Event analysis of BP and shell

Essay > Words: 6136 > Rating: Excellent > Buy full access at $1

Name

Professor

Institution

Event analysis of BP and shell

Date

Event analysis

Introduction

According to Dreman & Berry, (1995, pp21-36), There are multiple argument concerning the effects of announcement on the stock prices, such events usually attract a lot of attention. There are also arguments that such events attract the attention of speculators thereby increasing stock volatility. Events may mean a lot to the shareholder and the potential investors. Such events usually lead to increased activity in the stock of the companies in question and may either mean convenience or detrimental depending on the nature of the company (Harris, 1986; Ho, 1998; Jaffe, 1974).

Company information

BP and shell are both global; company dealing in oil and gas, with its presence in 80 countries around the world. The companies’ main products include gasoline, energy and other petrochemical products. The companies are operating two main segments in the petrochemical sector: Exploration & Production. And Refining & Marketing. Under exploration and production, the company explores for oil and natural gas, develops  oil nd gas fields, midstream transportation, storage and processing., The company trades natural gas, liquefied natural gas, power and natural gas liquids. However, on the refining and marketing segments, the company supply and trades, refining, manufactures, markets and transports crude oil, petroleum and petrochemicals products. Recent activities include the acquisition of 83% of Companhia Nacional de Acucar e Alcool in April 2011, as well as the acquisition of Verenium’s lignocellulosic biofuels business in 2010

The company financials report as at 3rd march include:

Prev Close:

47.84

Open:

47.42

Bid:

46.26 x 100

Ask:

55.00 x 1000

1y Target Est:

52.93

Beta:

1.8

Next Earnings Date:

N/A

Day’s Range:

47.28 – 47.69

52wk Range:

33.62 – 49.09

Volume:

6,740,414

Avg Vol (3m):

7,587,520

Market Cap:

150.24B

P/E (ttm):

5.89

EPS (ttm):

8.06

Div & Yield:

1.92 (4.00%

Shell

DETAILS

Previous Close

73.62

Open

72.90

Day’s High

73.00

Day’s Low

72.51

Volume

1.87 Mil

Avg Daily Vol (13 Wks)

2.71 Mil

Bid

72.01

Bid Size

1,000

Ask

73.78

Ask Size

1,000

52-Wk High

77.97

52-Wk Low

57.97

Dividend Rate

3.36

Yield

4.62

Normality plot of the bp shares

Descriptive statistics for the bp data

Anderson-Darling

A-Squared

2.802

p

0.000

95% Critical Value

0.787

99% Critical Value

1.092

Mean

604.298

Mode

581.1, 579, 587, 573.7, 641.1

Standard Deviation

25.909

Variance

671.280

Skewedness

0.194

Kurtosis

-1.314

N

116.000

Minimum

560.000

1st Quartile

581.100

Median

600.800

3rd Quartile

628.503

Maximum

655.400

Confidence Interval

4.765

for Mean (Mu)

599.533

0.95

609.063

For Stdev (sigma)

22.950

29.752

for Median

588.700

612.000

 

Objectives of the study:

The main objectives of this study are:

  1. To examine the effects of announcement on underlying stock prices
  2. To determine whether the companies’ announcement are legitimate news or terrible news

Data and Technique

The data for the two companies are used are based on a number of common assumptions about the

T test for Shell

t-Test 1-sample

Test Mean

1889.75

Confidence Level

0.95

N

112

Average

1889.75

Test Stdev

p 1-sample Stdev

Stdev

151.0336

151.0336

0.964

SE Mean

14.27133

T

0.000

TINV

1.658697

p – One sided

0.5

Accept Null Hypothesis  because p > 0.05 (Means are the same)

p – two sided

1

Accept Null Hypothesis  because p > 0.05 (Means are the same)

T test bp

t-Test 1-sample

Test Mean

604.2978

Confidence Level

0.95

N

116

Average

604.2978

Test Stdev

p 1-sample Stdev

Stdev

25.90908

25.90908

0.965

SE Mean

2.405598

T

0.000

TINV

1.658212

p – One sided

0.5

Accept Null Hypothesis  because p > 0.05 (Means are the same)

p – two sided

1

Accept Null Hypothesis  because p > 0.05 (Means are the same)

It is imperative to know that the standards levels of t statistics are as shown below:

level

One tailed

Two tailed

1%

2.33

2.55

5%

1.66

1.95

10%

0.97

1.25

We set the day that the company released the information’s defined as day zero, then the daily returns for the first 30 days is calculated to determine the linearity of the data. This is as shown below:

Rit the 30 days around the first day “zero”: t = -30, -29,…-1, 0, 1,…, 29, 303

Then it is also advisable to calculate the total daily returns Rmt, for these days on the market of another firms, either Shall or BP. However, this may be done to compare group of companies having the same risk or operating in the same industry. The returns can however, be easily adjusted for market performance as well as risks.  This will help in getting the excess returns for both Shell and BP during the period. For example, when the CAP model (capital assets pricing model) was used for controlling risk (Aitken, 2000, p 5)

Then the abnormal returns are defined as differences ARit= Rit–Rmt5

Then finally the average of the abnormal returns is calculated overall the N events in the whole sample:

The Cumulate the returns that are given for the first T days to CAAR are calculated:

From mother studies, there is enough proof that during the pre announcement the reaction from then investors varies in form from the reaction of the investors during post announcements. The preannouncement drifts that occur prior to the earning are usually because of insider trading. This is always against the strong for efficiency. However during the post announcement drift the reaction of the investors are against the semi strong form (MacKinlay, 1997).

Kabir, & Vermaelen (1996, pp 1591–160) states that, the t statistics is useful for determining if the excess returns during the announcements are very different from zero. This was arrived at by getting the t statistics for all the values of n and dividing the average excess returns by some predetermined standard error

T statistic (excess return/day t) = Average Excess Return / Standard Error

If the t statistics are statistically significant, the event affects returns; the sign of the excess return determines whether the effect is positive or negative.

Abnormal returns

When announcement are made most investors try to determine whether announcement are good for the company or bad for the same company: in the case we should determine whether the company had registered returns on the day of the announcements (Manne, 1966).

We assume that then return of the company on the day of announcements was 0.5. Further investigation finds that the returns for the same companies over the past 3 months were 0.3 on the business day. And the S&P INDEX had risen by 0.4%. Additionally, the CAPM estimated coefficient was found to be a=-0.1% and b=1.1.what was the response of the investors on the announcements (Barber, Griffin, and Baruch, 1994, p 23: Lorie, &. Neiderhoffer, 1968, pp 35-51: Kwong, &, Wong, (1984, pp, 905–917).

Market-adjusted abnormal returns

First we compute the original abnormal return for the announcement using the market-model, this is based on a number of factors amongst them the date of announcement, the nature of the company and performance of the company in prior events

Then we use the market market-adjusted model:

Then the mean adjusted model:

Conclusion

Is the market efficient?

Accordion to Grigori et al, (2008, p3), an efficient market is the market in which the market price is actually the unbiased estimate of the true value of the real investment. In this regard, it is healthy to say that the market is efficient. It also important to note that the market prices of the two companies are biased. Kyle, (1985, pp 1315–1335) enthuses that, this is because the prices are either low or high than the true value. However, it is important to note that the deviation are random and are not easy to determine the degree of randomness,  based on the fact that when the stock of  BP had more ratios they were overvalued than the stocks of Shell.  Finally i would like to say that market efficiency is difficult to state because it depends on the risk adjustment procedure, magnitude of the issues and selection bias, (Dreman, 1998; Ball, 1978, pp 103-126).

Herring, et al, (2010, p 347),  argues that, market efficiency is relative as there is likelihood that the market may be efficient o a group of investors and not efficient to other, this explains why the shareholder of BP may view the market as efficient while the investors of shell do not hold the same view.  This may only be attributed to the differential tax and transaction cost. These factors confer advantages to some shareholders relative to other.

The efficient of the market makes it difficult for any group of shareholder to beat the market through the same strategy despite the availability of the precise information tom the investors. They cannot consistent find the undervalued stocks.  The efficiency of the market makes it difficult for investors to use the price information both current and past to beat the market putting them at disadvantages land leaving them speculating.

From the three analyses, the abnormal return was positive, meaning that the announcement was a good for the investors. In most cases, the abnormal returns and the final cumulative returns for the past 12 months are usually measures of just averages. They are also the cumulative effects of the announcements on the made by the company; this is also same to the dividend and earnings announcements

Bibliography

Grigori Erenburg, Janet Kiholm Smith, and Richard L. Smith, (2008). “The Paradox of‘Fraud-on-the-Market Theory’: Who Relies on the Efficiency of Market Prices?”, Working Paper, 12.

Brad M. Barber, Paul A. Griffin, and Baruch Lev1994., “The Fraud-on-the-Market Theory and Indicators of Common Stocks’Efficiency,” The Journal of Corporation Law,

Aitken C.G.G. (2000), “Interpretation of Evidence, and Sample Size Determination,” in Joseph L. Gastwirth, ed., Statistical Science in the Courtroom,

Lorie, J., and V. Neiderhoffer (1968): “Predictive and Statistical Properties of Insider Trading,” Journal of Law and Economics, 11, 33–51.

MacKinlay, A. C. (1997): “Event Studies in Economics and Finance,” Journal of Economic Literature, 35, 13–39.

Manne, H. G. (1966): Insider Trading and the Stock Market. The Free Press, New York.

Harris, L. (1986): “Cross-Security Tests of the Mixture of Distributions Hypothesis,” Journal of Financial and Quantitative Analysis, p. 39.

Ho, B. M. (1998): Public Companies and their Equity Securities: Principles of Regulation under Hong Kong Law. Klumer Law International, London.

Jaffe, J. F. (1974): “Special Information and Insider Trading,” Journal of Business, 47, 410–428.

Kabir, R., and T. Vermaelen (1996): “Insider Trading Restrictions and the Stock Market: Evidence from the Amsterdam Stock Exchange,” European Economic Review, 40, 1591–1603.

Kwong, K. S., and K. A. Wong (1984): “The Behavior of Hong Kong Stock Prices,” Applied Economics, 16, 905–917.

Kyle, A. (1985): “Continuous Auctions and Insider Trading,” Econometrica, 53, 1315–1335.

Herring, Richard; Diebold, Francis X.; Doherty, Neil A. (2010). The Known, the Unknown, and the Unknowable in Financial Risk Management: Measurement and Theory Advancing Practice. Princeton, N.J: Princeton University Press. p. 347.

Dreman David N. & Berry Michael A. (1995). “Overreaction, Underreaction, and the Low-P/E Effect”. Financial Analysts Journal 51 (4): 21–30.

Ball R. (1978). Anomalies in Relationships between Securities’ Yields and Yield-Surrogates. Journal of Financial Economics 6:103-126

Dreman D. (1998). Contrarian Investment Strategy: The Next Generation. Simon and Schuster.

.............

Type: Essay || Words: 6136 Rating || Excellent

Subscribe at $1 to view the full document.

Buy access at $1
CategoriesUncategorized