Author: shyam

Internal Bar Strength

Definition

Internal Bar Strength (IBS) is based on the position of the day’s close in relation to the day’s range: it takes a value of 0 if the closing price is the lowest price of the day, and 1 if the closing price is the highest price of the day. The IBS effect may be related to intraday over-reactions to news or market movements, which are then “corrected” the next day.

IBS = (Close – Low)/(High – Low)

It is a mean-reversion strategy.

Back test

The paper from Alexander Soffronow Pagonidis claims that low IBS values are associated with high returns, while high IBS values are associated with low returns. Average returns when IBS is below 0.20 are .35% while average returns when IBS is above 0.80 are -0.13%.

We put this to the test on 16 NSE indices. Calculating IBS and trading at the close. To keep things simple, we assumed that we can trade at closing prices. Buy at the close if IBS is below 0.2, and sell at the close if IBS exceeds 0.8, exit the position at the following market close. If a back test on indices proved promising, we figured we would try this out on individual stocks next. However, IBS returns trailed buy-and-hold by a significant margin.

ibs

Using IBS to trade mean reversion, as the author intended, is a losing proposition. What if we do the reverse?

ibs_inverse

It “works” for about half the indices – could be pure luck.

Conclusion

It looks like IBS either doesn’t hold for Indian markets or for the indices we tested.

Source: The IBS Effect: Mean Reversion in Equity ETFs (pdf)

Equity curves: IBS Mean Reversion (pdf)

Trading turnover throughout the day

Turnover, defined as volume over total number of shares outstanding, is not constant throughout the day. If you plot turnover over a trading day, it typically traces a ‘U’ shaped plot.

turnover.BSL

turnover.MEP

turnover.INFY

Notice how turnover is the highest in the first half-hour and the last-half hour of trading? Turns out, it is a global phenomena. It follows that if you want liquidity, then it is enough if you show up for the last half-hour of trading.

Related:
Trading Day of Month Returns
Equity Returns at the Turn of the Month
Improving VWAP Strategies: A Dynamic Volume Approach

Monthly Recap: May you live in Interesting Times…

world.2016-04-29.2016-05-31

Equities

Major
DAX(DEU) +1.50%
CAC(FRA) +0.92%
UKX(GBR) -1.16%
NKY(JPN) +1.74%
SPX(USA) +2.15%
MINTs
JCI(IDN) +0.02%
INMEX(MEX) -1.61%
NGSEINDX(NGA) +7.83%
XU030(TUR) -9.43%
BRICS
IBOV(BRA) -9.81%
SHCOMP(CHN) -0.84%
NIFTY(IND) +4.21%
INDEXCF(RUS) -2.70%
TOP40(ZAF) +2.50%

Commodities

Energy
Ethanol +6.67%
Heating Oil +7.38%
Natural Gas +7.49%
Brent Crude Oil +2.90%
RBOB Gasoline +0.68%
WTI Crude Oil +6.55%
Metals
Copper -7.49%
Platinum -8.82%
Silver 5000oz -10.11%
Gold 100oz -5.52%
Palladium -12.65%

Currencies

USDEUR:+2.47% USDJPY:+2.45%

MINTs
USDIDR(IDN) +3.65%
USDMXN(MEX) +7.17%
USDNGN(NGA) +0.00%
USDTRY(TUR) +5.40%
BRICS
USDBRL(BRA) +4.23%
USDCNY(CHN) +1.54%
USDINR(IND) +1.69%
USDRUB(RUS) +3.25%
USDZAR(ZAF) +10.07%
Agricultural
Lean Hogs +3.33%
Lumber +2.78%
Orange Juice +20.00%
Wheat -2.56%
White Sugar +2.88%
Coffee (Robusta) +6.04%
Corn +2.81%
Cotton +0.28%
Soybean Meal +18.55%
Soybeans +5.17%
Cattle -2.34%
Cocoa -3.81%
Coffee (Arabica) +1.70%
Feeder Cattle +5.01%
Sugar #11 +7.46%

Credit Indices

Index Change
Markit CDX EM -0.78%
Markit CDX NA HY -0.24%
Markit CDX NA IG +0.19%
Markit iTraxx Asia ex-Japan IG +0.55%
Markit iTraxx Australia -4.39%
Markit iTraxx Europe -0.16%
Markit iTraxx Europe Crossover +3.18%
Markit iTraxx Japan -4.31%
Markit MCDX (Municipal CDS) -2.00%
The US Dollar rallied, NIFTY was the best performing equity index…

International ETFs (USD)

global.etf.performance.2016-04-29.2016-05-31

Nifty Heatmap

NIFTY 50.2016-04-29.2016-05-31

Index Returns

index.performance.2016-04-29.2016-05-31

Market Cap Decile Performance

deciles.perf.2016-05-31

Top Winners and Losers

PIDILITIND +17.11%
LT +17.45%
SRTRANSFIN +25.19%
ADANIPORTS -19.33%
RCOM -16.46%
CIPLA -11.98%
Mirroring the performance of sector indices…

ETF Performance

INFRABEES +5.44%
BANKBEES +4.88%
NIFTYBEES +3.96%
JUNIORBEES +2.15%
PSUBNKBEES +0.93%
CPSEETF -1.09%
GOLDBEES -2.63%
Infrastructure caught a bid. But for how long?

Yield Curve

yieldCurve.2016-04-29.2016-05-31

Bond Indices

Sub Index Change in YTM Total Return(%)
0 5 -0.03 +0.73%
5 10 -0.01 +0.73%
10 15 -0.10 +1.38%
15 20 -0.00 +0.71%
20 30 -0.01 +0.79%
Bonds put in a decent performance…

Investment Theme Performance

Low Beta/Volatility outperformed momentum…

Equity Mutual Funds

Bond Mutual Funds

Indian bonds are cheap…

ust-ind-10yr-spread.2016-04-29

Institutional flows

fii-investments.2014-01-01.2016-05-31

dii-investments.2014-01-01.2016-05-31

Book of the Month

Everything is interpreted through the prism of our own experiences.

The Confidence Game: Why We Fall for It . . . Every Time

The Confidence Game: Why We Fall for It… Every Time by Maria Konnikova (Amazon)

Trading Day of Month Returns

An analysis of daily returns of the NIFTY 50 index between 1998 and 2015 shows that:

  1. The first few and the last few trading days of a month are the best days to be long.
  2. Middle of the month returns are more volatile and, on an average, negative.
  3. Cumulative returns of a strategy that is long on the first 5 and the last 5 days of the month and short the others is 1,112.154% vs. 636.1821% of a buy and hold strategy.

day-of-month

Further reading: An Anatomy of Calendar Effects (SSRN)

A Brief Note on Monte Carlo

When we back-test a strategy against the historical prices of an instrument, say, the NIFTY 50 index, we have to keep in mind that historical values are just one path of the many paths that the instrument could have taken.

For example, 10 tosses of a fair coin can result in TTTTTFFFFF and TFTFTFTFTF with equal probability. If your strategy is path dependent (as most strategies are,) then just because it was successful in one trial (historical prices) doesn’t mean that it would have been successful in all (or majority) of them.

The simple thing to do after a successful back-test against historical prices is to run a Monte Carlo simulation to check if the strategy comes out ahead in most of them. This can be setup by assuming returns are normally distributed and running a simulation using the mean and standard deviation of the sample.

For instance, in the recent past, daily NIFTY 50 returns have exhibited a mean of -0.0003742873 and std. dev. of 0.01079387. When you run a simulation and plot the results over the actual closing prices of the index, you get the resulting chart:

monte-carlo.NIFTY

How many of these paths will result in a total equity wipeout of the back-tested strategy?