Category: Investing Insight

Investing insight to make you a better investor.

Factors: Buy All of Them

Can’t decide between quality, low-volatility, high-alpha? Why not buy all of them?

Our previous post discussed how you can use the historical performance of different factors to avoid falling into a factor-trap. However, can factor investing be further simplified?

The NSE Strategy Indices

The NSE has published a whole library of factor indices. Some of them are pure factors – like quality, low-volatility, etc – and some are hybrids – like alpha-quality-low-volatility (sort of like a shampoo-conditioner-face-wash 3-in-1.) You can explore their website if your curious.

The question is, what if you just took quality, low-volatility and high-alpha (a proxy for momentum) and just equal weighted it? Why choose when you can have all? This is the essence of the Multi-Factor approach to factor investing.

Equal-weighted Factor Portfolio

Even if you did a quarterly rebalance, you did better than NIFTY 50.

Since 2010, an equal-weight alpha/low-vol/quality/value factor portfolio gave an annualized return of 12% vs. NIFTY 50’s 8.88%.

While alpha and low-vol are price-based factors, quality and value are based on company fundamentals. What if, we just equal weighted the price-based factors?

Equal-weighted Alpha and Low-Volatility Factor Portfolio

Given the out-performance of the low-volatility factor, we see a significant boost to an equal weighted alpha/low-vol portfolio compared to equal weighting all the factors.

To summarize returns since 2010,

equal-weight all factors: 12.02%

equal-weight only alpha and low-vol: 13.76%

NIFTY 50: 8.88%

Caveats

Before transaction costs, we see that factor indices have beaten the NIFTY 50, historically. However, investors should bear these points in mind while looking at index back-tests:

  1. Index Inception – the date from which the index was constructed (since 2005.)

  2. Launch – the date on which the index was launched (in 2018.)

  3. Invested – the date from which a significant amount of money gets invested in the index (in 2019.)

  4. Re-balance frequency – how often does the index rebalance?

At launch, these indices have incorporated over 13 years of historical data. One can’t discount the possibility that there might be some over-fitting to increase their marketability.

Typically, index performance dips once the AUM crosses a tipping point. And given that India has a 0.1% STT (Securities Transaction Tax,) the higher the re-balance frequency, the worse the performance.

The true test of these indices will occur when real money is invested in them over two or three complete cycles.

Conclusion

Both Factor Rotation and Multi-Factor approaches have their pros and cons. However, the one thing that remains common is that these take time to play-out. There are huge year-over-year variances in performance and investors need to stick to an approach long enough for alpha to emerge.

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Factor Rotation

Is there a away to time factors?

Factor investing is the process of constructing portfolios of stocks by isolating certain statistical properties that have shown to out-perform over the long term. For example, investing in stocks that rank high in the “Quality” factor. Here are some factors that were discussed previously on FreeFloat:

  1. Introduction and Fama-French 5 Factors.

  2. Momentum investing.

  3. The Low-volatility Factor.

No Holy Grail

While factor portfolios are expected to out-perform over the long-term (say, 30 years,) there is a strong chance that they under-perform over an individual’s holding period (5-10 years.) This leads to sub-par investment returns due to out-of-favor factors.

For example, Value vs. Growth.

Is Value/Growth Dead?

Consider IWD, the iShares Russell 1000 Value ETF, IWF, the iShares Russell 1000 Growth ETF and IWB, iShares Russell 1000 ETF.

2000 through 2010, value out-performed.

2010 through 2020, growth out-performed.

Whether you were a “Value” investor or a “Growth” investor, you saw 10-years (!) of under-performance.

Persistence of Out-performance

To avoid under-performing, an investor can:

  1. Market-cap index (avoid choosing.)

  2. Predict (good luck with that.)

  3. Follow the herd (FOMO.)

Turns out, option #3 works pretty well and is robust.

Individual factors can be reliably timed based on their own recent performance.

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

Rule: Buy whatever worked in the last month

Worked in the US

Worked in India

The problem, however, is that India has STT (Securities Transaction Tax) that the US doesn’t. And with a high turn-over strategy, STT can completely sap whatever alpha was produced.

Rule: Buy the one with the Best Average Returns over the last 6-12 months

Worked in the US

Worked in India

Forward-Test

The back-test showed that for the US markets, investors can rotate into the factor that performed the best in the last month. And for India, investors can average into the one that had the best average 6-12 month returns.

US

The Factor Momentum strategy beat plain-vanilla momentum (MTUM) and S&P 500 (SPY). It also avoided the Corona Cliff in March 2020.

India

The Indian version of the Factor Momentum strategy is a mixed bag. Since it averages over a longer time-period, it is (understandably) slow to respond to sudden events. The dive during the Corona Cliff and subsequent performance is inline with the back-test. Thus far, it has marginally out-performed NIFTY 50 and MIDCAP 100 after STT and brokerage and we remain optimistic about its future.

Conclusion

Factor Rotation holds promise. Research, back-tests and forward-tests confirm. The trade-off is pretty clear as well: shorter look-backs/shallower draw-downs vs. transaction costs. Since trading US equities is nearly friction free, investors can use shorter look-backs. Indian investors will have to stomach deeper draw-downs given transaction taxes.

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The Low Volatility Anomaly

More returns for less risk

Our previous post on Momentum highlighted the inherent cliff-risk in the “buy-high, sell-higher” strategy. But even before Fama French published their 3-Factor paper, researchers had found that the “high risk = high reward” relationship is quite fragile. In 1972, Haugen, Robert A., and A. James Heins, studying the period from 1926 to 1971, came out with a working paper – On the Evidence Supporting the Existence of Risk Premiums in the Capital Market (ssrn) – that concluded that over the long run stock portfolios with lesser variance in monthly returns have experienced greater average returns than their ‘riskier’ counterparts (wikipedia).

Basically, a portfolio of low-volatility stocks will out-perform the market. More returns for less risk.

What Explains the Anomaly?

There are two main trains of thought on why this anomaly persists.

  1. It is difficult to short high-beta stocks and buy low-beta stocks. Because, if it were easy, one could construct a zero-beta portfolio with positive expected returns… and this anomaly would vanish.

  2. Stocks of companies with predictable earnings exhibit low-volatility. So, low-volatility is essentially high-quality – a known investment factor.

Historical Performance

While low-volatility has out-performed market indices, it is magic wand that makes drawdowns disappear.

India

US

Risks

The biggest risk in a portfolio of low-volatility stocks is that a large proportion of it could be held by weak hands – investors who are drawn to it primarily because of its low-volatility. And when faced with a drawdown that is steeper than historical experience, they can simultaneously head for the exits, resulting in a cascading drop in price. The triggers could be a missed earnings estimate, an industrial accident, etc. While momentum investors are used to being routinely hit in the head, a small shove can push low-volatility investors down a cliff.

Portfolio Construction

A portfolio of low-volatility stocks vs. a low-volatility portfolio of stocks.

While initial research focused on stocks that had low-volatility, a collection of low-volatility stocks can result in a portfolio with high-volatility if the correlations among them are high. To illustrate, consider two low-volatility stocks who’s volatility varies through time like this:

If you put them in the same portfolio, what happens to the portfolio volatility?

Now, what if you picked two stocks whose volatility were inversely correlate? In theory, you can mix to high-volatility stocks and get a low-volatility portfolio.

Resulting in:

Source: Low Volatility: Stock vs. Portfolio, StockViz

Min-Vol vs. Low-Vol

A Min-Vol portfolio tries to optimize the overall portfolio volatility. A completely different approach to having a portfolio of Low-Vol stocks. In the US, there are two large ETFs that track these different approaches: USMV – the iShares Edge MSCI Min Vol USA ETF, and SPLV – the Invesco S&P 500 Low Volatility ETF.

It appears that Min-Vol has an edge over Low-Vol in most scenarios.

Conclusion

While Momentum of Min/Low Volatility can appear to be diametrically different strategies, there are ways to mix them up in the same portfolio to achieve a lower-volatility momentum or a higher-return-low-vol outcomes.

However, at the end of the day, retail investors will forever be at the mercy of market beta. So, irrespective of which flavor of jam you like, the kind of bread you eat makes the biggest difference!

Enjoy the discussion:

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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.

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The Momentum Factor

8

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.

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