Invest Without Emotions
A recent Verdad Capital newsletter, Dan Rasmussen points out that it is impossible to scale value investing as defined in academia. Almost all of “value” ETFs and actively managed funds are completely avoiding the cheapest stocks (high book-to-market) while instead owning primarily stocks that are more expensive (low book-to-market).
And what explains this puzzle? Strategy capacity.
The cheapest stocks are disproportionately small in terms of size and volume. This means that an active manager looking to choose, say, the best 40 of these stocks would be unable to manage more than $200M or so. The average small value fund tracked by Morningstar has $1.3B of assets under management. It is close to impossible to deploy that amount of capital exclusively in the cheapest two deciles of the stock market.
This is true for Indian mutual funds as well. Most managers claim to be “value” investors while actually hugging the index with a GARP/momentum tilt. Given the size of most of these funds, there is no way they can invest in value.
Strategy capacity should be one of the questions investors should ask of their fund managers/advisers. Especially advisers of direct equity portfolios who do not know the aggregate exposure that their subscribers have across portfolios.
In the book Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are (Amazon,) author Seth Stephens-Davidowitz provides a peek into how “big data” can help us understand the world around us provided we know how to ask the right questions. And that, depressingly, people are consistently lying to themselves.
The author goes on to show how some of the most successful companies in recent memory are based on capitalizing on the difference between what people say they are and what they really are.
The book also touches on a common problem faced by quants who are trying to use big data for building trading models: the curse of dimensionality. We discussed this in our GEM and SMA series of articles (GEM, SMA) – there is no single “best” look-back period for calculating momentum or moving averages. There are trade-offs involved and there is always risk. These issues tend to snowball when using large, multi-dimensional data-sets to a point where it is hard to discern signal from noise.
The principal take-away from this book is that big-data is useful to answer questions typically raised in the social sciences and public policy. But a poor fit where the underlying data is heteroscedastic or the system itself is complex-adaptive.
Recommendation: must read!
NIFTY 50 volatility the last couple of years have been extremely low by historical standards. If you look at the rolling median of weekly returns over 50 weeks (about a year), you can see how the range has narrowed:
The standard deviation, a popular measure of volatility, has come down as well:
As the charts illustrate, the markets have been moving in tight ranges. And narratives have been built around it:
Narratives can change overnight. And if the last few months have taught us anything, the market drives the narrative. Also, new investors have only seen a low volatility environment and think it to be “normal.” So any reversion to the old volatility regime would be a rude awakening. Are we really in a new world or is volatility about to revert to its longer-term mean?
Code and additional charts on github.
Also read our Volatility Collection.
From an Indian investor’s point of view, the attractiveness of US stocks are many:
Most investors looking to invest in the US would be better off buying and holding the S&P 500 ETF (SPY) or its momentum equivalent (MTUM.) However, for those who are looking for a more active portfolio, we ported our relative momentum strategy to the US markets last year.
Our dynamic relative momentum strategy applies a trailing stop-loss of 5% on each constituent, everyday, and kicks out stocks that have sunk below it. Replacements are chosen based on their relative momentum scores.
While the strategy is still recovering from the 18% drawdown that occurred in September 2018, it has beaten MTUM and SPY handily over the entire period. The stats are updated every day here. Please bookmark!
Our static relative momentum strategy follows a once-a-month rebalance schedule. Unless any of the constituent stocks undergoes a delisting/merger etc., the algorithm is run at the beginning of the month and the portfolio is held for the entire month.
The strategy is still recovering from the 30% drawdown that occurred in June 2018 but it has out-performed both MTUM and SPY handily in the last 20- and 50-days. The stats are updated every day here. Please bookmark!
The dynamic strategy suffers from higher transaction costs but is likely to experience shallower drawdowns and be more responsive to market trends. Unlike in India, the US doesn’t have STT. So if you pay a flat brokerage (rates from Interactive Brokers can be as low as $1 per trade) then dynamic is the way to go. Here is the static vs. dynamic experience in Indian markets:
There is a ~15% return differential, July’16 through Feb’19, between the two versions.
Here is how investors should weigh the alternatives, in order from simple to complex, cheap to expensive:
We are happy to further explore these options with you. Drop us a note!
Related: US vs. Indian Midcaps
