James Gentle - Statistical Analysis of Financial Data: With Examples in R

CHAPMAN & HALL/CRC

Texts in Statistical Science Series

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Statistical Analysis of Financial Data

With Examples in R

James E. Gentle

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James E. Gentle

First edition published 2020

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Library of Congress Cataloging-in-Publication Data

Library of Congress Control Number:2019956114

ISBN: 978-1-138-59949-9 (hbk)

ISBN: 978-0-429-48560-2 (ebk)

To Mara

This book is intended for persons with interest in analyzing financial data and with at least some knowledge of mathematics and statistics. No prior knowledge of finance is required, although a reader with experience in trading and in working with financial data may understand some of the discussion more readily. The reader with prior knowledge of finance may also come to appreciate some aspects of financial data from fresh perspectives that the book may provide. Financial data have many interesting properties, and a statistician may enjoy studying and analyzing the data just because of the challenges it presents.

There are many texts covering essentially the same material. This book differs from most academic texts because its perspective is that of a real-world trader who happens to be fascinated by data for its own sake. The emphasis in this book is on financial data. While some stale datasets at the books website are provided for examples of analysis, the book shows how and where to get current financial data, and how to model and analyze it.

Understanding financial data may increase ones success in the markets, but the book does not offer investment advice.

The organization and development of the book are data driven. The book begins with a general description of the data-generating processes that yield financial data. In the first chapter, many sets of data are explored statistically with little discussion of the statistical methods themselves, how these exploratory analyses were performed, or how the data were obtained. The emphasis is on the data-generating processes of finance: types of assets and markets, and how they function.

The first chapter may seem overly long, but I feel that it is important to have a general knowledge of the financial data-generating processes. A financial data analyst must not only know relevant statistical methods of analysis, the analyst must also know, for example, the difference between a seasoned corporate and a T-Bill, and must understand why returns in a short index ETF are positively correlated with the VIX.

While reading ?.

These questions are addressed in an appendix to , discusses computer methods used to obtain real financial data such as adjusted closing stock prices or T-Bill rates from the web, and to mung, plot, and analyze it.

The software used is R. Unless data can be obtained and put in a usable form, there can be no analysis. In the exercises for the appendix, the reader is invited to perform similar exploratory analyses of other financial data.

In the later chapters and associated exercises, the emphasis is on the methods of analysis of financial data. Specific datasets are used for illustration, but the reader is invited to perform similar analyses of other financial data.

I have expended considerable effort in attempting to make the Index complete and useful. The entries are a mix of terms relating to financial markets and terms relating to data science, statistics, and computer usage.

This book is about financial data and methods for analyzing it. Statisticians are fascinated by interesting data. Financial data is endlessly fascinating; it does not follow simple models. It is not predictable. There are no physical laws that govern it. It is big data. Perhaps best of all, an endless supply of it is available freely for the taking.

Access to financial data, and the financial markets themselves, was very different when I first began participating in the market over fifty years ago. There was considerably more trading friction then; commissions were very significant. The options markets for the retail investor/trader were almost nonexistent; there were no listed options (those came in 1973). Hedging opportunities were considerably more limited. There were no ETFs. (An early form was released in 1989, but died a year later; finally, the first Spider came out in 1993.) Most mutual funds were actively managed, and charged high fees.