Beating the Market: Alphalytics Flagship ETF Strategy

The Alphalytics Quant Multi-Strategy (AQM) portfolio specializes in systematic trading of exchange-traded funds (ETFs) to achieve high performance every year and long-term capital gain.

By diversifying capital across 30 ETFs and six classes of investment strategy, AQM aims for superior returns with low drawdown in various market conditions.

TLDR

AQM is a high-performance investment strategy with medium risk that targets 25 percent CAGR.

Relative to similar funds, AQM aims for higher returns at a lower risk.

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AQM investment objectives

• 25 percent annualised returns;
• 1.5 Sharpe Ratio;
• 15 percent drawdown (maximum cumulative monthly); and
• To consistently beat the market in the long run.

AQM is an alpha-seeking strategy with a focus on high risk-adjusted returns.

In contrast to a smart beta or leveraged beta strategy – which involves taking on increased leverage or more risk – AQM aims for consistent outperformance.

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Key value proposition

Unlike traditional funds that require investors to trade-off between high risk/high return, AQM offers an industry-leading 3:2 return-to-risk ratio – three units of annualised gain for every two units of risk (maximum cumulative monthly drawdown).

With its multi-strategy approach, AQM seeks to provide upside participation and downside protection.

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Why you should trust Alphalytics

• 56.3 percent gross returns: Live, auditable track record (19 months from Oct 2019 to Apr 2021);
• 5.6 percent drawdown (Feb 2020): Compared to the S&P 500 experiencing a 20 percent drawdown (maximum cumulative monthly) during the 2020 Covid market “stress test” (Jan to Mar 2020); and
• AQM’s live performance continues to be consistent with its backtest results (by both absolute and adjusted-risk return measures).

Source: AQM research

Now, even though perfectly negatively correlated assets do not exist in real life, it is easy to see the value of a portfolio of diversified instruments using multiple, anti-correlated asset classes.

Therefore, a good starting point in constructing a diversified portfolio is to understand correlation between major asset classes.

Below is an asset class correlation matrix by Guggenheim Investment based on Bloomberg data (2011 to 2020).

Asset class diversification only mitigates some volatility and drawdown risk – and, most importantly, is not sufficient to help investors weather all market conditions.

In addition, numerous academic and practitioner studies quantify that correlation between assets tend to increase during periods of market downturn – precisely when diversification is expected to do its job of protecting a portfolio.

Hence, asset diversification, though fundamentally sound and necessary, is only a partial solution in the creation of a stable, high risk-adjusted portfolio.

Because financial markets have become progressively more integrated and correlated, multi-asset diversification can no longer protect investors from severe market losses.

Furthermore, correlation between assets is never a constant; it fluctuates according to the different phases of economic cycle. (Case in point: The Covid market meltdown in Q1 2020 when all asset classes experienced a major plunge.)

We have made the case that anti-correlation is an effective defensive component for a long-term portfolio; and that (large) drawdown is bad for wealth accumulation.

In addition, we observe that correlation between asset classes are shrinking. (Assets are increasingly correlated.)

Where then do we find anti-correlation?

We would argue in strategy diversification.

Primer on trading model diversification

Given the limits of asset class diversification as an effective hedge, one approach to constructing a stable portfolio is to use a variety of trading models.

Think of a trading model as a subset of an investment strategy.

For instance, value investing is a well-known investment strategy.

Three examples of trading models that are subsets of value investing are:

1. Classic value investing: The wonderful company with a fair price approach that is represented by Warren Buffett and Charlie Munger; this trading model focuses on finding competitive-moat-protected businesses at reasonable prices.
2. Value short: David Einhorn and Bill Ackman are two prominent investors who bet on overvalued stocks.
3. Value creation: Zhang Lei famously said, “We are entrepreneurs who happen to be investors” when describing Hillhouse, the $13 billion fund in China he founded. This typically involves hands-on management and adding value to the firm beyond capital investment.

For the purpose of our discussion: An investment strategy is a broad, widely accepted, and publicly available investment philosophy.

A trading model is a variation of an investment strategy; and a trading model is often proprietary to the Portfolio Manager.

AQM uses six different classes of investment strategy:

1. Adaptive asset allocation
2. Long/short
3. Momentum and reversal
4. Statistical arbitrage
5. Trend following and market timing
6. Volatility trading

Based on these six time-tested investment strategies, AQM consist of 33 trading models – these 33 trading models have uncorrelated return-to-risk profiles to achieve trading model diversification. (See also this post on a multi-strategy approach.)

To illustrate, below are two trading models used in AQM:

If you prefer to skip the technical explanation of trading models, jump ahead to “AQM results”.

The Alphalytics Team is constantly working on methods to reduce portfolio volatility and drawdown.

To achieve the level of diversification beyond that offered by traditional asset classes, AQM invests in a series of trading models with uncorrelated return profiles.

To generate alpha, AQM focuses not only on asset diversification but on investment strategy and trading model diversification.

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First AQM trading model: Global Equity Dual Momentum

The GEDM trading model is inspired by Dual Momentum Investing authored by Gary Antonacci.

Antonacci demonstrated that by combining relative-strength momentum and absolute momentum, investors can take advantage of intra-market trends while avoiding large market drawdown.

At each monthly rebalance, the GEDM model will first compare the past three months’ performance (or absolute momentum) of Vanguard Total Bond Market Index (VBMFX) with that of the risk-free rate.

If VBMFX’s 3-month return exceeds that of the risk-free rate, the model will invest in one of the following list of securities:

• SPDR S&P Midcap 400 ETF Trust (MDY)
• Invesco QQQ Trust (QQQ)
• Vanguard FTSE Emerging Markets ETF (VWO)
• iShares 20+ Year Treasury Bond ETF (TLT)

The security selected for purchase is based on its relative performance which is calculated as the asset’s total return over the past three months.

However, if VBMFX’s 3-month return is below that of the risk-free rate, the model will only hold the relatively low risk iShares 7-10 Year Treasury Bond ETF (IEF).

Source: AQM research

AQM’s GEDM model generated an annualised return of 17.3 percent while S&P 500 only produced 7.4 percent.

In terms of downside risk, S&P 500 suffered a maximum drawdown of 52.6 percent during the 2008 financial crisis, while GEDM managed to limit maximum drawdown to 19.2 percent.

The model produced a Sharpe Ratio of 1.06, while the market produced only a ratio of 0.46.

In sum, the GEDM model achieved receive higher returns at a lower risk, compared to the board equity market (S&P 500).

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Second AQM trading model: Contango Trend Following

VXX ETN trades like a stock in an exchange. (However, unlike stocks, owning VXX does not give an investor a share of a corporation.)

Ideally, VXX would track the CBOE’s VIX index — the market’s de facto volatility indicator. However, since there is no investment vehicle that directly tracks the VIX, Barclays choses to track the next best option: VIX futures.

VXX suffers from negative roll yield when the CBOE VIX futures curve is in contango. The VXX fund must “roll” its near-term futures contracts to further-term contracts as their expiration date nears.

By purchasing further dated contracts at higher price and selling near dated contracts at lower price, VXX suffers from value decay.

Since VIX futures curve is in contango more than 80 percent of the time, VXX will inevitably suffer value decay over time, as shown by the below price chart.

Knowing this, the CTF model is designed to capture the decay value of these instruments systematically using typical trend-following techniques. (Trend following is not new and is often used by CTA funds to trade futures instruments.)

In the case of AQM’s CTF model, it only takes short positions to capture decay value.

Trading models in AQM could be classified according to below six classes of investment strategy.

Backtest of AQM produced a 33 percent CAGR and Sharpe Ratio of above 2 while maintaining a drawdown of less than 11 percent (maximum monthly cumulative).

From a reward-risk perspective, CAGR versus drawdown ratio of overall portfolio is significantly higher than that of each individual model.

The benefit to investors: AQM puts less capital at risk to gain superior returns.

Backtest results show the consistency of AQM to weather market downturns in 2015 and 2018; and ability to generate positive profits year-after-year since 2009.

Source: AQM research

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Drawdown Analysis

In addition, it is important to study the nature of AQM returns during each major market downturn.

An analysis of major market downturns over the past two decades shows that AQM suffered a lesser drawdown than the broad equity market (S&P 500).

This signals a high level of confidence for AQM’s ability to weather various market conditions.

AUM capacity and superior ETF selection

AQM is designed with institutional investors in mind and sets a high bar for its ETF selection criteria.

AQM trades highly liquid, U.S.-listed ETFs: Each ETF needs to have more than USD 200 million in AUM and an average daily trading volume exceeding USD 25 million.

In addition, these ETFs must have a trading history of more than 5 years; majority of our ETFs have 10 or more years of trading history.

Below is a representative list of ETFs traded in AQM. Notice that these ETFs have AUM and daily trading volume way above the defined thresholds.