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Tullett Prebon Plc Stock Return Analysis - Case Study Example

Summary
"Tullett Prebon Plc Stock Return Analysis" paper discusses the stock return analysis of Tullett Prebon. The work calculated beta for the riskiness of the investment and comparison between short term and long term. Long-term investors obviously require to gauge the investment over a long period of time. …
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Extract of sample "Tullett Prebon Plc Stock Return Analysis"

  • Executive summary

This paper discusses stock return analysis of Tullett Prebon plc. The work calculated beta for the riskiness of the investment and comparison between short term and long term. Long term investors will obvious require to gauge the investment over long period of time while short term investors would wish to do this over a short period of time. The company price relativity help in in establishing the price relative strength which assist in stock selection process. The portfolio manager will be able to compare the company performance against the bench mark like in this case, the TLPR group is better. Then lastly it was

  • Table of Contents

Executive summary1

Table of Contents2

Task 13

Task II5

Task III7

Reference8

  • Task 1

The reported beta for Tullett Prebon plc as per (http://www.reuters.com) is 0.71 while calculated beta is 0.003703 showing that the beta calculated is much smaller than recorded beta.

One of the factors that can results into difference in beta is time factor. It can be noted that time frame provided by the beta calculated by (http://www.reuters.com) is unknown while the calculated beta like the one we have just calculated is well known. It is quite unique as it provide the end users a given time frame through which they gauge the riskiness of their investment (Fama and French 2012). Long term investors will obvious require to gauge the investment over long period of time while short term investors would wish to do this over a short period of time. Therefore, the one calculated here is in better position for the end users to choose compared with already calculated (Fama and French 2012). From our calculation, the calculated beta is:-

Variable

Results

Covariance

0.008365

variance

2.259323

Beta

0.003703

It shows that the calculated beta is 0.003703 while given beta is 0.71 indicated that given beta is more riskier compared to the calculated beta therefore, it is difficult to make conclusion as per the given beta simply because it is as from which period when the stock was riskier unlike we know very well that in the calculated when we can estimate the time period when the stock was riskier (Kelley and Tetlock 2013).

It should be noted that most given beta are calculated using the American standards that of S&P 500 index but in this case, we are using FTSE 250 of the United Kingdom. Therefore, in circumstances like in our case where the equities extends beyond U.S. borders, given beta would not be the best measure for determining how risky a stock is. Therefore, the calculated beta of 0.003703 in this case, the company is better placed for investment decision purposes

When you calculate beta on your own, you are is a better place to determine the reliability of the beta you are calculating through the determination of coefficient of determination or the so called r-squared (Kelley and Tetlock 2013). This is a crucial tool that can enable one to know how well the beta you have established measure the risk. On the other hand, given beta, an analyst is not able to establish how reliable the beta is because you may not understand the process through which it was calculated through.

It is important that most given beta are calculated using the capital asset pricing model (CAPM) which in most cases bases their argument on theoretical basis rather than practical basis. We have used regression technique which normally allow for better explanation of returns concerning the market rather than the theoretical explanation of the overall stock return as used in the capital asset pricing model.

However, self-calculated beta involve so many things which consume time and requires a lot of accuracy in the process. Since the given beta are machine generated, the probability of their accuracy is higher compared to self-calculated beta. Therefore, there is certainty that you are accurate on the beta you have calculated since they also depend on the secondary data.

  • Task II

In the appendix II, it shows the TLPR group/FTSE 250 price relative. From the data analysis, the first value of relative price is 1.46% varies increases in circumstances the TLPR value is more than FTSE 250 and declines when the TLPR Group value is less than FTSE 250. On the relative percentage change, that of the TLPR group is less than the percentage change in FTSE 250.

The company price relative help in in establishing the price relative strength which assist in stock selection process. The portfolio manager will be able to compare the company performance against the bench mark like in this case, the TLPR group is better than the market benchmark of FTSE 250.

Since the goal of every portfolio manager is to outperform the benchmark, and relative price. The price relative rises when a stock shows relative strength and is outperforming its benchmark. Conversely, the price relative falls when a stock shows relative weakness and is underperforming its benchmark.

The trend line are shown in the figure below including the trend line equation on the relative price differences between FTSE 250 and the TLPR group stock prices.

The price shows the movement of stock prices and the changes are indicated above. The outperformance is clear in specific dates as shown above.

The stock relative price can also be used in the general market analysis. It can be used through breaking down the stock into different sectors. The chartist’s graphs can be used of the comparison between the market leader and the market laggard. From the analysis, the TLPR group can be classified as market leader due to the fact that it outperforms the market indicators. Chang, Christoffersen and Jacobs (2013) noted that that market is classified as offensive in circumstances when the technology and consumer discretionary sectors are in the lead. At the same time, it is in defensive mode in circumstances when consumer staples, healthcare and utility sectors are in the lead. In our case, the market is in defensive mode since TLPR group is within the utility sector. Therefore, we can conclude that the company is in the right direction and the portfolio managers here are performing extremely well as compared with the industry based indicators. The investors should consider investing in the TLPR group using the price relative performance index (Chang, Christoffersen and Jacobs 2013).

  • Task III

In comparing the stock performance in short term and long term, the short term beta seems less riskier comparing with long term analysis of beta. Therefore, based on the beta, the short term analysis provides good estimates since it is less risky compared with the long term. The analysis is shown in the table below:-

Long term

Short term

Covariance

0.009120547

-0.002353767

variance

2.414066013

4.70048578

Beta

0.003778085

-0.00050075

This also indicate that the short term investment might have lower return due to the fact that it is less riskier while long term investment have higher return due to higher risk involve. Fama and French (2012) states that the investment with higher returns tends to be more risky than ones which are less risky. It is quite unique as it provide the end users a given time frame through which they gauge the riskiness of their investment hence long term and short term beta analysis.

  • Reference

Fama, E.F. and French, K.R., 2012. Size, value, and momentum in international stock returns. Journal of financial economics, 105(3), pp.457-472.

Kelley, E.K. and Tetlock, P.C., 2013. How wise are crowds? Insights from retail orders and stock returns. The Journal of Finance, 68(3), pp.1229-1265.

Chang, B.Y., Christoffersen, P. and Jacobs, K., 2013. Market skewness risk and the cross section of stock returns. Journal of Financial Economics, 107(1), pp.46-68.

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