Author: shyam

Book Review: Fragile by Design

Fragile by Design (Amazon) by Calomiris and Haber explains how the banking system of every country is the result of a unique set of starting conditions and how politics and culture shape their overall stability and purpose. They call it the Game of Bank Bargains.

My personal take-away from this is India is screwed. We will never get rid of our public banks who will conspire with our politicians to bleed our country dry. Politicians will find our banks ready and willing to impose an inflation tax on us. Who could blame them when hardly 2% of our population pays an income tax? We seem to have borrowed from the worst elements of US, Mexican and Brazilian banking systems. No Hope.

Read the book!

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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.

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Book Review: Fantasyland

Fantasyland: How America Went Haywire (Amazon,) is about how over the years, Americans have become less rational and more prone to magical thinking.

The author, Kurt Andersen, posits that Americans have self-selected both from a genetic and cultural point of view to believe in fantasies. The rise of social media and the internet has allowed everyone to live in his “own” truth, a fantasy land. He touches upon all weird, non-scientific things that Americans believe to be true, their outlandish religious beliefs, gun ownership and so on.

The main take-away for me was this: a liberal government can keep its populace fed and make sure that the country progresses scientifically and economically. However, it cannot give its citizens purpose. Purpose is something everybody seeks. And if the government cant give it, then religions/cult/tribes will fill the void. And the more vulnerable you are economically, the more you seek out a tribe to be part of.

Read the book!

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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.

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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.

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