• Lo and MacKinlay: Chapters 8, 9
  Princeton University Press Online Edition
  
• Sean D. Campbell, A Review of Backtesting and Backtesting Procedures
• Paul Cohen, (Alternate link)

• José Vidal, Fundamentals of Multiagent Systems, Chapter 1.
• Palmer et al., An Artificial Stock Market, Artif. Life Robotics (1999) 3:27-31
• Kendall and Su, The Co-Evolution of Trading Strategies in a Multi-Agent Based Simulated Stock Market...
• LeBaron, Agent-based computational finance: Suggested readings and early research, J. Economic Dynamics and Control (2000) 24:679-702
• van den Bergh et al., On Intelligent-Agent Based Analysis of Financial Markets

An afterthought from our risk class on 4 April:

• Read Michael Lewis' brief article on the Black-Scholes pricing model. Beware: the opinions expressed in this article are not necessarily those of this course or the university! Click here for a PDF version.

• Bolland and Connor, A Constrained Neural Network Kalman Filter for Price Estimation in High Frequency Financial Data
• Zhuang and Chan, Volatility Forecasts in Financial Time Series with HMM-GARCH Models
• O, Lee, Park and Zhang, Stock Trading by Modelling Price Trend with Dynamic Bayesian Networks

• (Before class) Finish Kevin Murphy's tutorial on graphical models.
• (Before class) Welch and Bishop, An Introduction to the Kalman Filter
• (Before HW5) L. R. Rabiner, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proceedings of the IEEE, vol 77, no 2, 257--287, 1989.
• (Before HW5) T. Ryden, T. Terasvirta, and S. Asbrink, Stylized Facts of Daily Return Series and the Hidden Markov Model, J. Applied Econometrics, 13, 217--244, 1998.

• Tal Levy's Handout on American vs. European Options
• Olivier Guéant's Handout on the relationship between interest rates and implied volatility

Reading for 7 March:

• The Economist, Better than beta?
• Charniak, Bayesian Networks Without Tears
• Heckerman, A tutorial on learning with Bayesian networks
• Shenoy and Shenoy, Bayesian Network Models of Portfolio Risk and Return
• Optional: Begin Kevin Murphy's tutorial on graphical models (up to but not including "Temporal Models")

There are two papers on artificial neural networks applied to finance, and one on ARCH/GARCH models for nonlinear time series, which will be revelant in our future reading.

• http://www.eecs.harvard.edu/~parkes/cs286r/spring08/reading3/Kaastra.pdf
• http://www.eecs.harvard.edu/~parkes/cs286r/spring08/reading3/ARCH.pdf
• http://www.eecs.harvard.edu/~parkes/cs286r/spring08/reading3/zhang01.pdf

• Linear Classifiers:
  We will develop the linear classifiers in class.
• Support Vector Machines
  • http://www.eecs.harvard.edu/~parkes/cs286r/spring08/reading3/svm.pdf
• Neural networks:
  • http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html
  • http://www.ibm.com/developerworks/library/l-neural (Google Cache)

Your peers' submitted homeworks on asset classes from last week are not required reading.

1. Voit, Chapters 1, 2
   PDF Excerpt
   You should study Chapter 2 as thoroughly as possible; while it is brief, it is crucial. Chapter 3 is optional this week.
   
2. Taleb, Chapter 10
   PDF, pp. 135-161 PDF, pp. 162-164
3. Lo and MacKinlay: Introduction, Chapters 1, 2
   PDF Excerpt
   
4. [Now Optional for this week] Ross, Chapters 4-6
   You will need to be familiar with the material in chapters 1-3.