Click Here To Purchase Forecasting in Financial and Sports Gambling Markets: Adaptive Drift Modeling

Author: William S. Mallios
Publisher: Wiley
ISBN: 978-0-470-48452-4

Forecasting in Financial and Sports Gambling Markets focuses on econometric models that adapt to evolving markets and drift as compared to statistical models which are based on recent information and have a relatively short life.

William Mallios explains the econometric tools available for modeling financial and sports betting scenarios. In the course of the book, several examples of applications for sports gambling markets and financial market are provided with special cases such as the effect of fixing markets.

Discussion on market efficiency and its determinants are provided with an introduction to adaptive drift modeling in the context of quantifying the opportunities in gambling and financial markets.
The opacity and dynamics of markets is explained by describing the dilemma between social and economic efficiency and the efficient market hypothesis in reality.There is also an explanation about the importance of several recent developments in the financial markets including the financial crisis and how some market players like hedge funds are adapting to these changes.

Basic adaptive model concepts such as the adaptive ARMA process and time varying volatility are introduced after a background of modeling practises in quant funds who develop parsimonious models and compete for staff and similar strategies.

Studies in Japanese candlestick charts develop insights into market psychology and is a very convenient method to chart simultaneous time series and price volatility. These charts are used to detect timing points. The impact of special cases such as  Black Monday and  insider trading on these charts are also discussed.

The extension of the chapter on candlesticks is the treatment of candlesticks for major league baseball and explanation into how to apply the concepts to MLB team/player performance.
Modeling details on single equation adaptive drift modeling, variable selection and identifying the reduced model, reduced model estimation, and adaptive GARCH processes are covered. Single equation modeling of sports gambling markets is covered with application of adaptive drift modeling techniques and modeling profile of several sports team and individual players.

Co-integration based models for simultaneous financial time series and system of simultaneous time series with feedback between series highlight the application of adaptive drift and volatility modeling. There are good case studies of info tech stocks in 2000 and modeling of co-integrated time series associated with NBA and NFL.

There is a treatment of additional topics including categorical forecasting, risk assessment, bayesian discrete analysis and logistic regression analysis, and allocating monies in sports gambling markets.

Forecasting in Financial and Sports Markets provides a good balance between several cases in financial markets and sports betting markets where statistical methods such as co-integration are of use and the actual mechanics of constructing and using models that deploy these statistical methods. Although not an introductory book, it should appeal to the intermediate readers who are looking not just for a cookbook but a book which will help them gain intuition in using adaptive drift methods to real world applications.