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...

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 estimate 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)
es<-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 e
ors.
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 e
ors.
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 e
ors.
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
oadly 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, occu
ed 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?...