Market Neutrality

Market neutrality is one of those buzz words thrown around quite a lot in finance; several hedge funds claim their strategies as being market neutral and use it as their main marketing tool. Some quantitative strategies are also oriented towards that goal; pairs trading is a prime example, but one can also include segments of statistical arbitrage in that broad area.

This begs the question why market neutral? To answer this question we must first discuss what market neutrality means. Consider the daily return for a stock i denoted R_i. We can decompose the returns between the market related (systematic) portion F and the stock specific (idiosyncratic) portion \Theta, yielding the following equation:

R_i = \beta_i F + \Theta_i

Which is nothing more than an ordinary least squares regression model decomposing the return of stock i into a systematic component \beta_i F and an idiosyncratic (uncorrelated) component \Theta_i. The market neutrality is obtained by eliminating the systematic portion of the returns, equivalent to say:

\beta_i F = 0


R_i = \Theta_i

Effectively, getting rid of the market exposure and only exposing ourselves to the portion of the return based on stock i specific profile, hence market neutrality. Now back to the initial question: why market neutral? Simply put; we want to make a bet on a security without at the same time betting on the direction of the market. In a relative value strategy like pairs trading where we are betting on the outperformance of securities relative to each other, regardless of where the market goes, market neutrality takes all its sense.

However market neutrality is not only considered in relative value strategies. Imagine an investor trading a portfolio of strategies. The market exposure of this particular investor can be thought as the capital weighted average of the individual strategy betas:

\beta_p = \frac {Q_j}{\sum Q_j} \beta_j

Where Q_j is the dollar amount invested in strategy j.

Keeping in mind the first equation we can also decompose the return of the portfolio in a similar fashion, composed of a systematic and idiosyncratic (strategy ensemble specific) component. In an attempt to obtain market neutrality, one could short (buy) market futures or the corresponding ETF in order to satisfy the second equation, effectively neutralizing the portfolio returns’ exposure to the market.

While this approach does not necessarily improve returns, it has the benefit of potentially better sheltering one against market storms by reducing exposure. Targeting a market neutral approach also has the benefit producing uncorrelated returns. A recent post by Marketsci explain that most investors don’t seem to look for absolute returns, but if you find yourselves in the category that would prefer absolute to relative returns, taking a look at market neutrality may be worth your time. I personally like market neutral strategies and if interest warrants, I could dive deeper into different techniques to obtain market neutrality that I find more reliable than ordinary least squares, like quantile regression.


8 thoughts on “Market Neutrality”

  1. This is the Beta Neutral version of Market Neutral but you could also consider the Dollar Neutral version, being long one dollar while short one dollar. In the presence of a market storm I would wager that the loss would be less with this version if all correlations goes to one.

    1. Good comment, indeed it is beta neutral, in a future post I will take a look at cash neutral and see how it performs, it is a work in progress for the moment. – QF

  2. Another thing worth investigating is how much of the percieved Beta exposure you manage to hegde ex post. i.e íf you calculate the realised beta will it sum to zero. This will not always be the case, the R-squared of the Beta calculation can sometimes be extremely low and the realised hedge quite poor.

  3. many market neutral strategies, including pairs trading, are often based not on returns but price with the underlying theory and analysis being co-integration which allows one to model the relationship between non-stationary time series. there are, of course, numerous issues with this approach, involving how you estimate the relationship (OLS versus dynamic linear models), how you determine if the relationship is stationary, and the period over which you perform the analysis, ie lookback. very nice blog, please keep up the good work. if you are interested in this approach, please let me know. I’ve written some scripts in R to analyze and back-test pairs using co-integration.

    1. Hey trey,

      Good comment. I take a look at it in the next post and I use different methods to seek neutrality. If you care to share, I would very much like to see your scripts you can hit me at quantumfinancier at gmail dot com – QF

  4. Trey any chance to get a peek ar those scripts? Also very interested in any techniques for deciding suitable pairs to test for cointegration.

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