Project 1 Firm: JP Morgan Chase 1). Plotted Prices for JPM: The first graph here shows the daily prices of the JP Morgan stock from January 2nd, 2019 to January 20th, 2021. Looking at this graph, we...

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The firm for the project is JP Morgan Chase. The first 5 questions are already done. The project uses R code for some question and he required code is attached.


Project 1 Firm: JP Morgan Chase 1). Plotted Prices for JPM: The first graph here shows the daily prices of the JP Morgan stock from January 2nd, 2019 to January 20th, 2021. Looking at this graph, we can see a huge decline in the stock price going from the beginning of February 2020 to the beginning of May 2020. This was obviously due to the COVID-19 pandemic that forced a recession at this time. The JP Morgan stock price has gone up since, and is reaching new highs in today’s market. Plotted Returns for JPM: This graph shows JP Morgan stock returns beginning in January of 2019 all the way to January of 2021. The stock returns go up and down and are quite volatile. The biggest instance of this volatility occurs at the same time as the stock prices went down, when the COVID-19 pandemic first began in February of 2020. Confidence Interval for JPM 2020: 95%: -0.004006674 0.004439564 99%: -0.005333674 0.005766564 Confidence Interval for JPM 2019: 95%: -0.00004150197 0.002884834 99%: -0.0005012626 0.0033445942 · For both 2019 and 2020, JPM has a confidence interval including 0, meaning that this is a significant variable for testing T-statistic for JPM 2020 = 0.1004528 · This t-statistic shows that we are very close to accepting the null hypothesis that the mean for JPM 2020 is 0. T-statistic for JPM 2019 = 1.904371 · Because this t-statistic is a relatively Confidence Interval for S&P 500 2019: 95%: 5.747738e-05 2.013632e-03 99%: -0.0002498568 0.0023209662 T-statistic for S&P 500 2019 = 2.075143 One tail and two tail tests for S&P 500 in 2019: alpha.one.tailed alpha.two.tailed CV [1,] 0.100 0.200 -1.284933 [2,] 0.050 0.100 -1.650947 [3,] 0.025 0.050 -1.969460 [4,] 0.010 0.020 -2.341296 [5,] 0.005 0.010 -2.595558 [6,] 0.001 0.002 -3.123018 · Probability here is 3.9%, which means we cannot reject the null hypothesis. Confidence Interval for S&P 500 in 2020: 95%: -0.001784885 0.003417872 99%: 0.002602297 0.004235284 T-statistic for S&P 500 2020 = 0.6151732 · We are 95% confident that the mean for the S&P 500 2020 is between -0.0017 and 0.0034 and 99% confident that the mean is between 0.0026 and 0.0042. Even though this t-statistic is really low, we are still 99% confident that the mean is not 0 and therefore, we can reject the null hypothesis. One tail and two tail tests for S&P 500 in 2020: alpha.one.tailed alpha.two.tailed CV [1,] 0.100 0.200 -1.284920 [2,] 0.050 0.100 -1.650923 [3,] 0.025 0.050 -1.969422 [4,] 0.010 0.020 -2.341236 [5,] 0.005 0.010 -2.595479 [6,] 0.001 0.002 -3.122886 One tail and two tailed tests for JPM profits in 2020: alpha.one.tailed alpha.two.tailed CV [1,] 0.100 0.200 -1.284920 [2,] 0.050 0.100 -1.650923 [3,] 0.025 0.050 -1.969422 [4,] 0.010 0.020 -2.341236 [5,] 0.005 0.010 -2.595479 [6,] 0.001 0.002 -3.122886 Probability is 92%, this means that we cannot reject that JP Morgan’s mean is equal to zero. One tail and two tailed tests for JPM profit in 2019: alpha.one.tailed alpha.two.tailed CV [1,] 0.100 0.200 -1.284933 [2,] 0.050 0.100 -1.650947 [3,] 0.025 0.050 -1.969460 [4,] 0.010 0.020 -2.341296 [5,] 0.005 0.010 -2.595558 [6,] 0.001 0.002 -3.123018 Probability is 5.8%, this means that we can reject that JP Morgan’s mean is equal to zero Confidence Intervals for Amazon: 95%: -0.004006674 0.004439564 99%: -0.005333674 0.005766564 T-statistic for Amazon = 0.1004528 · We are 95% confident that the mean for Amazon is between -0.0004 and 0.0044 and 99% confident that the mean is between -0.005 and 0.006. The t-statistic tells us that we probably cannot reject the null hypothesis that the mean is equal to 0. One tail and two tailed tests for Amazon alpha.one.tailed alpha.two.tailed CV [1,] 0.100 0.200 -1.284920 [2,] 0.050 0.100 -1.650923 [3,] 0.025 0.050 -1.969422 [4,] 0.010 0.020 -2.341236 [5,] 0.005 0.010 -2.595479 [6,] 0.001
Answered 3 days AfterFeb 12, 2021

Answer To: Project 1 Firm: JP Morgan Chase 1). Plotted Prices for JPM: The first graph here shows the daily...

Mohd answered on Feb 15 2021
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6) Why is this regression used in finance? Do a scatter plot. Graph histogram of residuals. Discuss the CAPM model. Has the relationship changed between 2019 and 2020?
Regression analysis has a few applications in account. For instance, the measurable strategy is crucial to the Capital Asset Pricing Model (CAPM). Basically, the CAPM condition is a model that decides the connection between the normal return of a resource and the market hazard premium.
The analysis is additionally used to figure the profits of protections, in view of various variables, or to estimat
e the exhibition of a business. Learn additional gauging strategies in CFI's Budgeting and Forecasting Course!
1. Beta and CAPM
In money, regression analysis is utilized to compute the Beta (unpredictability of profits comparative with the general market) for a stock. It very well may be done in Excel utilizing the Slope work.
2. Determining Revenues and Expenses
When gauging budget summaries for an organization, it very well might be helpful to do a numerous regression analysis to decide how changes in specific presumptions or drivers of the business will affect income or costs later on. For instance, there might be an exceptionally high connection between the quantity of salesmen utilized by an organization, the quantity of stores they work, and the income the business produces.
mod1<- lm(CSCO~MKT,data=DF2)
mod2<- lm(CSCO-RF~MKTRF,data=DF2)
a<-resid(mod1)
v<-resid(mod2)
res<-as.data.frame(cbind(a,v))
hist(res$a)
hist(res$v)
We have used regression to evaluate the relationship and effect of these variables on the response variable.
In this scenario, regression tells about the influence of MKTRF on our response variable. MKTRF is Statistically significant predictor with p value less than 0.05, and has a beta coefficient of 1.026. Beta coefficient is significant and positive. It has a positive influence on response variables. If we increase the value of MKTRF then the response variable will increase by 1.02688 times of MKTRF. We also have small standard errors.
In this scenario, regression tells about the influence of MKTRF on our response variable. MKTRF is Statistically significant predictor with p value less than 0.05, and has a beta coefficient of 1.026. Beta coefficient is significant and positive. It has a positive influence on response variable. If we increase the value of MKTRF then the response variable will increase by 1.02688 times of MKTRF. We also have small standard errors.
In this scenario, we have taken confidence level 99 percent. MKTRF is Statistically significant predictor with p value less than 0.01, and has a beta coefficient of 1.192. Beta coefficient is significant and positive. It has a positive influence on response variable (CAPM 2019). If we increase the value of MKTRF then the response variable will increase by 0.8615 times of MKTRF. We also have small standard errors.
7) What are the Fama and French factors? Why are they used? Discuss the three factor, five factor model and momentum.
The Fama-French three-factor model was developed by University of Chicago professors Eugene Fama and Kenneth French. The Fama-French Three-factor Model is an extension of the Capital Asset Pricing Model (CAPM).
The Fama-French model aims to describe stock returns through three factors: (1) market risk, (2) the out performance of small-cap companies relative to large-cap companies, and (3) the out performance of high book-to-market value companies versus low book-to-market value companies. The rationale behind the model is that high value and small-cap companies tend to regularly outperform the overall market.
The central theme of the model is that it permits financial backers to weigh their portfolios so they have more noteworthy or lesser openness to every one of the particular danger components, and accordingly can target all the more accurately various degrees of anticipated return.
The Fama-French Three Factor Model gives an exceptionally valuable instrument to understanding portfolio execution, estimating the effect of dynamic administration, portfolio development and assessing future returns. The Three Factor Model has supplanted the Capital Asset Pricing Model (CAP-M) as the most broadly acknowledged clarification of stock costs in the total and financial backer returns.
In 1993, Fama and French thought of the three-factor model with its two extra factors being size and worth (for example book to showcase esteem). The three-factor model was a critical improvement over the CAPM on the grounds that it adapted to outperformance propensity yet it didn't clarify a few peculiarities nor the cross-sectional variety in expected returns especially identified with benefit and venture (ValueWalk, 2015).
The Fama-French five-factor model which added two variables, productivity and speculation, occurred after proof demonstrated that the three-factor model was a deficient model for expected returns since it's three components disregard a great deal of the variety in normal returns identified with benefit and venture (Fama and French, 2015).
Use of the five-factor model
The hypothetical beginning stage for the five-factor model is the profit markdown model as the model expresses that the estimation of a stock today is needy upon future profits. Fama and French utilize the profit rebate model to get two new factors from it, venture and benefit (Fama and French, 2014).
8) Do size and book-to-market help explain your firm? What type of regression is this? Why is this regression used? (why is it important in finance?) Is beta affected? Do investment and profits help explain your firm? Explain. What these factors are. How about momentum?...
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