Invest Without Emotions
Is the low-volatility anomaly overrated?
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Investors may be giving up on the significantly higher returns of a momentum strategy in favor of slightly lower drawdowns of a low-volatility strategy. They maybe better off managing overall portfolio risk through a bond allocation rather than tilting away from momentum.
In the book Atomic Habits: An Easy and Proven Way to Build Good Habits and Break Bad Ones (Amazon,) author James Clear lays out a step-by-step guide on how to adopt better habits.
An excerpt from the book that hit the spot for me:
Your current habits are not necessarily the best way to solve the problems you face; they are just the methods you learned to use. Once you associate a solution with the problem you need to solve, you keep coming back to it.
Habits are all about associations.
You see a cue, categorize it based on past experience, and determine the appropriate response.
Every action is preceded by a prediction. Life feels reactive, but it is actually predictive.
Our behavior is heavily dependent on these predictions. Put another way, our behavior is heavily dependent on how we interpret the events that happen to us, not necessarily the objective reality of the events themselves.
Most investors are well aware of biases that they should overcome. However, when the hypothetical becomes real, they feel powerless to control their behavior. How do you go about making the right choices by default? By making them a habit. And this book will put you on a path where you can make better choices.
Recommendation: Must read!
Most of the time, beta swamps alpha. Take the case of the Roubini Country Insights model, for example. It claims to “rank countries based on an analysis of over 2500 data points from institutions such as the Bank of International Settlements and the World Bank.” Also, “these data points cover each country’s demographics, quality of education, healthcare and ability to innovate, and will look at the country’s growth potential in political and social spheres, as well as its top-down macro-economic situation.” It sounds like it does everything that a smart investment manager with a long-only global equities mandate should be doing. And you would expect such a smart model to add significant alpha.
Thanks to Barclays, a bunch of equity indices based on this model have been available for a while now. We were curious as to how these performed vs. their corresponding plain-vanilla market-cap weighted cousins.
Developed markets: MSCI World (black) vs. Insights (green)
Emerging markets: MSCI EM (red) vs. Insights (blue)
The value add from the smart-beta quantitative “Insights” model, roughly about 1% a year, seems skinny compared to all the work that must have gone into it. 2500 data points is a big dataset but it looks like most of them have no effect on equity returns. This also ties into the curse of dimensionality when dealing with complex adaptive systems – more data typically subtracts from the model.
As an investor, it probably would have been easier to stay invested in one of the cap-weighted indices, just accepting the beta, rather than reach for that 1% extra with fancy sounding strategies.
Previously, we looked at how inflation adjusted returns for the NIFTY, from 1991 through 2016, was 5% annualized. How does it look for bonds?
At the short-end of the curve, it looks like inflation ate away all of the returns… and some more.
Code and charts on github.
In Part I of Index Valuations, we showed how the relative PE (price-to-earnings ratio) and PB (price-to-book ratio) of the NIFTY 50 and NIFTY MIDCAP 50 indices have varied over time. What would a portfolio that weighted each of these based on the relative valuation ratio look like?
Suppose, the relative ratio (R) = Ratio(MIDCAP)/Ratio(NIFTY)
Then, at the end of every month, re-weight the protfolio so that portfolio (S1) = R * NIFTY + (1-R) * MIDCAP, and
portfolio (S2) = (1-R) * NIFTY + R * MIDCAP
Ratio can either be PE or PB
PE based weights:
PB based weights:
It looks like:
You can track and map this strategy to your portfolio using the PB weighted NIFTY/MIDCAP Theme.
Code and charts on github.