How Do You Beat Stock Markets?

The best information we can ever provide investors is the mechanics of how we think about macro conditions over time rather than what we think about them at any particular time. Consistent with this idea, we present our Macro Mechanics, a series of notes that describe our mechanical understanding of how the economy and markets work. These mechanics form the principles that guide the construction of our systematic investment strategies. We hope sharing these provides a deeper understanding of our approach and ongoing macro conditions.

The US equity market has far outperformed its peers globally and significantly outperformed most active managers domestically. Since 2010, the S&P 500 has provided investors with a return-to-risk profile consistent with those of a sophisticated asset manager, generating 13.3% annualized returns over cash at 17.2% volatility, i.e., a Sharpe Ratio of 0.78. Given the conservative nature of most institutional investors, even if they have matched or somewhat outperformed this risk-adjusted return, they were unlikely to have maintained such a high volatility profile, leading to underperformance. Combining these risk-adjusted and absolute return characteristics with the proliferation of low-cost ETFs— the S&P 500 has become the primary benchmark against which most investors measure success.

We think it is unlikely that equities can continue to perform at this pace over the long term. Over a very long history, beginning in the 1900s, the equity market has had a Sharpe Ratio closer to 0.35, i.e., about half of what we have seen since 2010. That is our long-term expectation. However, how that long-term expectation comes to fruition is a question whose answer extends beyond the capabilities of reasonable quantitative investing. We may have another two decades of exceptional equity returns followed by a lost decade, a re-run of the technology bubble followed by an exceptional bust, or any one of the infinite number of permutations of the future. Those long-term future paths are unknowable, and any strategy that seeks to bet on those paths is much closer to a coin flip than durable alpha in its odds of success.

Thus, given the centrality of US equity markets today, we think it is our role as active investors to seek to add value to this core component of investor portfolios. If most investors seek equity-like exposure, we think it’s our job to add an edge to that exposure. We believe that with reliable, durable, and uncorrelated alpha, it is possible to outperform the equity market significantly over a full investing cycle. In this note, we share our thoughts on the levers that we think we can pull to create these outcomes.

We think there are four big levers that we can pull to create durable outperformance versus the S&P 500:

• Sector Selection
• Beta Timing
• Dynamic Risk Control
• Strategy Overlays

We think pulling these levers can create a portfolio that seeks to maintain significant or leveraged upside capture to bull markets and limited or short exposure to bear markets while maintaining a much more moderate drawdown profile versus the benchmark. As ever, our approach is driven by a macro-based edge leveraged via quantitative implementation. Macro alpha signals operate at mid-frequency, i.e., big changes occur over months, not days. As such, we do not think it is reasonable to expect a strategy to outperform equities at every rolling junction. Rather, we seek to engineer strategies that outperform over a full investment cycle. We define a full investment cycle as one where markets have experienced bull and bear markets with a generalized upward drift. With these characteristics explained, let’s turn to our levers.

Sector Selection
As we begin our process of adding alpha to the benchmark, we must start by understanding the benchmark. The S&P 500 (our chosen benchmark for US equities) is an index that tracks the performance of 500 of the largest publicly traded companies in the United States. For a stock to be included in the S&P 500, it must show positive profitability and maintain a sizable market capitalization. The constituents of the index itself are weighted by their outstanding market capitalization. The larger the market capitalization of a company, the greater the share it occupies. The dominant driver of market capitalization over time is price rather than shares outstanding. As such, the S&P 500 has a very large bias toward medium-term price momentum for the largest, profitable companies in the stock market.

Now, momentum in itself is not a bad thing, in fact it is a valuable tool in the toolkit for investor. However, it is just one factor which can be predictive of asset market performance. We think adding factors to this mix can add some modest edge to the S&P 500. Said differently, we think the first step to generating alpha is to make beta better.

Given our expertise is in macro, we leverage our systematic macro process to achieve this. Particularly, our macro process lends itself well to sector selection. At the index level, the most dominant driver of changes to the equity market are changes in growth expectations, which flow through to earnings expectations, which flow through to the index, and various sectors. We find that responsiveness to big growth-induced moves is fairly uniform in terms of their price impact on sectors. As such, only very modest incremental alpha is achieved by playing big moves like recessions, economic booms, etc., via sector tilts. However, we find that there is considerably more information available in the distribution of profits in the economy. Thus, for a given level of growth, we can search for the biggest winners or losers in the cross-section. For instance, during an investment boom, we would prefer technology equities as our primary long exposure. Alternatively, during an inflationary surge, we would prefer energy stocks. We can map these relationships for every sector. Effectively, we seek to align the portfolio with cross-sectional macro trends rather than just price trends. Accounting for such macro factors adds an additional layer of protection and return to a standard S&P 500 portfolio.

There are a wide variety of alternative implementations possible here, but we use macro implementations to stay with our primary expertise. The purpose of this approach is to add a modest amount of sector selection alpha to S&P 500 exposure. By itself, this is nothing to write home about, but when combined with our other levers, it can be quite powerful.

Beta Timing
Choosing sectors that look more attractive during a given macroeconomic backdrop offers us additional returns versus the index and better downside protection. While it may offer a better beta, it is still exposed to all the risks embedded in equity beta. The primary risk that all equities are exposed to is the risk of a persistent and pervasive economic contraction, i.e., a recession. Secondarily, equities are often at risk of more minor corrections when they have become rich versus bonds, commodities, and cash in a manner inconsistent with their fundamental economic drivers. Our systematic macro process consistently evaluates the probability of these outcomes to trade equities long and short— we call this beta timing.

Crucially, this process is mechanically and empirically uncorrelated to the underlying market and depends entirely on whether our systems can anticipate what’s likely to transpire. We can use these signals to achieve higher confidence in whether equity markets will likely outperform or underperform. We can take these quantitative estimates of over/underperformance to estimate potential returns and use them to lever up or scale down our equity exposure. When equities are cheap and in an economic expansion, these signals dial-up portfolio exposure. Conversely, when equities are expensive and the economy is at risk of a recession, this component may take equity exposure to zero or even enter a net-short position. We find that adding this layer of beta timing to our sector selection process is extremely accretive to total portfolio performance. If the drivers of the returns streams are adequately differentiated, the benefits of adding sector tilts in addition to beta timing can improve the gross portfolio Sharpe Ratio 1.5-2x.

Dynamic Risk Control
Thus far, we have largely discussed generating better signals, which primarily increase total returns. However, we cannot simply seek to generate returns without direct consideration of measures of risk. Equity markets are extremely episodic in their risk— their volatility profile remains stable for long periods only to shoot upwards during times of crisis. Significant and persistent drawdowns usually accompany these volatility spikes.

Now, while our signals may provide outsized returns during these periods of high volatility, they run the risk of running high leverage at locally elevated volatility levels. In the event that these bets are offside, as they will often be, we run the risk of significant losses. As such, we think capping the total amount of risk, regardless of the amount of signal generated, is a prudent decision to boost investors’ staying power in an investment strategy. From our experience, most investors look for conformable equity-like risks with drawdowns of approximately 20%. We can cap our risk-taking to avoid exceeding a pre-specified drawdown threshold like this one. The further we are from this drawdown threshold, the more unconstrained our portfolio risk-taking; the closer we get, the more we dial down risk. We want to take the most risk when we have the most signal, but we want to do that within reason. This dynamic process is different from traditional volatility targeting, which imposes a constant level of portfolio risk regardless of signal strength.

We find this dynamic version of risk-taking significantly additive to portfolio performance. While dynamic risk control doesn’t directly enhance returns, it mitigates drawdowns and facilitates better risk-adjusted returns. Overall, dynamic risk control allows us to press our edge when we feel strongly but tempers our bets so that we take risks consistent with investors’ risk tolerance.

Strategy Overlays
Combining sector selection, beta timing, and dynamic risk control creates a return stream that consistently changes its gross exposure to equities. When there is a lot of signal, there is a lot of exposure. When there is little signal, the systems maintain very modest exposures. We cannot know how these signals will be distributed over the next few months or even years. Often, our signals may dial down their exposure, taking significantly less volatility than the S&P 500. Traditionally, this decrease in exposure would increase cash positions. However, we can further enhance portfolio returns by taking that cash and investing actively in bonds and commodities at an extremely low volatility. Maintaining a very low volatility for these exposures allows us to make sure we do not have excessive drift from the benchmark but can also reliably add returns above cash to the portfolio, further boosting the potential for long-term outperformance. We find these overlays to be extremely additive when our strategies have begun to reduce exposures, but equity markets may continue to power ahead. During these periods, the additional excess returns generated through these overlays help bolster the portfolio’s returns, allowing total returns to maintain pace with the benchmark without loading on the increasing risks embedded in the market.

Conclusions
Thus, while beating the equity market is a challenging and complex objective, we think it is attainable.

We believe an integrated approach to sector selection, beta timing, dynamic risk control, and active overlays can engineer a portfolio that durably and significantly outperforms the equity market over a full investing cycle.

We visualize the outcome of applying the principles we have described here in the readout below:

Coming soon for clients. Stay tuned.

Until next time.