has hijacked the world of finance." (Marketing Moving, June 2017)
“a great description of what has happened, a thorough explanation of what is currently going on, and a solid argument for what should be done in the future.” (Significance, April 2018)
From the Inside Flap
PRAISE FOR THE MONEY FORMULA
"This book has humor, attitude, clarity, science and common sense; it pulls no punches and takes no prisoners."
Nassim Nicholas Taleb, Scholar and former trader
"There are lots of people who'd prefer you didn't read this book: financial advisors, pension fund managers, regulators and more than a few politicians. That's because it makes plain their complicity in a trillion dollar scam that nearly destroyed the global financial system. Insiders Wilmott and Orrell explain how it was done, how to stop it happening again and why those with the power to act are so reluctant to wield it."
Robert Matthews, Author of Chancing It: The Laws of Chance and How They Can Work for You
"Few contemporary developments are more important and more terrifying than the increasing power of the financial system in the global economy. This book makes it clear that this system is operated either by people who don't know what they are doing or who are so greed-stricken that they don't care. Risk is at dangerous levels. Can this be fixed? It can and this book full of healthy skepticism and high expertise shows how."
Bryan Appleyard, Author and Sunday Times writer
"In a financial world that relies more and more on models that fewer and fewer people understand, this is an essential, deeply insightful as well as entertaining read."
Joris Luyendijk, Author of Swimming with Sharks: My Journey into the World of the Bankers
"A fresh and lively explanation of modern quantitative finance, its perils and what we might do to protect against a repeat of disasters like 2008-09. This insightful, important and original critique of the financial system is also fun to read."
Edward O. Thorp, Author of A Man for All Markets and New York Times bestseller Beat the Dealer
- Item Weight : 13.7 ounces
- Paperback : 264 pages
- ISBN-10 : 1119358612
- ISBN-13 : 978-1119358619
- Dimensions : 5.9 x 0.6 x 8.8 inches
- Publisher : Wiley; 1st edition (June 12, 2017)
- Language: : English
- Best Sellers Rank: #909,039 in Books (See Top 100 in Books)
- Customer Reviews:
Top reviews from the United States
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Paul Wilmott and David Orrell are both good applied mathematicians, authors, and critics, with Paul being the consummate insider, or “quant”, while David is the outsider, having already taken on academic economics with his insightful book “Economyths”. Their chatty dialogue explains the key role of the Black-Scholes formula in setting realistic prices for various kinds of financial options, but also emphasizes its limitations, so often ignored in practice. They find it astonishing how many quants actually believe in the Efficient Market Hypothesis, but note how it excuses all manner of selfish behavior, with extraordinary pay and bonuses.
Using his insider background, Wilmott exposes a litany of “mathematical tricks for betting on the markets”, noting that traditional quant concepts like “modern portfolio theory” and “value at risk” tend to “fail just when you need them most…when apparent stability breaks down”. However, hedge funds are good at looking “for small pockets of predictability… while they last”. Mostly they exploit financial derivatives, which “often are used to make highly leveraged bets – so models are critical for risk assessment.” Reading his description of the now infamous “collateralized debt obligations” and “credit default swaps”, it struck me that these derivatives functioned as a kind of pyramid scheme, to shift risk from insiders at the top of the pyramid to the dupes like us at the base, the toxic mortgages camouflaged by corrupt credit rating agencies. Yet the mystique of the clever mathematics has served, quite literally, as “the perfect get-of-jail card”
Meanwile Orrell asks whether “it is really possible to model markets as a kind of physical system?” (a la Issac Newton). The empirical and mathematical answer is that markets exhibit “complex dynamics that resist numerical prediction”, with power law distributions replacing Gaussians, not at all surprising in view of the variety of irrational actions identified by behavioral economists like Kahneman and Tversky. Even savvy investors need to heed Keynes admonition that “markets can stay irrational longer than you can stay solvent”. Wilmott and Orrell cite mathematical biology as a better example of applied mathematics. That is, good biological models are not based on Newton-type scientific laws, but rather statistics and observations inform simple mathematical formulae that offer key qualitative insights into particular situations, in keeping with the chaotic patterns of real world economic behavior (booms, busts, unpredictability, etc).
However I think that comprehensive explorations and simulations, based on analysis of “big data”, using higher dimensional non-linear networks and systems, will also offer insights and guidance in the future, despite their “black box” nature. Hurricane tracking is done manually, eyeballing a selection of Monte Carol simulations of complex forces, building off past experience. Why not do things like this for economic and financial forecasting - to continually update vastly more realistic models of risk?
Also not addressed is that one of our biggest challenges is to identify better tools than interest rates to guide the economy. For example, “Modern Monetary Theory” provides a sound basis for restoring the tools of fiscal policy in general, and of the government as employer of last resort in particular. And, Peter Barnes, in “With Liberty and Dividends for All”, shows how to use universal ownership of wealth-generating assets for the same purpose. In addition, the Fed could be pouring money directly into infrastructure public banks to develop renewable energy, affordable housing, public transit, etc., instead of pumping it into Wall Street to support speculation.
That is, financial economics could be facilitating the economy that we need, instead of an economy of greed. Wilmott and Orrell do hint at the possibility of monetary reform but mostly they give us worthy, but common place, prescriptions such as breaking up the big banks, stronger regulation, a financial transaction tax, simplicity, and transparency. The unaddressed problem here is the need for a “political revolution” to overcome the power of big money to resist even the most commonplace of reforms. Strangely, they do not cite other insider critiques, such as the Yves Smith blog “NakedCapitalism.com” or her great book “ECONned” on the crash of 2008. Yet this book does give quantitative types a much better understanding of what we’re up against, and it is an opening for some “beyond Wall Street” sequels.
The first half (Chapters 1-5) provides an engaging albeit somewhat standard background and history: the random walk/Brownian motion model, the efficient market hypothesis (EMH) and the observation that stock prices changes are more variable than the former predicts; fundamental analysis and technical analysis; "modern portfolio theory", the idea there is an optimal portfolio, based on assumptions for numerical values of future mean growth and correlations, and noting that future correlations are hard to predict; "value at risk" and the difficulty in assessing extreme events. They give a good explanation of the background and meaning of the Black-Scholes pricing model and basic "vanilla" options via dynamic hedging and mention more exotic options. They describe the explosive growth in CDOs and CFSs in the run-up to 2007-8.
The second half can be summarized as miscellaneous commentary on what the quants have done: here are a few examples. There are intrinsic difficulties in applying analogs of Black-Scholes to options (such as interest rates or credit risk) that are not explicitly traded and therefore cannot be directly dynamically hedged. The copula method for guessing correlations is bizarrely arbitrary and unjustifiable. There are no plausible "standard toy models" for interest rates or volatility. They give an informative classification of quant jobs (junior quant; model validation; quant developer; risk management; research quant; front office; quant trader).
The most distinctive features and their central critiques are in Chapters 9 and 10. They give cute and memorable examples of the many misaligned incentives within the industry, and discuss systemic risks exemplified by the financial crisis of 2007–2008. In an Epilogue they give detailed suggestions for making the profession more socially responsible (partly along the the "skin in the game" theme emphasized by Taleb). I won't try to summarize their analysis here, but it should all be required reading for anyone wishing to comment knowledgeably on the quant world.
In terms of what it says, this book is excellent, so my minor criticisms concern what it doesn't say. Some comments as a professional mathematician:
They do not distinguish clearly between the specific Brownian motion model and the general martingale model; the latter is what the EMH predicts.
In chapter 7 it is observed that estimates of future volatility change weekly, but so they should: the issue in testing a model is whether they fluctuate more than a martingale should.
Their rhetorical question "why do abstract fields such as measure theory have a stranglehold on [finance models]?" has a partial answer I give at the start of a graduate course. Measure theory is the operating system (OS) underlying probability theory. By analogy, you can learn to use your iPad or iPhone apps without knowing the device has an OS, but if you want to do novel developments then you need to have an interface with the OS.
The presumption that Math Olympiad champions would do more socially beneficial work ("mathematician cures cancer!???") in other fields suggests the authors have never spent an hour in a roomful of such people.
They make a big deal of attacking the EMH, pointing out correctly that many people have profited by finding and exploiting different small deviations from EMH predictions. But (in principle and surely also in practice) if these are indeed different deviations then their overall effect should be stabilizing. Their exposition tends to conflate this with the "herd behavior" of everyone ignoring a risk or planning to get out before the day of reckoning, the background to the 2007-8 crisis.