Studies - Posted on Tuesday, September 21, 2010, 11:35 PM GMT +1

5 Comments


Sep Tuesday 21

The Power of Momentum

Most recently Michael Stokes at MarketSci had – as always – an excellent and very inspiring posting about Bank of England Andrew Haldane‘s The Power of Momentum Strategy, (a couple of days) before picked up and discussed by Felix Salmon and EconomPic (hats up to both as well).

The Power of Momentum Strategy goes long the market (on close of the last business day of the month) when the market was up the previous month, and goes short the market if it fell the previous month (therefore buying strength and selling weakness).

Michael showed that the well-known wisdom ‘if something sounds too good to be true, it probably (regularly) is‘ unfortunately applies to Andrew Haldane’s strategy as well due to the fact that he used Robert Shiller’s “Irrational Exuberance” data set for historical prices, which is fine as long as month-end prices are used, not the average price for the month (which is on the one hand not obtainable at the end of the month, and on the other hand regularly a significant improvement of one’s entry price; on an up month the average price of the month will more often be lower than the month-end price, and vice versa).

But being always on the lookout for the holy grail of trading strategies, and inspired by a commentator on Felix Salmon’s article, I thought it would be interesting to check if what doesn’t work with entries/exits triggered at month-end, might very well work for different time frames and frequencies (daily, weekly, quarterly, semiannual and annual).

To make a long story short: Table I shows the S&P 500‘s historical performance (since 1/1/1930, data is NOT adjusted for dividend payments, and I did not use Robert Shiller’s “Irrational Exuberance” data set) assumed one went long | short on close of the last session of one of those periods/frequencies listed below in the event the S&P 500 rose | fell the previous period (e.g. on a week-end to week-end, end-of-quarter to end-of-quarter basis and so on). It’s than a buy | sell and hold strategy until the end of the next period (up to an entire year), and the next buy | sell decision has to be made.

  • Strat. #1 PoM (w)‘: weekly,
  • Strat. #2 PoM (m)‘: monthly (orifinal strategy),
  • Strat. #3 PoM (q)‘: quartely,
  • Strat. #4 PoM (s)‘: semiannual,
  • Strat. #5 PoM (a)‘: annual, and
  • Strat. #6 PoM (d)‘: daily.


Here is the link to the stats in a more ‘readable’, original size: Statistics 1

The original The Power of Momentum strategy (PoM (m)) – although profitable over the course of the last 80 years – would not even beat a riskless asset like the 30-day FED FUNDS, while weekly and annual momentum strategies acted more or less like a money shredder. Surprisingly (in opposite to the annual strategy) the semiannual strategy came very close to a buy and hold approach (Benchmark), beating the S&P 500 benchmark at least with respect to the maximum drawdown and maximum time in a drawdown.

Of course eye-catching the daily momentum strategy (long on a higher close, short on a lower close), even if not accounting for transaction costs. But as the respective equity curve shows, profits were made over a relatively short period of time (2 decades, the high period of trend following strategies), and for the most part lost over an even shorter period of time (the last decade, the high time of mean-reversion strategies).

Table II below shows the S&P 500‘s respective periodic returns (broken down into weekly, monthly, quartely, semiannual and annual time frames) and a couple of performance metrics (since 1/1/1930, S&P 500 data is NOT adjusted for dividend payments):

Here is the link to the stats in a more ‘readable’, original size: Statistics 2

Although the daily momentum strategy (‘PoM(d)‘) outperformed the other momentum strategies and the benchmark as well during any time frame (weekly, quartely … basis), and was profitable in more than 7 out of every 10 years, is shows the highest (by a wide margin) std. deviation in annual returns (32.39%), and there were some severe drawdowns during every time frame (end-of-period to end-of-period basis), even annually (94.19%, in contrast to the benchmark 55.08% on a year-end basis).

And last but not least the equity curves for all of those strategies listed above, except the daily momentum strategy.

Especially the fact that profits and losses are regularly made and lost over a relatively short period of time provides a telling argument for making trading strategies ‘adaptive‘, means to adjust rules and parameter to the ongoing changes in market conditions, affected by bull and bear markets and/or trend-following or mean-reversion tendencies, among others.

P.s.: Please accept my apologies for the inferior quality of equity curves. This is the next project I’ll be working on …

Successful trading,

Frank

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Remarks: Due to their conceptual scope – and if not explicitely stated otherwise – , all models/setups/strategies do not account for slippage, fees and transaction costs, do not account for return on cash and/or interest on margin, do not use position sizing (e.g. Kelly, optimal f) – they’re always ‘all in‘ – , do not use leverage (e.g. leveraged ETFs) – but a marginable account is mandatory – , do not utilize any kind of abnormal market filter (e.g. during market phases with extremely elevated volatility), do not use intraday buy/sell stops (end-of-day prices only), and models/setups/strategies are not ‘adaptive‘ (do not adjust to the ongoing changes in market conditions like bull and bear markets).

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Disclaimer

The information on this site is provided for statistical and informational purposes only. Nothing herein should be interpreted or regarded as personalized investment advice or to state or imply that past results are an indication of future performance. The author of this website is not a licensed financial advisor and will not accept liability for any loss or damage, including without limitation to, any loss of profit, which may arise directly or indirectly from use of or reliance on the content of this website(s). Under no circumstances does this information represent an advice or recommendation to buy, sell or hold any security.

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(Data courtesy of MetaStock , and for data import, testing, surveys and statistics I use MATLAB from MathWorks)

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Comments (5)

 

  1. Jeff Pietsch says:

    Very good Frank. I also like how your FTD/ MR chart puts the recent flatness in perspective time wise. With respect to the second/ momentum chart. How closely have you looked at building a complimentary adaptive blend of periodicity? Best, Jeff

  2. Manny says:

    Thank you for the information. I understand the entry part, just needed a little more clarification on the exit.

  3. CarlosR says:

    I’m guessing a little here, but what I thought Jeff meant was this: since at the end of your post you discussed the need for strategies to be adaptive, have you looked at making a strategy that would switch from daily to quarterly (or whatever), so that it was always using a strategy that was profitable?

    Since the original strategies were momentum strategies, this would amount to taking the momentum of the momentum, it would seem to me.

    But maybe Jeff had something completely different in mind, I’m not sure.

    • TradingTheOdds says:

      CarlosR,

      thanks.

      There were times when (periodically, especially on a day-to-day basis) buying strength or selling weakness was either a receipt for disaster (mean-reversion was dominant), and times when it seemed to be the wholy grail of trading strategies. The trick would be to figure out (e.g. quantified by an indicator) in what environment you currently are at time of investing (e.g: an indicator of 75 at the end of a session would indicate a 75% probability of a daily (weekly, monthly, …) follow-through of the previous sesiion’s strength/weakness), to probably most profitable holding period for a momentum strategy (daily, weekly, …), and how much (position sizing) to bet on each trade, continuously monitoring (and quantifiying) the market’s mean-reversion/trend following behavior.

      I haven’t done that yet, but would be an interesting project.

      Best,
      Frank

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