Author: shyam

The Government of India Undertakers

There is perhaps no bigger wealth destroyer than the government.

“If you put the federal government in charge of the Sahara Desert, in 5 years there’d be a shortage of sand” – Milton Friedman

It sounds logical that State Owned Enterprises (SOE) will lag their private sector counterparts. The most obvious reason being that incentive structure of both sectors makes a lot of difference.

A company run where everyone is driven to achieve more efficiency and compete in order to deliver value to its shareholders and managers are incentivized to achieve that goal will always do better than SOEs.

But in India it’s not mere underperformance but pure wealth destruction, which cannot only be attributed to lack of business acumen or slow agility.

It’s mix of that with heavy load of political influence. The short-sightedness and stupidity of the government and at the end the government not thinking about using these enterprises to create wealth.

Here is evidence of 20 biggest well-known names and their performance over the last 10 years not counting dividends.

Only 3 companies and actually only BPCL delivering 20%+ annualized returns over the last 10 years and this is also can be attributed because the government is interested in divesting it’s majority stake in it. So it’s the fact the government is getting out is driven the stock, that’s some vote of confidence by the market on the capacity of the state.

Now let us look at performance of the PSU indices vs Nifty

Consistent underperformance by State owned banks versus private sector in the last 10 years.

Far more underperformance than outperformance.

Apples and rotten apples

We rest our case.

In general just stay away from a government owned companies.

Post Script

The story goes (and it is perhaps apocryphal) that Gorbachev sent a key aide to London to learn a thing or two about what the British were doing well, which the Soviets clearly weren’t.

The British played good hosts and Gorbachev’s aide was taken for a tour of the city with places like the London Stock Exchange and the London School of Economics being on the itinerary.

As Yuval Noah Harari writes in Homo Deus—A Brief History of Tomorrow: “After a few hours, the Soviet expert burst out: ‘Just one moment, please… We have been going back and forth across London for a whole day now, and there’s one thing I cannot understand. Back in Moscow, our finest minds are working on the bread supply system, and yet there are such long queues in every bakery and grocery store.”

Gorbachev’s aide was surprised that in London there were no lines in front of supermarkets and shops for bread, even though millions of people lived in the city. The aide ended up saying: “I haven’t seen a single bread queue. Please take me to meet the person in charge of supplying bread to London. I must learn his secret.”

Of course, it need not be said, there was no one in charge for supplying bread to the city of London. And this is precisely why there were no queues.

Source: Why capitalism won

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The Relative Value Trap

Throwing away the wheat and keeping the chaff.

Our previous discussions on timing revolved around changing exposures to all factors. This includes both price based factors (volatility, momentum) and fundamental factors (quality, value.) While my personal preference, given its ease of implementation and low turnover, is factor rotation, multi-factor investing is a legitimate contender as well.

However, in the absence of liquid ETFs and index funds that track these individual factors, implementing such strategies is non-trivial and comes with cost-challenges and tracking errors. At the same time, being exposed to a single factor is a test of endurance – very few investors have the patience and time-horizon to stick through the ebb and flow single-factor portfolios. We touched upon this on our post of Magic Formula, a deep-value investment strategy.

So, is there a simple way to time a single factor?

Today, we pick the Value factor and run through some intuitive approaches to time it.

The Market Price-to-Book

Value factor strategies use relative value rankings to create portfolios. The problem with relative value is that if the market as a whole is expensive, which it usually is at the peak of bull markets, then the portfolio ends up with junk that can’t find a bid even during a bullish regime. Consequently, when the market inevitably turns, these portfolios suffer steep drawdowns.

What if, we were long the value factor only when the market is between a one-standard-deviation of historical valuations?

Timing the Value Factor

We go long the NIFTY500 VALUE 50 TR index if NIFTY 50 (alternately, MIDCAP 50) price-to-book is between bands. Stay in cash otherwise.

Using the NIFTY 50 PB ratio holds promise with annualized returns of the strategy clocking in at 8.75% vs. buy-and-hold’s 6.41% and shallower drawdowns to boot. However, we expect costs and taxes to pare away most of those excess returns.

Also, the problem with strategies that “go to cash” is that it brings with it cash management problems. Investors find other uses for the cash in their account and it is quite possible that when the time comes to buy, the cash would be blocked for use some where else.

Using Market PB to Manage Exposures

An alternative to timing the Value factor is to change the relative exposures of large and mid-cap allocations based on the market’s price-to-book. Here, you are always invested in the market but over-weight the cheaper index.

Suppose, the relative ratio, R = MIDCAP PB/NIFTY PB

Then, at the end of every month, re-weight the portfolio so that,

S1 = R * NIFTY + (1-R) * MIDCAP

On a monthly rebalance frequency, S1 clocks in a slightly higher annualized return compared to NIFTY 50 TR. However, it remains a disappointment after you consider transaction costs and taxes.

Combining Value and Momentum

What if, we first applied a relative-momentum cut-off before ranking stocks based on value? And managed risk by having a 10% trailing stop-loss? Would that overcome the adverse selection problem? After all, you are applying a value filter on stocks that are already moving higher.

We setup such a portfolio, Value in Flight, to track such a strategy.

What leaps out of this performance chart is that momentum over-powers value both on the upside and on the downside. During a bull market, investors likely fool themselves into believing that they have invested in a “value” portfolio while it is momentum driving returns.

Besides, using momentum with a stop-loss increases churn.

The bottom half of the chart shows how churn increases with market volatility.

It appears that combining value and momentum incurs all the costs of a momentum strategy and all the downsides of a relative-value strategy.

Conclusion

We tested three intuitive approaches to avoid adverse selection in relative value strategies. The first one used market valuations to time the value factor while the second used it to change relative weights within a portfolio. The third tried to combine momentum and value. None of these approaches make sense after considering transaction costs and taxes.

While the number of blind alleys in investment strategy research is potentially unlimited, we believe that the last word on this subject is yet to be written.

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Book Review: Merchants of Doubt

In Merchants of Doubt: How a Handful of Scientists Obscured the Truth on Issues from Tobacco Smoke to Climate Change (Amazon,) authors Oreskes and Conway describe how the Big-Tobacco’s efforts to obfuscate evidence against smoking founded an entire industry dedicated to spreading FUD against science.

History shows us clearly that science does not provide certainty. It does not provide proof. It only provides the consensus of experts, based on the organized accumulation and scrutiny of evidence.

There are always uncertainties in any live science, because science is a process of discovery.

The inherent uncertainties involved in the scientific exploration of a topic provides the opening for the Merchants of Doubt. By highlighting these uncertainties and engaging in relentless campaigns of doubt-mongering, these MoDs have twisted the scientific process and created an anti-science brigade.

With the rise of radio, television, and now the Internet, it sometimes seems that anyone can have their opinion heard, quoted, and repeated, whether it is true or false, sensible or ridiculous, fair-minded or malicious. The Internet has created an information hall of mirrors, where any claim, no matter how preposterous, can be multiplied indefinitely. And on the Internet, disinformation never dies.

Is it any wonder that there are people who still believe that the Earth is flat?

Recommendation: When you read this book along with This Is Not Propaganda, you’ll want to kill-off all social media/messaging companies and see yourself agreeing with the basic plot of Utopia. So, avoid, to preserve sanity.

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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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