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Market Analysis & Position Sizing (Both Equally Necessary)

Market Analysis & Position Sizing (Both Equally Necessary). =. We have no method, no framework, no paradigm, for the equally important, dark nether-world of position sizing. We have a plethora of market analysis, selection and timing techniques…..but. Part 1. Optimal f.

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Market Analysis & Position Sizing (Both Equally Necessary)

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  1. Market Analysis & Position Sizing(Both Equally Necessary) = We have no method, no framework, no paradigm, for the equally important, dark nether-world of position sizing. We have a plethora of market analysis, selection and timing techniques…..but

  2. Part 1 Optimal f

  3. Everyone, on Every Trade, on Every “Opportunity” Involving Risk, has an f value (whether they acknowledge it or not): f = | Biggest Losing Outcome for 1 Unit | / f$ f$ = Account Equity / Units Where: (also f$ = | Biggest Losing Outcome for 1 Unit | / f Example: -$10,000 Biggest Losing Outcome, $50,000 Account, and I have on 200 shares, (2 units ): f$ = 50,000 / 2 = 25,000 f = | -10,000 | / 25,000 = .4

  4. f$ and GHPR Invariant to Biggest Loss BiggestLoss ff$ GHPR –0.6 .15 4 1.125 –1 .25 4 1.125 –2 .5 4 1.125 –5 1.25 4 1.125 –29 7.25 4 1.125

  5. Trajectory Cone (Bell-Shaped on all 3 Axes)

  6. The distribution can be made into bins. A scenario is a bin. It has a probability and An outcome (P/L)

  7. 2:1 Coin Toss

  8. Mathematical Expectation 2:1 coin toss: ME = .5 * -1 + .5 * 2 = -.5+1 = .5

  9. f value example – 2:1 Coin Toss • $10 stake • Worst Case Outcome -1 • I’m wagering $5 (5 units) • f$ = 10 / 5 = 2 (one bet for every $2 in my stake) • f =|-1| / 2 = .5 • When biggest loss is manifest, we lose f% of our stake – 50% in this case

  10. The Mistaken Impression Multiple made on stake = 1 + ME/|BL| * f (a.k.a Holding Period Return, “HPR”)

  11. Optimal f is an Asymptote

  12. The Real Line ( f )

  13. f after 40 plays

  14. f after 40 plays 40 Plays 1 Play

  15. f after 40 plays At .15 and .40, makes the same, but drawdown changes At f=.1 and .4, makes the same, But drawdown changes!

  16. f after 40 plays Beyond .5, even in this very favorable game, TWR (multiple) < 1, meaning you are losing money and will eventually go broke if you continue

  17. f after 40 plays Points of Inflection: Concave up to concave down. Up has gain growing faster than drawdown.(but these too migrate to the optimal point as the number of holding periods grows!)

  18. f after 40 plays

  19. Most Favorable Blackjack Condition Optimal f = .06 or risk $1 for every $16.67 in stake

  20. Part 2 The Leverage Space Portfolio Model

  21. Modern Portfolio Theory

  22. Why The Leverage Space Model is Superior to Traditional (Modern Portfolio Theory) Models: • Risk is defined as drawdown, not variance in returns. • The fallacy and danger of correlation is eliminated. • Valid for any distributional form – fat tails are addressed. • The Leverage Space model is about leverage, which is not addressed in the traditional models.

  23. Leverage has 2 Axes – 2 Facets The instant case of how much I am levered up f How I progress my quantity with respect to time / equity changes

  24. The fallacy and danger of correlation • Fails when you are counting on it the most – at the (fat) tails of the distribution. • Traditional models depend on correlation – Leverage Space model does not. • cl/gc (all days) r=.18 (cl>3sd) r=.61 (cl<1sd) r=.09 • f/pfe (all days) r=.15 (sp>3sd) r=.75 (sp<1sd) r=.025 • c/msft (all days)r=.02 (gc>3sd) r=.24 (gc<1sd) r=.01

  25. f after 40 plays

  26. Why The Leverage Space Model is Superior to Traditional (Modern Portfolio Theory) Models: • Risk is defined as drawdown, not variance in returns. • The fallacy and danger of correlation is eliminated. • Valid for any distributional form – fat tails are addressed. • The Leverage Space model is about leverage, which is not addressed in the traditional models. (on both axes of “Leverage”)

  27. Part 3 The Leverage Space Model Software Implementation

  28. Link for how to gather your data and create scenarios & probabilities: http://parametricplanet.com/rvince/ScenariosExample.xls

  29. http://parametricplanet.com/rvince/register.html

  30. Here is the data I am using (this is from the link example from the previous slide) :

  31. Date,Equity Jan-07,617.00 Feb-07,664.00 Mar-07,673.00 Apr-07,751.00 May-07,887.00 Jun-07,849.00 Jul-07,781.00 Aug-07,851.00 Sep-07,942.00 Oct-07,834.00 Nov-07,804.00 Dec-07,789.00 Jan-08,791.00 Feb-08,813.00

  32. Get java at: http://java.com/en/download/index.jsp

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