Weekly Recap: Holacracy

world.2015-04-30.2015-05-08

Equities

Major
DAX(DEU) +2.23%
CAC(FRA) +0.87%
UKX(GBR) +1.24%
NKY(JPN) -0.39%
SPX(USA) +1.47%
MINTs
JCI(IDN) +1.88%
INMEX(MEX) +1.62%
NGSEINDX(NGA) -0.92%
XU030(TUR) +0.33%
BRICS
IBOV(BRA) +1.64%
SHCOMP(CHN) -5.31%
NIFTY(IND) +0.12%
INDEXCF(RUS) +1.20%
TOP40(ZAF) -0.99%

Commodities

Energy
Heating Oil -0.83%
Natural Gas +5.22%
Brent Crude Oil -1.76%
WTI Crude Oil -0.18%
Ethanol +1.91%
RBOB Gasoline -2.24%
Metals
Platinum -0.08%
Copper +1.04%
Palladium +2.72%
Gold 100oz +0.38%
Silver 5000oz +1.86%

Currencies

USDEUR:+0.01% USDJPY:+0.13%

MINTs
USDIDR(IDN) +1.15%
USDMXN(MEX) -1.55%
USDNGN(NGA) +0.16%
USDTRY(TUR) +0.78%
BRICS
USDBRL(BRA) -1.32%
USDCNY(CHN) +0.10%
USDINR(IND) +0.81%
USDRUB(RUS) -1.29%
USDZAR(ZAF) +0.10%
Agricultural
White Sugar +1.17%
Coffee (Robusta) -2.17%
Corn -0.83%
Feeder Cattle -0.72%
Soybean Meal -1.53%
Soybeans +0.36%
Sugar #11 +1.22%
Wheat -0.21%
Cattle -5.08%
Cotton -2.85%
Lean Hogs +5.52%
Lumber -7.84%
Orange Juice +1.21%
Cocoa -0.45%
Coffee (Arabica) -3.79%

Credit Indices

Index Change
Markit CDX EM -0.03%
Markit CDX NA HY -0.59%
Markit CDX NA IG +2.67%
Markit iTraxx Asia ex-Japan IG -0.22%
Markit iTraxx Australia +0.48%
Markit iTraxx Europe +1.76%
Markit iTraxx Europe Crossover +10.94%
Markit iTraxx Japan +2.28%
Markit iTraxx SovX Western Europe -0.21%
Markit LCDX (Loan CDS) +0.00%
Markit MCDX (Municipal CDS) +0.35%
Can you believe that after all the drama, the NIFTY actually ended this week in the green?

Nifty Heatmap

CNX NIFTY.2015-04-30.2015-05-08

Index Returns

For a deeper dive into indices, check out our weekly Index Update.
index performance.2015-04-30.2015-05-08

Sector Performance

sector performance.2015-04-30.2015-05-08

Advance Decline

advance.decline.line2.2015-04-30.2015-05-08

Market Cap Decile Performance

Decile Mkt. Cap. Adv/Decl
1 (micro) -3.91% 60/69
2 -6.40% 64/64
3 -3.74% 63/65
4 -2.57% 58/70
5 -3.22% 55/73
6 -3.93% 54/74
7 -3.29% 56/72
8 -2.15% 64/63
9 -3.23% 57/71
10 (mega) -2.96% 66/63
Outside the NIFTY, things looked bad across the board…

Top Winners and Losers

HINDALCO +8.05%
BAJAJ-AUTO +8.95%
ABIRLANUVO +19.35%
SRTRANSFIN -18.54%
BANKBARODA -14.14%
EXIDEIND -10.61%
Nuvo rallied on retail merger plans…

ETF Performance

CPSEETF +0.42%
NIFTYBEES -0.14%
GOLDBEES -0.81%
INFRABEES -1.51%
JUNIORBEES -2.32%
BANKBEES -3.84%
PSUBNKBEES -4.45%
PSU banks got shellacked…

Yield Curve

yield Curve.2015-04-30.2015-05-08

Bond Indices

Sub Index Change in YTM Total Return(%)
GSEC TB -1.07 +0.50%
GSEC SUB 1-3 -0.77 +1.81%
GSEC SUB 3-8 -0.63 +2.30%
GSEC SUB 8 -0.28 +2.63%
Coupons kept the dream alive…

Investment Theme Performance

Equity Mutual Funds

Bond Mutual Funds

Thought for the weekend

Zappos, an online shoe retailer, announced that the company was eliminating managers and attempting to make Zappos “a fully self-organized, self-managed organization by combining a variety of different tools and processes.” The move to self organize involves the adoption of Holacracy, a system of governance that emphasizes the distribution of authority.

Subsequently, about 14% of the company’s workforce, or 210 out of 1,503 employees, quit.

Source: Zappos Watches 210 Employees Self-Manage Themselves Out The Door

Index Update 09.05.2015

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 2015-05-08 png

index momentum best 50 2015-05-08 png

index momentum worst 365 2015-05-08 png

index momentum worst 50 2015-05-08 png

Refactored Index Performance

50-day performance, from February 24, 2015 through May 08, 2015:

Trend Model Summary

Index Signal % From Peak Day of Peak
CNX AUTO SHORT
9.89
2015-Jan-27
CNX BANK SHORT
13.42
2015-Jan-27
CNX ENERGY SHORT
30.10
2008-Jan-14
CNX FMCG SHORT
10.93
2015-Feb-25
CNX INFRA SHORT
50.20
2008-Jan-09
CNX IT SHORT
88.30
2000-Feb-21
CNX MEDIA SHORT
30.20
2008-Jan-04
CNX METAL SHORT
55.67
2008-Jan-04
CNX MNC SHORT
5.36
2015-Mar-12
CNX NIFTY SHORT
8.95
2015-Mar-03
CNX PHARMA SHORT
12.48
2015-Apr-08
CNX PSE SHORT
27.69
2008-Jan-04
CNX REALTY SHORT
88.96
2008-Jan-14
The MNC index should be real “blue-chip” index – lowest draw-down of the whole bunch.

Correlation Update 09.05.2015

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.

Machine Learning Long-Short Trend Following

Introduction

Our previous post discussed how a simple SMA On/Off Switch based tactical algo can be enhanced by a volatility metric. We generated significant alpha by following a simple rule:

Go short if either or the volatility signal or the 50-DMA indicates a negative bias and long otherwise.

But what if we trained a machine on the same data and allowed it to decide when to go long and short?

Support Vector Machines

We fed an SVM our volatility metric and the percentage distance from 50-day SMA. A 5-year training set was used to predict the next year daily long/short. We will not delve into the details of how SVMs work, Wikipedia does a decent job introducing the concept.

Performance

To make it easier to compare, we plot the wealth-charts for the NIFTY and BANKNIFTY indices side-by-side.

The black line is the Machine Learning Long-Short Model and the blue line is buy-and-hold. NIFTY and BANKNIFTY since 2011:

nifty.machine.learning.2011

banknifty.machine.learning.2011

NIFTY and BANKNIFTY since 2013:

nifty.machine.learning.2013

banknifty.machine.learning.2013

Cumulative Returns

Buy-and-hold has two big advantages over a trading strategy: transaction costs and tax treatment. Here is how the different strategies compare with buy and hold:

NIFTY SVM

BANKNIFTY SVM

It appears that the ML(V + 50-DMA) Long Short strategy works better on the BankNifty than on the Nifty. The out-performance of the ML model on the BankNifty more than compensates for transaction costs and taxation.

Conclusion

The ML model outperformed the NIFTY by an average of 12% in the last 4-years and the BANKNIFTY by 94% in the same period. The out-performance on the BANKNIFTY is considerable enough to warrant further exploration.

Basis Trades using Futures

Introduction

When we discussed cash-futures basis, it was pointed out that the fair value of a futures contract is a function of the underlying price, interest rates, dividends and time to expiration. The same logic applies to the fair value of contracts across expiration dates. For example, as of close on April 30, 2015, NIFTY futures contracts had the following values: 8177.35 (April), 8244.05 (May), 8275.30 (June).

Some of our clients wanted us to check if this basis can be traded. Is it possible to profit from going long the near contract and short the far contract on a consistent basis? Before we look at profitability, lets chart the basis.

The basis

Here is how the basis between different contracts look (2000 through now):

NIFTY.futures.basis

Here is the summary statistic of the basis:

summary statistics

Here is the same data with futures expiry dates removed:

summary statistics

With the extreme values removed, we can now check if we can trade the nearest expiry contract with the farthest.

50-day Average Basis Trade Back-Test

Lets take a look at the Near vs. Farthest basis and draw a 50-dma through it:

NIFTY.futures.basis.50dma

The basis is not stable and what’s worse, it appears to be trending. Lets try our simple trading rule: go long the basis if it is above 50-dma and short if otherwise.

Here’s how the back-test works out (2005 through now):

NIFTY.futures.basis.50dma.trade.2005

Lets check the back-test on a smaller subset (2010 through now):

NIFTY.futures.basis.50dma.trade.2010

A ~20% profit in a 10 year time-frame is barely enough to cover transaction costs. Besides, it looks like the strategy hit a wall in 2010.

Conclusion

It appears that the basis trade described above is not profitable enough after considering transaction costs and taxes. Also, whatever meager profits were there seem to have been arbitraged away lately.