Tag: momentum

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

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.

The All Star Backtest

Profit by investing in stocks that have hit their all time highs

We launched the All Star Portfolio last week along with our discussion on momentum strategies. It is a great way for investors new to momentum (or equity investing, in general) to follow along a systematic momentum portfolio. We like this particular strategy because:

  1. Lower risk compared to other momentum strategies.

  2. Go-to cash if there aren’t viable candidates.

  3. A wide trailing stop-loss to exit outliers.

  4. Works with top-300 stocks by market cap – doesn’t depend on small caps to drive performance.

  5. Lower churn compared to other momentum strategies.

Historical Performance

We start from 2010 and rebalance monthly. Observe the drawdowns and the relative out-performance of the All Star.

The reason for the lower drawdowns is the ability of the strategy to stay in cash when things are bad. While we can take up to 25 positions, the slots are not always filled. An equal weighted portfolio (4% per slot) with only 5 positions will have 80% in cash.

Moreover, the per-position max loss during the time frame has been less 10% in a given month.

By keeping a trailing stop-loss of 15%, we ensure that only the true outliers are caught by it and not the run-of-the-mill corrections that are bound to happen.

The Momentum Factor

Only buy stocks that go up…

The Fama French 5-Factors came in two installments. The first 3 were published in 1992 and the rest in 2014. They capture “fundamental” factors, i.e., factors that can be derived from looking at balance-sheet and income statements. In 1993, Jegadeesh and Titman published their ground-breaking work on momentum: Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency (pdf)

Their paper singularly propelled momentum (or, trend-following) into the mainstream by giving it the academic vigor it lacked earlier. While its findings may not have been earth-shattering for most traders, most professional investors looked at momentum strategies as something that “traders” did and avoided them. But within a decade of the paper being published, momentum strategies were firmly established as a “legitimate” strategy that could be allocated to.

Basic Design

The J/T paper constructs a long-short portfolio by ranking stocks based on their prior 12 month returns (skipping the most recent month.) The top decile portfolio is called the “losers” decile and the bottom decile is called the “winners” decile. In each month, the strategy buys the winner portfolio and sells the loser portfolio. This is the classic 12_1 momentum, where 12 denotes the formation period and the 1 is the number of skip months.

The reason for the skip month is to account for the short-term reversal effect associated with momentum. Some researchers, like Fama and French, do 12_2 momentum, where the most recent 2 months are skipped. However, a more recent study by Medhat and Schmeling (2018) finds that once we discard the stocks with the lowest turnover, equity returns exhibit short-term momentum rather than short-term reversal. So, the short-term reversal effect may not be as prevalent as is usually thought and one could even go with a 12_0 momentum.

The French Momentum Library

Luckily for us, Ken French (of the Fama French fame) regularly updates factor statistics on his Tuck School of Business webpage.

While the momentum factor (MOM) has out-performed the market factor (MKT = rm – rf = Market risk premium), over the long term, it is not without its share of pain.

Long-only Momentum

As “retail” investors, we typically invest in long-only portfolios. This makes momentum investing an extremely gut-wrenching ride. One day, you might be thinking which Greek island you are going to buy and the next day you might be scrambling to pay rent.

Momentum strategies have been packaged as an ETF for retail investors in the US for a while now. PDP, the Invesco DWA Momentum ETF, and MTUM, the iShares MSCI USA Momentum Factor ETF, are the 800-pound gorillas in the room.

The differences in performance highlight the different ways momentum strategies can be implemented.

The Indian story is relatively new. A monthly-rebalance momentum strategy has delivered superior returns (although, with a massive dose of heartburn.)

Measures of Momentum

Once the J/T paper was out, academics got to work and systematically mapped out more than a dozen different ways to setup up momentum portfolios. The most common ones are:

  1. 12_2/12_1/12_0: These are the “original” momentum portfolios formed by only looking at absolute returns.

  2. Relative: For each stock, create a distribution of relative returns over every other stock in the universe and use it to drive portfolio formation.

  3. Acceleration: Rank stocks based on how well they have performed over the last 6-months vs. their preceding 6-month returns.

  4. CAPM-α: Rank stocks based on their α over an index.

  5. Sharpe ratio: Rank stocks based on their Sharpe ratios.

  6. Idiosyncratic/Residual: Rank stocks based on what is left after fitting a Fama-French 5-factor model. i.e., whatever cannot be explained by the 5-factors.

  7. 52-week or All-time Highs: A portfolio of stocks who’s prices have hit either one-year of all-time highs.

These are further combined with some sort of risk management measure, like a stop-loss or a trend overlay. So, based on the universe of stocks, frequency of rebalancing, momentum measures and risk-management technique applied, there are hundreds of different “momentum” portfolios that can be created.

Conclusion

While momentum is now a well established investment strategy, it is not an easy one to be married to. Differences in portfolio construction: formation periods, skip months/weeks, stock universe, stop-losses, etc. have a big impact on overall performance.

While momentum definitively underlines the “no pain, no returns” maxim, in a twist of irony, academics discovered the “low-volatility” anomaly. What if, investors can take less pain for more returns? Stay tuned for our next Free Float!

Introducing the All Star Portfolio

Given the large number of choices in front of investors these days, we felt that there should be an on-ramp for those who want to just follow along a systematic strategy without committing their portfolios.

So, we built a momentum portfolio that is easy for first-time investors to follow along. We call this the All Star Portfolio and is based on stocks hitting their all-time-highs. Just subscribe to our substack and receive emails whenever there is a change in the portfolio.

Factor Momentum Everywhere

Earlier this year, AQR had published a paper that showed momentum behavior also exists in equity factors (like value, quality, etc.) and not just in vanilla equities.

In this article, the authors document robust momentum behavior in a large collection of 65 widely-studied, characteristic-based equity factors around the globe. They show that, in general, individual factors can be reliably timed based on their own recent performance. A time series “factor momentum” portfolio that combines timing strategies of all factors earns an annual Sharpe ratio of 0.84. Factor momentum adds significant incremental performance to investment strategies that employ traditional momentum, industry momentum, value, and other commonly studied factors. Their results demonstrate that the momentum phenomenon is driven in large part by persistence in common return factors and not solely by persistence in idiosyncratic stock performance.

Factor Momentum Everywhere – Tarun Gupta, Bryan T. Kelly (AQR)

We put this idea to the test by constructing a long-only portfolio with five of the strongest factors – Momentum, Quality, Low-volatility, Value and Small-cap. The strategy was to go long whatever factor had the best returns over the last 12-months. We also looked at going long the best factor from the previous month. In both cases, the portfolio was re-balanced every month.

The representative indices and ETFs used for this back-test can be perused from the code: factor-momentum-india.ipynb and factor-momentum-US.ipynb

Results

The strategy using a 12-month formation period was a disappointment. There was no discernible improvement over a buy-and-hold of the large-cap index.

India (12-month Formation)
US (12-month Formation)

However, the one-month formation period widely out-performed the large-cap benchmark.

India (1-month Formation)
US (1-month Formation)

Needless to say, shorter the look-back period, larger the number of trades. So we added another back-test that averaged factor returns over 6-through-12 months to check if there was an acceptable middle-ground. Turns out, there is.

India (6..12-month Formation)
US (6..12-month Formation)

Forward Test

We setup US portfolios back in May this year when we first got to know about this paper. The 12-month and the 6…12-month formation period portfolios can be found here and here. They both seem to have out-performed SPY and MTUM so far.

We constructed the Factor Momentum 6-12 Theme for Indian equities that tracks the last strategy outlined above but given the lack of liquidity in factor ETFs, it trades the underlying stocks directly.

Investing in Factor Momentum

Indian investors can use brokers like TD Ameritrade or Interactive Brokers to invest in US stocks. The trades are posted on the slack channel mentioned on the pages linked above. You can execute the trades yourself by monitoring the messages on the channel.

If you are interested in executing this strategy on Indian equities, talk to us!

Related:
Factor Holding Periods for Excess Returns
Funding Your Dollar Dreams

Questions? Slack me!