Tag: momentum

High-to-Price (HTP) Momentum Backtest

Momentum investing strategies have historically produced high returns in Indian equities. The biggest problem with them has been deep drawdowns when the markets enter bear territory.

A number of risk management strategies like using moving averages, trailing stop-losses, and hedging have been discussed on this blog before. These strategies, either standalone or in combination with each other, have provided investors with significant protection against momentum crashes. These are “exogenous” techniques, i.e. they are not part of the strategy itself but is imposed by portfolio management infrastructure. The advantage of these techniques is that the default is to be always invested in the market. It is risk-management’s job to control exposure.

Alternatively, endogenous risk-management techniques are those that are baked into the investment strategy itself. Our All Star strategy is a prime example of a momentum strategy that reduces exposure to equities by design. If enough stocks are not hitting their all-time-highs, it simply sits in cash. When you combine this with one of the exogenous risk-management techniques, you end up with a high Sharpe portfolio.

The advantage of high Sharpe strategies is that you can use leverage to amplify returns. However, if you are a “cash-and-carry” investor then it might be too conservative. Is there a momentum strategy that sits between All Star and traditional momentum?

High-to-Price (HTP) Momentum

A new paper, Büsing, Pascal and Mohrschladt, Hannes and Siedhoff, Susanne, Decomposing Momentum: Eliminating its Crash Component (SSRN,) outlines a new way to slice the 52-week momentum strategy to avoid crash risk. It describes a ranking system based on High-to-Price (HTP) where HTP = ln(Phigh/P0) where Phigh is the stock’s 52-week high price and P0 is its price at the beginning of the period.

A monthly rebalanced HTP long-only portfolio looks promising. It sidestepped quite a few whipsaws and has a better drawdown profile than NIFTY 50.

There have been periods where the strategy couldn’t find 25 stocks to go long and it only had a handful of positions or was all cash. However, the degree of overlap between constituents in consequent months is quite high indicating that the portfolio is likely to experience very low churn. In this aspect, it is very similar to the All Stars strategy.

At a line-item level, there have been instances where some stocks have tanked more than 30% in the month. However, the skew is, by-and-large, positive.

Investing in HTP Momentum

The real world doesn’t line-up perfectly to match the end-of-the-month rebalancing activity outlined in our backtest. To make this strategy investible, it needs to have some risk-management strategy in place on a clearly defined universe of stocks and has to be dynamically rebalanced.

We present our High-to-Price (HTP) Momentum Theme that consists of a portfolio of 25 stocks selected from the top 300 stocks by market-cap that rank high on their HTP scores. A 10% trailing stop-loss ensures that errant positions don’t drag down the whole portfolio. It is ideal for investors who can accept a bit more risk than All Stars for potentially higher returns.

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Factor Momentum Performance Update

Factor performance tends to be sticky. If Value, Momentum, Quality, etc. out-performed in the recent past, they continue to out-perform in the near-future.

AQR wrote a paper on it back in January 2019: Factor Momentum Everywhere. More recently, the folks at Research Affiliates extended the research in their Factor Momentum paper. Based on AQR’s research, we setup a portfolio that mimics this for both US and Indian stocks in December 2019. We called it Factor Momentum III and Model Momentum Theme.

The performance of the US portfolio has been gangbusters. It sidestepped the Corona Crash of 2020 and has been on a tear since then. The Indian experience, however, has been disappointing.

US Factor Momentum

The Indian portfolio suffered from its inability to go into cash/bonds during crashes. Being fully invested took a bite out of its overall performance.

India Factor Momentum

The Indian version comes up short even if you compare its stats with its component factor portfolios.

India Factor Momentum Statistics

The intuition behind the Radar Plot above is that the larger the area under the points, better the strategy. Model Momentum is in pink and it pales in comparison to most of its constituents. Surprisingly, the Financial Strength Value Theme (light green,) that is rebalanced annually, beat out everything else.

What explains the underperformance?

  • Not being able to go into cash/bonds meant a larger hill to climb during recoveries. However, cash is a double-edged sword. If you get the timing wrong, you might end up going into cash after the bottom and watch the market recover helplessly. Unless the trend formation period is really short, cash is not a viable option.
  • High transaction costs can also be playing a role here. The difference between Gross and Net returns is ~15%. Not as high as a pure momentum strategy but not trivial either. Also, US portfolios do not incur STT and brokerages are essentially zero.
  • Maybe 20-months is too short a window to judge such a slow-moving strategy. The research looks solid and maybe all we need is to give it some time?
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Global Equities Momentum

A slice of Dual Momentum

Gary Antonacci created the Global Equities Momentum (GEM) model that applied dual momentum to stock and bond indices. It toggles between stocks and bonds using 12-month trailing returns. And when it toggles to “stocks,” it chooses between US equities and International (ex-US) equities based on whichever posted higher returns in the previous 12-months. The model uses the S&P 500 index as a stand-in for US equities and the WORLD ex USA index for international stocks.

Investors can use the ETFs SPY/VOO for the S&P 500, SCHF for World ex-US DMs and AGG for bonds while replicating this strategy.

The best part about this strategy is its simplicity. It takes just 3 inputs and anybody can set it up on Google Sheets. Execution is as simple as it gets because at any given point in time, it is long just one ETF. Also, given that it uses a 12-month look-back, it is less prone to whiplashes, resulting in a lower trading frequency.

Specifications & Expressions

When you automate systematic strategies, you need to nail down its exact specifications. In this case, they are mainly: inputs, look-back periods and traded instruments.

The original version of the Strategy uses the S&P 500 and World ex-US both for inputs and as proxies for the traded instruments. However, there is no reason why they both should be the same. Also, what is so magical about a 12-month look-back period anyway? Why can’t it be 6 -months, a month or an average of the last 6-months?

The Strategy only describes a broad idea with one set of Specifications and Expressions out of a multitude. It can (and should) be adapted to fit one’s risk profile and investment horizon.

The Momentum Expression

The easiest tweak to the original strategy is to swap out the traded equity instruments with their momentum counterparts.

At the final step, when it comes to executing the trade, you can use MTUM, the US Momentum ETF instead of SPY/VOO and IMTM, the DM ex-US Momentum ETF instead of SCHF.

Long-only momentum ETFs are highly correlated to their market-cap counterparts but have the potential to juice returns in bull-markets. Since we are trend-following anyway, why not go a step further up the risk-curve and embrace momentum as well?

This is the basic idea behind our Global Equities Momentum I strategy.

The Look-Back Specification

Picking a look-back for trend-following strategies is fraught with data-mining bias. One could potentially test 100s of periods and pick one that gave the best results historically. The data-mined look-back could even work in forward tests but inexplicably, and suddenly, fail in real portfolios.

The safest thing to do would be to not change the look-back periods outlined in the original research. However, the world would’ve changed since its first publication. How do you strike a balance between the two?

This is what we’ve tried to do in our Global Equities Momentum II strategy.

Long look-backs are slow at reacting to rapidly changing markets. Some might say that this is a bug while some might argue that this is a feature. Shorter look-backs, on the the other hand, can react faster but are prone to head-fakes and whiplashes.

The second version of our GEM strategy tries walk the fine line by taking the average of 6- through 12-month returns. It tries to hew close to the original research while acknowledging that the world has gotten faster since it was first published.

No Free Lunch

While the strategy adapts to the broad, slow-moving macro theme of US equity under-performance vis-à-vis rest-of-the-world (were it to occur,) it is not immune to getting whiplashed due to short and steep market dislocations like the COVID crash of March 2020. The strategy got into bonds just when the equity markets were recovering and stayed there until well-after. It is simply not possible to avoid all landmines when it comes to investing.

While we ran our back-tests, we tried a fair amount of permutations and combinations. Some where discarded in spite of having better risk-adjusted returns because they lacked internal consistency. While some slipped into data-mining territory in spite of our best efforts to avoid it. Readers interested in the process and the code can read through our GEM Collection.

Related: ETFs for Asset Allocation

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Momo "Rapid-Fire" Momentum

High octane strategies for your portfolio

The biggest advantage that retail investors have is that they don’t have to worry about managing a huge portfolio with different types of investors with differing time-horizons and expectations. And of course, there’s the straightjacket of mandates that bind professional investors.

The problem with bucketing yourself as a “value investor,” “contrarian,” “growth,” or “momentum guy” is that you lose the biggest advantage that you have: flexibility and the ability to adapt to the market. Mandates, or lack thereof

Broadly, at a meta-level, investment strategies can either be Ferraris or busses but not both. They are built with different uses in mind. A Ferrari is not going to be able to seat 40 people or tug a 40 ton rig. And you don’t build a bus to go 0 to 60 mph in 3 seconds.

As a retail investor, your life becomes a lot simpler if you decide upfront if you want to drive a Ferrari or take the bus. But once you get on one, be at peace with your decision. Most investors would be better off taking the bus: DCA/SIP into a mutual fund, don’t chase performance, focus on asset allocation and increase your income and savings over time.

However, just because taking the bus is “right” according to conventional wisdom, doesn’t mean that everybody should be forced to get on one. Just like how you have Ferraris, buses and everything else in-between on the road, there are a wide range of investment strategies outside of the mainstream “at-scale” investment vehicles like mutual funds, PMS, managed accounts, etc.

Momo: The Ferrari Of Investment Strategies

Momentum is a well known Fama-French factor. The problem with momentum portfolios have always been the massive left-tail: when markets are volatile, the drawdowns have been heart-breaking. It doesn’t matter if the portfolio is long-only or long/short, there is no escaping the momentum whiplash.

Then there is the question of rebalancing frequency. To scale a momentum fund, managers need to trade-off transaction and impact costs with being responsive to the market. And that means leaving a fair bit of alpha on the table.

This is the constraint of driving a bus. It can be a fast bus. But it is still a bus.

However, what is true for professional investors and funds is not necessarily true for you, the retail investor.

Do It Often, Do It Better

Most of the early factors were researched at a time when compute power and data were hard to come by. Researchers took the short-cut of using monthly returns to run their analysis because it made the problem more tractable. That set a precedent that is being followed to this day: the monthly rebalance schedule.

The problem with a monthly or a quarterly rebalance schedule is that the market has got a lot faster since the days the papers were written. We live in a world where data is abundant and compute power is a fraction of what it used to be. And trading costs have crashed to a small fraction of what it was 30 years ago.

The world changed.

There is no reason why the market shouldn’t be sampled more frequently.

Some Left-Tails Can Be Docked

A higher frequency approach lends itself to better risk management. It allows for a more responsive position sizing system based on market volatility and the ability to employ “stop-loss” exits on individual positions.

While drawdowns are not entirely avoidable given the nature of the markets, it is quite possible to protect the portfolio against the extremely deep ones. And the deep ones seem to occur at least once every three years, or so.

Avoiding the worst of the drawdowns allows for faster compounding of the portfolio.

Momos are risk-managed, frequently sampled momentum strategies.

Our Experience With Momos

We have been running Momo portfolios for both Indian and US markets for a while and we do it for all three flavors of momentum: Relative, Velocity and Acceleration. We’ll get into the differences between these in later posts but irrespective of the flavor, the “container” within which they run are identical.

The flavors wax and wane depending on the market – there is really no way to quantifiably claim that one is better than the other. In terms of personal preference, I would rank Relative Momentum first, Velocity and then Acceleration. To keep things concise, we show Relative “Momo” Momentum performance below.

US Equities

Indian Equities

Does It Scale?

When we discuss these strategies with professional fund managers, the most common question that comes up is “does it scale?”

And the answer is: No.

It doesn’t scale to professional break-even levels. For eg: for an Indian PMS to break-even, it at least needs Rs. 100 cr in AUM. There is no way the Indian Momos scale up to that level.

But it really doesn’t matter to you, the retail investor. Remember: professional investors are driving a bus, you need not.

Trade-Offs

The market abhors a free lunch. So the next questions is: “What are the trade-offs?”

  1. Risk management is not free. There are always trading costs/taxes that affect the final outcome. But the known-knowns are factored into the performance metrics shown above.

  2. Execution lags. There is always a delay between when the trades are triggered and when the execution takes place. This can be narrowed down by automation to a de minimis.

  3. Compliance. There could be employer, broker or regulator imposed limits on how frequently positions can be churned in certain accounts. Momos would be a poor fit in these circumstances given that any deviation from the model triggered trades can lead to substantial deviation in performance.

Next Steps

If you decide that taking the bus is not for you, then we can help. Have a look at the Momo strategies linked below and let us know if you are interested. We are here to help.

US Momos

Relative Momentum

Velocity

Acceleration

Indian Momos

Relative Momentum

Velocity

Acceleration

Check out our completely automated strategies: stockviz.biz/themes

Investing in the US? We got you covered on us.stockviz.biz/themes

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Cross-Asset Time-series Momentum

Trend-following systems typically use the past performance of a particular asset to trigger a buy or a sell on that asset. A research paper that came out in 2019 looked at whether the historical performance of multiple assets can be used to trade them.

Pitkäjärvi, Aleksi and Suominen, Matti and Vaittinen, Lauri Tapani, Cross-Asset Signals and Time Series Momentum (January 6, 2019). Available at SSRN: https://ssrn.com/abstract=2891434

From the abstract:

We document a new phenomenon in bond and equity markets that we call cross-asset time series momentum. Using data from 20 countries, we show that past bond market returns are positive predictors of future equity market returns, and past equity market returns are negative predictors of future bond market returns.

Unfortunately, the paper did not look at Indian markets to check if this worked. So, we rigged up a simple backtest to see for ourselves.

Rules

A simplified equity-bond cross-asset trading strategy at the beginning of month t can be constructed as follows: Compute the past 12-month equity return (E past) and the past 12-month bond return (B past). If:

a) E past is positive and B past is positive: Buy equity
b) E past is negative and B past is negative: Sell equity
c) E past is negative and B past is positive: Buy bonds
d) E past is positive and B past is negative: Sell bonds
e) Otherwise, invest in the risk-free rate.
Hold the portfolio for one month and then repeat the same procedure in month t+1 (source.)

Backtest

We used the NIFTY 50 TR index to represent equities, NIFTY GS 10YR index for bonds and the CCIL Index 0-5 TRI for risk-free rate.

Since our risk-free index starts only from 2004, our backtest only goes back 16 years. However, the markets have been through a lot since then, so it is unlikely we are losing much by not being able to go back much earlier.

The 12-month look-back approach massively under-performs the NIFTY 50 TR buy-and-hold. We shortened the look-back to 3-months to see if we could make the strategy more responsive to trend reversals.

To our dismay, we saw only marginal improvements in overall returns but the draw-down profile of the long-only portfolio was much better.

Conclusion

While the approach outlined in the paper might be valid for the selected subset of markets, it fails a simple backtest on Indian market indices.

Code for the backtest can be found on github.

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