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I realize that the statistical analysis of financial data is a huge topic, but that is exactly why it is necessary for me to ask my question as I try to break into the world of financial analysis.

As at this point I know next to nothing about the subject, the results of my google searches are overwhelming. Many of the matches advocate learning specialized tools or the R programming language. While I will learn these when they are necessary, I'm first interested in books, articles or any other resources that explain modern methods of statistical analysis specifically for financial data. I assume there are a number of different wildly varied methods for analyzing data, so ideally I'm seeking an overview of the various methods that are practically applicable. I'd like something that utilizes real world examples that a beginner is capable of grasping but that aren't overly simplistic.

What are some good resources for learning bout the statistical analysis of financial data?

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  • $\begingroup$ Given the current state of national economies due to the rescue of the various financial institutions, one may question the value of accepted knowledge in this field, save for greater fool theory. $\endgroup$
    – James
    Jul 22, 2010 at 15:06
  • $\begingroup$ @James: Alternatively, this might argue for more education in the field, not less... $\endgroup$
    – Shane
    Aug 13, 2010 at 18:11
  • $\begingroup$ @Shane: Yes, absolutely. I was being slightly facetious in my comment, but I do believe important insights are being withheld by the private-sector community as competetive advantages, which is ultimately to the detriment of an efficient global economy. $\endgroup$
    – James
    Aug 18, 2010 at 11:18

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You might start with this series of lectures by Robert Shiller at Yale. He gives a good overview of the field.

My favorite books on the subject:

Beyond that, you may want some general resources, and the "bible" of finance is Options, Futures, and Other Derivatives by John Hull.

Lastly, in terms of some good general books, you might start with these two:

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You should check out http://area51.stackexchange.com/proposals/117/quantitative-finance?referrer=b3Z9BBygZU6P1xPZSakPmQ2, they are trying to start one on stackexhange.com

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Ed Thorpe started the whole statistical arbitrage thing. He has a website, and some good articles.

http://edwardothorp.com/

You should also read Nassim Taleb's "Fooled By Randomness".

Also, go on Google Scholar and read the top articles by Markowitz, Sharpe, Fama, Modigliani. If you don't have full access, go to the nearest college and get a community library card.

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    $\begingroup$ These are good references, but I disagree with your assessment of Ed Thrope as the founder of this field. Statistical analysis of financial data and statistical arbitrage are not the same thing: one would perform statistical analysis for most financial analysis (e.g. modern portfolio theory). $\endgroup$
    – Shane
    Jul 20, 2010 at 17:20
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    $\begingroup$ I agree, Markowitz definitely invented portfolio theory $\endgroup$
    – Neil McGuigan
    Jul 20, 2010 at 17:55
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    $\begingroup$ Looks like Louis Bachelier started it all in 1900 en.wikipedia.org/wiki/Louis_Bachelier $\endgroup$
    – Neil McGuigan
    Sep 15, 2010 at 21:58
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More from an economics perspectives I think these two sets of lecture notes are very good:

http://home.datacomm.ch/paulsoderlind/Courses/OldCourses/FinEcmtAll.pdf

http://personal.lse.ac.uk/mele/files/fin_eco.pdf

The first provides econometric methods for analysing financial data whereas the second provides the financial economics theory behind the models being applied. They're both MSc level texts.

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Also good is "Statistical Analysis of Financial Data in S-PLUS" by Rene A. Carmona

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Check out Wilmott.com as well. It's oriented toward more advanced practitioners, but if I had to choose one person from whom to learn financial math, it would be Paul Wilmott. Brilliant but grounded.

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Oren, it is useful to define what aspects of finance you intend to tackle. Statistics is a tool when seen from the econometrics perspective (in terms of assessing the plausibility of a proposed model / theory) or can be the first or primary line of attack when seen from the machine learning side - that is you go low on the domain knowledge and rely more on constructing a feature space and applying algorithms. (However, the task of constructing a useful feature space is dependent on deep domain knowledge).

To get a handle on the theoretical aspects of finance - I'd recommend say:

To learn how to apply statistics / econometrics coupled with the theory:

The books recommended above, ones by David Ruppert, Eric Zivot, Ruey Tsay are useful, however, I would recommend Chris Brooks' & Ruppert's texts first, followed by Taylor's.

Paul Soderlind's notes, and Kevin Sheppard's notes (both available online) are quite good.

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I like Risk and Asset Allocation by A. Meucci. This book is a bit more advanced than Ruppert's book, but still very user-friendly.

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Kennedy's Guide to Econometrics is a good survey of techniques in econometrics--not detailed enough to get your hands dirty, but very good for discovering what techniques are being used.

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