Post on 12-Jan-2016
Momentum Based Investment Strategies
Presented by Bruce Vanstonefor the Australian Shareholders Association
This material is presented for educational purposes only.
I am not a financial advisor, and this material is not advice.
In many cases, the material represents research findings.
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Disclaimer
Who am I?◦ Dr. Bruce Vanstone (Bond University)◦ Research Discipline
Computational Finance Algorithmic Trading
◦ Trader and Academic◦ Consultant to Fund Managers
Example: Porter Capital Management
What is this all about?◦ Momentum!
Overview
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What is it?◦ Momentum refers to stocks that have exhibited
past over-performance; it is a measure of that degree of over-performance
Is it credible?◦ Short answer: Yes◦ Long answer: …this presentation!
Why should I care?◦ It has very low correlation to how you probably
already invest
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Momentum
Academics focus on the EMH, and the RWT◦ Random Walk Theory
Theory of ‘independence’
◦ Efficient Markets Hypothesis Theory of ‘information’
Anything which doesn’t fit is labelled an ‘anomaly’ in finance
Momentum is classified as an anomaly
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Momentum: The ‘Short’ Answer!
‘discovered’ by DeBondt & Thaler (1985) Since then, it is one of the most widely
researched topics in academic finance◦ Rouwenhorst: demonstrated momentums
outperformance in every European market (80-95)◦ Griffin: demonstrated outperformance in over 40
countries ‘Globally, momentum profits are large and
statistically reliable in periods of both negative & positive economic growth’
Demonstrated in forex, commodities and even real estate markets
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Momentum: The ‘Short’ Answer!
Much of the remaining information in this presentation is based on simulations using reconstructed data concerning stocks, delistings, index memberships etc
We can use simulations to learn a lot about the markets we trade and invest in
For this presentation, I have focused on the ASX200
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Momentum: The ‘Long’ Answer !
Basic principle: stocks exhibiting out-performance tend to continue to do so for a while
How do we measure out-performance?◦ By ranking the degree of change over a pre-
defined period Example metric: % increase over 12 months
That's the metric I used in the rest of these slides, but the whole idea is actually very robust to choice of metric
Funds use different periods, and sometimes, things like change in growth rate etc instead of change in price
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Momentum: the basics
Basic momentum testing is normally built on the j/s/k methodology◦ j = past ranking
period◦ s = skip period
(if used)◦ k = future
holding period
Momentum: conceptually
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Basic assumptions of momentum are:◦ Stocks going up strongly are likely to continue for a
measurable time in the future◦ Stocks going down strongly are likely to continue
for a measurable time in the future
OK, lets stop right there!
For momentum to be valid, these ‘assumptions’ must be true◦ ... and interestingly enough, we can test them
easily!
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Underlying assumptions
Great!.... because we can test this with any credible definition of stocks going up or down…
For this example, I have used these rules:
◦ Stocks going up: price higher than the last 200 days◦ Stocks going down: price lower than the last 200
days
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Up/Down assumption
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Example: (‘Up’)
Example: (‘downs’)
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Create two tests:
UP : buy all stocks with price higher than for the last 200 days, sell after 1 year
DOWN: buy all stocks with price lower than for the last 200 days, sell after 1 year
For momentum assumption to be valid, these should be quite different outcomes
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Up/Down simulation
01/01/2000 -> 31/12/2014 Buy $5,000 worth of stock every time the
buy condition is true◦ Standard transaction costs, membership etc
included
What can we learn about our market?
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Up/Down Outcome
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Outcome – Up versus Down
‘UP’ rules
‘DOWN’ rules
(previously, slide 10)
Basic assumptions of momentum are ◦ Stocks going up strongly are likely to continue for
a measurable time in the future◦ Stocks going down strongly are likely to continue
for a measurable time in the future
Seems like the assumptions could be viable
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Back to momentum
Next ‘assumption’ is the stronger the stock is going up, the better◦ Why? Because momentum is about buying the
top group of stocks travelling in the required direction
◦ Example: top 20
Best way to do this is to go back to the j/s/k description
Example: 12/0/12
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Momentum
For these kinds of tests, we observe the difference between the top (example) 20 and the market (using an index)
Again, not too difficult to do using simulations
So…. What can we learn?◦ $1m, standard txn costs, membership etc
included
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12/1/12
Momentum versus market
MOM = rank stocks by last 12 months price change, skip, rotate, hold top 20◦ i.e. 12/1/12
MKT = buy XAORD at start of period, sell at end
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Momentum/Market simulations
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Outcome – MOM versus MKT
‘MOM’
‘MKT’
These simulations appear to support the two basic assumptions required, namely:
Assumption 1◦ Stocks going up strongly are likely to continue for
a measurable time in the future◦ Stocks going down strongly are likely to continue
for a measurable time in the future
Assumption 2◦ the stronger the stock is going up, the better
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Momentum Simulations
These simulation results are in line with previous research findings by other academics in different markets◦ Do a little searching on Google or Google Scholar!
These simulation results are also quite robust to the different “j/s/k” settings
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Momentum Simulations
Think of momentum as a general ‘principle’ rather than a measure
Then it makes sense to think about momentum on things besides price, like:◦ Earnings◦ Fundamental ratios
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Further thoughts on Momentum
Some Real Results – ASX200
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Some Real Results – S&P500
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30-Sep-13
29-Nov-13
31-Dec-13
31-Jan-14
28-Feb-14
31-Mar-14
30-Apr-14
31-May-
14
30-Jun-14
31-Jul-14
31-Aug-14
30-Sep-14
31-Oct-14
30-Nov-14
31-Dec-14
31-Jan-15
80000
90000
100000
110000
120000
130000
140000
S&P500 Prime Momentum Unaudited
$ 127,784
Thank you for your time!
If you are interested, you can follow my real results at www.vanstonetrading.com
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Questions