Category: Investing Insight

Investing insight to make you a better investor.

Asset Allocation and Taxes

Simple portfolio asset allocations should start with a mix of equities and bonds. Typically, a 60/40 or a 70/30 split between them is suggested as a good starting point. The big questions are:

  1. What are the trade-offs between 60/40 and 70/30?
  2. Should you stick to large-caps or use mid-caps for the equity leg?
  3. Should you rebalance every month or is an annual rebalance enough?
  4. Should you invest the legs separately or opt for an equity-oriented balanced fund?

60/40 vs. 70/30 * Large vs. Mid-caps

60/40, monthly rebalance
70/30, monthly rebalance

From a drawdowns point of view, using the large-cap NIFTY 50 index seems to deliver a smoother ride to the investor. However, there is hardly any difference between the drawdown profile of the 60/40 vs. that of the 70/30. From a returns point of view, a 70/30 portfolio has about a point over the 60/40.

So, risk-averse investors should probably go with a large-cap 70/30 mix. And for those who want reach a bit, a mid-cap 70/30 should do the trick.

Monthly vs. Annual Rebalance

The less frequently you rebalance your portfolio, the less you pay out in transaction costs. However, with a lower frequency of rebalances, you run the risk of one piece of your portfolio overshadowing the rest and dictating the overall risk of the portfolio.

70/30, annual rebalance

For a 70/30 portfolio, it appears that an annual rebalance has negligible effect on portfolio returns or drawdowns.

Tax impact – DIY vs. Mutual Fund

If you choose to implement the legs of the portfolio separately, then you create a taxable event every time you rebalance. A mutual fund, on the other hand, has no such drag.

70/30 large-cap, after tax vs. equity-oriented hybrid fund
70/30 mid-cap, after tax vs. equity-oriented hybrid fund

Taxes seem to lop-off about 2% of annualized returns in the DIY portfolio while the mutual fund gets to compound it throughout. In both the large-cap and mid-cap scenarios, an equity-oriented hybrid fund comes out ahead.

If you were set this up as an SIP, then it is possible to avoid selling positions by just buying the asset that has fallen below its target. So taxes predominantly dent lumpsum investment returns.


If you are an SIP investor, then a DIY 70/30 large-cap or mid-cap portfolio (if you are willing to bear a bit more volatility) should do the trick. But lumpsum investors should probably shop around of a decent equity-oriented hybrid fund.

Related: Allocating a Two-Asset Portfolio

Check out the code for this analysis on pluto: 60/40 and 70/30. Questions? Slack me!

MSCI Country Momentum Index Correlations

In MSCI Country Index Correlations, we looked at country index correlations through time. Here is a quick update that “flattens” out the rolling correlation of the momentum versions of these indices with the MSCI INDIA MOMENTUM Index.

Three-year Rolling Correlations

MSCI Country Momentum Index 3-year Rolling Correlations

Five-year Rolling Correlations

MSCI Country Momentum Index 5-year Rolling Correlations


Momentum is a lose proxy for sentiment and the tides of optimism floats all boats. All equity markets are correlated with each other – some strongly (HONG KONG) and some weakly (CANADA.)

The median correlations across both 3- and 5-year rolling periods are greater than +0.70 between INDIA MOMENTUM and EMERGING MARKETS MOMENTUM.

Cumulative Returns of INDIA and EM MOMENTUM (MSCI)

No market is an island. Sentiment is tail that wags the dog.

Low Volatility: Stock vs. Portfolio

Lower the Risk, Higher the Returns?

Typically, investors expect higher returns from high risk investments compared to low risk ones. However, realized returns are the opposite of what they expect.

High Beta vs. Low Vol

This anomaly, where lower risk systematically results in higher returns, has spawned a number of “betting against beta” strategies. A common approach is to rank a universe of stocks by volatility and create a portfolio off the top-N. However, this approach could lead to a highly volatile portfolio if relative correlations are not considered.

1+1 = 0

Here are two stocks with their volatility plotted against time:

correlated low-vol stocks

If you create a portfolio off these two stocks, what happens to portfolio volatility?

A portfolio made off low-vol stocks can be high vol

In the worst case scenario, where all the volatilities are correlated, portfolio volatility can end up being a sum of all component volatility.

If low-vol stocks can create a high-vol portfolio, can high-vol stocks create a low-vol portfolio? Yes! It all depends on how the volatilities are correlated.

inversely correlated high-vol stocks

In the best case scenario, portfolio volatility can be a very low constant value if the components are inversely correlated.

a low-vol portfolio created off volatile components

It doesn’t matter if individual volatilities are high or low. What matters is the correlation of volatilities.

Portfolio Optimization

A simple ranking of stocks will not help in creating a low-vol portfolio. What we need is a holistic approach that considers the correlation of volatilities and optimizes the entire portfolio.

One way to go about this is to use gradient descent. Start with a random portfolio and go in the direction that minimizes variance (min-var) or expected tail loss (min-ETL)

min-var and min-ETL backtest

With a monthly rebalance, the chances of the portfolio getting trapped in a local-minima are low. And the backtest looks promising.

Investing in Low-Volatility Portfolios

Equity investors can map our Minimum Variance and Minimum ETL Themes to their portfolios to gain exposure to these low-vol strategies.

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


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!

Factor Holding Periods for Excess Returns
Funding Your Dollar Dreams

Questions? Slack me!

Reducing Crash Risk in the Nifty Alpha Indices

The NSE has a couple of strategy indices – the NIFTY Alpha 50 Index and the NIFTY100 Alpha 30 Index – based on historical CAPM alphas. The former selects 50 stocks from the largest 300 stocks whereas the latter selects 30 stocks from the NIFTY 100 index.

First, a look at a simple buy-and-hold strategy.

Buy-and-Hold Curves


The alpha indices have out-performed the plain-vanilla NIFTY 50. However, what jumps out off the page is the sheer depth and length of the drawdows that these indices have made.

Even though they give vastly better returns than the NIFTY 50 index, the lived experience would be too painful for most investors. Is there a way to reduce these drawdowns while retaining most of the out-performance?

In a 2012 paper, Momentum has its moments, Barroso and Santa-Clara outline a way in which historical volatility could be used to reduce momentum crashes.

Strategy Outline

The basic idea is that momentum risk is time-varying and sticky. And, periods of high risk are followed by low returns.

rolling 100-day sd
rolling 200-day sd

To test this theory out on the Alpha indices, we first split the time series into halves. The first to “train” and the second to “test.” We need a training set because we are not sure what the appropriate look-back for calculating risk should be (we check 100- and 200-days). The test set is a check of out-of-sample behavior of the strategy.

The theory laid out in the paper, that periods of high risk is followed by periods of low returns, is true. Subsequent returns when std. dev. is in the bottom deciles show large negative bias. Also, perhaps indicating a bit of mean-reversion, returns after std. dev. is in the 7-9th decile, have fat right tails.

NIFTY ALPHA 50 200-day cumulative returns by 200-day sd decile
NIFTY100 ALPHA 30 200-day cumulative returns by 200-day sd decile

Train In-Sample

The next question is the appropriate lookback and deciles for calculating the std. dev. Running this on the training set, we find:

training set: NIFTY ALPHA 50 – 200
training set: NIFTY100 ALPHA 30 – 200
training results

A strategy that goes long Alpha when std. dev. is in the 1-5 deciles side-steps severe drawdowns in the training set. Note, however, that it under-performed buy-and-hold during the melt-up of 2007.

Test Out-of-Sample

Applying a 200-day lookback on both the indices over the test set, we find that the strategy continues to side-step drawdowns but no longer out-performs buy-and-hold by a large margin.

test set: NIFTY ALPHA 50 – 200
test set: NIFTY100 ALPHA 30 – 200
test results


Using historical volatility (std. dev.) reduces drawdown risk in Alpha indices. But it comes at the cost of reduced overall returns over buy-and-hold over certain holding periods. However, given the magnitude of the dodge in 2008 and 2016, it is well worth the effort (and cost) if it helps keep the discipline.

Check out the code for this analysis on pluto. Questions? Slack me!