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We analyze the performance of mutual funds from a multiple inference perspective. When the number of funds is large, random fluctuations will cause some funds falsely to appear to outperform the rest. To account for such “false discoveries,”
a multiple inference approach is necessary.
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by Michael W. Brandt
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Lecture Notes by Prof Jiang Wang

Chapter 1: Introduction to Finance
Chapter 2: Present Value
Introduction to Part B: Valuation
Chapter 3: Fixed-Income Securities
Chapter 4: Common Stocks
Chapter 5: Forwards and Futures
Chapter 6: Options
Introduction to Part C: Time Value and Price of Risk
Chapter 7: Historical Asset Returns
Chapter 8: Time Value of Money
Chapter 9: Risk
Chapter 10: Portfolio Theory
Chapter 11: Capital Asset Pricing Model (CAPM)
Chapter 12: Arbitrage Pricing Theory (APT)
Introduction to Part D: Coporate Finance
Chapter 13: Efficient Market Hypothesis
Chapter 14: Capital Budgeting
Chapter 15: Real Options
Chapter 16: Financing Decisions



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150 page book by William T. Ziemba

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by Jun Liu and Jun Pan
This paper studies the optimal investment strategy of an investor who can access not only the bond and the stock markets, but also the derivatives market. We consider the investment situation where, in addition to the usual diusive price shocks, the stock market experiences sudden price jumps and stochastic volatility. The dynamic portfolio problem involving derivatives is solved in closed-form. Our results show that derivatives are important in providing access to the risk and return tradeos associ- ated with the volatility and jump risks. Moreover, as a vehicle to the volatility risk, derivatives are used by non-myopic investors to exploit the time-varying opportunity set; and as a vehicle to the jump risk, derivatives are used by investors to dis-entangle their simultaneous exposure to the diusive and jump risks in the stock market. In addition, derivatives investing also aects investors' stock position because of the in- teraction between the two markets. Finally, calibrating our model to the S&P 500 index and options markets, we nd sizable portfolio improvement for taking advantage of derivatives


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Is Credit Dead?
Value-At-Risk
The Origin of Poker, Page 1 and Page 2
Masters of the Poker Bluff
The Education of a Poker Player
21st Century Disclosure
The Unbearable LIghtness of Cross-Market Risk
Monopoly 101, Part I
Monopoly 101, Part II
What is the Interest Rate in Hell?
The Dakota Option Part I
The Dakota Option Part II
The Dakota Option Part III
The Theory of Risk Management
Modern Portfolio Theory at Fifty
Hedge Fund Risk Management
Risk: The Ugly History
Time Enough for Counting
Ere Your Credit Advance
Wald's Series
Whose LIfe is it Anyway?
A Wall Street Rocket Scientist in King Arthur's Court
Table Stakes
Six Degrees of Idiocy
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Carl Chiarella
Chih-Ying Hsiao
Abstract
This paper studies the impact of stochastic volatility (SV) on optimal investment decisions. We consider three different SV models: the generalized Stein/Stein model, the Heston Model and an extended Heston Model with a CEV (Constant Elasticity of Variance) volatility process and derive the the long-term optimal investment strategies under each of these processes. Since the volatility is not a directly observable variable, we need to deal with a partial information problem. In constructing the optimal investment strategies based on the estimation results for the ASX 200 Index, we provide the investment recommendations calculated on a range of parameter values within the standard error bands obtained from the estimation procedure. In order to obtain the investment strategies based on the CEV volatility model, we provide a computational solution since analytical solutions are no longer available. All three investment strategies based on the three SV models suggest a positive intertemporal hedging term in addition to the static mean-variance portfolio. However, in their details the three investment strategies differ from each other. We also find that the investment strategies are quite sensitive to the CEV parameter in the extended Heston model.
Key words: Asset allocation under stochastic volatility, partial information problem, extended Kalman filter, the Heston model, CEV process.

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Risk Management For Intelligent Trading Portfolios (3 Part Series)
Part I: Risk Measurement in Derivatives Portfolios
[Adobe Acrobat *.pdf ] [HTML]
Part II: Risk Management for Computerized Trading Models
[Adobe Acrobat *.pdf ] [HTML]
Part III: Artificial Intelligence in Trading and Risk Management
[Adobe Acrobat *.pdf ] [HTML]
First Published at Applied Derivatives Trading, www.adtrading.com.

Adaptive Portfolio Trading Strategies For Forex Portfolios.
[Adobe Acrobat *.pdf ] [HTML]
First Published with BNP Research Papers (Working Papers), Banque Nationale De Paris, Global Markets Research, bfi.bnp.fr

Artificial Intelligence based Risk Management Strategies for Trading Portfolios
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Presentation slides [HTML] (note: download files approx. 250K each)
First Published with Unicom Seminars, www.unicom.co.uk


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want to look ahead, see Last Year's Lecture Notes, below.


Lecture Note 1 -- Allocating Investment Capital (Revised 1/02/01).
Lecture Note 2 - The CAPM (revised 1/09/01)
Lecture Note 3 -- Using the CAPM (Revised 1/16/01)
Lecture Note 4 -- Testing the CAPM (Revised 1/26/01)
Lecture Note 5 -- Multi-Factor Models and the APT (Revised 2/14/01)
Lecture Note 6 -- Active and Passive Portfolio Management (Revised 2/27/01)
Lecture Note 7 -- Measuring Managed Fund Performance (Revised 3/5/01)


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