CHAPTER-4 EMPIRICAL STUDY ON TRADING CHARACTERISTICS …shodhganga.inflibnet.ac.in/bitstream/10603/40042/9/09_chapter_4.pdf · EMPIRICAL STUDY ON TRADING CHARACTERISTICS OF ETFs IN

CHAPTER-4

EMPIRICAL STUDY ON TRADING

CHARACTERISTICS OF ETFs IN INDIA

Empirical Study on Trading Characteristics of ETFs : In this chapter the trading characteristics of exchange traded funds listed on

the National Stock Exchange (NSE) are examined. Exchange Traded Funds, or

ETFs as they are commonly known, are tradeable securities which derive their

value from a pre-defined basket of securities which are constituents of an index or a

benchmark like a commodity price . These types of ETFs derive their value (and

volatility) from the market movements of the underlying stocks,which comprise the

portfolio, and these funds are similar to index funds managed by institutional

portfolio managers. They carry certain unique trading features unlike other mutual

fund products, the unique trading characteristics empirically studied are:-

Tracking its underlying benchmark trading price.

Tracking the returns of its underlying benchmark.

Trading near to their Net Asset Values (NAVs).

The empirical study is split into four parts and hypothesis is framed

to test the relation between the trading variables of the ETF, Regression

supported by t-stat is applied to test the hypothesis.

1. REGRESSION ANALYSIS ON TRADING PRICES OF ETFs AND

BENCHMARKs

2. REGRESSION ANALYSIS ON RETURNS OF ETFs AND BENCHMARKs

3. REGRESSION ANALYSIS ON TRADING PRICES AND NAVs of ETFs

4. REGRESSION ANALYSIS ON ONE DAY TIME LAG ON TRADING

PRICES of ETFs

4.1 REGRESSION ANALYSIS ON TRADING PRICES OF ETFs AND BENCHMARKs

In this regression analysis the relation between trading price of the ETF(the

dependant variable) and the trading price of the benchmark( the independent

variable) was analyzed, The beta (βi) coefficient defines the price relationship

between ETF and Underlying INDEX / BENCHMARK, for example NIFTYBEES

trading price should be equal to 1/10th of ―CNX NIFTY INDEX‖ according to its

trading characteristic , So if regression beta is equal to 0.10 it implies that

NIFTYBEES trading price is exactly tracking the S&P CNX NIFTYs trading price.

HYPOTHESIS-I

H0: TRADING PRICE OF ETF IS INDEPENDENT OF THE TRADING PRICE OF BENCHMARK

HA: TRADING PRICE OF ETF IS NOT INDEPENDENT OF THE TRADING PRICE OF BENCHMARK

TP-ETFi = αi + βi TP-BENCHMARKi + ei ----------- (1)

where

TP-ETFt = Trading Price of ETF TP-BENCHMARKi = Trading Price of Benchmark H0: βi = 0 and HA: βi ≠ 0

TABLE 4.1 REGRESSION ANALYSIS ON TRADING PRICES OF

ETFs AND BENCHMARKs

S.NO. ETF

TP-ETFi = αi + βi TP-

BENCHMARKi

αi βi R2 t-stat

1 NIFTYBEES -2.39 0.1 1 456.64

2 QNIFTY -17.95 0.1 0.99 261.63

3 JUNIOR BEES 2.09 0.01 1 577.29

4 BANKBEES -6.27 0.1 1 638.59

5 RELBANK -29.53 0.1 0.92 103.5

6 KOTAKPSUBK -2.6 0.11 0.99 286.13

7 PSUBANKBEES 8.73 0.1 0.99 316.19

8 SHARIAHBEES -0.03 0.1 0.96 146.71

9 GOLDBEES 121.66 0.91 0.99 1240.62

10 GOLDSHARE 124.73 0.9 0.99 828.8

11 KOTAKGOLD 113.41 0.91 0.99 1027.09

12 RELGOLD 108.46 0.89 0.99 857.6

13 QGOLDHALF 50.24 0.46 0.99 1253.14

Out of the 8 Equity ETFs, ―JUNIORBEES‖ is supposed to trade at 1/100th

of its benchmark trading price all others are to trade at 1/10th of its benchmark

price, when we observe the above Table 4.1 we find that ―JUNIORBEES‖ beta is

equal to 0.01 which means it is exactly tracking its benchmark price ,

the other seven Equity ETFs regression betas are equal to 0.10 which

means they are also exactly tracking their benchmark prices except for

―KOTAKPSUBK‖ beta is slightly different at 0.11,

t-stat is also very high which leads to the rejection of the null hypothesis. Or

in other words we can say that trading prices of ETFs are not independent of the

trading prices of its underlying benchmark‘s.R2 of the regression for all the 8

Equity ETFs are near to 1 or 1 which implies that regression analysis is suitable to

test the relation between the variables.

Similarly the betas for all the 5 Gold ETFs are between 0.90 & 0.95 implying

that they are almost tracking the 1 gram ―Spot Gold Prices‖ except for

―QGOLDHALF‖ which is supposed to trade at half gram spot gold price the reason

for its beta being 0.46, t-stat is high which implies that the null hypothesis ( that

there is no relation between Gold ETFs trading prices and Spot gold prices ) is

re jected. R2 of the regression for all the 8 Equity ETFs are near to 1 or 1 which

implies that regression analysis is suitable to test the relation between the variables

We can conclude that Equity ETFs trading prices are exactly tracking their

benchmarks trading prices compared to Gold ETFs, since the regression betas for

Gold ETFs are supposed to be equal to 1 but they had recorded betas between 0.90

and 0.95 only, but their tracking ability is satisfactory which is not too low.

4.2 REGRESSION ANALYSIS ON RETURNS OF ETFs AND BENCHMARKs

In this regression analysis the relation between returns of the ETF(the

dependant variable) and the returns of the benchmark( the independent variable)

was examined, The beta (βi) coefficient defines the return relationship between ETF

and Underlying INDEX / BENCHMARK.

Beta implies the level of systematic risk, If beta is bigger than unity, the ETF

moves more aggressively in comparison to the benchmark and if beta lies bellow

unity, the ETF follows a conservative investing policy, if beta is equal to unity ETF

moves with the benchmark.

The failure of ETFs to accurately replicate the performance or returns of

underlying indices is defined as tracking error. In this section, we use the three

methods that are suggested by Frino and Gallagher (2001) in order to estimate the

tracking error of sample‘s ETFs.

The column named TE! concerns the estimation of tracking error by the standard

error‘s residual of regression—(2) . The columns TE2 and TE3 are related to the

calculation of mean absolute difference and the standard deviation of differences

among ETFs and indices‘ returns respectively. Finally, the last column

(TE1+TE2+TE3)/3 represents the average tracking error after thecombination of

all three methods calculations.

HYPOTHESIS-II

H0: RETURN ON ETF IS INDEPENDENT OF THE RETURN ON BENCHMARK. HA: RETURN ON ETF IS NOT INDEPENDENT OF THE RETURN ON BENCHMARK.

R-ETFi = αi + βi R-BENCHMARKi + ei ----------- (2)

where

R-ETFt = Return on ETF R-BENCHMARKi = Return on Benchmark

H0: βi = 0 and HA: βi ≠ 0

TABLE 4.2 REGRESSION ANALYSIS ON RETURNS OF ETFs AND

BENCHMARKs

S.

NO. ETF

R-ETFi = αi + βi R-BENCHMARKi TE1 TE2 TE3

(TE1+ TE2+

TE3) / 3

αi βi R2 t-stat

1 NIFTYBEES 0.01 0.89 0.87 81.7 0.5 0.3 0.49 0.42

2 QNIFTY 0.03 0.69 0.57 33.65 0.9 0.7 0.98 0.85

3 JUNIOR BEES 0.01 0.91 0.71 49.08 0.8 0.6 0.85 0.76

4 BANKBEES 0.02 0.86 0.8 63.72 0.75 0.6 0.79 0.71

5 RELBANK 0.08 0.47 0.15 13.31 2.1 1.6 2.31 2.01

6 KOTAKPSUBK 0.04 0.7 0.38 24.64 1.8 1.2 1.91 1.65

7 PSUBANKBEES 0.03 0.74 0.55 33.76 1.4 1 1.48 1.29

8 SHARIAHBEES 0.04 0.48 0.1 9.77 2 1.33 2.14 1.83

9 GOLDBEES 0.013 0.75 0.77 56.19 0.45 0.37 0.53 0.45

10 GOLDSHARE 0.017 0.69 0.69 46.78 0.5 0.43 0.6 0.51

11 KOTAKGOLD 0.013 0.74 0.74 52.45 0.48 0.4 0.56 0.48

12 RELGOLD 0.015 0.72 0.7 47.69 0.51 0.43 0.6 0.51

13 QGOLDHALF 0.019 0.71 0.71 48.47 0.5 0.42 0.59 0.51

(TE means TRACKING ERROR OF ETFs)

TE1 standard error residuals of above regression (which is standard deviation of residuals)

TE2 average of absolute return differences between ETF and Benchmark

TE3 standard deviation of return differences between ETF and Benchmark

From the above regression Table 4.2 we observe that out of the 8 Equity

ETFs ―JUNIORBEES‖, ―NIFTYBEES‖, ―BANKBEES‖ had recorded high betas

when compared to ―RELBANK‖ & ―SHARIAHBEES‖ that had recorded very low

betas implying that the former are tracking the returns of the benchmark better

than the latter, and we also observe that all the 8 betas are below unity which

implies that Equity ETFs follows a conservative investing policy and they are

deviating in tracking the returns of their benchmarks .

Betas of the Gold ETFs are low they lie between 0.7 & 0.75 which implies

low correlation of returns with its benchmark (Spot Gold Prices), and alphas are

positive which implies that they are earning an excess return that is not connected to

the benchmark.

When we observe the tracking errors of Equity ETFs we find that

―NIFTYBEES‖ had recorded the lowest tracking error making it the best Equity

ETF and ―RELBANK‖ had recorded the highest tracking error during the study

period. The above result says that ETFs tracking diversified indices had recorded

low tracking error compared to ETFs tracking specific sector indices, as it is seen

from ―NIFTYBEES‖ tracking error of 0.42 and ―RELBANKS‖ tracking error of

2.01.

Tracking Errors of all 5 Gold ETFs are similar they lie between 0.40 & 0.55

but ―GOLDBEES‖ had recorded the lowest tracking error which gives it a better

rank than its competitors, since lower the tracking error better the ETF

performance .

We can conclude that on average the tracking error of Gold ETFs is lower

than the tracking error of Equity ETFs.

4.3 REGRESSION ANALYSIS ON TRADING PRICES AND NAVs of

ETFs

According to the trading characteristic of ETFs they are supposed to trade at

their NAVs, In this regression analysis the relation between trading price of the

ETF(the dependant variable) and the Net Asset Value of the ETF ( the independent

variable) was examined,

If regression beta is bigger than unity, we conclude that ETFs are traded at

premium and if beta is less than unity, we note that they are traded at discount, if

they are equal to unity they are trading at their net asset value.

HYPOTHESIS-III H0: TRADING PRICE OF ETF IS INDEPENDENT OF IT‘S NET ASSET VALUE. HA: TRADING PRICE OF ETF IS NOT INDEPENDENT OF IT‘S NET ASSET VALUE.

TP-ETFi = αi + βi NAV-ETFi + ei ----------- (3) where

TP-ETFt = Trading Price of ETF NAV-ETFt = Net Asset Value of ETF

H0: βi = 0 and HA: βi ≠ 0

TABLE 4.3 REGRESSION ANALYSIS ON TRADING PRICES AND

NAVs of ETFs

S.NO. ETF TP-ETF i = αi + βi NAV-ETFi

αi βi R2 t-stat

1 NIFTYBEES 2.84 0.99 1 551

2 QNIFTY -5.26 1.01 0.99 399.53

3 JUNIOR BEES 0.9 0.99 1 509.11

4 BANKBEES 3.49 1 0.95 141.36

5 RELBANK 3.29 0.91 0.9 94.4

6 KOTAKPSUBK -1.78 1.01 0.99 352.92

7 PSUBANKBEES -0.76 1 0.99 373.59

8 SHARIAHBEES 1.54 0.98 0.97 157.85

9 GOLDBEES 13.16 0.99 1 1520.01

10 GOLDSHARE 36.29 0.98 1 821.03

11 KOTAKGOLD 21.05 0.99 1 1147.39

12 RELGOLD 22.62 0.99 1 975.17

13 QGOLDHALF 3.78 1 1 1400.54

From the above regression Table 4.3 we observe that out of the 8 Equity

ETFs ―QNIFTY‖ and ―KOTAKPSUBK‖ are trading at a premium ―BANKBEES‖

& ―PSUBANKBEES‖ are exactly trading at its NAV whereas ―NIFTYBEES‖,

―JUNIORBEES‖,‖ RELBANK‖ AND ―SHARIAHBEES‖ are trading at a discount.

If the beta is equal to 1 it implies that the ETF is trading at its NAV if it

either trades at a premium or discount arbitraging occurs whereby the authorized

participants swap the basket of stocks with the ETF units, the arbitrage continues

until the trading price matches the net asset value of the ETF.

Among the Gold ETFs ―GOLDBEES‖, ―GOLDSHARE‖, ―KOTAKGOLD‖,

AND ―RELGOLD‖ are trading at a discount, only ―QGOLDHALF‖ is exactly

trading at its NAV.

t-stats are very high for both Equity and Gold ETFs leading to the rejection

of the null hypothesis, and proving that there is a relation between trading prices

and net asset values of ETFs.. R2

for all of them is near to 1 or 1 implying

that regression analysis is best suited to test the relation between the

variables.

4.4 REGRESSION ANALYSIS - ONE DAY TIME LAG MODEL

ON TRADING PRICES of ETFs

In this regression analysis the relation between trading price of the ETF on

day t (the dependant variable) and the trading price of the ETF on day (t-1 the

independent variable) was examined, if beta is near to 1 there exists a strong

relation between two consecutive days trading prices. In other words it is being

tested whether price of day (t) has got some bearing on the price of previous day (t-

1) or not.

Based on the regression results of historical data the regression model is

applied to test the price relation for an extended period of 9 months trading prices

to test the results, this results are also presented below the regression results.

HYPOTHESIS-IV H0: TRADING PRICE OF ETF IS INDEPENDENT OF PREVIOUS DAY‘S TRADING PRICE HA: TRADING PRICE OF ETF IS NOT INDEPENDENT OF PREVIOUS DAY‘S TRADING PRICE

TP-ETFt = αi + βi TP-ETFt-1 + ei ----------- (4) where

TP-ETFt = Trading Price of ETF at time t TP-ETFt-1 = Trading Price of ETF at time t-1 H0: βi = 0 and HA: βi ≠ 0

TABLE 4.4 REGRESSION ANALYSIS - ONE DAY TIME

LAG MODEL ON TRADING PRICES of ETFs

S.NO ETF TP-ETFt = αi + βi TP-ETFt-1

αi βi R2 t-stat

1 NIFTYBEES 8.16 0.99 0.99 264.23

2 QNIFTY 8.21 0.99 0.99 262.75

3 JUNIOR BEES 1.39 0.99 0.99 314.95

4 BANKBEES 10.55 0.99 0.99 348.19

5 RELBANK 11.82 0.99 0.99 260.78

6 KOTAKPSUBK 3.95 0.99 0.99 285.74

7 PSUBANKBEES 3.5 0.99 0.99 307.9

8 SHARIAHBEES 5.13 0.96 0.94 116.27

9 GOLDBEES 4.06 1 1 688.4

10 GOLDSHARE 3.47 1 1 843.77

11 KOTAKGOLD 3.62 1 1 805.62

12 RELGOLD 3.49 1 1 813.68

13 QGOLDHALF 1.8 1 1 822.37

From the above Table 4.4 we observe that the regression betas of all the 8

Equity ETFs are almost near to 1 and Gold ETFs betas are exactly 1 proving that

there is a one day time lag relation between the trading prices of ETFs, observing

yesterdays price we can estimate tomorrows price movement. The t-stats are high

proving that the null hypothesis is false, or in other words there is a strong relation

between two consecutive days trading prices of ETFs. R2 is 1 or near to 1 for all

the sample ETFs in this regression analysis proving that regression

analysis is a good test to understand the relation between the trading

prices.

4.5 One Day Time Lag - Actual Price (vs) Predicted

Price The one day time lag regression is tested for line fitness by comparing the

actual price and predicted price, for an extended period of 9 months from (1/4/2013

to 31/12/2013), the actual and predicted price average and volatility (standard

deviation) are compared. In this regression analysis the relation between trading

price of the ETF on day t (the dependant variable Y) and the trading price of the

ETF on day t-1 ( the independent variable X) was examined

TABLE 4.5 SHOWING ACTUAL AND PREDICTED AVERAGE PRICE

AND STANDARD DEVIATION FOR ―NIFTYBEES‖

ACTUAL- Y PREDICTED- Y

AVERAGE PRICE 596.6268 596.6268

STANDARD DEVIATION 25.39969 24.50874

CHART 4.1

The Average Actual Price and Predicted Price of

―NIFTYBEES‖ over the extended period of the study was same, there is a very small gap in their standard deviation, but with small adjustments to the predicted price we can estimate next day‘s movement of ETFs Trading Price. The standard error of the

regression is 6.686289

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TABLE 4.6 SHOWING ACTUAL AND PREDICTED AVERAGE PRICE AND STANDARD DEVIATION FOR ―QNIFTY‖


AVERAGE PRICE 613.4039 613.4039


CHART 4.2

The Average Actual Price and Predicted Price of ―QNIFTY‖

over the extended period of the study was same, there is a small

gap in their standard deviation, but with small adjustments to

the predicted price we can estimate next day‘s movement of

ETFs Trading Price. The standard error of the regression is 8.024914

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TABLE 4.7 SHOWING ACTUAL AND PREDICTED AVERAGE PRICE AND STANDARD DEVIATION FOR ―JUNIORBEES‖


AVERAGE PRICE 118.130107 118.130107


CHART 4.3


―JUNIORBEES‖ over the extended period of the study was

same, there is a very small gap in their standard deviation, but

with small adjustments to the predicted price we can estimate

next day‘s movement of ETFs Trading Price. The standard error of the

regression is 1.364688, which is very low compared to the regression of other ETFs.

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TABLE 4.8 SHOWING ACTUAL AND PREDICTED AVERAGE PRICE AND STANDARD DEVIATION FOR ―BANKBEES‖


AVERAGE PRICE 1126.699305 1126.699305


CHART 4.4


―BANKBEES‖ over the extended period of the study was same,

there is a small gap in their standard deviation, but with small

adjustments to the predicted price we can estimate next day‘s

movement of ETFs Trading Price. The standard error of the regression is

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TABLE 4.9 SHOWING ACTUAL AND PREDICTED AVERAGE PRICE AND STANDARD DEVIATION FOR ―RELBANK‖


AVERAGE PRICE 1,136.41 1,136.41


CHART 4.5

The Average Actual Price and Predicted Price of ―RELBANK‖ over the extended period of the study was same, there is a small gap in their standard deviation, but with small adjustments to the predicted price we can estimate next day‘s movement of ETFs Trading Price. The standard error of the regression is 25.34049

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TABLE 4.10 SHOWING ACTUAL AND PREDICTED AVERAGE PRICE AND STANDARD DEVIATION FOR ―KOTAKPSUBANK‖


AVERAGE PRICE 269.8452 269.8452


CHART 4.6


―KOTAKPSUBANK‖ over the extended period of the study was same, there is a small gap in their standard deviation, but with small adjustments to the predicted price we can estimate next day‘s movement of ETFs Trading Price. The standard error of the


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TABLE 4.11 SHOWING ACTUAL AND PREDICTED AVERAGE PRICE AND STANDARD DEVIATION FOR ―PSUBANKBEES‖


AVERAGE PRICE 278.5849 278.5849


CHART

4.7

The Average Actual Price and Predicted Price of ―PSUBANKBEES‖ over the extended period of the study was same, there is a small gap in their standard deviation, but with small adjustments to the predicted price we can estimate next day‘s movement of ETFs Trading Price. The standard error of the


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TABLE 4.12 SHOWING ACTUAL AND PREDICTED AVERAGE PRICE AND STANDARD DEVIATION FOR ―SHARIAHBEES‖


AVERAGE PRICE 130.2608 130.2608


CHART 4.8


―SHARIAHBEES‖ over the extended period of the study was same, there is a small gap in their standard deviation, but with small adjustments to the predicted price we can estimate next day‘s movement of ETFs Trading Price. The standard error of the


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TABLE 4.13 SHOWING ACTUAL AND PREDICTED AVERAGE PRICE AND STANDARD DEVIATION FOR ―GOLDBEES‖


AVERAGE PRICE 2,714.68 2714.6797


CHART 4.9

The Average Actual Price and Predicted Price of ―GOLDBEES‖ over the

extended period of the study was same, there is a small gap in their standard

deviation, but with small adjustments to the predicted price we can estimate next

day‘s movement of ETFs Trading Price, from the above graph we find that actual

and predicted lines are over lapping each other, which implies that actual and

predicted are almost similar. The standard error of the regression is 34.55216526

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TABLE 4.14 SHOWING ACTUAL AND PREDICTED AVERAGE PRICE AND STANDARD DEVIATION FOR ―GOLDSHARE‖


AVERAGE PRICE 2,678.05 2678.052

STANDARD DEVIATION 155.037 151.6727 CHART 4.10

The Average Actual Price and Predicted Price of ―GOLDSHARE‖ over the





predicted are almost similar. The standard error of the regression is 32.20789608.

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TABLE 4.15 SHOWING ACTUAL AND PREDICTED AVERAGE PRICE AND STANDARD DEVIATION FOR ―KOTAKGOLD‖


AVERAGE PRICE 2,704.62 2704.61738

STANDARD DEVIATION 169.016 165.6117354 CHART 4.11

The Average Actual Price and Predicted Price of ―KOTAKGOLD‖ over the






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TABLE 4.16 SHOWING ACTUAL AND PREDICTED AVERAGE PRICE AND STANDARD DEVIATION FOR ―RELGOLD‖


AVERAGE PRICE 2,634.70 2634.704


CHART 4.12

The Average Actual Price and Predicted Price of ―RELGOLD‖ over the






0.00

500.00

1,000.00

1,500.00

2,000.00

2,500.00

3,000.00

3,500.00

1

10

19

28

37

46

55

64

73

82

91

10

0

10

9

11

8

12

7

13

6

14

5

15

4

16

3

17

2

18

1

Наз

ван

ие

оси

RELGOLD ACTUAL PRICE (VS) PREDICTED PRICE

actual y

predicted y

TABLE 4.17 SHOWING ACTUAL AND PREDICTED AVERAGE PRICE AND STANDARD DEVIATION FOR ―QGOLDHALF‖


AVERAGE PRICE 1,358.24 1,358.24


CHART 4.13

The Average Actual Price and Predicted Price of ―QGOLDHALF‖ over the






0.00

200.00

400.00

600.00

800.00

1,000.00

1,200.00

1,400.00

1,600.00

1 9

17

25

33

41

49

57

65

73

81

89

97

10

5

11

3

12

1

12

9

13

7

14

5

15

3

16

1

16

9

17

7

18

5

Наз

ван

ие

оси

QGOLDHALF ACTUAL PRICE (VS) PREDICTED PRICE

actual Y

predicted Y

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