Invest Without Emotions
Investing insight to make you a better investor.
This is an update to our continuing coverage of the performance of portfolio management services. Read the first one for an intro.
Median PMS performance continues to drag Small and Midcap mutual funds. However, Equity Intelligence seems to be in a league of its own:
How did diversification across Midcap equity, bonds and gold work out for Indian investors over the last 10 years? Not too shabby, as it turns out:
Combined portfolio – Annualized: 12.16%; Max drawdown: -42.42%
Gold only portfolio – Annualized: 9.69%; Max drawdown: -21.49%
Equity only portfolio – Annualized: 12.55%; Max drawdown: -59.39%
Bond only portfolio – Annualized: 7.99%; Max drawdown: -8.52%
*Not including transaction charges/taxes.
The idea is that you start with mostly Equity and Gold in the portfolio and rebalance at the end of every year so that at the end of 10 years, you end up with mostly Bonds.
Notice the drawdown of the equity vs. that of the portfolio. You end up with similar returns but with lower volatility.
If you remove Gold from the equation and go with only Equity and Bonds:
Combined portfolio – Annualized: 11.38%; Max drawdown: -49.90%
Equity only portfolio – Annualized: 12.55%; Max drawdown: -59.39%
Bond only portfolio – Annualized: 7.99%; Max drawdown: -8.52%
Even though a diversified, rebalanced portfolio makes sense on the surface, the tax treatment on Gold and Bonds make an annual rebalance an expensive affair.
Code and detailed results are on Github.
A recent paper on SSR, Testing Random Walk Hypothesis: An Empirical Analysis of National Stock Exchange Indices (pdf), had me wondering if the NIFTY could indeed be modeled as an ARIMA(1,1,1) process as the author asserts.
As a first step, I wanted to check if ARIMA(1,1,1) is a given. What would be best fit be across rolling windows of different sizes? Turns out that for the most part, the best fit is ARIMA(0,0,0) aka, white noise. And the second best fits apply less than 20% of the time (Code and Results.)
Second, I wanted to check if ARIMA(1,1,1) has any forecasting ability. It does appear so (Code and Results.)
Buy & Hold Annualized return: 13.25% vs. Long/short NIFTY with different look-backs:
200: 16.75%; 500: 17.41% and 1000: 14.28%
*Not including transaction costs.
Although there is a slight advantage in using an ARIMA(1,1,1) model, I have a hard time reconciling the first set of results with the second. The advantage could very well be random.
Last month, we took a stab at measuring a fund’s alpha over a basket of ETFs (link.) The rationale was that the index often chosen by the mutual fund is not easily accessible to the investor. We saw how mutual fund alpha varies over time. We then asked the question: What if we just invested in the basket instead of buying the fund?
We did a study of the top 10 equity mutual funds by AUM back in March-2011 and found that 4 out of 10 funds under-performed their ETF baskets and 2 out of 10 funds could be replaced by an ETF basket without compromising too much on returns. That is, only 4 out of 10 fund out-performed the ETF basket setup for them.
The code, inputs and results are on github.
Indian bull vs. the US bull since 2012:
| SYMBOL | Cumulative Returns | Annualized Returns |
|---|---|---|
| SPY |
115.63
|
15.25
|
| EPI |
70.47
|
10.36
|
| INDY |
78.93
|
11.35
|
| SCIF |
74.11
|
10.79
|
| PIN |
50.53
|
7.85
|
| INP |
68.36
|
10.10
|
| INCO |
164.49
|
19.69
|
| INDL |
20.98
|
3.58
|
| INXX |
38.16
|
6.15
|
Investing in the Indian story has been a drag on returns for US$ ETF investors.
The code is on github.