What do you think of strategies displayed on timelyportfolio.blogspot.com?

I really like the fact that there is some code to reproduce the strategies, but they seem very elementary because he does not control for risk factors.

By that I mean that you can have a super complex strategy with lots of bells and whistles, but if in the end your strategy is mostly long, and that for the period you consider the market is up, you are merely looking at 1 big point and not 5000 independent samples from your strategy even though you might have this illusion.

How do professional deal with this, can you point at good papers for it ?


Glad people are reading. Simple with more history in terms of time and indexes is better in my book. I have spent 13 years reading over 200 research papers, incorporating complicated and advanced techniques, and studying very reputable buy side research with no improvement in results. Readers are on their own to extend to lots of markets including Nikkei with 90% drawdown over 20 years. Also equity markets have an upward bias making reliable robust shorting techniques extremely difficult. I save the currency, bond, and commodity markets with long term mean reversion and no upward bias for potential shorts.

See and hear Emmanuel Derman for much more discussion from someone who knows far better than me.

Absolutely love the discussion. Look forward to reading other responses.


I don't think risk factors are that important here. This is a simple market timing strategy where you're either fully exposed to the market or not exposed at all. All he needs to show is that he's adding value above buy-and-hold by all of the in-and-out trading.

  • $\begingroup$ with this approach, I can show you some excellent backtesting that adds a lot of value.. $\endgroup$ – nicolas Jul 10 '11 at 9:48
  • $\begingroup$ I'd be interested to hear how you'd improve this approach. It's pretty simplistic, but seems to show some efficacy to at least avoid the largest drawdowns. $\endgroup$ – Frank Fingerman Jul 10 '11 at 12:23
  • $\begingroup$ you can 'improve' with more data-snooping and more overfitting. but is that a real improvement ? $\endgroup$ – nicolas Jul 10 '11 at 12:38
  • $\begingroup$ @nicolas you might consider linking to a particular strategy since that site is updated frequently. I do not think he is data snooping in general. I think he comes up with a strategy and tests it. I don't see where he is doing any sort of optimization in-sample. Sure, the results will be different under different market conditions, but you have to start somewhere. $\endgroup$ – user508 Jul 11 '11 at 15:02
  • $\begingroup$ I think all his strategies dont deal with that very common question. do you see one that does ? $\endgroup$ – nicolas Jul 11 '11 at 21:47

I think the way to see the real effect in a backtest is to produce the distribution achieved with zero skill. You can get one point from this distribution by starting with the same initial portfolio, then do random trading through the time period conditional on obeying the same set of constraints. Do that several times to get the approximate distribution.

The initial portfolio can have a big effect on what you see. So at the very least using different starting portfolios can give you some information.

A fuller discussion of this is at http://www.portfolioprobe.com/2010/11/05/backtesting-almost-wordless/

  • $\begingroup$ very interesting. In the langage of this presentation, I was then referring to isolating risk factors, contained within the mean random path performance diferential. $\endgroup$ – nicolas Mar 2 '12 at 18:17

On the Expected Performance of Market Timing Strategies, a recent working paper by Hallerbach from Robeco Asset Management, attempts to construct a rigorous framework for evaluating market-timing strategies.

We derive expressions for the Information Ratio (IR) that can be expected from market timing strategies in non-parametric and parametric settings. Our results hold as superior approximations and lift Grinold’s [1989] “Fundamental Law of Active Management” to an operational level. In addition, we separate “time series breadth” (the timing frequency per strategy) from “cross-section breadth” (the number of separate sub-strategies) because they contribute differently to performance. We provide theoretical proofs that implementing volatility-weighted bet sizes, both in the time series context of a single underlying market and in the cross-section context of multiple underlying markets, maximizes the expected timing IR. Our theoretical results can be used as a benchmark for and reality check on the back tested performance of timing strategies. We confirm the accuracy of our results by simulating timing strategies for currencies, equities and fixed income.


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