Quant Strategies in 2018

Quant Strategies – Performance Summary Sept. 2018

The end of Q3 seems like an appropriate time for an across-the-piste review of how systematic strategies are performing in 2018.  I’m using the dozen or more strategies running on the Systematic Algotrading Platform as the basis for the performance review, although results will obviously vary according to the specifics of the strategy.  All of the strategies are traded live and performance results are net of subscription fees, as well as slippage and brokerage commissions.

Volatility Strategies

Those waiting for the hammer to fall on option premium collecting strategies will have been disappointed with the way things have turned out so far in 2018.  Yes, February saw a long-awaited and rather spectacular explosion in volatility which completely destroyed several major volatility funds, including the VelocityShares Daily Inverse VIX Short-Term ETN (XIV) as well as Chicago-based hedged fund LJM Partners (“our goal is to preserve as much capital as possible”), that got caught on the wrong side of the popular VIX carry trade.  But the lack of follow-through has given many volatility strategies time to recover. Indeed, some are positively thriving now that elevated levels in the VIX have finally lifted option premiums from the bargain basement levels they were languishing at prior to February’s carnage.  The Option Trader strategy is a stand-out in this regard:  not only did the strategy produce exceptional returns during the February melt-down (+27.1%), the strategy has continued to outperform as the year has progressed and YTD returns now total a little over 69%.  Nor is the strategy itself exceptionally volatility: the Sharpe ratio has remained consistently above 2 over several years.

Hedged Volatility Trading

Investors’ chief concern with strategies that rely on collecting option premiums is that eventually they may blow up.  For those looking for a more nuanced approach to managing tail risk the Hedged Volatility strategy may be the way to go.  Like many strategies in the volatility space the strategy looks to generate alpha by trading VIX ETF products;  but unlike the great majority of competitor offerings, this strategy also uses ETF options to hedge tail risk exposure.  While hedging costs certainly acts as a performance drag, the results over the last few years have been compelling:  a CAGR of 52% with a Sharpe Ratio close to 2.

F/X Strategies

One of the common concerns for investors is how to diversify their investment portfolios, especially since the great majority of assets (and strategies) tend to exhibit significant positive correlation to equity indices these days. One of the characteristics we most appreciate about F/X strategies in general and the F/X Momentum strategy in particular is that its correlation to the equity markets over the last several years has been negligible.    Other attractive features of the strategy include the exceptionally high win rate – over 90% – and the profit factor of 5.4, which makes life very comfortable for investors.  After a moderate performance in 2017, the strategy has rebounded this year and is up 56% YTD, with a CAGR of 64.5% and Sharpe Ratio of 1.89.

Equity Long/Short

Thanks to the Fed’s accommodative stance, equity markets have been generally benign over the last decade to the benefit of most equity long-only and long-short strategies, including our equity long/short Turtle Trader strategy , which is up 31% YTD.  This follows a spectacular 2017 (+66%) , and is in line with the 5-year CAGR of 39%.   Notably, the correlation with the benchmark S&P500 Index is relatively low (0.16), while the Sharpe Ratio is a respectable 1.47.

Equity ETFs – Market Timing/Swing Trading

One alternative to the traditional equity long/short products is the Tech Momentum strategy.  This is a swing trading strategy that exploits short term momentum signals to trade the ProShares UltraPro QQQ (TQQQ) and ProShares UltraPro Short QQQ (SQQQ) leveraged ETFs.  The strategy is enjoying a banner year, up 57% YTD, with a four-year CAGR of 47.7% and Sharpe Ratio of 1.77.  A standout feature of this equity strategy is its almost zero correlation with the S&P 500 Index.  It is worth noting that this strategy also performed very well during the market decline in Feb, recording a gain of over 11% for the month.

Futures Strategies

It’s a little early to assess the performance of the various futures strategies in the Systematic Strategies portfolio, which were launched on the platform only a few months ago (despite being traded live for far longer).    For what it is worth, both of the S&P 500 E-Mini strategies, the Daytrader and the Swing Trader, are now firmly in positive territory for 2018.   Obviously we are keeping a watchful eye to see if the performance going forward remains in line with past results, but our experience of trading these strategies gives us cause for optimism.

Conclusion:  Quant Strategies in 2018

There appear to be ample opportunities for investors in the quant sector across a wide range of asset classes.  For investors with equity market exposure, we particularly like strategies with low market correlation that offer significant diversification benefits, such as the F/X Momentum and F/X Momentum strategies.  For those investors seeking the highest risk adjusted return, option selling strategies like the Option Trader strategy are the best choice, while for more cautious investors concerned about tail risk the Hedged Volatility strategy offers the security of downside protection.  Finally, there are several new strategies in equities and futures coming down the pike, several of which are already showing considerable promise.  We will review the performance of these newer strategies at the end of the year.

Go here for more information about the Systematic Algotrading Platform.

Beating the S&P500 Index with a Low Convexity Portfolio

What is Beta Convexity?

Beta convexity is a measure of how stable a stock beta is across market regimes.  The essential idea is to evaluate the beta of a stock during down-markets, separately from periods when the market is performing well.  By choosing a portfolio of stocks with low beta-convexity we seek to stabilize the overall risk characteristics of our investment portfolio.

A primer on beta convexity and its applications is given in the following post:

 

 

 

 

 

 

 

 

 

 

In this post I am going to use the beta-convexity concept to construct a long-only equity portfolio capable of out-performing the benchmark S&P 500 index.

The post is in two parts.  In the first section I outline the procedure in Mathematica for downloading data and creating a matrix of stock returns for the S&P 500 membership.  This is purely about the mechanics, likely to be of interest chiefly to Mathematica users. The code deals with the issues of how to handle stocks with multiple different start dates and missing data, a problem that the analyst is faced with on a regular basis.  Details are given in the pdf below. Let’s skip forward to the analysis.

Portfolio Formation & Rebalancing

We begin by importing the data saved using the data retrieval program, which comprises a matrix of (continuously compounded) monthly returns for the S&P500 Index and its constituent stocks.  We select a portfolio size of 50 stocks, a test period of 20 years, with a formation period of 60 months and monthly rebalancing.

In the processing stage, for each month in our 20-year test period we  calculate the beta convexity for each index constituent stock and select the 50 stocks that have the lowest beta-convexity during the prior 5-year formation period.  We then compute the returns for an equally weighted basket of the chosen stocks over the following month.  After that, we roll forward one month and repeat the exercise.

It turns out that beta-convexity tends to be quite unstable, as illustrated for a small sample of component stocks in the chart below:

A snapshot of estimated convexity factors is shown in the following table.  As you can see, there is considerable cross-sectional dispersion in convexity, in addition to time-series dependency.

At any point in time the cross-sectional dispersion is well described by a Weibull distribution, which passes all of the usual goodness-of-fit tests.

Performance Results

We compare the annual returns and standard deviation of the low convexity portfolio with the S&P500 benchmark in the table below. The results indicate that the average gross annual return of a low-convexity portfolio of 50 stocks is more than double that of the benchmark, with a comparable level of volatility. The portfolio also has slightly higher skewness and kurtosis than the benchmark, both desirable characteristics.

 

Portfolio Alpha & Beta Estimation

Using the standard linear CAPM model we estimate the annual alpha of the low-convexity portfolio to be around 7.39%, with a beta of 0.89.

Beta Convexity of the Low Convexity Portfolio

As we might anticipate, the beta convexity of the portfolio is very low since it comprises stocks with the lowest beta-convexity:

Conclusion: Beating the Benchmark S&P500 Index

Using a beta-convexity factor model, we are able to construct a small portfolio that matches the benchmark index in terms of volatility, but with markedly superior annual returns.  Larger portfolios offering greater liquidity produce slightly lower alpha, but a 100-200 stock portfolio typically produce at least double the annual rate of return of the benchmark over the 20-year test period.

For those interested, we shall shortly be offering a low-convexity strategy on our Systematic Algotrading platform – see details below:

Section on Data Retrieval and Processing

Data Retrieval

 

 

More on Strategy Robustness

Commentators have made the point that a high % win rate is not enough.

Yes, you obviously want to pay attention to other performance metrics also, such as profit factor. In fact, there is no reason why you shouldn’t consider an objective function that explicitly combines various desirable performance measures, for example:

net profit * % win rate * profit factor

Another approach is to build the model using a data set spanning a different period. I did this with WFC using data from 1990, rather than 1970. Not only was the performance from 1990-2014 better, so too was the performance during the OOS period 1970-1989.  Profit factor was 2.49 and %Win rate was 70% across the 44 year period from 1970.  For the period from 1990, the performance metrics increase to 3.04 and 73%, respectively.

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So in this case, it appears, a most robust strategy resulted from using less data, rather than more.  At first this appears counterintuitive. But it’s quite possible for a strategy to be over-condition on behavior that is no longer relevant to the market today. Eliminating such conditioning can sometimes enable strategies to emerge that have greater longevity.

WFC from 1970-2014 (1990 data)

Performance

Optimizing Strategy Robustness

Below is the equity curve for an equity strategy I developed recently, implemented in WFC.  The results appear outstanding:  no losing years in over 20 years, profit factor of 2.76 and average win rate of 75%.  Out-of-sample results (double blind) for 2013 and 2014:  net returns of 27% and 16% YTD.

WFC from 1993-2014

 

So far so good. However, if we take a step back through the earlier out of sample period, from 1970, the picture is rather less rosy:

 

WFC from 1970-2014

 

Now, at this point, some of you will be saying:  nothing to see here – it’s obviously just curve fitting.  To which I would respond that I have seen successful strategies, including several hedge fund products, with far shorter and less impressive back-tests than the initial 20-year history I showed above.

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That said, would you be willing to take the risk of trading a strategy such as this one?  I would not:  at the back of my mind would always be the concern that the market might easily revert to the conditions that applied during the 1970s and 1980’s.  I expect many investors would share that concern.

But to the point of this post:  most strategies are designed around the criterion of maximizing net profit.  Occasionally you might come across someone who has considered risk, perhaps in the form of drawdown, or Sharpe ratio.  But, in general, it’s all about optimizing performance.

Suppose that, instead of maximizing performance, your objective was to maximize the robustness of the strategy.  What criteria would you use?

In my own research, I have used a great many different objective functions, often multi-dimensional.  Correlation to the perfect equity curve, net profit / max drawdown and Sortino ratio are just a few examples.  But if I had to guess, I would say that the criteria that tends to produce the most robust strategies and reliable out of sample performance is the maximization of the win rate, subject to a minimum number of trades.

I am not aware of a great deal of theory on this topic. I would be interested to learn of other readers’ experience.