Dividend Policy, Risk Analysis, Factor Models, Option Pricing, Regression Analysis, and Corporate Finance

        An entire capital market refers to a market model that operates within the context of ideal market conditions. In this market, supply and demand interchangeably correlate to determine the set prices of various products in the scope of operations. An entire market comprises of several interdependent companies whose control of the market in terms of costs is overly limite, and thus each firm owns a relative market share Those above increasingly vary from the core prospects as exhibited by an efficient market. A dynamic market thus posits a particular scenario which frequently highlights the universal sharing of essential information, as well asthe random flow of prices for stocks as its significant features. In an efficient market, the randomness of stock prices majorly occurs due to the prevalent market’s preference based on recent events at the expense of past trends. According to analytical perspectives, an efficient market operates within the stringent confines of critical assumptions set forth to create a useful framework for which this particular market thrives.

Weak, semi-strong, and healthy form levels of efficiency

        The scope of these inherent assumptions is mostly reflected in the variety of forms which categorically define the suitability of this market from several dimensions. An efficient market assumes three consequential types namely; weak, semi-strong, as well as active forms all of which present differing features at large. These forms primarily are set apart mostly by the influence of information in regards to price formation, as well as the role played by past events in determining stock prices within and without the inherently efficient market in the scope of operations. By and large, thus, in a weak form public information is widely available to all consumers and marketers with the consequence that completely undervalues the role played by performances from the past in establishing prospective results at large. A semi stabl, efficient market for, on the other hand, represents a scenario where non-public, as well as public informatio,  is available to all the market players. A robus, capable market form hails a trend whereby marketers haveconsiderable access to a wide array of critical informatio is precluding; public, as well as private information.

Howdo the assumptions about our capital markets effect our financial models?

        Fiscal models play a core role more so when it comes to providing a suitable platform that increasingly aims to analyze myriad fiscal trajectories throughout operations critically. Different effects arising from diverse market forms harbor a wide array of actionable impactson the prevalent financial models on several fronts.

What does the empirical work seem to imply about the various levels of efficiency? 

        The incorporation of critical perspectives transcending the scope of empirical evidence in the world of economics seems to havelent much credence to the value of ideal theoretical suppositions. Back in time, a series of conventional theories such as Capital Asset Price Modeling, as well as Efficient Form Hypothesis created a suitable framework whose fundamental tenets mostly served to offer an explanation about the prevalent behavioral dispositions experienced in varied financial models. A critical analysis of a substantial amount of considerable evidence posited conflicting information that upended the erstwhile reliance on ideal presuppositions which earlier ascertained the significant role played by logic, as well as rationality in determining the decision making processes of varied individuals at large.

        To date, an increasing amount of critical empirical evidence seemingly indicates the irrelevance of contemporary theories in explaining the behavioral dispositions of the real world market. This analogy increasingly aims to support the argument which highlights the prevalent complexities revealed in the behaviors of varied individuals throughout their financial decision making processes. People tend to act irrationally when it comes to investments and purchasing assets owing to the set disparities brought on preferences, as well as tastes. To that end, it should be noted that a series of reputable empirical evidence to date tends to comprehensively analyze myriad prevalent anomalies brought on by myriad behavioral tendencies exhibited by countless individuals over the scope of their financial decision making processes. Fundamental concepts therein now seemingly relate to the diverse influence posited by several irrational and illogical decisions made by varied individuals in the scope of their financial decisions.

Behavioral finance and its implication to financial models

    Behavioral economics as an above economic terminology refers to popular and or contemporary theoretical perceptions which seemingly ascribes to the potential of logic in making business decisions. Decision making plays a critical role in the scope of making suitable fiscal choices. Behavioral finance comes across with an appropriate avenue whose core tenets increasingly aim towards the incorporation of logic as it relates to enhancing our decision making processes from several diversified dimensions.

An essential facet in understanding the foundation of major financial models point out the role played by unpredictable behavior in creating new commercial models on several fronts.

Using the data that you created for LE 11.1 from January 2014 – Decembe 2018 for Morgan Stanley, JP Morgan, Regeneron and the S&P Composite on monthly holding period returns. You will first need to subtract the risk free rate from the company returns and the S&P Composite. The monthly risk – open allowance can be obtained from the Fama – French database in Wharton.  Estimate the beta coefficient for the three companies using the variance/covariance matrix and the correlation matrix from LE 11.1. 

The beta is calculated by dividing Covariance with variance. Covariance evaluatyes how stocks are moving together. If the cobariance is positive than it means the stiocks move in the same direction when prices increase or decrease. On the other han, if covariance is negative than it means that the stocks will move inthe opposite direction. In the above tabl, the beta of JPM, MS & REGN is computed. It can be seen that The JPM corporations stocks are more volatile than the stocks of other tweo companies. JPM is 142% more volatile than S&P. REGN is 64% more volatile.

 Perform a simple regression analysis using Morgan Stanley, JP Morgan, and Regeneron as the dependent variable and the S&P Composite as the independent variable.   Remember, you will need to regress risk premiums. This is accomplished by subtracting the risk free rate from the Fama – French database for the company returns and the S&P composite.  This will result in three separate regressions, one for each of the companies.  Discuss the statistical properties of your results.   How do these estimated betas compare to the estimates that you obtained from the variance/covariance matrix in question 4?  Bloomberg provides a modified beta to calculate risk estimates.  How is this determined and why do they give a modified beta?

SMB (Small Minus Big) is the average return on the three small portfolios minus the average return on the three significant collections,

HML (High Minus Low) is the average return on the two value portfolios minus the average return on the two growth portfolios,

MKTRF is the excess return on the market or also called the risk premium of the market. It is calculated as the value-weight return on all NYSE, AMEX, and NASDAQ stocks (from CRSP) minus the one-month Treasury bill rate (from Ibbotson Associates).

 Mom is a momentum factor developed by Chart.

    Treating the three company’s returns as the dependent variables, perform a multiple regression analysis using the four factors from the Fama – French model.  Remember you want to use the risk premiums of the companies.  The market return in Fama – French has already subtracted the risk free rate so you will not have to do this using the Fama – French data.     Please interpret the empirical results.  (Hint: To run a multiple regression in Excel, you need to identify the first and last observation for the independent variables and make sure the variables are in columns next to each other.)  How do the empirical results in this question compare to the observed findings from the simple regression model in question 3?

 Please discuss the Black & Scholes model and the binomial model approach to option pricing.  What are the advantages and disadvantages of these two approaches?  Determine the price of a call and put option assuming that the exercise price is $105, the value of the stock is $101 the risk-free rate is 2.05% the standard deviation of returns on the capital is 28%, and the option hassix months remaining to maturity.  What is the price sensitivity of the call and put options to changes in the price of the stock?  Would the sensitivity be different if the exercise price in this example was $103?  Please explain.

 Black Scholes Model deploys a wide array of critical options usable in determining the price of a call option as influenced by several variables precluding, but not limited to; common price of stock, option type, volatility, risk-free rate, time, as well as the strike price. The binomial mode, on the other hand, uses a repetitive technique whose core tenets increasingly enhance determination of specific points in time which manifests throughout specific period transcending dates of valuation, as well as the expiration of options. Both the binomial and black shoe model deploys a common framework for analyzing stock options overcome time. These two financial models harbor pros and cons as assessed by critical perspectives concerning their suitability.

  Pros and cons of the binomial model of stock pricing

One specific advantage presented by the binomial model is its ability to provide an individual with a multifaceted avenue for carrying out assessments about changes in asset prices, as well as the suitable options available in varying periods. This enhances the prospects of an individual’s decision making processes thereby leading to the promotion of sound investment. Also, this model further enhances transparency over the scope of price and options volatility.

Cons of the binomial model of stock pricing

 A resulting limitation of this particular model in calculating stock options ascribes to its inherently complex procedures which consume most time in the scope of striving to come up with a suitable financial decision making process.

 Pros and cons of black shoe model of stock pricing Primary merit presented by the black shoe model ascribe to its capacity to provide an easy avenue for computing prices of stocks thereby serving to offer insightful analysis to investors relative to returns on investments.

Cons of the binomial model of stock pricing

While the model overly offers better understanding of relevant computations indicating the value of returns on investments, this particular model lacks a favorable avenue for displaying transparency concerning the prevalent changes in prices overtime.

 Calculations

C = S × N (d1) - Xe-rt × N(d2)

C= call option

S= stock price

X = exercise price

N (d1) and N (d2) = standard normal distribution

N (d1) = {(In s/x) + [r + (s.d/2)^2 ] × time, t}/ {s.d × (time)^1/2}

 = 101 × 0.0478 – (105^-369) × 0.2444

The variability of Call price and option price are interdependent on the underlying exercise price of the market. Call and exercise prices are directly proportional while put prices are indirectly proportional to exercise price.

 Call = $6.6386

Put = $9.5825
d1: -0.0478
d2: -0.2444

Change in sensitivity calculations

 = 101 × 0.05 – (103^-369) × 0.1466

= Call = $7.4751
Put = $8.439
d1= 0.05
d2 = -0.1466

 A reduction in underlying price from 105 to 103 subsequently leads to a decrease of put prices while increasing call prices.

 Graphically illustrate when possible and provide formulas for the development of the Mean-   Variance (Markowitz) model to the Capital Asset Pricing Model (CAPM), the Arbitrage Pricing Theory (APT), and multi-factor models like Fame and French. These illustrations and equations can be copied and pasted as long as the source is sited.  The answers should be in your own words. How are the models different from each other and how are they similar?

Similarity of models

Both models are used to calculate stock prices based on the underling information in the market.

Arbitrage and Capital asset pricing model uses the beta function to predict future option prices of assets

 Disparity of models

Fama and French, as well as Arbitrage Capital Theory,  are multifaceted models which provide many distribution price prospects while CAPM is a singular model that analyses future options about the sensitivity of prices in the market.

Capital pricing model

Arbitrage Pricing model

This exercise deals a lot with factor models to estimate risk estimates for a company.  Why is this a relatively difficult thing to accomplish for equities?  What are the advantages and disadvantages of using the Capital Asset Pricing Model (CAPM) as an estimate of risk? 

 The capital asset pricing model operates within the confines of stringent principles whose core tenets largely favor the estimation of risks for equity Because CAPM uses one base year to calculate risks, it provides a suitable platform for estimating risks affiliated with ownership. Also, in using CAPM to calculate estimated risks, over reliance on the assumption that investors possess a wide array of the financial portfolio makes for an excellent foundation to calculate risk estimates

Similarly, the risk rate free enables investors to be lend, and borrow at relatively open rates.

In Finance, many times there is an expression used that says that dividends are irrelevant.  Please explain what is meant by this expression.  How could signaling and the clientele effect come into play concerning a company’s dividend policy?    How could you replicate a dividend policy even if the company is a growth company that doesn’t pay dividends?

Irrelevance of dividends is attributed to its low impact in a firm’s stock in a scenario that allows for an entire market. Signaling and clientele effect play a core role in the scope of determining a company’s investment portfolio. Signaling connotes the integration of dividends about establishing a company’s fiscal trajectory. Thus a rise in profits signals better future potential, and the converse holds. On another note, the clientele effect aids a company to carry out critical assessments based on investors’ preferential dividend policies on several fronts.

Companies may implement the neutral divided policy whose core tenets increasingly aim to send a message that it uses its cash for further financial investments. This way, it reduces its fixed rates of interest accordingly.

Please discuss the problems of auto correlation, multi-col linearity, and heterosexuality in a regression model. Why will this affect our empirical regression results?