Tag: quant

An Equity, Bond and Gold Portfolio

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 Setup

  • Annual rebalance.
  • Bonds start at 1%, the rest is divided between Gold (10%) and Equities.
  • The total return index for the 5-10 year tenure published by CCIL is used as a proxy for Bonds.
  • The MID100 FREE index is used as a proxy for Equities.
  • The GOLDBEES ETF is used as a proxy for Gold.
  • Period under observation: 2007-04-01 through 2017-03-31.

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.

Returns

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.

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Can NIFTY be modeled using ARIMA?

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.

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Replacing Mutual Funds with ETFs

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.

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IPOs: An Update

Retail news outlets are once again gaga over phenomenal listing day returns of IPOs. Investors will do well to take a step back and take in the scene:

  • Median listing day returns since 2010 has been 1.22%
  • The best year (since 2010) was 2014 with median returns of 1.435%; the worst being 2012 with -2.76%
  • Picking winners over a large sample size (the listed universe) is easier than picking from a smaller lot of IPOs

      • The code and data is on github.

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Residual Momentum

The conventional way of implementing momentum strategies rank either relative or absolute time series returns of a universe of stocks. If either market beta, value or the small-cap premium had a big hand in driving equity returns during the formation period, then the momentum portfolio will be overweight those factors. This leads to steep momentum crashes, or so the theory goes.

In their paper on Residual Momentum, Blitz, Huij and Martens propose ranking stocks based on the residuals obtained after fitting their return series to the Fama-French Three factor model. They argue that a portfolio created this way outperforms vanilla momentum strategies.

We have created an automated residual momentum strategy, Momo (Residual) v1.0, that implements the residual momentum strategy outlined in the paper.

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