Category: Investing Insight

Investing insight to make you a better investor.

Trend Following vs. Trend Prediction, Part I

Traditional equity momentum strategies are variations of algos that try to figure out “trending” stocks so that they can be ranked to create a long/short portfolio. The key thing to remember is that these algos are following a trend, the prediction that a trending stock will continue to trend is implicit. However, using machine learning techniques, stocks can be ranked based on their predicted returns over a future time frame.

The simplest momentum strategy looks only at a price series. However, it quickly runs into problems when additional factors are overlaid on top of basic momentum. For example, you may want to filter out volatile stocks out the basket. You can do this either by setting a maximum volatility level or by weighing both momentum and volatility to arrive at a combined rank. Either approach leads to ad hoc decisions of cut off levels and the ratio with which to weigh each of those factors. Luckily, typical machine learning algorithms can work with multiple factors and weigh them based on the training set you supply.

The biggest drawback of using machine learning is that the larger the number of factors/features you use, the less explainable the resulting model becomes. As a trader, if you want to use any of these models, you should have a fairly good idea of what is going in, how the model is setup and what exactly is the model getting trained with.

As a first step in taking a crack at this, we have setup four machine learning algos. Two of them use SVRs and the other two use LR to train on data that is either return-series only or a combination of returns and volatility. You can have look at them here.

We will have more of these machine learning models out as we ramp up our understanding of these models. Stay tuned!

Categories Tags ,

Transaction Cost Analysis of a Momentum Strategy

Momentum strategies have been on a tear over the last few years and have generally out-performed pure-value strategies. When we compare momentum returns with mutual funds, the most common criticism we encounter is that mutual fund returns are after transaction costs whereas our “Theme” returns are before transaction costs.

Momo (Relative) v1.1 vs ABSL S&M Fund (Annualized returns are 85.50% and 35.12%, respectively.)

The challenge we face in showing post-cost returns is that we offer different brokerage slabs to different types of clients, making a one-cost-fits-all analysis impossible. However, we can show how different brokerage slabs impact returns.

A gross return of 83.82% translates to returns of 74.20%, 69.58% and 65.08% for brokerage slabs of 0.1%, 0.05% and 0% respectively (STT of 0.1% was assumed.) Momentum out-performs even after transactions costs.

Categories Tags ,

Should You Buy What Mutual Funds Buy? [Update]

Back in November-2015, we had concluded that it does not make sense following mutual fund entries and exits from individual stocks. Using an expanded data set of 260 funds, we still reach the same conclusion. Median returns over 10, 20 and 50 days on additions were 0.49%, 1.81% and 4.88% whereas exits clocked -0.01%, 0.90% and 3.27%.

Direct equity investors would do well to ignore what fund managers a doing.

Code and results are on github.

Categories , Tags

Can Beta Dispersion be used for Market-Timing?

The paper Beta Dispersion and Market-Timing (SSRN) argues that one can predict crashes by tracking the dispersion of betas of the constituents of an index. The intuition presented in the paper is that when beta dispersion is high, any shock to the high beta stocks could spill over to the low beta stocks and create a broad market correction.

Although the paper proceeds to present a back-test on the US S&P 500 index, there some questions that need to be answered before deploying this strategy:

  1. What is the performance if you remove 2000 and 2008 from the data? Perhaps most of the out-performance can be attributed to skipping these two periods purely due to chance?
  2. Are the results robust over different markets? Perhaps it is unique to the US?
  3. What happens if you change the look-back period of beta calculations? Perhaps it is being data-mined?
  4. What happens if the calculations are continuous rather than sampled at the end of the month? Perhaps its an end-of-the-month effect?

Unfortunately, we don’t have a robust data-set to put this theory to test. However, the chart of the cumulative returns of the NIFTY 100 index vs. the beta-dispersion of its components does not lead to the same conclusion made in the paper.

The code for this analysis is on github.

Categories , Tags

Mutual fund portfolio overlap and Active Share

There is a problem of plenty when it comes to mutual funds – direct growth schemes alone number into the high 200s. Investors have responded to this bewildering array of choices by going in for the ‘unlimited buffet’ option. They end up making small investments into a large number of funds. By doing so, they end up owning the whole market – paying active management fee for a passive investment. There are two things investors should keep in mind before adding a new fund to their investment:

  1. What is the new fund’s portfolio overlap with the existing investments?
  2. How different is the new fund’s portfolio from a large-cap and mid-cap index?

The first answer will tell you whether to add the new fund to your portfolio. The second will tell you if you should just replace the fund with an index ETF.

For example, say you own HDFC Mid Cap Opportunities and you are wondering if you should also buy the Birla Sun Life Midcap fund. Here’s how the fund portfolios overlap:

The funds have about 18 stocks in common and a fairly large number of stocks that are not in any of the indices. Given the differing styles, perhaps it makes sense to add the new fund to the portfolio.

The second, also called “Active Share,” shows how different the portfolio is from an index. For example, DSP Blackrock Technology.com Fund has a 26% overlap with NIFTY 100 and a 5% overlap with NIFTY MID100 FREE. Whereas, the HDFC Large cap Fund has a 95% overlap with the NIFTY 100 index. It probably makes sense to replace the latter with an index fund.

For more details about the analysis and its results, please peruse the notebook on github.

Categories , Tags