ANALYSIS USING R STUDIO AND EXCEL

ANALYSIS USING R STUDIO AND EXCEL

TABLE OF CONTENTS

TOC o "1-3" h z u Tasks PAGEREF _Toc124258107 h 3I. PAGEREF _Toc124258108 h 3II. PAGEREF _Toc124258109 h 4III. PAGEREF _Toc124258110 h 5IV. PAGEREF _Toc124258111 h 8V. PAGEREF _Toc124258112 h 11VI. PAGEREF _Toc124258113 h 13Reference List PAGEREF _Toc124258114 h 14

TasksI.

Figure 1: Dataset chosen for analysis

(Source: Self-created using R studio)

The company that is chosen for the analysis is Google. The stock portfolio of the company is available on the R studio platform. The close value and the volume of the stocks for the company are also added which has been used for the analysis.

Figure 2: Expected Return values

(Source: Self-created using MS Excel)

The above figure contains the expected return values for the assets of the company. The percentage of expected return is also provided for the analysis. The expected return percentage (Er) is 1.60%.

Figure 3: Correlation

(Source: Self-created using MS Excel)

The correlation for the assets of the company is provided (Bhowmik and Wang, 2020). The correlation has been calculated in MS Excel and the value has been calculated to 0.832549294.

II.

Figure 4: Risk Prediction using Solver

(Source: Self-created using MS Excel)

The risk prediction in the company is solved using the Solver function in MS Excel. The solver function has been provided in MS Excel.

Figure 5: Sharpe Ratio for 1.50% return

(Source: Self-created using MS Excel)

The sharpe ratios has been calculated using MS Excel and its formulas. The sharpe ratio has been calculated using the risk percentage of 1.50%.

Figure 6: Frontier Efficient Curve

(Source: Self-created using MS Excel)

The frontier efficient graph has been obtained using the Risk and Return of the company. The curve has been obtained in MS Excel software. A default theme has been chosen for the implementation of the curve in MS Excel.

III.

Figure 7: Code Implementation of Linear Analysis

(Source: Self-created using R studio)

The code implementation for Linear Analysis is provided in the above snip. The code has been plotted in the R studio platform (Endri et al. 2020). Packages like Tidyverse and ggpubr are installed and applied for the linear regression.

Figure 8: Output of Linear Analysis

(Source: Self-created using R studio)

The linear analysis has been done using the R studio platform. The plot has been plotted against Open and Close (Zhang et al. 2019). The plot came in the form of a straight line along with scatter values.

IV.

Figure 9: Code Implementation for finding the return value

(Source: Self-created using R studio)

The code implementation for finding the return value is provided in the above snip. The code has been plotted in the R studio platform (Watkins, 2020). A graphical plot is also provided for the analysis.

Figure 10: Output plot for finding the return value

(Source: Self-created using R studio)

The output for the return value has been plotted and the snip is provided above. The plot has been made for the return value against the date. The output has been implemented in the R studio platform.

Figure 11: Code Implementation for finding volatility

(Source: Self-created using R studio)

The above snip contains the code implementation for finding the volatility of the assets of the company. For the implementation, many packages had to be installed. The output came in the form of a graph. The code has been implemented in the R studio platform.

Figure 12: Output plot for finding the volatility

(Source: Self-created using R studio)

The output for the volatility has been plotted and the snip is provided above. The plot has been made for the volatility against the date. The output has been implemented in the R studio platform.

V.

Figure 13: Code Implementation of GARCH model

(Source: Self-created using R studio)

The GARCH model has been chosen for the analysis over the ARCH model. The model has been implemented in the R studio platform.

Figure 14: Output for GARCH model

(Source: Self-created using R studio)

VI.The output for the GARCH model has been implemented in the RF studio platform. The GARCH model is implemented over the ARCH model for better analysis and explanation of the goals of the report. The GARCH model provides estimated and improved results for volatility over The ARCH model. Moreover the GARCH model is easy to implement in R studio than the ARCH model.

Reference ListJournal

Watkins, M.W., 2020. A step-by-step guide to exploratory factor analysis with R and RStudio. Routledge.

Zhang, G., Ali, S., Wang, X., Wang, G., Pan, Z. and Zhang, J., 2019. SPI-based drought simulation and prediction using ARMA-GARCH model. Applied Mathematics and Computation, 355, pp.96-107.

Endri, E., Abidin, Z., Simanjuntak, T.P. and Nurhayati, I., 2020. Indonesian stock market volatility: GARCH model. Montenegrin Journal of Economics, 16(2), pp.7-17.

Bhowmik, R. and Wang, S., 2020. Stock market volatility and return analysis: A systematic literature review. Entropy, 22(5), p.522.

Salisu, A. and Adediran, I., 2020. Uncertainty due to infectious diseases and energy market volatility. Energy Research Letters, 1(2), p.14185.