Category: Investing Insight

Investing insight to make you a better investor.

India Hedge funds under-perform a simple Momentum strategy

Long/short equity funds, which make up a bulk of the India-specific funds, are up 47% year-to-date.

LiveMint recently ran an article, “India hedge funds outperform, but investors remain sceptical.” (link)

I don’t have access to the report quoted in the article and I am not sure if the returns are after fees and profit-sharing. But what is surprising is that these funds under-performed a basic, un-levered, momentum strategy. In the same period, our Momentum 200 Theme is up 61.54% and you can take all of it to the bank.

If you are charging 2 and 20, you should do better. Besides, most of them haven’t been through a down-cycle. And yes, Momentum will totally get crushed in a down-cycle, but at least investors know what they are getting into. For all you know, hedge funds, being the black-boxes that they are, could be just long momentum.

Turns out investor skepticism is warranted after all.

momentum.performance

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The Trouble with Buy-and-Hold Forever…

Interesting report titled “MYTH: Time reduces risk” by Ineichen Research & Management:

Some academic finance literature suggests that time diversifies risk, meaning that investing for the long term reduces risk. Disciples of buy-and-hold strategies also believe in the idea of time diversification. The logic is that if one has a very long investment horizon, one can recover from large losses. The counter argument is that time actually amplifies risk. The logic here is that over the longer term, more bad things can happen and the probability of failure and destruction is higher.

We think time diversification is a myth. Time amplifies risk. It is true that the annual average rate of return has a smaller standard deviation over a longer time horizon. However, it is also true that the uncertainty compounds over a greater number of years. Unfortunately, the latter effect dominates in the sense that total return becomes more uncertain the longer the investment horizon. Furthermore, betting on the long term might not be applicable for most investors. After all, the long term is nothing else than many short-term periods joined together.

I think there is a fair amount of confusion between what “long-term investing” entails and “buy-and-hold-forever” type of investing and investors get into all sorts of trouble because of that. (here)

Also, the paper talks about diversification being the only free lunch. But the problem is best described by Howard Marks (here):

  • If you concentrate your portfolio, your mistakes will kill you.
  • If you diversify, the payoff from your successes will be diminished.

You should read the entire article:

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RSI through a Support Vector Machine, Part Deux

Yesterday, we asked a question: How would an SVM (Support Vector Machine) train if we gave it a 14-day RSI and 50-day SMA of the Nifty index? The goal was to use the SVM to first see if it can figure out a relationship between RSI and NIFTY and then check if we can turn that into a set of trading rules.

If you look at the predictions that the SVM gave for 2006, you can see two distinct areas where it went short (red contours) and where it went long (blue contours.) But the funny thing is, it went long when RSI > 50 (when the market is supposed to be overbought) and short when RSI < 40 (supposed to be oversold.)

svm-rsi-nifty-2006

The kicker is that it followed the trend (x-axis) more than RSI (y-axis). In terms of predictive power, trend seems to be way more powerful than RSI, at least for the year 2006.

To check if we can actually setup any trading rules (trend x RSI = 4 combinations for buy/sell), we ran yearly training data through an SVM to check if there were any stable relationships. Here’s the video:

The contours change year to year, with little stability between them. Basically, a trading strategy based on RSI is going to be random.

Related: Using SMA to Reduce Volatility of Returns

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Mangling RSI through a Support Vector Machine

Conventional wisdom has it that RSI values over 70 to represent overbought market conditions and under 30 to represent oversold market conditions. But where did these numbers, 70 and 30, come from? We already tested two naive RSI strategies that bombed spectacularly. We were curious as to what an SVM (Support Vector Machine) would do if we gave it a 14 day RSI and 50-day SMA of the Nifty index. This is what came out of it:

svm-rsi-nifty

The bifurcation between long and short is pretty well defined. And is seems that trend overshadows RSI. Is RSI even relevant?

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