Developing Financial Analysis Tools [Video]

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Developing Financial Analysis Tools [Video]

Atmajitsinh Gohil

Evaluate financial products in R by using financial packages via simple and easy-to-follow instructions
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Video Details

ISBN 139781787285125
Course Length5 hours and 4 minutes

Video Description

As most of the data on the web or residing in a database is not structured in the right way, the course will assist viewers in developing skills to manipulate, transform, and evaluate raw input data. Through the concept of tidy data and visualization tools, viewers will be able to analyze trends and study the financial markets.

Once users have developed a good understanding of financial markets and financial data, the next three sections (3, 4, and 5) will introduces users to the concepts of basic statistics, time series analysis, and forecasting. Viewers will use a variety of basic R functions and forecast package to understand statistics and perform time series analysis.

By the end of this volume users will be able to use R, learn the use of Shiny apps, understand the concept of tidy data, and generate R markdown files for sharing information.

Style and Approach

This course is shows how to build finance models and helps analyst solve their quantitative finance problems.

Table of Contents

Laying the Foundation of Financial Markets
The Course Overview
Performing Time Value of Money in R
Evaluating the Equity Markets Using the quantmod Package in R
Fundamental and Technical Analysis in R
Bond Valuation in R
Yield Curve
Introduction to Derivatives
Introduction to Shiny
Financial Data Extraction and Cleaning
Extracting Financial Data from Various Sources
Extracting Data from the Web
Understanding Tidy Data Using the tidyquant Package
Visualizing Financial Data in R
Generating a User Interface in Shiny
Creating a Shiny Application
Basic Statistics Using R
Summarizing Financial Data in R
Outlier Analysis in R
Calculating Asset Return
Understanding Population and Sample Data
Evaluating Skewness and Kurtosis in Financial Data in R
Probability Distribution of Asset Returns
Market Volatility, Correlation, and Covariance in R
Introduction to Time Series Modeling
Basic Features of a Time Series
Decomposition of Time Series Data
Stationarity and Autocorrelation in R
Stationarity and Autocorrelation in R (Continued)
Time Series Modeling in R
Time Series Modeling in R (Continued)
ARIMA in R
Advance Topics in Time Series Modeling
Forecasting in R
Accuracy of Forecast
Cointegration
Cointegration (Continued)
Vector Autoregression (VAR)

What You Will Learn

  • Install R and R Studio and get help in R.
  • Work with R and the R Studio console.
  • Perform basic arithmetic and linear algebra calculations in R
  • Explore the various data structures in R
  • Import and export datasets from various sources to R
  • Write loops in R and an introduction to internal looping functions

Authors

Table of Contents

Laying the Foundation of Financial Markets
The Course Overview
Performing Time Value of Money in R
Evaluating the Equity Markets Using the quantmod Package in R
Fundamental and Technical Analysis in R
Bond Valuation in R
Yield Curve
Introduction to Derivatives
Introduction to Shiny
Financial Data Extraction and Cleaning
Extracting Financial Data from Various Sources
Extracting Data from the Web
Understanding Tidy Data Using the tidyquant Package
Visualizing Financial Data in R
Generating a User Interface in Shiny
Creating a Shiny Application
Basic Statistics Using R
Summarizing Financial Data in R
Outlier Analysis in R
Calculating Asset Return
Understanding Population and Sample Data
Evaluating Skewness and Kurtosis in Financial Data in R
Probability Distribution of Asset Returns
Market Volatility, Correlation, and Covariance in R
Introduction to Time Series Modeling
Basic Features of a Time Series
Decomposition of Time Series Data
Stationarity and Autocorrelation in R
Stationarity and Autocorrelation in R (Continued)
Time Series Modeling in R
Time Series Modeling in R (Continued)
ARIMA in R
Advance Topics in Time Series Modeling
Forecasting in R
Accuracy of Forecast
Cointegration
Cointegration (Continued)
Vector Autoregression (VAR)

Video Details

ISBN 139781787285125
Course Length5 hours and 4 minutes
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