1. The Evolution of a Vector Vest User
Timing Systems, Strategies, and other topics
Ric Denton
May 31, 2005

2. Agenda Sources Introductory Comments & Premises
Vector Vest Timing System
Strategies
Dividend Strategy
Bottoms Up Strategy
Risk Management
Monte Carlo Tools
Summary

3. Sources Kaufman, Trading Systems and Methods, 3rd edition
Ehlers, Rocket Science for Traders, 2001
Vector Vest Ride the Wave Trading System
Luenberger, Investment Science, 1998
Taleb, Fooled by Randomness, 2001
Schwager, Market Wizards, several volumes
O’Neil, How to make money in stocks, 3rd edition
Pring, Introduction to Technical Analysis, 1998
Sornette, Why Stock Markets Crash, 2003
Pruitt/Hill, Building Winning Trading Systems, 2003
Stridsman, Trading Systems and Money Management, 2003
Conway, Professional Stock Trading, 2003
And many more

4. Personal Evolution Snapshot
Belief in fundamental analysis
Actual Experience: Companies mislead, analysts mislead
Actual Experience: Huge portfolio drawdowns
Belief that we are likely in a secular bear market
Adopt methods that will work in both bull and bear markets
Adoption of hybrid technical & fundamental approach
Development of Vector Vest strategies
Months/years of backtesting
Questions about Vector Vest market calls
Perception that market timing is far more critical than details of strategy (with exceptions)
Development of my own timing systems
Control over assumptions, behavior of system
Development of non-Vector Vest strategies as well

5. Vector Vest – Basic Features
Daily download – not for day trading
Excellent screening tools
Limited to system variables and black box variables
Includes industry groups and sectors
Tends to be earnings based, not balance sheet based
Supports portfolio management
Backtesting supported back to 1996
Watchlists supported
Option trading supported
Like most such software tools, look elsewhere for statistics
Competes with TC2000, has similarities to the AAII tools
Good system for building algorithms, not quite as strong in the charting area

6. Timing - Premises Timing the stock market is possible
Reduce risk (drawdowns or other measures)
Increase returns
Academics still don’t believe it
Most of the time most of all stocks are correlated with the market – Go long with uptrends, go short with downtrends
It is the portfolio behavior that counts, not individual stocks (within limits)
The time frames that are chosen are extremely important from standpoint of transaction costs, slippage, liquidity issues, etc.

7. Timing Scales Day trading – not supported by Vector Vest
VVC + CCI: 3 to 15 days
Swing trading: Days to multiple weeks
Roughly the Ride the Wave time scale
Longer term: Months to a year
Buy and hold: Years (if you believe it)
It is extremely important to decide which time scale you are comfortable with
Amount of effort involved
Achievable returns versus risk

8. Vector Vest Timing System (MTI)
My personal preference: I don’t like black boxes
MTI is a good indicator for the time scale it is operating on (~ 65 day MAs, with ~14 day ROCs)
It has been modified somewhat over the years
Decision overlay for making Ride the Wave calls using MTI has drifted over the years
Be very careful in following this system unless you have extensively backtested first.
Example follows to demonstrate what can happen

9. Screen Grab from Vector Vest

10. Critical Dates, last slide
August 11, 2003 – “kisses” the 40 day MA
September 30, 2003 – same kiss
October 29, 2003 – same kiss
November 26, 2003 – same kiss
December 16, 2003 – same kiss
February 9, 2004 – same kiss
Example strategy pulled from : “ Prudent
Investors should take and protect profits. Aggressive Investors and traders should play the market to the downside. For those of you who are Riding the Wave, we are short with a combination of QQQ, IWM, and stocks from the Hold your Nose
Strategy.”
Meanwhile, look for these dates in Market Calls. You won’t find them. They have been filtered with the benefit of hindsight.

11. Impact of misleading calls
Huge opportunity cost – much of the 2003 upswing would have been missed
Took one out of the market just as the cycle reversed to the upside.
To be fair, many timing systems can have this or other problems. Possible Conclusions:
Know and understand your system.
My previous system suffered the same problem!
Use a diversity of styles and timing scales
Consider other timing systems as well

12. Second example Compare the VVC versus the MTI in the next screen grab
Climbing out of deep troughs with the right strategy can be highly rewarding
Now compare the Vector Vest Strategies for the deep troughs
How do you know when a rally is fading?
Again, it comes down to the decision overlay to the MTI, not the market timing indicator itself!
Prior to 2003, the mother of all rallies started Oct 11, How clear was it at that time? Did you catch it?
Since there will always be ambiguity, choose more than one style Play it safe.

13. Screen grab over 4 years

14. MTI – don’t miss the forest for the trees
Above 1.55:
Don’t buy more longs
Get ready to sell your long portfolio on downticks
Between 1.00 to 1.55 and rising:
Many conservative long strategies work
Between 1.00 to 1.55 and falling:
Even here, you can hold on to conservative long portfolios
Sell aggressive long portfolios
Do not go short unless you are very experienced

15. The MTI forest (cont.) Crossing 1.00 and falling:
Go short – the sooner the better
Between 0.6 to 1.00 and falling:
Shorting opportunities
Sell any longs
Between 0.4 to 1.00 and rising
Cover shorts
Employ aggressive long strategies if you are experienced
Below 0.6:
Don’t sell new shorts
Get ready to cover existing shorts on first upticks

16. MTI Strategies in Graphical Format
“Risky” short region
Safer short range (“Stinky” shorts)
Contrarian strategy range
Portfolios in general
Focus on Long Portfolios
C > i.e. Contrarian above 1.0
M > i.e. Momentum above 1.0
C < i.e. Contrarian below 1.0

17. Summary thoughts on timing systems
The central problem is to get a fast response in identifying new trends and trend reversals
Simultaneously need low false alarms
Must also be on the time scale of interest
If you intend to trade, familiarize yourself with several timing systems
Choose the one that works best for you
What is the time scale?
Recognize that most systems are black boxes – do you trust their claims?
My personal choice? Develop my own system
My own mistakes, my own assumptions

18. Strategies – The importance of styles
Different styles = Diversity = Safety
Address first a safe dividend strategy with zero to little trading
Timing not involved at all
Rebalancing annually is optimal
Tax efficient
Next, a “bottoms up” strategy
Aggressive
Extensive use of timing
Needs careful risk management
High payoff
Tax inefficient

19. Sources for developing strategies
First, what are the objectives of the strategy?
Then go to Sources
Vector Vest strategies – learn them, learn how to backtest to achieve reliability
Vector Vest strategy modifications
AAII Journal – a host of winning strategies
Available online at modest annual fee
Need to convert them to Vector Vest
O’Shoughnessy – figure out how to reverse engineer from a “master’s” strategy
Morningstar a great source

20. Turning to Dividend Strategy Development
Why not just look for highest dividend yields?
Select the column DY in Vector Vest stock viewer and simply organize by descending DY
Problem: There’s lots of garbage mixed in with some high dividend stocks – what about safety?
Going for high income while risking capital loss is a bad tradeoff – go for “equity income”
Need a strategy with a set of rules to filter for quality, and then sort by DY
Performance metric: Best combination of dividends and capital appreciation
ARR/Std.Dev (Sharpe Ratio) or ARR/MDD

21. Quality filter rules
Dividends don’t exist in a vacuum, a good stock should have earnings growth to support dividend growth
Hence Stock GRT > 0 or some other value
But long term we can’t pay out more dividends than are supported by earnings and earnings growth
Stock GRT > DG
Have they actually increased dividends over time?
DG > 0 or some other value

22. Quality filter rules What is the safety of the dividend payout?
DS > 20 or DS > 40 or DS > 60 or
And on and on go the rules, for example, close by having a liquidity constraint
AvgVol*Price > 400,000
You will almost certainly find many REITs at the top when sorting by DY:
How much performance do you want sacrifice in favor of diversification away from REITs?
Generally I don’t go above % from any one sector

23. Do the backtesting
I use the QuickTest function, which is really convenient
Enter each result into an excel spreadsheet
Drawdowns also using QuickTest, plugging in many dates to find the worst drawdown during a given year
Does this result in the performance that you are looking for?
How does it compare to the benchmark Morningstar sources?
Make added changes to the screening and backtest again, and again, and
Based on the final results, decide to use the screen for real investments or continue to refine the approach

25. Dividend portfolio Results

26. Cross-Reference Sites
Dividenddetective.com – Harry Domash
Value Line – either soft copy or in any public library
MSN Moneycentral.com - Stock Scouter gives lots of useful information
ValuePro.net – Looks at discounted cash flow and compares current price to intrinsic value

27. What can be learned No substitute for backtesting
Not every attempt at a new strategy results in immediate success
Even then, insight is usually gained from the runs that didn’t work out
Do not be overwhelmed by backtesting
Doing the backtesting for these runs took only minutes each time, including the testing for drawdowns using QuickTest
Sidebar comment: You too can beat the mutual funds with their scandalous fees

28. Turning to Aggressive Strategies
Those contests with 1000% returns?
Forget ‘em: Impractical, illiquid stocks
Need to insert liquidity constraints for results that you can hope for
What level of drawdown will you accept?
20% typical, 40% not uncommon
Drawdowns just as important as ARR in evaluating returns

29. Liquidity Avoid moving the market
Your $$ investment must be a small fraction of total daily volume times stock price
An AvgVol*Price > is the minimum constraint needed hope for low bid-ask spreads
This isn’t nearly good enough – check out the new Vector Vest simulation parameters. Better values to consider are
Your shares purchased < 1% of daily AvgVol
Your $$ investment < 0.1% of Market Cap

30. Liquidity (continued)
Share constraint needs to be inserted into all strategies. Example:
Call Total portfolio investment “Port”
Use 7 stock portfolio
1% of daily volume constraint per stock becomes: .07*Price*AvgVol > Port, with Port in dollars
Similarly Market Cap constraint at the maximum level of 0.1% becomes
7000*Shares*Price > Port, with Port in dollars
Using again a 7 stock portfolio
Strange factor due to converting Vector Vest shares, which are displayed in millions

31. Liquidity (continued)
Implications for backtesting
“Port” needs to be adjusted to account for amount of current capital
Many strategies are great when capital (Port) is initially small, but do less well as Port becomes large
Decline in returns for larger Port is a classic problem with small high return mutual funds
Backtesting results demonstrating decline in performance for larger investment amounts follows
Uses simple liquidity constraint of AvgVol*Price
Dollar investment < .05 AvgVol*Price

32. Example Runs for a long portfolio – Liquidity (AvgVol
Example Runs for a long portfolio – Liquidity (AvgVol*Price) = 500,000 with 15 stocks

33. But does it scale? Liquidity at 5 million with 15 stocks – a $4 million portfolio in longs

34. But does it scale? Liquidity at 20 million with 25 stocks, a $25 million portfolio in longs

35. Getting practical with Bottoms Up
My version on next slide
Note the constraints on volume and % ownership
Note the affect of higher “Port” values
Moves lower down the original sort list, lowers performance
Moving lower also increases the probability for higher drawdowns
There is an important additional volume clue as to which Bottoms Up stocks are safest (based on backtesting)
“Capitulation sell-off” of volume > 3 or 4 times AvgVol
Generally, best results when VST/RT > 10
Bottoms Up best when coming out of a deep trough and up to about when MTI crosses 1.17 plus two weeks
Using only 7 stocks is “optimal”
But only for this piece of my overall portfolio
Never devote one’s entire portfolio to only 7 stocks!

42. Bottoms Up Backtesting
Typical backtest starting on slide following
Backtest start in July 2000
My own timing system used
A “Sloppy” timing system with regard to trend changes requires careful the money management
Bottoms up is a dangerous strategy unless you have well thought out timing rules and lots of backtesting experience
Using MTI, be careful to develop your own buy sell rules
Jump on the right upticks but bail if the MTI starts heading south again
Analyze the Bottoms up stocks to get more informed
~ Half are dead cat bounces
Survivorship bias in multi-year testing
I don’t use stops except on the overall portfolio

43. Bottoms Up Backtesting Results

44. Backtesting Results (cont.)

45. Equity Curve, Bottoms Up Backtest

46. Plateau after July 2003: Bad Times or Constraint-Driven?
New Backtest
Original Backtest

47. Distribution of Percentage Gains

48. Percentage gain versus vol & ownership constraint (000’s)
Cycle: to
7 stocks
One day lag
“Port” is the contraint value

49. Bottoms Up Risk Management
Feeding in investment in stages reduces drawdowns with some cost in performance
Put in ~35% on the bottoms up buy signal
Wait for a ~5% gain in this portfolio before more investment
Dividing up next investments into 2 tranches is somewhat better than using only one tranche
Next tranches based on re-running strategy at day of investment
Portfolio stop is used
If first tranche loses 12% or more, sell off
If stops on individual stocks are used, they need to be very wide (50% or so)

50. Other Portfolio Regimes
Shorting
Many available in Vector Vest
Look at Bear Market Mutual Funds, giving very reliable performance with the right timing system
Momentum (MTI > 1.1, or Russell 2000 > 50 day SMA
Many good Vector Vest strategies
Rising tide lifts all boats – don’t have to get too clever
Also use “portfolio 123” for expanded non-black-box sorting functions (especially the Growth at a Reasonable Price – GARP - strategies)

51. Money Management & Optimal Bet Sizes
Look at each portfolio period as a single “bet”
Compounding over n periods starting with equity X0 :
Xn = R1R Rn X0 where Ri is the return per period
The Ri are random variables describing the returns as determined from backtesting
Extend formula to include bet size:
Xn = (α R α) (α R α) (α Rn α) X0
Log utility: p1 ln( α*(1 + r1) +1 - α) + p2 ln(α*(1 + r2) α) + . .
where pi and ri are probabilities and returns found from backtesting
If the random variables have only two outcomes, maximizing the log utility above yields the Kelly formula for optimal bet size α = W – (1 - W)/R, where
α is the bet size as a fraction of the current equity
W is the % of time a “win” occurs
R is the ratio of win amount / loss amount

52. Key Questions
How much leverage is it safe to use for each strategy taken separately
What is the return for each strategy?
Since M > and C > compete for attention, what is the optimal mix between them?
What is the return?
What is the variation?
Trade off return versus drawdowns by mixing portfolios

53. Contrarian strategy modeling

54. Maximizing Log Utility – Contrarian
At 100% margin, Contrarian return per period is exp (.14) = 15%

55. Probabilities and Returns – Other Portfolios
C > and C < are similar to “Contrarian” portfolio (C > when MTI > 1.13)
M > is a Sharpe ratio based momentum portfolio for when MTI > 1.13

56. Log Utility for the long portfolios
M >
C >
C <
At 100 % margin, M > return per period is exp (.235) = 26.5%
C > return per period is exp (.178) = 19.5%

57. Money Management & Optimal Bet Sizes
Mix two (or more) portfolios to determine performance as a function of bet sizes, starting with initial equity X0:
Xn = (α R1 + β S1 + 1 – α - β) (α Rn + β Sn + 1 – α - β) X0
where
α and β are the betting fractions for each portfolio
Ri and Si are random return variables corresponding to the two (independent) portfolios
Monte Carlo simulations based on backtested results using two win outcomes and two loss outcomes give a good characterization
Simulation results determine the average portfolio return
α and β are adjusted to reduce the low outlier results
We do this for the case of mixing M > and C >

58. Results for M > and C >

59. Example Monte Carlo result for 7.5 years

60. Example Monte Carlo result for 7.5 years

61. Summary Trading & Timing Systems – not everybody wants to be a trader
MTI works as a timing system if you are very clear about how to use it – don’t follow any advice blindly, develop your own rules
Consider other timing systems as well
Trading requires significant attention to detail
Many strategies
Usually, a mixture of fundamental and technical parameters are appropriate
Balance your aggressive strategies with conservative strategies
Dividend strategy with Bottoms Up is an example balance
Investment of time and energy is important in any strategy you choose
Vector Vest Simulator is a useful tool that could give new insights to the “best” strategies

62. Ric Denton: Colorado Springs Email at ricdenton@adelphia.net
Contact Information
Ric Denton: Colorado Springs
Telephone (719) at
Some topics such as the Monte Carlo approach were very brief. Feel free to call if you have any questions on the slides.