CAPM MODEL AND P2P LENDING

The analysis was conducted on Lloyds of London (bank), E-on (utility provider) and Amazon (retail). The historical price movement of the stocks was analysis using a 3 years period starting with year 2016, 2017 and 2018.

Topic 1: The effect of the Brexit vote on the CAPM Betas.

Part A.

The  is a single factor model which helps estimate the beta of the model by analysing the linear regression between the percentage returns for stock as the dependent variable and the percentage of returns for the market index (S&P) as the independent variable. The capital asset pricing model (CAPM) helps ascertain the expected return on the asset. In doing so, there are some underlying economic forces in the market which make the stocks move synonymously move together.  The slope () represents the beta which denotes the linear relationship between the stock returns and the market while the error terms  represents the residual returns that cannot be explained by the market fluctuations.

The CAPM assumes that the parameters used exhibit a linear relationship and there is no multicollinearity and autocorrelation among the variables. According to Elbannan, M.A. (2014), the beta is a measure of the systematic risk by assessing the level of volatility of the stock in the market. A stock with a beta of one has the price of the security moving along with the market while a beta of less than one has a less volatile security as compared to the market movements. A security with a beta of zero has an expected return equal to the risk free rate implying that such a stock does not possess any systematic risk, K and also the stock has no relationship with the market movements (Zabarankin, M., Pavlikov. and Uryasev, S., 2014). The analysis below investigates three securities namely Lloyds of London (Bank), E-on (utility provider) and Amazon (retailer). The results of the analysis are analysed below.

Lloyds of London (bank)

 Regression Statistics Multiple R 0.250284 R Square 0.062642 Adjusted R Square 0.03586 Standard Error 0.07132 Observations 37

There is a correlation of 25 percent between the Lloyds of London stock and that of the market index. The r square (goodness of fit) is equal to 0.062642 which shows that movements in the price index explain 6.2642 percent of the historical price movement of Lloyds of London stock.

 Coefficients Intercept -0.00887 X Variable 1 0.738794

The beta of Lloyds of London (bank) is 0.738794. The beta was less than 1 indicating that the theoretical price of the portfolio was less volatile as compared to the market.

E-on (utility provider)

 Regression Statistics Multiple R 0.024794 R Square 0.000615 Adjusted R Square -0.02794 Standard Error 0.069007 Observations 37

There is a correlation of 2.5 percent between the E-On stock and that of the market index. The r square (goodness of fit) is equal to 0.000615 which shows that movements in the price index explain 6.2642 percent of the historical price movement of E-on (utility provider)

 Coefficients Intercept 0.007204 X Variable 1 0.06858

The beta of E-On is 0.06858. The beta was less than 1 indicating that the theoretical price of the portfolio was less volatile as compared to the market.

Amazon (retail)

 Regression Statistics Multiple R 0.604161105 R Square 0.365010641 Adjusted R Square 0.346868088 Standard Error 0.058909749 Observations 37

There is a correlation of 60.42 percent between the Amazon stock and that of the market index. The r square (goodness of fit) is equal to 0.365010641 which shows that movements in the price index explain 36.5010641 percent of the historical price movement of Amazon stock.

 Coefficients Intercept 0.013896889 X Variable 1 1.78972658

The beta of Amazon is 1.78972658. The beta was greater than 1 indicating that the theoretical price of the portfolio was more volatile as compared to the market.

Part B. Split the sample into the pre-Brexit vote period (23 June 2016) and the post-Brexit vote period and repeat the analysis for the two periods. For each of the three firms, calculate the pre-Brexit CAPM Beta and the post-Brexit CAPM Beta.

Pre-Brexit

E-On

 Coefficients Intercept -0.01603 X Variable 1 1.555715

The beta of E-On stock before the brexit was 1.55715. The beta more than 1 indicating that the theoretical price of the portfolio was highly volatile as compared to the market.

Lloyds

 Coefficients Intercept -0.02066 X Variable 1 0.857292

The pre-brexit beta of Lloyds stock was 0.857292. The beta less than 1 indicating that the theoretical price of the portfolio was less volatile as compared to the market.

Amazon

 Coefficients Intercept 0.018288 X Variable 1 1.457319

The pre-brexit beta of Amazon security before the brexit was 1.457319. The beta was higher than 1 indicating that the theoretical price of the portfolio was highly volatile as compared to the market.

Post-Brexit

E-ON

 Coefficients Intercept 0.008103 X Variable 1 -0.15473

The beta of E-On stock after the brexit was -0.15473. The beta less than 1 indicating that the theoretical price of the portfolio was less volatile as compared to the market. The beta before the brexit for E-On security was higher than that after the brexit period. It can then be deduced that through brexit, the volatility of the E-ON stock decreased.

Lloyds

 Coefficients Intercept -0.00321 X Variable 1 0.94295

The post-brexit beta of Lloyds stock was 0.94295. The beta less than 1 indicating that the theoretical price of the portfolio was less volatile as compared to the market. The post-brexit beta for Lloyds was higher than that after the pre-brexit period implying that the effect of brexit saw the increase in beta of the Lloyds stock hence increased volatility of the stock.

Amazon

 Coefficients Intercept 0.014175 X Variable 1 1.944217

The post-brexit beta of Amazon security after the brexit was 1.944217 which was higher than 1 indicating that the theoretical price of the portfolio was highly volatile as compared to the market. As compared to the pre-brexit beta of Amazon, the post-brexit beta was higher than that of the pre-brexit suggesting that after brexit, the volatility of the Amazon stock increased by 48.6898 percent over the 2 years period.

Part C. Comment on your results. Do you have empirical evidence to suggest that the value of the Beta coefficients has changed as a result of the Brexit vote? If yes, which firms/industries are affected the most? Why? Discuss your results with reference to the strengths and limitations of CAPM.

The analysis of the beta was done over a monthly analysis of stock prices 2 year period (2014-2016) before the brexit and 2 years period (2016-2018) after the brexit. The beta after the brexit for Lloyds and Amazon security was higher than the pre-brexit beta while the pre-brexit beta coefficient of E-On utility company was higher than that of the post-brexit period.  The Brexit vote has significance influence on the price movements of the stocks. While the the volatility of the E-ON stock decreased following the Brexit, the beta of the Lloyds and Amazon stock increased in volatility. From the analysis, it is quite clear to see that the model is simple to use and execute since it only required performing a regression analysis on the linear equation. Notably, portfolio diversification is a good means of hedging out risks since it smooths out the unsystematic risk by smoothing out the negative performance of poor performing stocks (Berk, J.B. and Van Binsbergen, J.H., 2016).

CAPM model faces a setback when it comes to the risk-free rate especially because it keeps on changing on a daily basis. CAPM is based on assumptions and it at times leads to unrealistic real-life situation. According to Barberis, N., Greenwood, R., Jin, L. and Shleifer, A. (2015), it is unrealistic for investors to lend and borrow at risk-free rate. Thus, the minimum required rate slope may end up being less steep leading to a lower return than what the actual results of generated by the model.

Topic 2: Peer to peer (P2P) lending

In the current digital world, it is completely indispensable to match innovation technology with the arising financial needs. Financial intermediation is not devoid of technological advances. Technology and the financial world have increasingly integrated and intersected in interesting ways. One of the most recent and visible means of integration has been the P2P market lending which give lenders and borrowers a platform to interact together. The innovation has indeed created new opportunities in the financial world particularly among financial institutions (Hsueh, S.C. and Kuo, C.H., 2017).

The P2P lending model utilizes online technology platforms to connect investors with individuals and business who are in search of capital. The model is popular among borrowers since it allows them to access funds at lower interest rates unlike most traditional credit providers. The model also provides a new investment opportunity for Retail investors. P2P service providers earn by charging a service fee on each facilitated loan and use of technology application process decreases transaction costs thereby making it more efficient and user-friendly than conventional lending. As a financial intermediation in social democracy, the borrowers and lenders in P2P lending have to engage in loan pacts (Serrano-Cinca, C., Gutierrez-Nieto, B. and López-Palacios, L., 2015). One of the popular P2P lending institutions is the publicly traded lending club and the privately-held prosper.

The rapid growth in the financial intermediation sector has been brought about by the development of more efficient technological platforms. Moreover, the fact that it is possible to perform more secure transactions, generate competitive returns as well as expand the virtual networks gives an upper hand to P2P lending. Compared to traditional lending enterprises such as bank, P2P platforms has some common similarities shared by these market lending facilities such as profit making objective (Guo, Y., Zhou, W., Luo, C., Liu, C. and Xiong, H., 2016). Unlike popular thought, P2P Company is an intermediary and not a lending institution. By just looking at the profile of borrowers, lenders using P2P can choose who to invest in. Though the P2P loans are unsecured, they can be transformed into securities and other lenders can purchase them. P2P lending is unregulated by the government and uninsured by agencies such as FDIC. It is important to be cognizant of this factor when choosing P2P lending.

Some of the opportunities that exist with regards to P2P lending is the formulation of liquid secondary trading markets. Presently, the P2P lending offers hold-to-maturity investments. In the nearby future, the securitization of marketplace will contribute immensely to the growth of the financial sector. Given the high returns, transparency and liquidity the P2P loan lending services should be regulated by the government. Moreover, some corporations offering P2P services have merged other firms in the marketplace lending platforms the ability to access new evaluated-pricing services and this will enable clients be able to access be informed about portfolio risks on a timely basis (Li, C.H., Lai, V.S.K., Cheung, W. and Cui, X., 2018).

The innovation of the P2P financial platform businesses poses challenges to legislators and regulators in several respects including: the “easy way” of modifying existing finance and banking laws are not sufficient. Also, the internal organization and knowledge of staff is not well suited to regulating crowd funding. Also, the capital mediation by crowd funding platforms calls for a different regulatory approach than banking. In the long run, the P2P lending platforms will require dedicated, single European regulatory framework.

P2P platform performs the brokerage function of financial intermediation. The key factors of consideration are the risk and maturity of the loans (Serrano-Cinca, C., Gutierrez-Nieto, B. and López-Palacios, L., 2015). To boost the efficiency and sustainability in financial intermediation, P2P should consider curbing the moral hazard impediments so much embedded in the many of the modern financial intermediation.

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