Applied Quantitative Methods for Trading and Investment Applied Quantitative Methods for Trading and Investment is intended as a quantitative finance
Applied Quantitative Methods for Trading and Investment
Applied Quantitative Methods for Trading and Investment is intended as a quantitative
finance textbook very much geared towards applied quantitative financial analysis, with
detailed empirical examples, software applications, screen dumps, etc. Examples on the
accompanying CD-Rom detail the data, software and techniques used, so that contrary to
what frequently happens with most textbook examples, they clarify the analysis by being
reasonably easily reproducible by the reader.
We expect this book to have a wide spectrum of uses and be adopted by financial
market practitioners and in universities. For the former readership, it will be of interest
to quantitative researchers involved in investment and/or risk management, to fund man-
agers and quantitative proprietary traders, and also to sophisticated private investors who
will learn how to use techniques generally employed by market professionals in large
institutions to manage their own money. For the latter, it will be relevant for students
on MSc, MBA and PhD programmes in Finance where a quantitative techniques unit is
part of the course, and to students in scientific disciplines wishing to work in the field of
quantitative finance.
Despite the large number of publications in the field of computational finance in recent
years, most of these have been geared towards derivatives pricing and/or risk manage-
ment.
1 In the field of financial econometrics, most books have been subject specific,
2 with very few truly comprehensive publications.
3 Even then, these books on financial econometrics have been in reality mostly theoretical, with empirical applications essentially
focused on validating or invalidating economic and financial theories through econometric
and statistical methods.
What distinguishes this book from others is that it focuses on a wide spectrum of meth-
ods for modelling financial markets in the context of practical financial applications.On
top of “traditional” financial econometrics, the methods used also include technical analy-
sis systems and many nonparametric tools from the fields of data mining and artificial
intelligence. Although we do not pretend to have covered all possible methodologies,
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