Defining a market VS characterizing a market

When trying to analyse market data it is common practice to use techniques borrowed from different fields to transform the data. Examples of these are probability distributions of price returns, moving averages, autocorrelations, rolling volatility estimates,  data mining techniques and pretty much any kind of operation we use to treat the data/build an indicator.

Nothing wrong per se in using any of these methods, but I see a problem arising when we try to define the market behaviour by means of these. In general, most of these methods cause a a great loss of information about the actual market structure. Again, this is not necessarily wrong, and actually I find it a needed step in the analysis: markets are so complex that filtering out some “noise” is necessary, but one has to be aware of what’s being considered as noise.

Take empirical probability distributions/pdf of daily returns: they are great in that they tell you more or less what’s the range of possible events on a daily basis, but nothing more really. A market could trend up for some time and then sell off right back to its original level and have one probability distribution, or it could trade on a range for the whole same period, and still have exactly the same distribution of returns. In this case, an important piece of information that gets lost (among others) is the time evolution of returns (and hence of course of their moments).
In this case, combining the knowledge of returns probability distribution with an analysis of autocorrelations could clarify the picture, as it could the use of a rolling probability distribution with a smaller time window. These expedients would allow us to use more information but of course we would still suffer a loss of information (but this is also part of our goal).
Similar argument goes for moving averages…all they tell us is how the average price has moved in the last X days . They, alone, don’t tell us anything about the actual range of the price movements, nor about how the average price will move in the future.

What I am trying to say here is that markets have a very fine and complex structure and trying to fully define them is not only almost impossible, but also not needed from a trading point of view.
If we consider markets as an ocean of information, trading consists in finding a small but meaningful  and recurrent wave (or better, many waves) out of this ocean and to ride it until it changes direction. Spotting the wave is only half of the game, building  an algorithm to ride it is the other half.
Likewise, my approach so far has been to look for small inefficiencies characterizing a market and try to take advantage of them while limiting the impact of other factors emerging from the inherent limitations of the data transformation technique in use.

PS: Happy New Year everyone!



About mathtrading

My name is Andrea La Rosa and I am a quant trader based in the UK. In the past I worked as a quant in the prop desk of an investment bank, before deciding to fully dedicate myself to quantitative trading.
This entry was posted in Trading Strategies Design and tagged . Bookmark the permalink.

5 Responses to Defining a market VS characterizing a market

  1. Ellie K says:

    I don’t approach a trading strategy with the intent of fully describing a market, not quantitatively. On second thought, maybe I do. I suppose that describing distribution of returns as a particular probability distribution, with specific parameter values, whether higher-order moments or shape (like for gamma distribution), can be considered to be “describing” a market.

    The reason I’m commenting is because I’m trying to determine why I disagreed with the first part of your post. It logically supported your conclusion. And your conclusion is exactly, perfectly accurate, in my experience as an active investor, and former buy-side trader:

    “look for small inefficiencies characterizing a market and try to take advantage of them while limiting the impact of other factors”

    Also, I agree, that fully describing a market is unnecessary. In fact, it is close to impossible, for most of us. If we try, we are emulating the methodology used by central bankers and economists. That isn’t a trivial matter for them, despite all their access to data and resources. It is far less likely to be successful for us!

    Market volume is a limiting constraint for a large institutional money manager, For smaller investors, or a hedge fund running many different strategies, in different markets, that isn’t a problem.

    “look for small inefficiencies characterizing a market and try to take advantage of them while limiting the impact of other factors…”

    Quantitative methods may motivate the trading strategy. It is essential to understand the underlying reason for the existence of the inefficiency too. As long as you understand enough of the context, you should be okay. That is why I believe you are correct! Use your knowledge of non-quantifiable influences to limit the impact of those other factors 🙂

    • Ellie K says:

      EDIT! I’m sorry! 😮
      I didn’t mean to duplicate the passage cited. I was referring to this in the first quoted section:

      “Trading consists of finding a small but meaningful, and recurrent wave…”

  2. mathtrading says:

    Hi Ellie,
    Thanks for commenting. For me, looking at returns distribution is a way of characterizing a market, but certainly not a way of fully defining it.
    And I do agree with you, having a good understanding of the context surrounding a particular inefficiency is very useful, although strictly speaking I am not sure I would consider it a necessary condition (as long as one is aware of the lacking of a deep understanding and possibly underweights that particular strategy).


  3. Pingback: Overfitting, forecasting and trading | Math Trading

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