# 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:

$\beta_i F = 0$

Implying:

$R_i = \Theta_i$

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.

QF

# Why I do Things This Way

I must confess a few things. I started my journey in the investment world as a self-proclaimed value investor. I didn’t know any better and I figured; if it worked for Warren Buffet, it ought to work for me. So I read and read on the subject and a little later I was being introduced to financial theory in school; time vale of money, benefits of diversification and all that jazz. At that point I felt like the planets aligned, making money and the market was easy, we only had to consider companies as a series of future cash flows. I then learned to do fundamental valuation: discounted cash flow models, comparables analysis, financial ratios regression et. al. However it never really did it for me, I was always left with questions unanswered.

Looking for other more attractive venues for me, I was always hearing tales of those mythical investors that could predict the future with a single look at a chart. Looking forward to gain this level of perception, I started looking into visual chart analysis. At first I must say I was baffled by what appeared to be doodles on the chart. I remember that at some point early on someone was trying to persuade me that if my chart was forming a tea cup I found myself a pattern to trade on. I must admit I was perplexed. Nonetheless I stuck through and passed the stage of chart reader and graduated to the indicator stage. Then things started to look more appealing to me, I particularly liked how each indicator would put a specific aspect of a stock price series to the foreground and reducing the noise. However I couldn’t seem to find a way to use these indicators to develop a way to make money. Decidedly reaching the $1M mark before 25 years old was going to be more complicated than I had forecasted when I was younger. Then one day I came across the blogs on my blogroll; I was hooked. The method used in these blogs just made all the puzzle pieces fit together. They wouldn’t discredit any method per se but would question the methods, the underlying assumptions, and would use an outside the box thinking approach to answer question left unanswered. Rather than being strictly technical or fundamental, they would use quantitative methods to analyse the market and rigorously evaluate phenomenons. This no fad, down to earth and based on the scientific approach was exactly was I was looking for all this time without knowing it. While I am nowhere near the$1M mark, I have grown from an absolute approach trader to a seeing the shades of grey. Instead of looking for the Holy Grail strategy or approach, I now strive to constantly get better and get answers to my questions.

If you are a reader of my blog, I also assume you follow these blogs, and the one true great thing I hope you get away from it is not that new strategy published that scores a 40% annualized return in the backtest, or that awesome new indicator that outperforms this and that. Above all else, I hope that what you get from our blogosphere community is the desire to investigate and to constantly improve your trading. And that my friends, is the only way to succeed; and no fundamental or technical school of thoughts will ever give you that if you just blindly follow it without questioning the underlying principles.

QF

# Rotation System à la Quantum Financier

Rotation systems have generated a lot of virtual ink lately see CSS Analytics here, Engineering Returns here, then at MarketSci here for a few examples. I have recently been playing around with the concept but with a very different approach. I figured it might be interesting for readers to hear another approach to a similar problem.

Rotation systems are often based on two very simple concepts: momentum (relative strength) effect and negative correlation. The goal of such models is usually to allocate capital to securities trending on the upside. By selecting securities that are show negatively correlated behaviour we expect the securities to complement each other depending on the regime. For example one could include in the basket of available securities stocks and fixed incomes ETFs with the expectation to be long bond when stocks are not performing and vice-versa. This kind of model basically surfs the beta with the momentum of securities. This approach demands certain considerations when creating the model, and depending on the investor, various degrees of fancy maths are going to be used. When looking at creating such systems, we need to determine the following amongst others:

1. What securities are available and how are they selected?
The negative correlation amongst assets is a desirable feature here. We possibly also could include ETFs with different degrees of leverage to aggressively add beta when the time is right. Then we can select them based on heuristic, macroeconomic relationship, data mining etc.

2. How is momentum measured and ranked?
It could be using a simple normalized difference between prices of different time frames or a rate-of-change indicator. The RSI , DVO or TSI indicators are also good candidates. Alternatively you could fit a least square model and evaluate slope and residuals. You could also do some kind of factor decomposition and create a factor model to forecast momentum.

3. How do we allocate capital across securities?
A simple equal cash position could be used or we could use volatility to adjust the weight amongst securities. Mean-variance optimization with certain tweaks to it would perhaps make a good candidate.

4. How often do we recalibrate the strategy?
Most TAA systems recalibrate monthly and some rotation systems trade on a weekly time frame. The trick is to minimize the transaction cost while still capitalizing on the uptrend of selected securities and mitigating the drawdown. The appeal of such systems is usually their risk adjusted performance and we would not want to compromise it.

5. Do we consider alternative/innovative metrics in the system?
Michael at Marketsci recently talked about taking correlation during market shocks into considerations. This type of innovative approach seemed to work quite well for him, and really here sky is the limit. We only have to balance complexity and quality.

Without spoiling the next post, I can say that my rotation system will be very different from anything discussed so far on the blogosphere (as far as I am aware). Instead of using historical relative strength we will see how we can use machine learning classification and select securities using the forecast. I will also try to incorporate new metrics to help with system performance.

QF