An Engine, Not a Camera: How Financial Models Shape Markets (Inside Technology) (Hardcover)

~ Donald MacKenzie (Author)

Editorial Reviews

Review

"An Engine, Not a Camera is a compelling, detailed, and elegantly written exploration of the conditions in which finance economists help to make the world they seek to describe and predict. Donald MacKenzie has long been without equal as a sociologist of how late modern futures are brought into being and made authoritative. This is his best work yet."
—Steven Shapin, Franklin L. Ford Professor of the History of Science, Harvard University

"An Engine, Not a Camera provides an insightful appreciation of the ways in which financial models influence and shape the world they seek to understand."
— Anthony Hopwood, Times Higher Education Supplement

"Donald MacKenzie has long been one of the world's most brilliant social and historical analysts of science and technology. Here he provides an original, astute, and exhaustively researched account of the development of finance theory and the ways in which it is intertwined with financial markets. An Engine, Not a Camera is essential for anyone interested in markets and the forms of knowledge deployed in them."
—Karin Knorr Cetina, University of Konstanz and University of Chicago

"Having returned from an audacious incursion into the black box of modern financial markets, Donald MacKenzie shows how economic theory has succeeded in capturing and shaping them. This book will be of substantial interest to specialists in a range of fields including economics, finance theory, economic sociology, and science and technology studies. But MacKenzie's tour de force is to make clear, even to nonspecialists, that through complex technical issues, alternative forms of economic organization can be imagined and discussed."
—Michel Callon, École des Mines de Paris

"In one lifetime modern finance theory has revolutionized the arts of canny investing. MacKenzie knows this exciting story, and he tells it well."
—Paul A. Samuelson, MIT, Nobel Laureate in Economic Sciences (1970)

Product Description

Winner of the 2008 Viviana Zelizer Distinguished Scholarship Award given by the American Sociological Association’s section on Economic Sociology., Winner, 2007 British International Studies Association’s (BISA) International Political Economy Group (IPEG) Book Prize. and Winner of the 2005 John Desmond Bernal Prize awarded jointly by the Society for Social Studies of Science (4S) and the Institute for Scientific Information.

In An Engine, Not a Camera, Donald MacKenzie argues that the emergence of modern economic theories of finance affected financial markets in fundamental ways. These new, Nobel Prize-winning theories, based on elegant mathematical models of markets, were not simply external analyses but intrinsic parts of economic processes.

Paraphrasing Milton Friedman, MacKenzie says that economic models are an engine of inquiry rather than a camera to reproduce empirical facts. More than that, the emergence of an authoritative theory of financial markets altered those markets fundamentally. For example, in 1970, there was almost no trading in financial derivatives such as "futures." By June of 2004, derivatives contracts totaling $273 trillion were outstanding worldwide. MacKenzie suggests that this growth could never have happened without the development of theories that gave derivatives legitimacy and explained their complexities.

MacKenzie examines the role played by finance theory in the two most serious crises to hit the world’s financial markets in recent years: the stock market crash of 1987 and the market turmoil that engulfed the hedge fund Long-Term Capital Management in 1998. He also looks at finance theory that is somewhat beyond the mainstream—chaos theorist Benoit Mandelbrot's model of "wild" randomness. MacKenzie's pioneering work in the social studies of finance will interest anyone who wants to understand how America’s financial markets have grown into their current form.

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Product Details

• Hardcover: 389 pages
• Publisher: The MIT Press (May 1, 2006)
• Language: English
• ISBN-10: 0262134608
• ISBN-13: 978-0262134606
• Product Dimensions: 9 x 6.3 x 1.3 inches
• Shipping Weight: 1.5 pounds (View shipping rates and policies)
• Average Customer Review: 4.8 out of 5 stars  See all reviews (5 customer reviews)
• Amazon.com Sales Rank: #326,332 in Books (See Bestsellers in Books)

Customer Reviews

Most Helpful Customer Reviews

12 of 12 people found the following review helpful:

4.0 out of 5 stars A plausible case, August 29, 2007

By	Dr. Lee D. Carlson (Baltimore, Maryland USA) - See all my reviews (TOP 100 REVIEWER)    (REAL NAME)

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Many financial analysts and financial journalists have pointed to quantitative trading and the subprime mortgage markets as being the major cause behind the extreme volatility in the financial markets in the summer of 2007. This book therefore seems fitting for this particular time in financial history, if only at a bare minimum to educate the reader about the use of mathematical modeling in financial analysis and financial engineering. As the subtitle of the book indicates, the author's main thesis is that the use of mathematical models can actually change the dynamics of the markets themselves, moving them possibly to territories even more uncertain that they were invented to describe. Quantitative trading, now done by most of the major players in the financial markets, is dependent of course on mathematical modeling, some of which uses highly sophisticated reasoning patterns and artificial intelligence. Most of these models are proprietary, and therefore one cannot ascertain their efficacy in the acquisition of wealth for the organizations that deploy them. However, with a little pertinacity one can acquire a good understanding of their workings by studying the academic literature.

Some of the predominant models in the public domain are discussed in this book, mostly from an historical perspective but the author inserts some of the relevant mathematics in its appendices for the more mathematically sophisticated reader. In general the author makes a plausible case for his main thesis, but at times his conclusions are based on mere anecdotes, and he makes the typical mistake of imputing power and influence to individuals that is unsubstantiated. It is very tempting, especially among those individuals or institutions that are involved in trading, or even responsible for innovations in the same, to believe that they are the cause for some of volatility in the financial markets. But such claims, even if they seem reasonable or intuitively clear, must be substantiated with careful statistical analysis, which can be time-consuming and difficult, and few individuals it seems are willing to devote themselves to such a project. The author though is aware of this, for he states very early on in the book that historical sources may not be sufficient to allow one to decide if the influences are real. In addition, he cautions the reader to "look not just at what participants say and write but also at whether the processes in question involve procedures and material devices that incorporate economics."

The author labels the idea that economics as an academic project is actually part of economic processes the `performativity of economics', which he further breaks down into subclasses that serve to clarify the distinctions he wishes to make. One of these is more of a passive notion, called "generic" performativity, which is used to describe the participant's use of economic theories or data without emphasizing their effects on economic processes. If such effects take place, this is called "effective" performativity, which is then specialized to "Barnesian" performativity. The latter is used to describe the situations where the practical use of economic theory makes economic processes resemble what they are described to be by economic theory. Barnesian performativity is to be contrasted with `counterperformativity' where the actual use of economic models makes economic processes not resemble their description by these models. The author discusses how to detect Barnesian performativity, but warns of the difficulty in proving that movements in prices are following certain model predictions.

But aside from the qualitative/historical emphasis that the author makes in this book and the small number of unsubstantiated claims of model-market influence, the reader will take away a better understanding of such topics as the capital asset pricing model, the Black-Scholes-Merton model of option pricing, the Modigliani-Miller theory of capital structure, a description of Levy processes and their role in econometrics, and most interestingly, a different explanation for the demise of Long Term Capital Management. All of these topics, coupled with the intellectual honesty and literary skill of the author, make this book a highly interesting contribution to the financial literature.

9 of 9 people found the following review helpful:

5.0 out of 5 stars An Insightful Look into Finance's Twin Roles, December 18, 2006

By	Craig L. Howe "The Pointed Pundit" (Darien, CT United States) - See all my reviews (TOP 500 REVIEWER)    (REAL NAME)

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Both the science and the art and practice of finance have experienced phenomenal growth since the 1970s.

As a science, finance has evolved from a descriptive outpost on the economic frontiers to become of that discipline's central topics. During the same period, the financial markets changed from what often seems today like sleepy outposts of liquidity into dynamic centers for financial engineering. In the 1970s, the world was being introduced to commodity hedging and options trading. By the early part of the 21st century, derivatives contracts totaling more than $273 trillion were outstanding worldwide.

Donald MacKenzie, a sociology professor at the University of Edinburgh, argues in An Engine, Not a Camera, the trends are connected. Paraphrasing Milton Friedman, he argues the emergence of economic models were an engine of inquiry rather than a camera to reproduce empirical facts. As the science of finance became authoritative, the markets were altered. These new, Nobel Prize-winning theories, elegant mathematical markets models, were more than external analyses. They evolved into intrinsic parts of the financial process.

Beginning with a discussion of the work of Franco Modigliani and Merton Miller, the Capital Asset Pricing Model and Random Walk, MacKenzie takes the reader on a journey through the development of the Black-Scholes-Merton model, The Crash of 1987, Long-Term Capital Management and the Russian government's default in 1998 to bind the threads of his thesis.

Detailed, astute, well-written, and with much of the technical detail relegated to the appendices, this book weaves economics, financial theory, economic sociology and science and technology studies into an essential read for anyone with a serious interest in the financial markets.

7 of 8 people found the following review helpful:

5.0 out of 5 stars A great book, but not great for the reasons it thinks, January 24, 2009

By	Stephen R. Laniel (Cambridge, MA USA) - See all my reviews (REAL NAME)

By far the least interesting part of this fascinating book, it seems to me, is its ostensible purpose. The title more or less says it all: financial models don't merely describe the world around them; they play an active part in shaping that world. The shorthand for this concept is "performativity": MacKenzie wants to argue that financial models are "performative." (Ungainly as that word is, MacKenzie steps to the next level when he gives us "counterperformativity.")

In the economic world outside of finance, this is straightforward enough. If you spend enough time imagining that humans are rational actors who only look out for their own self-interest, for instance, and the theorems furthermore say that you should be a rational actor who only looks out for his own self-interest, you will -- suprise of surprises -- probably find yourself acting purely self-interested.

This is easy enough as an intuitive proposition. To really argue it, you'd need some data. The data would need to show that the model somehow creates the greed, or that people become greedy faster than they otherwise would have. You'd then be required to show that the causality doesn't run in the other direction: it's not that greedy people are just drawn to this model, but that they become greedy. Something like this.

Perhaps my tone suggests what I think about this, namely that it doesn't interest me very much. And so it goes for the bits -- and thankfully they are just bits -- about performativity in An Engine, Not A Camera. The finance markets are a good place to look for data: there's a lot of extremely high-quality stock-price data, at one-day resolution or lower, for the last few decades. And there happen to be a couple models -- the Capital Asset Pricing Model and the Black-Scholes option-pricing formula -- that are in wide use. Conditions couldn't be more ripe for testing some hypotheses.

One fundamental concept sitting underneath Black-Scholes is that opportunities for riskless profit don't persist; that is, there are no arbitrage opportunities. In order to determine the rational price for a stock option, we construct a riskless portfolio on the basis of that option. Since it's riskless, and since there are no arbitrage opportunities, our riskless portfolio must fetch the same price as any other riskless asset. (Treasury bonds with the same duration as the option are often taken to be riskless assets.)

If we want to show that financial models have a hand in shaping the financial world, we could show that Black-Scholes actually helped to destroy arbitrage opportunities. This is a straightforward enough exercise; it's an exercise that plays a central role in the fall of Long-Term Capital Management. There the story was pretty clear-cut: a hedge fund (and its imitators) sought and destroyed arbitrage opportunities so successfully that there were none left to find. In a perfectly efficient market, Long-Term Capital is broke. And so it was.

In that sense, financial models are performative. But there's also (wait for it) "counterperformativity," in which the model is self-negating. Index funds are a good example here. Index funds are an idea that basically sprang out of the Capital Asset Pricing Model, in which the market's own volatility is the baseline against which stock volatilities are measured (this measure of volatility is known as "beta"). Whenever a stock gets added to the S&P 500, that stock's price rises more than it rationally should. The reason is that index funds are required to buy that stock automatically now that it's part of the index. So index funds are a built-in source of arbitrage that wouldn't have existed without CAPM. (One might wonder why arbitrageurs, knowing this ahead of time, wouldn't swoop in and bid away the irrationality. I don't know the answer offhand, but I suspect that index funds' sheer size is part of the answer: stock purchases that large need larger short sales than arbitrageurs are prepared to conduct. Plus there are more restraints on short sales than on purchases.)

Like I said, I don't think much of this is very interesting. To me, MacKenzie's book functions much better as a history and semi-technical explanation of modern finance. Read it as history, rather than as sociology or philosophy.

Among other fun technical details in MacKenzie's book, I'd single out the Cox-Rubinstein model. It's a simplified model of option pricing, which leads to the Black-Scholes model in the limit as prices are allowed to change continuously. And it's an elegant piece of work.

The natural next steps after An Engine, Not A Camera are more formal works on option pricing and stochastic processes. We'd want to learn about martingales, Brownian motion, stochastic differential equations; and generalizations of Black-Scholes that allow for credit constraints, legal limitations on short-selling, discontinuous price changes, corporate dividends, and heavier-tailed price distributions. I'm going to be diving into Grimmett and Stirzaker in the near future.