FINANCE 656 - Global Asset Allocation and Speculative Strategies
This is an advanced investments course in applied global asset management. It presumes knowledge from a basic course in finance, as well as a prior course in investments. It presents applied theory, empirical data and applications. The course begins with an overview of global economies and global financial market risks and returns. Practical issues in active asset allocation are examined, including the Black-Litterman approach to using mean-variance analysis for global portfolio optimization (used by Goldman Sachs Asset Management). Both long-term, strategic asset allocation and short-term, tactical asset allocation issues and strategies are presented, simulated and critiqued.
Time-varying risk and risk premiums are of the essence of the materials in this course. The term structure of interest rates is analyzed in theory and empirical data for its information about changing economic growth and risk, using insights and econometrics from Breeden and Harvey. Fundamentals-based financial models are presented and considered in modeling changing risks and returns. Consumption risk and the consumption CAPM are examined and used to link assets’ profits and risks to the macro-economy.
The course also has a segment on dynamic, speculative strategies, using the diverse views of efficient markets theorists, Warren Buffett and Doug Breeden. The strategies and fall of Long Term Capital Management in 1998 and some issues of the 2007 mortgage market meltdown and credit crunch are presented and critiqued.
The Fuqua School of Business Honor Code is maintained in this course.
Prerequisites Students must have had basic finance, Finance 350, as well as Investments, Finance 352. Bond market, options and futures insights and examples will be used, so at least a basic background in fixed income and derivatives is important. Prior or simultaneous courses in Fixed Income and Derivatives would be quite helpful. Students must have had at least one course in statistical analysis. A facility with matrix algebra and probability and statistics through linear regression is essential. Some differential calculus and optimization techniques will be used.