In our last two blog posts, I went over some examples of opportunistic & event-driven trades that fit within our wheelhouse (ie. forced liquidity arbitrage taken advantage of in a market-neutral manner). In this post, I’d like to touch on the systematic element of our investment philosophy. I would recommend that you review our blog post “Our Investment Philosophy” as a starting point.

Finding The Elusive “Holy Grail”

The Whole is Greater than the Sum of its Parts

-Aristotle

Many traders seek a “holy grail” investment strategy. We don’t believe that such a thing exists. To be clear, in the context of an active trader or portfolio manager, a “holy grail” would be an investment strategy that, over the long-run, consistently generates high risk-adjusted returns (say 2.0+ Sharpe Ratio) with little to no correlation to major market factors. It’s also important to define “investment strategy” in this context as being a defined set of rules designed to capture a specific alpha.

That is not to say that there aren’t very high risk-adjusted returning investment strategies out there – there are. But how long will they last? The more lucrative an alpha source is, the more incentive there is for other sophisticated traders (and many are very well-incentivized!) to find and capture it. In general, the better the returns have been for a investment strategy, the faster those returns could decay – an uncovered “holy grail” may tarnish.

We believe that the real “holy grail” is the effective implementation of an idea rather than a single strategy. To us, that idea is a dynamic portfolio of uncorrelated investment strategies. Ideally, these are automated algorithms that capture structural alphas across the global financial marketplace (ie. taking the other side of higher time-frame or forced market participants, with the clues of their impact ascertained through the scientific process). The individual component strategies of such a portfolio may not have “holy grail” characteristics – however, the whole is greater than the sum of its parts.

With this in mind, we would argue that, in the long-run, it is actually more fruitful to look for multiple low-Sharpe component strategies and combine them within a dynamic regime-based meta-strategy, rather than gunning for the single 2.0+ Sharpe strategy. One could also make a reasonable argument that it is exponentially “harder” to find and implement high-Sharpe strategies (ie. we reason it should take less time / resources / intellectual capacity to find a 0.5-1.0 Sharpe strategy vs. a 2.0+ Sharpe given market competition – though of course it’s tough to tell to what degree).

Implementing this philosophy successfully should drastically improve portfolio robustness when compared to a single high-Sharpe strategy. Assuming the component alpha’s are “true” (ie. the backtests are not significantly biased / curve fit), there are two major sources of the increased robustness: 1) a portfolio of uncorrelated strategies has less failure points – ex. a few strategies could be “false” or decay and the overall portfolio would still generate alpha, and 2) the lower-Sharpe individual component strategies will generally have less risk of competition eating away at the alpha availability. Assuming the strategies are also “truly” uncorrelated (ie. they trade different markets, timeframes, styles, etc.), the systemic risk of the portfolio is greatly reduced and black swan events should have less impact on overall portfolio returns.

When scaled correctly, this investment philosophy results in what we believe to be the ultimate form of diversification. Of course, its implementation is no doubt easier said than done.

One thing is clear, though: it is impossible to implement this investment philosophy optimally without automation.

This is exactly why XMonetae has built out and will perpetually improve its technology-driven “assembly line” – designed to hypothesize, verify, and ultimately trade automated investment strategies within a dynamic, multi-asset portfolio in the most efficient way possible.