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Multi-Dimensional Equity Curve Analysis

June 1, 2010

It is hard to fault our tendency to think in only a few dimensions at one time. Our minds are designed for survival, and hence we rely on heuristics, experience and linear-thinking. But its important to think about these things in the stock market because it impacts our chances of survival in the brave new world of modern computing and artificial intelligence. While you look at a chart of Apple Computer, the computer algorithms at Goldman Sachs are  simultaneously analyzing  Apple, QQQQ, IBM, Microsoft, option prices, the US dollar, interest rates and a host of other inter-relationships. Their goal is to determine relative value, or to predict changes in the stock price. So while you are predicting a breakout or a breakdown by looking at historical patterns, they are looking in multiple dimensions at the “invisible” factors that affect Apple prices. Clearly they are going to be more accurate than you at predicting the future price of Apple,  no matter how good you are at reading charts. The market is a highly complex and interconnected web, and to look at the price of the underlying is to operate in only 2 dimensions.

This line of thinking affects strategies as well. It is by no means limited to complex real-time intermarket analysis —beyond the scope of even sophisticated traders. In fact, simply integrating multiple time frames in a comprehensive sequence will greatly assist in classification accuracy. This approach is well-documented by Brian Shannon of Alphatrends, and his book is highly recommended as both a theoretical and practical framework:  Basically, to properly predict direction you are better off aligning yourself with more than one time frame, and also giving added weight to the long-term time frame. This general concept affects equity curve analysis in unique and interesting ways.

When we are trying to determine whether or not to turn strategies on or off such as daily follow-through, we are only looking at one time-frame. Our assumption is that we can “time” daily-follow through with a reasonable degree of accuracy for the purposes of creating an adaptive approach.  However, much like the examples mentioned above, there is reason to believe that looking at more things simultaneously will improve our ability to make predictions.  In this case we can take a multiple time-frame approach and look at the spectrum of mean-reverting strategies, and ideally consider intermediate and long-term time frames as well. Furthermore, we can look at the factors that affect mean-reversion such as volatility and trendiness. From there we can use an even more layered approach by considering markets other than the S&P500, and perhaps even look at the individual sector or stock level.  This is the type of approach that is far less prone to random error, and data-snooping, and is much more “robust.” An effect such as mean-reversion is broad-based and driven by strong common factors that many of us many not currently understand, but nonetheless exist. Detecting such shifts in the tectonic plates of the markets requires a multi-dimensional approach to equity curve analysis.

5 Comments leave one →
  1. Dave Svilar permalink
    June 1, 2010 9:29 am

    I like the idea taking quantitative research and combining it with the “art” of trading, which is what I think you’re getting at here. I’ve read Shannon’s book several times and watch his weekly update (highly recommended) on StockTwits TV each week as part of my required homework as a trader. Good post.

    • david varadi permalink*
      June 2, 2010 2:34 am

      hi dave, thanks and much appreciated. definitely some interesting material there.

  2. June 1, 2010 7:53 pm

    multitimeframe analysis seems to make sense, however i have not found any studies that show it really gives you an edge in trading? Forgive me but Shannons book is like a mojority TA books – few well chosen examples and no studies to back up the claims ( i also read Cahen’s “Dynamic Technical Analysis”, which also deals with multitimeframe approach but suffers from similar drawbacks). Are there any rigorous data/studies showing the edge in multitimeframe analysis?

    • david varadi permalink*
      June 2, 2010 2:39 am

      hi jt, i agree with you that there is little out there on multi-time frame analysis, however many sources do give some hints at its usefulness. most tests that combine short-term indicators with long-term trends such as RSI2 and the 200ma are effectively minor examples of multiple time-frame analysis. there are no studies in the formal academic sense, but I have run such tests in the background that show very robust results. Of course the key is understanding that such analysis is done best without too many assumptions. I sense a lot of practitioners are reluctant to share a lot of material on that end. I think that is the case with many things that are the most useful–the most well-documented stuff is old news so to speak.


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