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A guide to modeling analyses for financial and sports gambling
markets, with a focus on major current events
Addressing the highly competitive and risky environments of
current-day financial and sports gambling markets, Forecasting in
Financial and Sports Gambling Markets details the dynamic process
of constructing effective forecasting rules based on both graphical
patterns and adaptive drift modeling (ADM) of cointegrated time
series. The book uniquely identifies periods of inefficiency that
these markets oscillate through and develops profitable forecasting
models that capitalize on irrational behavior exhibited during
these periods.
Providing valuable insights based on the author's firsthand
experience, this book utilizes simple, yet unique, candlestick
charts to identify optimal time periods in financial markets and
optimal games in sports gambling markets for which forecasting
models are likely to provide profitable trading and wagering
outcomes. Featuring detailed examples that utilize actual data, the
book addresses various topics that promote financial and
mathematical literacy, including:
Higher order ARMA processes in financial markets
The effects of gambling shocks in sports gambling markets
Cointegrated time series with model drift
Modeling volatility
Throughout the book, interesting real-world applications are
presented, and numerous graphical procedures illustrate favorable
trading and betting opportunities, which are accompanied by
mathematical developments in adaptive model forecasting and risk
assessment. A related web site features updated reviews in sports
and financial forecasting and various links on the topic.
Forecasting in Financial and Sports Gambling Markets is an
excellent book for courses on financial economics and time series
analysis at the upper-undergraduate and graduate levels. The book
is also a valuable reference for researchers and practitioners
working in the areas of retail markets, quant funds, hedge funds,
and time series. Also, anyone with a general interest in learning
about how to profit from the financial and sports gambling markets
will find this book to be a valuable resource.
Autorentext
William S. Mallios, PhD, is a consultant at Mallios and Associates, where he provides professional advisement to various financial, medical, and educational institutions. A Fulbright Senior Specialist, Dr. Mallios served as professor of decision sciences at California State University, Fresno, for more than twenty-five years and has provided consulting services for government organizations, including the Food and Drug Administration and Centers for Disease Control.
Zusammenfassung
A guide to modeling analyses for financial and sports gambling markets, with a focus on major current events
Addressing the highly competitive and risky environments of current-day financial and sports gambling markets, Forecasting in Financial and Sports Gambling Markets details the dynamic process of constructing effective forecasting rules based on both graphical patterns and adaptive drift modeling (ADM) of cointegrated time series. The book uniquely identifies periods of inefficiency that these markets oscillate through and develops profitable forecasting models that capitalize on irrational behavior exhibited during these periods.
Providing valuable insights based on the author's firsthand experience, this book utilizes simple, yet unique, candlestick charts to identify optimal time periods in financial markets and optimal games in sports gambling markets for which forecasting models are likely to provide profitable trading and wagering outcomes. Featuring detailed examples that utilize actual data, the book addresses various topics that promote financial and mathematical literacy, including:
Higher order ARMA processes in financial markets
The effects of gambling shocks in sports gambling markets
Cointegrated time series with model drift
Modeling volatility
Throughout the book, interesting real-world applications are presented, and numerous graphical procedures illustrate favorable trading and betting opportunities, which are accompanied by mathematical developments in adaptive model forecasting and risk assessment. A related web site features updated reviews in sports and financial forecasting and various links on the topic.
Forecasting in Financial and Sports Gambling Markets is an excellent book for courses on financial economics and time series analysis at the upper-undergraduate and graduate levels. The book is also a valuable reference for researchers and practitioners working in the areas of retail markets, quant funds, hedge funds, and time series. Also, anyone with a general interest in learning about how to profit from the financial and sports gambling markets will find this book to be a valuable resource.
Inhalt
Preface.
1.Introduction.
1.1 Favorable Betting Scenarios.
1.2 Gambling Shocks.
1.3 The Dark Side of Sports: The Fixes.
2.1 Changing Paradigms.
2.2 Modeling Commentaries.
2.3 Sports Hedge Funds.
2.4 Gambling Markets: Prohibition, Repeal and Taxation.
2.5 Quantifying the Madness of Crowds in Sports Gambling Markets.
2.6 Statistical Shocks: Alias Variables.
3.1 Dilemmas between Social and Economic Efficiency.
3.2 Towards a More Visible Hidden Hand.
3.3 Hedge Funds and Galapagos.
3.4 Lotteries: Market for Losers.
4.1 Quant Funds and Algorithmic Trading.
4.2 Market Volatility and Fat-Trailed Distributions.
4.3 Adaptive ARMA(1,1) Drift Processes.
4.4 Time Varying Volatility.
5.1 Bullish and Bearish Patterns from Chartist Perspectives.
5.2 Black Monday.
5.3 A Matter of Alleged Insider Trading.
5.4 Commodity Bubbles and Volatility.
5.5 Short Selling.
5.6 Terrorist Attacks and the Markets.
5.7 A Hollywood Romance: Spiderman and Tinkerbell.
5.8 Copenhagen and Climate Change: Exxon Mobile Buys XTO Energy.
6.1 The 2008 World Series: Philadelphia Versus Tampa Bay.
6.2 The 2008 Chicago Cubs: Visions of 1908 Heroics.
6.3 A Strange Set of Coincidences: A Plate Umpire's Affinity for a Pitcher.
7.1 Adaptive ARMA Processes.
7.2 Variable Selection: Identifying the Reduced Model.
7.3 Reduced Model Estimation: Single Equations.
7.4 Reduced Model Empirical Bayesian Estimation: Single Equations.
7.5 Single Equation Volatility Modeling: Adaptive GARCH Processes.
7.6 Modeling Monetary Growth Data.
7.7 Modeling GNP Deflator Growth.
8.1 Effects of Interactive Gambling Shocks.
8.2 End of an Era: Modeling Profile of the 1988-89 Los Angeles Lakers.
8.3 Spread Betting.
8.4 Modeling Profile of a Dream Team: The 1989-90 San Francisco 49ers.
8.5 Major League Baseball: A Data Intensive Game.
8.6 While Still Under the Curse: Modeling Profile of the 1990 Red Sox.
8.7 Portrait of Controversy: Modeling Profile of Roger Clemens with the 1990 Red Sox.
8.8 Pitcher of the Year in 1990: Modeling Profile of the Oakland's Bob Gibson.
9.1 The Cruse of Higher Dimensionality.
9.2 From Candlesticks to Cointegration.
9.3 Cointegration in Terms of Autoregressive Processes.
9.4 Estimating Disequilibria through Factor Analysis.
9.5 Simultaneous Time Series: Adaptive Drift Modeling.
9.6 Simultaneous Time Series: Adaptive Volatility Modeling.
9.7 Exploratory Modeling: Marathon Oil Company.
9.8 The High Tech Bubble of 2000.…