lecture25 - Lecture 25 Money Management Steven Skiena...

Info iconThis preview shows pages 1–10. Sign up to view the full content.

View Full Document Right Arrow Icon
Lecture 25: Money Management Steven Skiena Department of Computer Science State University of New York Stony Brook, NY 11794–4400 http://www.cs.sunysb.edu/ skiena
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Money Management Techniques The trading strategies we have studied point towards possible investment opportunities, but usually do not tell us how much we should invest in each. Money management issues are implicit in discussions of (1) risk vs. return, (2) portfolio optimization, and (3) market saturation. Properly allocating capital to investment opportunities can be as or more important than finding them in the first place.
Background image of page 2
Leveraged Trading Strategies That investment strategies must modulate risk and return in money management is apparent when studying the impact of leverage. Consider a strategy which borrows money at the LIBOR rate for one year, and invests it in a stock market index (here Nasdaq).
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Nasdaq Leveraged Trading 1990-2000
Background image of page 4
Nasdaq Leveraged Trading 2000-2007 The probability of going bust is as meaningful notion of risk as volatility. . .
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Managing Money When You Have an Edge You play a sequence of games, where: If you win, you get W dollars for each dollar bet If you lose, you lose your bet For each game, the probability of winning is p and losing is q = 1 - p You bet some fixed percentage f of your bankroll B each game, for you have (1 - f ) B if you lose and ( W - 1) fB + B if you win. The right value of f is called the Kelly Criterion.
Background image of page 6
Rigged in our Favor? Suppose we bet $1 on a fair coin, but one which pays $2.10 if it comes up heads? How much of our bankroll should be bet each time? Bet too much and we lose, even with the odds in our favor!
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
After 5000 Coin Tosses Ten straight tails leaves only 1/3 the bankroll at 10%, but almost 2/3 at Kelly (4.5%)
Background image of page 8
The Kelly Criterion: History Developed by John Kelly, a physicist at Bell Labs in a 1956 paper “A New Interpretation of Information Rate” published in the Bell System Technical Journal. He used Information Theory to show how a gambler with
Background image of page 9

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 10
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 01/02/2012 for the course FINANCE 347 taught by Professor Bayou during the Fall '11 term at NYU.

Page1 / 27

lecture25 - Lecture 25 Money Management Steven Skiena...

This preview shows document pages 1 - 10. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online