13.03 research plagiarism and academic integrity

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MIT190336

Subedi

Anjana

Course: Master’s in professional accounting

School: Melbourne Institute of Technology

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Unit title: Accounting Research

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Anjana Subedi

© MIT April, 2020

T1-2020 v1

Abstract
The major objective of the report is to examine whether during the period of Covid-19 pandemic the stock market is predictable. In precise, the report is focused in testing the EMH of Australian Stock Market during the Covid-19 pandemic. The report will use all the valid, reliable and accessible data to test the EMH on Australian stock market. Efficient market hypothesis is the process when financial institution use all the accessible economic and market data to determine the price of the assets.

Testing of the efficient market hypothesis is one of the most dominant area of research in the field of the capital market. There are large number of the study conducted around the word focused over testing this particular hypothesis. In this concern security marker is regarded as efficient in presenting the information of the overall stock market or individual stock. Besides providing the detail literature review regarding EMH the paper also examines the EMH of Australian stock market.

The finding of the study shows that there is market efficiency of Australian stock market during the period of COVID-19 and the value of the stock can be predictability during the period. It emphasis that the price of the assets are sensitive to the economic realities and are integrated with the information prevailing in the market.

Keyword: Hypothesis, Efficiency market, random walk theory.

Table of Contents

Abstract 1

1 Introduction 3

2 Literature review 3

2.1 Strong form EMH 4

2.2 Semi-strong form EMH 5

2.3 Week-form EMH 6

2.4 The random walk model 6

3 Research Objectives and question 7

3.1 Research objective 7

3.2 Research question 7

4 Methodology 8

4.1 Research method 8

4.2 Sources of data 8

4.3 Sampling design 9

4.4 Data analysis approach 9

4.5 Hypothesis formation 10

5 Data Analysis 11

5.1 Run test 11

5.2 Current state of efficient market hypothesis 12

6 Finding and discussion 13

7 Conclusions 15

8 References 16

9 Appendix 19

Introduction
The concept of efficient market hypothesis was independently developed by the Eugene F. Famma and Paul A. Samelson in the period of 1960s. The idea if the EMH is extensively used in the securities market for security pricing and price detection process. According to Fama (1970), when the price of asset is determined based on all the available economic realism information it becomes impossible for the participants of stock market to maximise their under the uncommon condition. The approach of the EMH regulates the trading of the stock at the respective fair value which prevent investor to practice uncommon trade such as it prevent investor to purchase a undervalued stock and sell of the stock at the inflation price. Under this condition in order to maximise the profit the investor needs to acquire high risk investment.

The efficient market hypothesis is one of the major area of discussion over a continued period of time. In the study, Dima & Milos (2009) outlines that the stock market can only be recognised as an efficient only when the prices of the security completely reflect the accessible information over economic realism. At the event when this this particular condition is matched the participants of the stock market cannot generate uncommon return which is higher than the return that can be achieved through holding a random portfolio including with its comparable risk. The concept of the EMH is based on the principle of “random walk”, this principle emphasis that the price series under the condition where all succeeding price changes form the market event of random departure based on the former price series. The EMH concept emphasis that stock are always traded in their respective fair value on exchange which prevent the investor to sell stock at inflation price or purchase the undervalued stock.

Literature review
The empirical literature relevant to the efficient market hypothesis consist of three different segment. The first studies emphasis on the probability of return (it discuss that whether the return is predictable based on the past assessable information or any other variables). The study emphasis over the possible events that have the potential to create a change in the price of assets (change in decision regarding capital structure, investment and distributed dividend). The third and last studies emphasis over the private information (this discuss that investor with the private information about market do not reflect market price).The study will only focus on the first theme of the EMH studies.

Fama in his study outlined that the efficiency of the market cannot be tested per share rather the process is based on the equilibrium model. In precise, testing about whether the information regarding the marker price is correct is only possible under the condition when there is appropriate price formation model (Tehseen, Ramayah, & Sajilan, 2017). The market is governed by the economic realism as the result the market do not achieve efficiency automatically. The major factor that is responsible for obtaining efficient market is the action of the investor to generate the higher return. But there is a paradox between the efficient market and the investor, under the event when market becomes efficient the investor is likely to stop looking for any possible inefficient to maximise the return, this event will leas market to inefficient position. Based on the paradox that inefficient arises in a regular basis but its disappearance is irregular as the investor accept it as end trade, so it can be determined that the market do not obtain automatic efficiency rather it is based on self-corrective mechanism.

Based on the Fama the EMH that the market can actually obtain consist of three different level of efficiency including strong form, semi-strong form and weak form EMH.

Strong form EMH
It is the most rigorous form of the EMH, which states that overall information prevailing in the market private or public is responsible for the price of stock. Market efficiency is the point when the return cannot be realised despite any level of information or research assessable by the investor (Dragota, Oprea, & Brasoveanu, 2019). This form of EM is recognised by the random walk theory. It outlines that securities value and market are not automatic as it consist of influence form past event. Under this form, the most appropriate method to maximise return is through the practice of buy and hold strategy, the priority is given to the fair trading rather than uncommon speculation.

It emphasis that the market obtain efficient only under the condition that overall information of the share value that can or cannot be accessible by the potential or existing investors, is precisely and quickly revealed into the market price. For example, when the existing market price value is determined to be lower based on some information that is held privately, than it is likely that the information holder will exploit pricing through the purchase of the shares. The demand for share continue in the market until the price of the share reflect the private information. In this point the investor will gain no additional benefit as a result they the demand for share decreases and the price of the share will stabilise at an equilibrium. From the overall market perspective, the strong form is the most compelling and rational form of EMH, but this event is not free from limitation (Jovanovic, Andreadakis, & Schinckus, 2016). The major downside of this market theory is that the practice is difficult to empirically conform, as the research is unlike to obtain the cooperation from the respective financial community section.

Semi-strong form EMH
The market efficiency under semi strong form assumes that market achieve efficient only under the event if all the available public information including historical price or even other variables reflect the market price of the stock. It indicates that the market will cope with all the information available for the public to determine the new price equilibrium of securities. The market efficiency depends upon the supply and demand caused from the prevailing public information in the market (Degutis & Novickyte, 2014). Investor who tends to accept this hypothesis emphasis that only those information that are not available to the public in the market can support the speculation to maximise the return.

Market efficiency under this form implies that existing price of stock continuously and rapidly adjust to the new information released for the public (Callado & Leitao, 2018). Under the hypothesis the stock price is forced to adjust to the public information. This form consider that neither technical nor fundamental analysis can assist to achieve a continuous excess gain the only approach to generate the excess gain is through the use of the non-public information. It is based on that at any given liquid marker and period of time the price of the securities completely reflect the overall public information. The theory concerned with this approach is random walk, where change in the price level is the result of the random departure in the historical prices. As the price of the share rapidly reflect the publicly accessible information so the preceding price becomes independent to existing price. As it is empirically difficult to test strong form so over this reason the semi-strong form becomes more valuable but the major drawback of this approach is that it lack on the intellectual rigorous.

Week-form EMH
The market efficiency under week form assumes that market achieve efficient only under the event if the existing price of the securities integrates all the available information regarding the past prices (Titan, 2015). This form of EMH assumes that the existing market price of security do not reject the information available on the previous price and other preceding price variable, which implies that forecasting of the future price based on the previous pries is meaningless. Under this form only the factor that makes sense to achieve a market efficiency is past price of the relevant securities other variables that this do not make sense under week form.

In the week form model, investor becomes unable to determine and rational pattern that allow them to anticipate the future price in order to generate abnormal return. It assume that the earning, volume data and price movement data are unable to influence the price of stock and as a result they cannot be used to forecast the future price. This model uses the random walk theory as the basis in order to test the week form. In an efficient market the flow of the information occurs in a random approach, the change in the price level of securities as the effect of the information also tends to be random. So the movement of the price in a week form is also random and the consecutive change in price are independent from each other. It simply advocates that future price of stock are random and past price cannot be used to influence the succeeding price by investor to maximise return.

The random walk model
The random walk model can be functionally represented as equation pt = pt−1 + μt.

Here, pt represent the price during the time t, pt-1is the immediate following price and random error is represented by μt. In a statistic a pure random is the event with the identical and independent distribution which are typically constant variance and zero mean. The change in the price level recognised as Dpt = pt – pt−1, is the μt which cannot be predicted based on the past price change.

From a contrast perspective the equation condition emphasis that the prediction of the security price at time period of t+1 is the same price level during the time t, it rationally implies that the forecasted return or loss during the period of holding is zero. So the evaluation of the previous price is meaningless as the parameter observed was the complete result of change.

Determining of the efficiency for a stock market is meaningful in order to provide justification for the excess investor return, which are the uncommon returned generated by investor beyond observed risk. Rational of random walk theory for respective EMH form are as follows:

· When the market is efficient under strong form, the existing market price is the most appropriate forecaster or the right price, use of all the assessable information whether private or public held. It state that consistently obtaining excess return is not possible even through the insider trading approach.

· When the market is efficient under semi-strong form, the existing market price is the most appropriate forecaster or the fair price, based on the publicly accessible information on the investment return and risk. The analysis of public information past price and other variables cannot be rational for generating consistent excess yield. It state that generating return beyond justified by risk is not possible for any investor.

· When the market is efficient under weak form, there is no correlation among the sequential prices, as a result achieving a consistent excess return is not possible. It emphasis on the historical price pattern.

Research Objectives and question
Research objective
The objectives of the research is as follows:

· To find out whether during the period of COVID 19 the stock market is predictable

· Testing the EMH of Australian stock market during the COVID 19

Research question
The research question is as follows:

· What is the influence of the COVID 19 in the predictability of the Australian stock market?

· What is the market efficiency of Australian stock market during the COVID 19?

Methodology
Research method
Research is the purposive investigation with the goal to determine the specific new meaningful information. Research method is concerned with the gathering data, analysis of the data and stating the discovery into the study. This report is based on the quantitative approach, it implies that the study will focus on presenting the finding through the exploration of the numerical analysis. The research objective and question evaluated in term of functional and numerical statement and the outcome are discussed in the study based on the numerical relation between variable. The objective of the quantitative approach is to evaluate numerical data and facts rather than discussing the research question over argument or theme (Goundar, 2012).

Sources of data
The research is based on the secondary data sources. The objective of the research is to determine the predictability of the stock market during the COVID-19 and testing the EMH of Australian market (Peon, Antelo, & Calvo, 2019). To determine the finding for this objective using the primary sources is not rational which was the major reason for only choosing the secondary data approach. As the testing requires analysis of the weekly and weekly Australian stock market the use of the primary sources is not possible. Tough the research only uses the secondary approach the data analysis and evaluation are valid and reliable. The study uses two different sources for collecting the data, which are as follows:

Fox market

Fox market is among the most reliable online channel that continuous and quick update on the events of the stock market. In order to gather require data about weekly return on Australian stock pattern over the period of COVID-19 fox market is used. The data are collected over the period of 51 weeks from August 2019 to July 2020.

Yahoo finance

Yahoo finance is the biggest sources of the financial data, it provides about weekly returns of the stock listed business from all around the globe (Lee, Lee, Chang, & Tai, 2016). Yahoo finance is the major sources of the data used in the study. The data was collected relevant to the Australian stock pattern over the period of COVID-19 from yahoo finance. The data are collected over the period of 51 weeks from August 2019 to July 2020.

Stock market

The biggest sources for the Australian stock data is the ASX website (Muralitharan, Agricola, Chandler, Coulepis, & Gray, 2012). The ASX provided overall information on the events and pattern relevant to the Australian company and stock market. It is the primary sources of data sources for the study.

Sampling design
Sampling technique

The study is based on the random sampling as the entire Australian market consist of large number of listed companies. It is not possible to evaluate the weekly return of the every company. In order to make the study a few company from the different industry sector within the ASX 100 list have been randomly chosen for the study.

Sampling size

As the Australian market is large which consist of thousands of listed company, so evaluation or incorporating weekly return of every single company into the study is not suitable. For the study, 25 Australian listed company have been incorporated into the study.

Data analysis approach
There are various analysis approach that is used in the research to evaluate the collected data. The data analysis approach of the study are as follows:

Runs test

It is a statistical technique that evaluates whether a data set occur randomly based on the specific distribution. The run test examines the occurring of the related events which are separated by those events that is typically different. Run test is useful to determine whether the data occur over the random series or they are influenced by any other variables. A run test also measure the data to determine whether the information is based on the random walk theory (Worthington & Higgs, 2009).

The run test is concerned with the test of the arbitrariness, in order to test information of the test are first arranged for and are tested with the + it can be summarised that the data are more distributed in the middle and with the – it can be summarised that the data are not normally distributed around the middle.

The test is concerned with measuring whether the capacity reasonably fits with the data collection, through denoting the data that surpass the capacity with the + and the capacity that underperform with -. In concerned with this, run test is appropriate but which utilizes the p value in order to test the capacity of the data.

Hypothesis formation
The objective of the research is to determine the predictability of the stock market during the COVID-19 and testing the EMH of Australian stock market (Shanaev, Shuraeva, & Ghimire, 2020). The hypothesis of the study is as follows:

Null hypothesis (HO):

There is no market efficiency of Australian stock market during the period of COVID-19 and the value of the stock is not predictability during the period.

Alternative hypothesis (H1):

There is market efficiency of Australian stock market during the period of COVID-19 and the value of the stock can be predictability during the period.

Data Analysis
Run test
The database consist of weekly stock price return of 25 ASX listed company. The data are collected over the period of 51 weeks from August 2019 to July 2020 from different sources including yahoo finance, fox and stock market. Based on the gather data run test is performed with the purpose to determine p value. The following function was performed in excel to determine the p value from dataset.

Based on the above matrix p value for each companies was generated from the run test. The major purpose for the use of the p value is test the efficiency of the market. Based on the p-value gained from the run test either the null hypothesis is accepted or rejected to determine efficiency of the market (Biau, Jolles, & Porcher, 2009). In order for the p value to become reasonably significant the value is measured against the value of level of significant. When testing under 5% level of significance p value is consider significant if the p value is less than 0.05 if the obtained value of p is more than 0.05 than the phenomenon is not consider as significant. Typically null hypothesis represent nonexistence of the significant relation and alternative hypothesis represent the existence of the significant relation. Therefore, for the phenomenon to be significant the p value must be less than the 0.05 level of significant so the null hypothesis is rejected and alternative hypothesis is accepted. The dataset consisting of the weekly price of the 25 companies from the period of from August 2019 to July 2020 is analysed in the run test and the respective p value of each companies is obtained. The research shows that the most of the company which is p value of 20 company was less than the value of the level of significant 0.05. So the null hypothesis was rejected and the alternative hypothesis was accepted. For the company with the p value less than level of significant 0.05 the null hypothesis is rejected, it shows that there is market efficiency of Australian stock market during the period of COVID-19 and the value of the stock can be predictability during the period. For the company with p-value greater than the level of significant 0.05 the null hypothesis is accepted it shows that there is no market efficiency of Australian stock market during the period of COVID-19 and the value of the stock is not predictability during the period.

Current state of efficient market hypothesis
As the stock market is a big area a typical data analysis with a limited few company it is not completely accurate to say that the data analysis generates the overall truth about the market hypothesis. Based on the observation against the EMS it can be recognised that a close picture of the EMH is generated. Though the investigation encompasses the accurate dataset from the market there are various principle consequences that prevent from generating the most accurate reflection of current state of EMH (Fakhry, 2016).

The major reason for which it is difficult to determine the EMH is due to the theory of EMH is experimentally refutable. Through the normal data analysis it is only possible to check the significant relation of EMH based on the phenomenon. In the study the price of the stock of 25 different ASX listed company have been analysed the price of the stock was collected from August 2019 to July 2020 this include the price of stock of these company before the period of COVID-19 and during the period of COVID-19. To determine the current state of Australia stock market EMH run test have been performed, which focus on measuring testing the hypothesis whether the is there is market efficiency of Australian stock market during the period of COVID-19 and the value of the stock is not predictability during the period. Through the data analysis it was observed that the p value of the 20 company in the test was less than the value of level of significant which implies that there is market efficiency of Australian stock market during the period of COVID-19 and the value of the stock is not predictability during the period. But the p value of the 5 company states that there is no significant relationship in this phenomena. While observing the 25 company it can be said that the current state of the Australia stock market is efficient which prevent investor from generating continuous abnormal profit. Under this event the only way for the investor to maximise their earning is through high level of speculation and high risk stock.

The data analysis only represent the small portion of the overall Australia stock market based on the result obtained it is reasonable to state that the market is efficient but the finding do not provide the evidence to determine the overall market efficiency picture of the Australian market, the test is merely based on testing the hypothesis rather than determining the most accurate state of the current level of market efficiency (Aktan, Sahin, & Kucukkaplan, 2018). The test is focused to determine the efficiency rather than the overall proficiency of the market So, based on this particular run test the it can be determined the Australia stock market operate in an efficient market during the period of 51 weeks from August 2019 to July 2020

Finding and discussion
The data analysis was focused to test the market efficiency hypothesis of the weekly return in individual stock of five different Australia listed company over the first half of the year 2020, which is the period of COVID-19 pandemic. Based on the different data analysis conducted in the study it shows that there is market efficiency of Australian stock market during the period of COVID-19 and the value of the stock can be predictability during the period. This implies that the economic realities affect the performance of the Australian stock market. During the first half of the year 2020, information prevailing regarding the COVID-19 was the major factor responsible for affecting the weekly return in the Australia stock market. Over this period of time, the weekly return in the stock decreased due to decrease in the price level of the stock. The information prevailing in the market regarding COVID-19 pandemic, lockdown, temporary shutdown of business and low trading was the major factor that caused the decrease in the weekly return.

Fig: Company with significant price changes due to COVID-19

The above figure 1, monthly return in individual stock of five different Australia listed company over the six month of 2020 from January to June, which is the period of COVID-19 pandemic. As represented by the figure, the initial return in the month of January of every company is relatively high but over the succeeding period the monthly return fluctuates, mostly the return of every company have decreased during this period.

In this period, the price of the stock was predictable as investor or the public completely aware about the economies realities and its impact on the overall economy (Birau, 2012). During the period Australian stock market operates under the market efficiency as the price of the stock integrated all the information prevailing in the market to determine the equilibrium price. Over the period the price of the stock and the weekly return initially decreased but over with the decreasing impact of COVID-19 information the price and weekly return achieved growth. The Australian stock market was effective as the market incorporated information and historical price to determine the fair value of the stock which prevented any investor from gaining the excess abnormal profit.

Conclusions
The approach of the efficient market regulates the trading of the stock at the respective fair value which prevent investor to practice uncommon trade such as it prevent investor to purchase a undervalued stock and sell of the stock at the inflation price. Under this condition in order to maximise the profit the investor needs to acquire high risk investment. The EMH concept emphasis that stock are always traded in their respective fair value on exchange which prevent the investor to sell stock at inflation price or purchase the undervalued stock. The EMH is based on the random wall model to determine the market efficiency. The finding of the study shows that there is market efficiency of Australian stock market during the period of COVID-19 and the value of the stock can be predictability during the period.

References
Aktan, C., Sahin, E. E., & Kucukkaplan, I. (2018). Testing the Information Efficiency in Emerging Markets. In Financial Management from an Emerging Market Perspective (pp. 50–67). https://doi.org/10.5772/intechopen.70369

Biau, D. J., Jolles, B. M., & Porcher, R. (2009). P Value and the Theory of Hypothesis Testing: An Explanation for New Researchers. Clinical Orthopaedics and Related Research®, 468(3), 885–892. https://doi.org/10.1007/s11999-009-1164-4

Birau, R. (2012). The Impact Of Behavioral Finance On Stock Markets. Annals of the „Constantin Brâncuşi” University of Târgu Jiu, Economy Series, 3, 45–50. Retrieved from https://www.researchgate.net/publication/258698903_THE_IMPACT_OF_BEHAVIORAL_FINANCE_ON_STOCK_MARKETS

Callado, A. A. C., & Leitao, C. R. S. (2018). Dynamics of Stock Prices and Market Efficiency. International Business Research, 11(6), 29–40. https://doi.org/10.5539/ibr.v11n6p29

Degutis, A., & Novickyte, L. (2014). The Efficient Market Hypothesis:A Critical Review Of Literature And Methodology. Ekonomika, 93(2), 7–23. Retrieved from https://www.researchgate.net/publication/324971038_THE_EFFICIENT_MARKET_HYPOTHESIS_A_CRITICAL_REVIEW_OF_LITERATURE_AND_METHODOLOGY

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Dragota, V., Oprea, D. S., & Brasoveanu, L. O. (2019). Market Efficiency, Predictions and Returns. ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, 53(3/2019), 59–76. https://doi.org/10.24818/18423264/53.3.19.04

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Appendix
Weekly return of five Australia listed company in first half of 2020:

CBA

WOW

BHP

COL

AYI

APT

ALQ

BPT

BLD

ANZ

19-Jul-20

74.43

39.23

38.8

21.2

100.24

75.05

7.87

1.485

6

18.72

12-Jul-20

72.6

38.86

37.92

19.9

100.12

67.37

7.4

1.515

6

18.47

5-Jul-20

70.63

38.51

36.19

21.25

89.42

72.31

6.78

1.415

6

18.3

28-Jun-20

71.57

37.77

36.26

19.3

89.48

67.5

6.69

1.545

6

19.19

21-Jun-20

69.27

36.39

36.05

17.5

84.45

57

6.53

1.52

4.78

18.8

14-Jun-20

68.68

36.55

35.01

`

90.48

58.69

6.87

1.625

5.2

18.75

7-Jun-20

67.32

36.67

35.99

17.1

97.7

51.86

6.84

1.555

5.2

18.92

31-May-20

68.73

37.06

36.33

16.4

108.11

50.61

7.23

1.705

5

19.77

24-May-20

63.75

35.34

34.64

15.6

86.15

47.41

7.15

1.615

5

17.89

17-May-20

58.7

34.16

34.32

15.7

84.78

44.51

6.9

1.58

5.5

15.23

10-May-20

59.6

35.16

31.67

16

77.34

41.2

6.28

1.43

5.1

15.44

3-May-20

59.6

34.7

31.4

15.8

85.98

39.88

6.4

1.51

5.27

15.73

26-Apr-20

58.84

34.45

29.84

15.5

82.34

29.16

6.65

1.445

5.3

15.75

19-Apr-20

58.88

35.89

30.54

16.4

83.34

27.01

6.11

1.365

5.26

16.02

12-Apr-20

61.06

37.4

31.28

15.5

86.36

29

6.49

1.385

5.4

16.56

5-Apr-20

61.76

35.1

31.5

15.3

92.14

22

6.18

1.485

5.41

16.54

29-Mar-20

60.11

35.5

30.33

15.2

75.09

19.55

5.64

1.295

5.44

15.79

22-Mar-20

57.66

34.8

29.03

15.6

82.82

19.1

5.39

1.045

5.75

15.47

15-Mar-20

59.91

37.47

27.01

15.9

76.53

12.44

5.35

1.08

5.75

16.02

8-Mar-20

66.36

37.05

26.72

17

95.68

23.24

6.29

1.415

5.72

18.8

1-Mar-20

73.93

38

32.19

17.8

103.05

32.94

7.77

1.65

5.85

22.14

23-Feb-20

81.78

38.8

33.6

17.82

102.86

33.17

8.36

1.755

6.63

24.83

16-Feb-20

88.8

43.45

38.22

18

115.7

38.99

9.89

2.09

4.78

27.24

9-Feb-20

90.99

43.14

38.65

17.98

116.9

38.61

9.76

2.11

5.2

26.61

2-Feb-20

84.8

42.72

38.77

18.5

117.7

39.44

9.52

2.38

5.2

26.03

26-Jan-20

85.26

41.84

39.4

18.46

117.87

38.55

9.65

2.67

5

25.75

19-Jan-20

84.94

40.84

40.45

18.46

121.49

37

9.66

2.7

5

25.9

12-Jan-20

84.05

39.04

40.6

18.48

123.28

33.22

9.65

2.77

5.5

25.42

5-Jan-20

82.5

37.82

39.9

18.48

121.24

31.13

9.44

2.67

5.1

25.12

29-Dec-19

80.31

36.35

39.15

18.38

139.63

30.27

9.15

2.6

5.27

24.73

22-Dec-19

81.07

37.41

39.64

18.42

137.72

30.58

9.37

2.57

5.3

24.78

15-Dec-19

81.25

37.24

39.72

18.1

136.48

29.27

9.4

2.57

5.26

24.8

8-Dec-19

80.06

37.42

39.35

18.2

130.8

28.7

9.14

2.61

5.4

24.71

1-Dec-19

78.99

38.36

37.36

18.46

133.8

29.9

9.04

2.49

5.41

24.59

24-Nov-19

80.82

39.76

38.23

18.2

130.78

31.6

9.05

2.4

5.44

24.84

17-Nov-19

79.6

39.08

37.19

18

127.31

30.49

8.72

2.32

5.75

24.86

10-Nov-19

80.69

38.96

37.28

18.6

128.38

32.91

8.29

2.38

5.75

25.41

3-Nov-19

79.17

37.68

37.3

18.4

128.08

26.97

8.22

2.4

5.72

26.25

27-Oct-19

78.24

37.66

35.7

18

125.99

27.45

8.15

2.35

4.78

26.19

20-Oct-19

80.7

37.93

35.77

18.52

124.9

29.3

8.09

2.35

5.2

28.03

13-Oct-19

79.64

37.49

34.79

18.6

122.93

29.65

8.07

2.35

5.2

27.76

6-Oct-19

78.77

37.11

35.82

18.58

122.02

35.12

8.21

2.37

5

27.49

29-Sep-19

77.59

36.32

35.3

18.6

121.15

34.15

8

2.43

5

27.17

22-Sep-19

81.54

37.54

36.84

18.6

132.29

36.57

8.14

2.56

5.5

28.68

15-Sep-19

82.18

37.43

37.75

18.6

135.57

33.05

8.13

2.57

5.1

27.91

8-Sep-19

82.14

36.43

37.13

18.5

139.16

31.94

8.18

2.51

5.27

27.85

1-Sep-19

79.54

38.06

36.31

18.6

125.84

33.88

8.05

2.49

5.3

27.07

25-Aug-19

79.05

37.77

36.29

18.4

125.41

30.98

7.67

2.45

5.26

26.74

18-Aug-19

77.4

35.99

35.42

18.2

121.57

24.54

7.67

2.22

5.4

26.64

11-Aug-19

75.12

34.99

36.17

18.58

123.99

23.26

7.46

1.805

5.41

26.39

4-Aug-19

79.42

35.4

37.29

18.3

129.31

24.17

7.18

1.885

5.44

27

BXB

CGF

CIM

DMP

EVN

FLT

GMG

ILU

JHX

MQG

11.07

4.37

6.08

75.54

6.38

11.04

16.05

9.22

28.9

127.84

11.29

4.49

5.9

74.01

6.08

10.46

15.59

8.92

28.51

125.18

11.12

4.42

6.06

71.64

6.13

10.54

15.24

8.76

26.38

119.8

10.84

4.57

6.16

72.2

6

11.4

15.81

8.78

27.65

122.02

10.74

4.6

6.02

67.79

5.35

11.32

15.28

8.53

26.38

119.19

11.01

5.32

6

68.32

5.25

13.61

15.25

8.38

26.7

121.53

10.95

4.92

6.02

62.7

5.5

14.3

14.76

8.76

25.89

115.6

11.22

5.09

5.68

65.42

5.6

15.39

15.05

9.01

26.95

118

11.64

5.01

5.6

62.33

6.1

13.08

15.37

8.22

25.95

109.97

11.05

4.63

5.58

59.3

5.89

11.29

14.65

8.11

23.55

102.62

11.07

4.24

5.6

57.9

5.73

10

14.22

7.64

21.54

105.08

10.58

4.76

5.98

56.95

5.39

10.76

14.36

7.3

21.63

105.19

10.49

4.53

5.4

56.01

4.72

10.05

12.95

7.55

20.7

96.92

10.79

4.05

5.8

47.9

5.18

9.12

12.95

7.3

17.81

95.01

11.58

4.79

6

52

4.74

10.52

13.72

7.58

19.24

100.41

10.93

4.93

6.6

49.8

4.54

11.56

13.86

7.29

20.27

99.5

11.25

4.08

6.02

48.8

4.1

9.91

12.11

7.08

19.02

84.3

10.31

3.6

7.16

50.57

3.86

19.15

11.13

6.73

17.44

80.01

10.24

3.85

8

47.28

3.62

26.5

11.44

6.8

18.07

85.06

10.33

6.64

8.04

55.36

3.79

32.62

13.76

7.38

23.39

116.48

11.63

8.27

8

56.9

4.35

39.48

15.2

8.21

27.7

131.93

11.96

9.09

8

55.58

4.04

38.97

14.98

8.34

28.23

134.83

12.89

10.32

7.5

62.78

4.51

39.75

16.39

9.54

30.31

151.34

12.65

10.1

7.5

58.2

4.23

39.31

16.44

9.53

30.5

148.58

12.7

8.94

7.3

54.81

3.77

41.47

15.24

9.74

31.4

146.96

12.63

8.95

7.3

54.89

3.71

43.85

14.89

9.72

31.8

144.77

12.32

8.72

7.3

55.52

3.77

44.28

15.02

9.44

31.57

145.86

12.29

8.63

7.22

55.45

3.9

44.37

14.65

9.55

30.44

144.14

12.1

8.34

7.2

56.73

3.58

44.6

14.03

9.32

29.56

140.35

11.67

8.27

7.2

53.99

3.67

44.09

13.5

9.23

28.06

138.37

12.04

8.37

7.5

53.87

3.74

44.04

13.74

9.51

28.56

140.23

12.21

8.38

7.1

53.86

3.55

43.39

13.73

9.41

28.52

140.25

12.07

8.2

7.04

52.25

3.53

44.03

13.67

9.28

27.86

136.31

12.36

7.88

7

50.75

3.89

42.44

14.44

9.43

28.81

135.1

12.55

8.14

7

52.82

3.91

42.24

14.82

9.52

28.96

138.05

12.37

8.06

6.8

52.4

3.87

39.71

14.27

9.23

29.01

134.88

12.41

8.09

6.7

53.31

3.99

42

14.64

9.1

28.85

137.96

12.03

8.07

6.78

48.9

3.93

43.25

14.18

9.07

27.66

137.74

11.98

7.85

6.7

51.03

4.24

43.11

14.37

9

25.07

134.38

12.23

7.73

6.9

50.95

4.15

41.6

14.32

8.73

25.57

134.98

11.94

7.77

7.22

49.9

4.17

46.48

14.26

8.35

25.23

133.82

11.31

6.86

7.32

48.93

4.65

47.81

14.18

8.02

24.77

129.39

10.67

6.93

7.34

47.96

4.61

47.45

14.22

7.85

24.28

126.44

11.12

7.46

7.46

47.28

4.58

47.81

14.05

7.89

24.72

132.09

11.19

7.56

7.46

47.65

4.77

47.93

13.83

7.72

24.69

131.05

11

7.29

7.58

46.53

4.54

46.51

13.68

7.7

23.48

129.29

10.99

6.96

7.66

45.7

5.07

46.1

13.68

7.6

23.02

127.08

11.02

6.71

7.66

42.77

5.18

43.05

14.52

7.15

22.55

123.84

11

6.72

7.6

42.85

4.89

44.84

15.5

7.53

23.06

124.79

12.35

6.69

7.7

40.14

5.22

45.68

14.59

8.36

22.59

118.54

12.8

6.57

38.7

5.46

14.91

8.7

21.68

122.78

MPL

NCH

NST

OSH

RIO

2.98

33.7

15.84

3.14

106.09

2.96

32.78

14.94

3.09

104.14

2.93

33.22

14.76

3

97.99

3.04

32.73

13.91

3.21

96.39

3.01

31.14

13.13

3.12

98.99

3.07

29.85

13.03

3.47

96.28

2.93

30.09

13.7

3.29

97.81

2.9

29.05

13.48

3.58

98.6

2.85

30.58

14.8

3.48

93.4

2.79

31.45

14.47

3.22

91.33

2.86

30.23

14.04

2.84

85.36

2.83

27.6

13.03

2.87

83

2.65

25.15

11.75

2.83

82.59

2.61

28.53

13.19

2.61

87.27

2.65

28.57

12.67

2.74

91.51

2.69

25.64

11.51

2.7

89.39

2.66

24.53

10.18

2.66

88.93

2.66

24.7

10.61

2.29

85.78

2.68

21.74

10.72

2.28

82

2.8

25.01

11.09

3.391

81.08

2.84

29.08

14.46

4.96

86.25

2.83

26.3

13.46

5.349

87.27

2.91

28.72

14.48

6.187

97.69

3.05

27.99

13.58

6.304

97.65

3.07

28.99

13.46

6.236

98.43

3.1

29.53

12.6

7.055

98.77

3.16

31.98

12.61

7.513

103.18

3.29

31.9

12.28

7.659

105.24

3.28

30.74

11.45

7.737

102.43

3.21

30.18

11.92

7.298

101.36

3.23

29.63

11.14

7.23

102.51

3.29

28.74

10.7

7.23

103.66

3.27

28.05

9.65

7.162

100.6

3.21

29.51

9.85

6.879

95.79

3.26

30.76

9.59

7.25

96.9

3.12

30.6

9.17

7.055

94.05

3.15

31.22

9.28

7.181

93.54

3.21

29.98

9

7.191

95.23

3.37

32.16

9.93

7.006

90.49

3.41

32.43

9.77

7.064

90.81

3.37

33.13

10.15

7.006

87.87

3.26

35.25

11.85

6.772

89.97

3.25

35.47

11.7

6.86

87.65

3.45

35.08

10.99

7.162

91.6

3.43

35.96

11.28

7.415

92.44

3.41

33.51

10.38

7.074

93.29

3.41

36.78

11.41

6.928

90.37

3.61

37

11.9

6.47

87.58

3.42

34.45

11.4

6.46

85

3.37

36.26

12.18

6.148

84.72

3.42

38.62

13.27

6.831

87.69

COL January February March April May June 16.55 14.21 15.16 15.51 15.36 17.170000000000002 WOW January February March April May June 41.84 38.380000000000003 35.1 35.75 35.340000000000003 37.28 BHP January February March April May June 39.4 33.6 28.98 32.35 34.64 35.82 CBA January February March April May June 85.26 81.78 61.82 62.69 63.75 69.42 AYI January February March April May June 117.87 102.86 85.66 86.59 86.15 95.74