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An Engine, Not a Camera: How Financial Models Shape Markets (Inside Technology) Hardcover – April 14, 2006

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Editorial Reviews

Review

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)

A brilliant, extremely lucid account of the connections between financial economics and the development of futures, options, and derivatives markets between the 1950s and 2001.

(Neil Fligstein American Journal of Sociology)

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 Shapi, Franklin L. Ford Professor of the History of Science, Harvard University)

Maggie Mort tells the fascinating and unusual story of the development of a high-tech submarine from the point of view of workers on the project.

(Michel Callon, Ecole des Mines de Paris)

About the Author

Donald MacKenzie is Professor of Sociology (Personal Chair) at the University of Edinburgh. His books include Inventing Accuracy (1990), Knowing Machines (1996), and Mechanizing Proof (2001), all published by the MIT Press. Portions of An Engine, not a Camera won the Viviana A. Zelizer Prize in economic sociology from the American Sociological Association.

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Product Details

  • Series: Inside Technology
  • Hardcover: 392 pages
  • Publisher: The MIT Press (April 14, 2006)
  • Language: English
  • ISBN-10: 0262134608
  • ISBN-13: 978-0262134606
  • Product Dimensions: 6 x 0.8 x 9 inches
  • Shipping Weight: 1.5 pounds (View shipping rates and policies)
  • Average Customer Review: 4.8 out of 5 stars  See all reviews (11 customer reviews)
  • Amazon Best Sellers Rank: #686,117 in Books (See Top 100 in Books)

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34 of 34 people found the following review helpful By Dr. Lee D. Carlson HALL OF FAMEVINE VOICE on August 29, 2007
Format: Hardcover Verified Purchase
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.
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15 of 15 people found the following review helpful By Craig L. Howe on December 18, 2006
Format: Hardcover Verified Purchase
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.
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8 of 8 people found the following review helpful By J. Gordon on June 27, 2008
Format: Hardcover Verified Purchase
MacKenzie has an interesting take on the development of financial models, technology and organized exchanges, focusing on the latter half of the 20th century. He sees the three components as interacting as markets evolve. For example, the index futures exchanges did not really take off until options pricing theory made it possible to create spreadsheets to assist traders in options pricing. The incorporation of computers greatly increased market efficiency.

MacKenzie analyzes the development of modern finance theory and its interplay with market evolution from a social-scientific, anthropological point of view. The people involved in the development of theory and markets take center stage; the author conducted dozens of interviews with academics and practitioners, and even reviewed the private papers of some. The amount of research incorporated into the analysis is impressive.

One word of caution: although the book does not contain much math, and what is included is relegated to appendices, a strong familiarity with the development of financial models, at the level, say, of Bernstein's "Capital Ideas", would be greatly beneficial to one's enjoyment of this book.

Summary: If you are interested in this topic -- read this book! You won't be disappointed.
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19 of 23 people found the following review helpful By Stephen R. Laniel on January 24, 2009
Format: Hardcover
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.
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