Market Neutrality: how it works in simple terms
By: M. Emmanuel
Tags: Finance, Stock Market, Data Science, Money

Market Neutrality: how it works in simple terms

The CAPM model and the Beta

Source: Pikrepo

Proprietary trading firms and hedge funds normally use market neutral portfolios, they do not do directional trading. This is so common that the iconic view of long-term value fund managers is much less common than we tend to think.

By directional trading I mean the way that small investors, retail traders, professional day traders and long term value investors usually do: a portfolio of assets (or just a single asset/instrument) is selected and a long or short position is taken with the expectation that it will increase (or decrease) its value in the market. For short-term traders the operation will last minutes, for long term traders will last months or years, but both groups base their strategy on an analysis that tries to determine the future direction of the asset/instrument price.

While directional trading is profitable when executed by a professional (much more profitable than market neutral portfolios, especially if leveraged instruments are used) it always poses much higher risks. Loses can be devastating on directional trading strategies if the wrong direction is chosen or under high volatility periods when markets trigger risk thresholds forcing to exit positions.

Investors (I mean professional investors with large amounts of money) usually value safety over profitability, therefore any investment strategy which reduces risk is usually more interesting than higher returns at higher risks. The industry is committed to model strategies that reduce risk over finding models with high profitability.

The industry is committed to model strategies that reduce risk over finding models with high profitability.

An additional point is that many directional strategies do not work with large capitals, as they are usually based on the fact that the applied capital is small enough to be noticed by the market. This is especially true for intraday.

Market Neutrality

Market Neutrality (also known as Dollar Neutrality) is a concept where the investor holds at the same time both long and short positions on the same underlying asset/instrument/commodity but with slightly different assets/instruments. The difference can be different options of the same underlying asset, different settlement periods in future contracts or two ETFs with the same underlying or even companies within the same industry (Ford/GM) — industries tend to move together —.

Pair trading

A well known and simple to understand market neutral portfolio is the one built by long and short positions on WTI and Brent. Both have the same underlying asset: oil. So whatever price does in each instrument, both prices are highly correlated and can not diverge much, if one rises, the other will rise too. The divergences found are related to short-term events (inventories, demand expectations, new pipelines) and any other aspect that might impact fundamentals on supply/demand (macroeconomics, geopolitics, etc.). Those divergences tend to be corrected over time and difference of prices in highly correlated assets/instruments tend to show a strong mean reversion property because, at the end, it is the same underlying asset: an oil barrel.

The most basic strategy (which would be more accurately described as a pair trading strategy) implies buying WTI and selling Brent (or the opposite) when prices diverge. The strong mean reversion property will make both prices converge again so the operator is actually trading the difference. This means that the profitability is — theoretically — decoupled with the actual price of the oil, because we are both short and long at the same time, for the same amount of dollars on the same commodity (oil).

Is market neutral risk-free?

A widely known example on how market neutral strategies are not necessarily risk-free investments is the LTCM case. LTCM operated hedged strategies and included Myron S. Scholes and Rober C. Merton (both awarded with Nobel prize in 1997) as part of their board. You could not think of better travel companions. Their models outperformed the market by a significant margin to later suffer from an organised liquidation. The case shows how theoretical models — no matter how good are the people behind them — can fail and how there is no such thing as zero risks.

Figure 1. When maths fail: Long Term Capital Management Hedge Fund. Profitability per invested dollar. Chart data extracted from: https://en.wikipedia.org/wiki/Long-Term_Capital_Management

Despite this case, Hedged strategies are much less risky than any other investment strategies. Especially when planned to get moderate returns. The drawdowns can be smaller and the risk can be better controlled. As a trade-off, their implementation is more complex (often using derivatives) and some strategies require large amounts of money to create truly diversified neutral equity portfolios. This is why the approach is not widely known (or used) by individual investors as probably strategies such as passive investment achieve good profitability and are much easier to understand and implement.

A much more recent event that shows how market neutral strategies involving correlated future contracts are not risk-free is the April 2020 contract CME WTI future contract case. In the middle of the worst pandemic in decades, the price collapsed into negative values (some people just learnt that day that futures can have negative quotes). So it was as crazy as it sounded: the market would pay you for getting 1000 oil barrels (get the money and the barrels). You would become a distressed owner of 1000 oils barrels (that nobody wanted at that time and likely without a single clue on how to sell them) to be delivered to a Chicago warehouse designated by a CME agent.

Anybody operating a pair with that contract in the wrong direction, with strong leverage and without risk control measures in place would be in high distress (maybe broke). So there are risks, yes there are.

Side note: COVID-19 WTI April Future Contract

Despite Terry Duffy from CME stated that ‘futures market worked to perfection’ (whatever that means) the fact is that the role of market makers during exceptional market conditions is controversial, as it is common to find that they fulfil their contractual obligations by providing liquidity at absurd levels. For the average trader, the moral from the WTI COVID-19 case can be summarized in a couple of new rules that I personally incorporated to my trading plan — even when I do not operate crude oil — : 1. never operate future contracts with physical delivery that are close to their settlement dates, or as a stronger and probably safer corollary: stay away from future contracts with physical delivery. 2. stay away of contracts without enough liquidity —for example, monthly versus quarterly — . Those are rules normally observed by every small trader but I never had that actually written until the event took place.

While the examples might be scary they are also very specific outliers. Market neutral strategies are by definition less risky than anything else because you are applying the concept of coverage and you are operating the marginal. Stop-loss thresholds are harder to hit because one position covers the other, but you still need to apply some common sense and risk management to avoid these “free-fall/trap” situations.

The CAPM model and the Beta

The Capital Asset Pricing Model can be considered as the main pillar of market neutrality. The theory was originally proposed by William T. Sharpe and basically models asset prices in two components: one is due to the general market (systematic) and another one (residual) is due to the individual asset.

Figure 2. The CAPM model: market and residual components

What CAPM model states is that assets move with the market (any bullish day will tend to sweep along prices in all assets, similarly an all-red day will drag prices down no matter what). What varies is how much the individual asset will follow the market. That figure is modelled as a Beta parameter. The Beta parameter represents how much the asset varies with respect to the market returns (rm).

The Theta value represents the residual component and the model (this is controversial though) says that it presents a strong mean reversion property.

The idea is that you build portfolios where Betas cancels each other. So you go long on certain assets and short on another set. An inverse ETF is selected as the short reference and regular equities are used as long positions, but the alternatives to building market neutral portfolios are diverse and can involve much more complex portfolios (including partially neutral portfolios).

The trading activity takes place on the residual components that are expected to be much more predictable due to its mean reversion property, while the overall market trend (or bias) is cancelled out by having the same amount of dollars in long positions than in short positions.

The model is also the basis of factoring, where the theta is estimated by the prediction of several factors.

Calculating the Beta

The first step of operating a market neutral strategy is to build a market neutral portfolio. My first portfolio ever built is based on S&P500 equities and an inverse S&P500 ETF, being my first objective to obtain a plain P&L equity curve.

To calculate asset betas covariance between underlying and asset under analysis is divided by market variance. Covariance represents the correlation between both assets while variance represents how much the asset (market) is moving from its mean.

In my example, daily equity data is downloaded from Polygon.io and incorporated into CSV files with daily OHLC data per asset. The daily returns are calculated for each asset and compared against the reference asset (inverse S&P500) obtaining each asset Betas. By doing this, a market neutral portfolio can be built where we shall be most of the time at break-even in our investment.

Figure 3. AAPL 60 days Beta vs S&P500 calculated using Polygon.io data

Figure 3 shows Apple (NASDAQ: AAPL) beta calculated daily session returns versus an estimated value of S&P500. Calculating Betas is the first step to build a market network portfolio as it gives us a quantitative value on how close is the asset price movement when compared with the market.

Daily returns and equity curve

Figure 4. AAPL vs SP500, daily returns per 1000$ invested.

Figure 5. AAPL vs SP500 equity curve per each 1000$ invested.

Figures 4 and 5 show daily returns and equity curve for a neutral portfolio made out of SP500 index and its most capitalised asset (AAPL). Even with just one asset, it can be seen how mean reversion is stronger and that the equity curve gravitates toward zero. The Beta selection and the fact of having built a really poor neutral portfolio (just one asset and the index) are the probable causes of the strong bias shown from its 0 average in 2016–2017.

While the example is not complete and can not be used as a trading strategy, it shows the way a market neutral portfolio operates. The underlying idea is that the combined returns show a stronger mean reversion than assets show in directional trading.

The way Betas are calculated can vary, as a certain period of time needs to be defined, but by no means a Beta can be seen as a static figure (as some textbooks implicitly state).

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