CAPM Analysis, Is CAPM Use the Best - Coursework Example

Statistical work Submitted by…………………….. Introduction This paper explores CAPM analysis by using one market index alongside a list of ten companies from mining, food and insurance industry. We collected weekly data from three sample periods. That is, from January to December of: 2006 - 2008, 2009 - 2011 and 2012 – 2014. Our choice of firms and industrial sectors guaranteed high market capitalization. Various regression analyses for the model: Stock Return (Ri)j = β (Rmj – Rfj) + Rfj Where Rfj = alpha (risk Free rate) for jth company Rmj = Market return for jth company; and Β = beta The regression results was summarized as displayed in Table 1. Table 1. Summary of regression results Company (Stock Ticker) Beta Alpha (Constant) R2 t-statistics Critical value AAL 0.0389747 (0.0082543) 0.0029485 (0.0027181) 0.0439 4.72 0.000 ADM -0.0031406 (0.0076713) 0.0035526 (0.0021348) -0.0018 -0.41 0.682 BLT -0.000029 (0.0005729) 0.0029974 (0.0026775) -0.0021 -0.05 0.960 AV 0.0000162 (0.0005467) 0.0080287 (0.002555) -0.0022 0.03 0.976 CCH 0.231253 (0.0428002) 0.002765 (0.0027629) 0.0572 5.40 0.0000 CPG 0.009858 (0.0439397) 0.0080691 (0.0025648) -0.0020 0.22 0.823 EVR 0.1343544 (0.0834369) 0.0018854 (0.0027034) 0.0034 1.61 0.108 RRS 0.2174586 (0.0488991) 0.0064729 (0.0030096) 0.0388 4.45 0.0000 RIO 0.0068945 (0.0087003) -0.0012772 (0.0043329) -0.0008 0.79 0.429 VED 0.0888437 (0.042878) 0.0057275 (0.0031713) 0.0070 2.07 0.039 We wanted to test the hypothesis: Ho: β = 0 (No significant beta) H1: β ≠ 0 (Significant beta exist. That is, beta explains market risk) From the table results, we rejected Ho for 5 companies (AAL, CCH, EVR, RRS and VED company) since t-statistic was greater than the critical value. This implied that significant beta existed for these 5 companies and their market risks were explained by beta. Beta is the gradient of the model line. Nonetheless, for ADM, BLT, AV, CPG and RIO Company, there was no significant beta to explain the market risk since t-statistic was less than the critical value. Generally, alpha, the constant, illustrated how much worse or better the company performed than CAPM predicted (negative alpha illustrated how much worse the fund performed and vice versa). The quality of our model was illustrated by our R2. While an R2 of 1.0 would imply that our model fit the weekly data perfectly (100%) and that the performance of the funds were explained by their risk exposure, as estimated by beta, this was not the case as evidenced in different R2, s in the Table 1. According to Reilly & Brown (2012), variation of stock returns is the concern of any shareholder. As such, most models are not interested in finding out the most convenient way of determining stock return but to see which macroeconomic factors determine the variation of stock return. CAPM is a simple model that is perceived on sound reasoning, some of the assumptions that look like the model are unrealistic. Rather than simply just broadening an existing theory, Albright, Winston & Zappe (2009) propos this concern by giving a completely different model: the Arbitrage Pricing Theory (APT). Being the opposite of CAPM, the current developed APT starts with the assumption that arbitrage opportunities should not be found in perfect financial markets. This thought is a little bit restricted than those needed to develop the CAPM. Bruns (2013) claims APT starts by assuming that there are n factors, which cause asset returns to significantly differ from their expected values. Is CAPM use the best? A lot of praise has been attached to the use of CAPM model. Some of these praises are argued as follows: First, the model offer analysis of diversified portfolio. The thought that investor have diversified portfolio, same to the market portfolio, removes unsystematic but precise risk. Secondly, it adopts systematic risk (beta). CAPM considers systematic. This has been assumed in other return models, like the dividend discount model (DDM). Systematic or market risk is a fundamental variable because it is one does not see it therefore cannot be completely mitigated. The model is also easy to use. CAPM is an easy calculation that can be easily stress tested to achieve a variety of possible outcomes to issue confidence around the desired rates of return. Lastly, the model shades light on financial risk variability of businesses. When a business questions opportunities, suppose the business mix and financing differ from the ongoing business, and then the other required return calculations, like other weighted average cost of capital (WACC) cannot be put to use. However, no model is 100% perfect, though each should possess a few traits that make it resourceful and applicable. CAPM, while being put to light for its unrealistic assumptions, issues a more important outcome than either the DDM or WACC in many scenarios. It can be calculated easily and stress-tested. When used in hand with other aspects of an investment mosaic, it can provide uneven yield data that can aid or eliminate a potential investment. Nothing is perfect! Just as other models, the CAPM also has its short-comings (Danthine & Donaldson, 2014). These short-comings can also be identified as follows: In this model, we presume that investors can borrow and lend at a risk-free rate is quite unrealistic. The government and individual investors borrow at dissimilar rates, thus the minimum required return line might actually provide a lower return. The model uses risk-free rate; a rate that is mostly used as the yield on short- term government securities. According to Chapman (2013), this creates volatility because the yield changes daily. The model also uses return on market, which is the sum of the capital gains and dividends for the market. But at any time the market return can be negative thus creating a problem so then a long-term market return is utilized to smooth the return. Nonetheless, according to Kristina Zucchi, in her article: The Advantages And Disadvantages Of The CAPM Model, she explained the merits of CAPM (as mentioned earlier). Kristina alludes that it`s might be the best model to better understand some puzzling features of foreign exchange. Sensitivity of results to sector characteristics Going by the results, the paper can pinpoint sector-wise return traits of stocks of London Stock Exchange. Here, a return of stocks listing in the LSE in a span of three sample periods was used. In consideration of weekly return and risk analysis, stocks in the mining Sector brought in the highest return. Stocks of the two industries have lower degree of risk as compared to those of mining sector. In consideration of the risk-return trade off, Chandra (2008) also found mining sector is the convenient filed to invest. Slow return in the food & allied and service sectors was gotten. Macroeconomic factors` have an effect on those selected industry return following multifactor stock return analysis proposed in the Arbitrage Pricing Theory (Vollmer, 2014) In analyzing the sector wise return traits of London Stock Exchange the division of the 3 sectors is follows; Mining, Insurance and Food processing. A weekly consideration of the closing data of the selected stocks and a conclusion has been reached that Mining sector generated maximum return. In this period, mining sector generally generated significant beta. Insurance and food processing come in at second and third respectively on the basis of return as evidenced in their beta and alpha during this time. There are proposals that stock return can be explained by the market return while others propose return can be explained much more broadly by firm specific, industry specific and macroeconomic variables (Jelicic, 2010). Not many made an effort to find out the intrigues of these factors on the sector wise return. Now on that perspective this essay sheds special attention for finding the effect of macroeconomic differences on the sector wise return with their return traits. According to Nowak (2014), who partly explored sector-wise fund`s performance, certain industries could not be explained by the macroeconomic factors, obviously there are other factors that put into consideration the explanations involved. A further detailed research can be made from point. Given that stock exchange is the backbone of the economy, economic conditions influence it adversely. The results found from the analysis sheds light on the fact that information that might not be reflected properly in the stock price may cause an imbalance due to the fact that investors may not take rational decision based on the information. This is also in line with the argument put forward by Peltomäki (2009). Conclusion To conclude, this paper has explored the CAPM analysis by using one market index together with a list of ten companies from mining, food and insurance industry. We gathered weekly data from three sample periods. That is, from January to December of: 2006 - 2008, 2009 - 2011 and 2012 – 2014. We have tested whether significant beta, that explain the funds` performances, exist or not. We have also mentioned the short-comings as well as the strength of CAPM analysis. Reference List Albright, S. C., Winston, W. L., & Zappe, C. J. 2009. Data analysis & decision making with Microsoft Excel. Mason, Ohio: South-Western/Cengage Learning. Bruns, F. 2013. Windfall profit in portfolio diversification?: An empirical analysis of the potential benefits of. S.l.: Diplomica. Chandra, P. 2008. Investment analysis and portfolio management. S.l.: Tata Mcgraw-Hill. Chapman, R. J. 2013. Simple tools and techniques for enterprise risk management. Hoboken, N.J: Wiley. Danthine, J.-P., & Donaldson, J. B. 2014. Intermediate Financial Theory. Burlington: Elsevier Science. Jelicic, N. 2010. Dynamic strategy and performance of German mutual fund managers: Evaluation of equity and fixed-income managers using conditional models. Hamburg: Diplomica-Verl. Kristina, Z. The Advantages And Disadvantages Of The CAPM Model, Web, February 10, 2015 http://www.investopedia.com/articles/investing/021015/advantages-and-disadvantages capm-model.asp Nowak, T. 2014. Non-life insurance-linked securities: Risk and pricing analysis. Verl. Versicherungswirtschaft. Peltomäki, J. 2009. Do investors benefit from the use of options and complexity of derivative strategy of a hedge fund?. Vaasa: Universitas Wasaensis. Elton, E. J. 2010. Modern portfolio theory and investment analysis. Hoboken, NJ: J. Wiley & Sons. Reilly, F. K., & Brown, K. C. 2012. Investment analysis and portfolio management. Mason, Ohio: South-Western Cengage Learning. Vollmer, M. 2014. A beta-return efficient portfolio optimisation following the CAPM: An analysis of international markets and sectors. Read More