Category: Your Money

Index Update 10.10.2015

MOMENTUM

We run our proprietary momentum scoring algorithm on indices just like we do on stocks. You can use the momentum scores of sub-indices to get a sense for which sectors have the wind on their backs and those that are facing headwinds.

Traders can pick their longs in sectors with high short-term momentum and their shorts in sectors with low momentum. Investors can use the longer lookback scores to position themselves using our re-factored index Themes.

You can see how the momentum algorithm has performed on individual stocks here.

Here are the best and the worst sub-indices:

index momentum best 365 2015-10-09 png

index momentum best 50 2015-10-09 png

index momentum worst 365 2015-10-09 png

index momentum worst 50 2015-10-09 png

Relative Strength Spread

CNX_500 relative-spread-index 50 2015-10-09 png

Refactored Index Performance

50-day performance, from July 29, 2015 through October 09, 2015:

Trend Model Summary

Index Signal % From Peak Day of Peak
CNX AUTO LONG
11.52
2015-Jan-27
CNX BANK LONG
14.42
2015-Jan-27
CNX COMMODITIES LONG
35.32
2008-Jan-04
CNX CONSUMPTION LONG
5.71
2015-Aug-05
CNX ENERGY LONG
33.93
2008-Jan-14
CNX FMCG LONG
7.96
2015-Feb-25
CNX INFRA LONG
53.04
2008-Jan-09
CNX IT SHORT
87.64
2000-Feb-21
CNX MEDIA LONG
18.06
2008-Jan-04
CNX METAL LONG
66.60
2008-Jan-04
CNX MNC SHORT
9.28
2015-Aug-10
CNX NIFTY LONG
8.97
2015-Mar-03
CNX PHARMA LONG
4.00
2015-Apr-08
CNX PSE LONG
33.36
2008-Jan-04
CNX PSU BANK SHORT
40.81
2010-Nov-05
CNX REALTY LONG
90.30
2008-Jan-14
CNX SERVICE LONG
9.02
2015-Mar-03
And just like that, momentum is back!

Correlation Update 10.10.2015

Nifty one year daily return correlations

Nifty one year daily return correlations

Nifty one month daily return correlations

Nifty one month daily return correlations

Bank Nifty one year daily return correlations

Bank Nifty one year daily return correlations

Bank Nifty one month daily return correlations

Bank Nifty one month daily return correlations

Midcap one year daily return correlations

Midcap one year daily return correlations

Midcap one month daily return correlations

Midcap one month daily return correlations

A lot of thick blue squares mean that positive correlations are high. Red squares mean negative correlations are high. Whites are the doldrums.

INDA vs. SPY Observed Volatility

The iShares MSCI India ETF (INDA) tracks the MSCI India Total Return Index, representing about 85% of the Indian stock market. As a follow up to our earlier post on the historical volatility of the NIFTY historical NIFTY volatility, we thought we’ll compare the volatilities of INDA and SPY, the S&P 500 ETF.

10-day volatility:
INDA.SPY.volatility.density.10

50-day volatility:
INDA.SPY.volatility.density.50

As expected, a dollar denominated emerging market ETF is more volatile than the S&P. File this away in your brain attic.

NIFTY Volatility, Historical Perspective

Was 2014 an anomaly?

Here’s a density plot of NIFTY volatility across 10-, 20-, 30-, and 50-day periods:

NIFTY.volatility.density.2014

And here’s how it was in 2004 (10-years ago):

NIFTY.volatility.density.2004

For those of who argue that the introduction of the pre-open auction call in 2010Gaps and the Pre-Open Call Auction skews these results, here’s how 2011 looked like:

NIFTY.volatility.density.2011

The unprecedented absence of a second “hump” in the volatility density plot for 2014 should give pause to investors looking for a repeat of 2014 anytime soon.

Reversion to higher volatility?

If you look at the 50-day volatility over different time-periods, you can observe how volatile volatility is:

NIFTY.volatility.density.50

This year’s observed volatility is closer to last-year’s than to its long-term mean. Here’s how 2015 has panned out so far:

NIFTY.volatility.density.2015

We should expect higher volatility as the initial bull-run wears off and volatility reverts. This will have a ripple effect on pretty much every investment/trading strategy.

Appendix

Year-wise NIFTY volatility density plots (pdf)

Will Your Strategy Outperform?

Came across an interesting paper: Will My Risk Parity Strategy Outperform? Robert M. Anderson, Stephen W. Bianchi, CFA, and Lisa R. Goldberg. Even though they discuss risk parity, they make some pretty interesting points that relate to all investment strategies.

Today’s alpha is tomorrow’s beta

… the introduction of new securities can have an indirect effect; a strategy that was seemingly profitable in the past might have been less profitable if the new securities had been available and thus made the strategy accessible to a broader class of investors.

Before index ETFs, there was no cost-effective way of replicating an index. For example, NIFTYBEES was listed in 2002 and came with an expense ratio of 0.80% while retail brokerage charges were in the 0.5-1.0% range. Replicating the NIFTY index before NIFTYBEES came around was expensive. So any backtest before 2002 that that tries to argue the benefits of buying-and-holding an index ETF is likely bogus. Similarly, today’s active management strategies available to a select few hedge-fund investors are tomorrow’s “smart beta” ETFs that will be available to anybody with a demat account.

Leverage is an external source of risk

The notion that levering a low-risk portfolio might be worthwhile dates back to Black, Jensen, and Scholes (1972), who provided empirical evidence that the risk-adjusted returns of low-beta equities are higher than the CAPM would predict.

There are periods when banks pull their lines of credit based on macro factors that has nothing to do with your strategy. For example, during the 2008 financial crisis, your bank/broker would have pulled your credit lines forcing you to sell near the bottom and preventing you from buying the bounce. Any strategy that uses leverage – risk-parity, for example – should factor this risk.

Performance depends materially on the backtesting period

Even if we were reasonably confident that one strategy achieved higher expected returns than another without incurring extra risk, it would be entirely possible for the weaker strategy to outperform over periods of several decades, certainly beyond the investment horizon of most individuals…

Besides, most strategies have a rebalancing frequency – once a month, once a year, and so on. The specific day you choose to rebalance can have a material impact on your strategy. For example, rebalancing during options expiry, corporate events, etc… can meaningfully skew your risk/returns.

Borrowing and trading costs can negate outperformance

Value-weighted strategies require rebalancing only in response to a limited set of events. The risk parity and 60/40 strategies require additional rebalancing in response to price changes and thus have higher turnover rates. Leverage exacerbates turnover.

There is huge execution risk involved in strategies that requires shorting of shares. Given the regulations surrounding SLBS – lending/borrowing allowed only on those securities that are listed in F&O and that too only in increments of lot-sizes – the friction involved in shorting stocks are prohibitive.

Execution drift

There is likely going to be a big difference between model execution prices and actual execution prices. For example, when we rebalance our Themes, we use the latest available price in our database. These prices themselves could be stale by over 10 minutes. These changes then have to percolate down to investors who execute them in the market. From start to finish, there could be a price gap of over 20 minutes – a significant source of drift between the ideal P&L and actual P&L.

Conclusion

Investors should have a deployment checklist for their strategies that addresses the issues raised above. What we have found is that most strategies that look good on a simple backtest don’t look that great when costs, variable periods, drift and half-lives are factored in.