Working with Financial Data in R

Name: Working with Financial Data in R
Brand: Loonycorn
Price: 19.99 USD
Availability: Discontinued

15.5 Hours

$19.99$99.99

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138 Lessons (15.5h)

You, This Course and Us
Promo_CareerInFintech3:16
Introducing Risk management
Risk Management - Slides and Source Code
Introduction12:30
Factor Risk Models10:26
Case Studies12:52
Mean Variance11:52
Correlations12:58
Outlining an Approach to Risk Management
Overall Approach12:53
Portfolio Mean Variance9:24
Factor Models9:17
Factor Variance Calc10:29
VaR11:34
VaR - Pros and Cons10:28
RIsk Modeling in Excel/VBA
Yahoo Finance10:33
Returns10:38
VBA Cov7:36
Factor Regressions10:12
Factor Model Risk5:56
Scenario Risk9:23
Va R Calc6:46
Risk Modeling in R
Data Frames5:26
Covariance Matrices based on Historical Return8:32
Factor Modeling8:34
Scenario-based Stress Tests4:35
VaR4:35
Risk Modeling in Python
Covariance Matrices based on Historical Return9:30
Factor Modeling7:26
Scenario-based Stress Tests and VaR8:35
Introducing Factor Analysis
You, This Course and Us1:45
Factor Analysis and PCA
Factor Analysis and the Link to Regression8:05
Factor Analysis and PCA7:02
Basic Statistics Required for PCA
Mean and Variance6:05
Covariance and Covariance Matrices11:47
Covariance vs Correlation3:20
Diving into Principal Components Analysis
The Intuition Behind Principal Components
Finding Principal Components7:12
Understanding the Results of PCA - Eigen Values4:07
Using Eigen Vectors to find Principal Components2:31
When not to use PCA2:26
PCA in Excel
Setting up the data6:52
Computing Correlation and Covariance Matrices3:27
PCA using Excel and VBA5:51
PCA and Regression2:56
PCA in R
Setting up the data5:16
PCA and Regression using Eigen Decomposition3:58
PCA in R using packages1:56
PCA in Python
PCA and Regression in Python6:42
Introducing Numerical Optimisation
Optimisation - Slides and Source Code
Introduction6:51
Balance3:33
Framing the Problem8:27
Solving the problem10:19
Applications6:46
PortfolioAllocation5:58
Regression6:57
Gradient Descent5:54
Linear Programming and the Simplex Method
Wyndor7:27
Standard Dual7:04
Micro Econ6:13
Graphical7:37
Simplex Intuition7:47
Simplex Mechanics8:48
Simplex Extensions7:43
Implementing Linear Programming in Excel
Outlining our Approach3:55
Assembling Data3:52
Linear Estimations6:51
Solver5:28
VBA for Covariance5:49
Quadratic Optimization7:30
Implementing Linear Programming In R
Introducing R3:30
Data frames5:15
Linear Estimates7:43
Quadratic Estimates6:25
Quadratic Programming in R6:48
Implementing Linear Programming in Python
Python for optimization5:21
Pandas3:14
Linear Estimates5:45
Quadratic Estimates6:07
Quadratic Optimization3:57
Understanding Integer Programming
Integer Programming6:03
LP Relaxation4:53
Flaws Naive LP7:00
Applications7:23
Either Or Constraints5:42
Unusual Forms7:18
Implementing Integer Programming in Excel
Integer Constraints4:29
Leverage and Long-bias Constraints3:29
Solver for Integer Programming4:45
Implementing Integer Programming in R
Implementing Integer Programming in R6:44
Implementing Integer Programming in Python
Integer Constraints3:45
Solving for Leverage in Python7:07
Introducing Linear and Logistic Regression
You, This Course and Us1:54
Connect the Dots with Linear Regression
Using Linear Regression to Connect the Dots9:06
Two Common Applications of Regression5:26
Extending Linear Regression to Fit Non-linear Relationships2:37
Basic Statistics Used for Regression
Understanding Mean and Variance6:05
Understanding Random Variables11:27
The Normal Distribution9:31
Simple Regression
Setting up a Regression Problem11:38
Using Simple regression to Explain Cause-Effect Relationships4:59
Using Simple regression for Explaining Variance8:09
Using Simple regression for Prediction4:06
Interpreting the results of a Regression7:27
Mitigating Risks in Simple Regression7:58
Applying Simple Regression
Applying Simple Regression in Excel11:57
Applying Simple Regression in R11:14
Applying Simple Regression in Python6:05
Multiple Regression
Introducing Multiple Regression7:05
Some Risks inherent to Multiple Regression10:08
Benefits of Multiple Regression3:49
Introducing Categorical Variables7:00
Interpreting Regression results - Adjusted R-squared7:04
Interpreting Regression results - Standard Errors of Co-efficients8:14
Interpreting Regression results - t-statistics and p-values5:34
Interpreting Regression results - F-Statistic2:53
Applying Multiple Regression using Excel
Implementing Multiple Regression in Excel8:54
Implementing Multiple Regression in R6:26
Implementing Multiple Regression in Python4:21
Logistic Regression for Categorical Dependent Variables
Understanding the need for Logistic Regression9:26
Setting up a Logistic Regression problem6:04
Applications of Logistic Regression9:57
The link between Linear and Logistic Regression8:15
The link between Logistic Regression and Machine Learning4:18
Solving Logistic Regression
Understanding the intuition behind Logistic Regression and the S-curve6:23
Solving Logistic Regression using Maximum Likelihood Estimation10:04
Solving Logistic Regression using Linear Regression5:34
Binomial vs Multinomial Logistic Regression5:23
Applying Logistic Regression
Predict Stock Price movements using Logistic Regression in Excel9:52
Predict Stock Price movements using Logistic Regression in R8:00
Predict Stock Price movements using Rule-based and Linear Regression6:46
Predict Stock Price movements using Logistic Regression in Python4:49

DescriptionInstructorImportant DetailsRelated Products

Discuss Risk Modeling, Optimization, Factor Analysis & Regression in R

LoonycornLoonycorn is comprised of two individuals—Janani Ravi and Vitthal Srinivasan—who have honed their respective tech expertise at Google and Flipkart. The duo graduated from Stanford University and believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Description

A financial portfolio is almost always modeled as the sum of correlated random variables. The Great Recession and many other financial mishaps can be attributed to poor risk modeling. In this course, you'll explore the many capabilities of the R programming language in relation to risk modeling, factor analysis, numerical optimization, linear regression, and logistic regression. By course's end, you'll have a firm understanding of how to use R to create more accurate models and make smarter financial decisions.

Access 138 lectures & 15.5 hours of content 24/7
Model risk using covariance matrices & historical returns
Understand factor analysis & its link to linear regression
Discuss principal components, Eigenvalues & Eigen vectors
Apply PCA to explain the returns of a technology stock like Apple
Explore the classic linear programming problem setup & the primal and dual problems
Implement simple & multiple regression in Excel, R, and Python
Discover applications of logistic regression

Specs

Details & Requirements

Length of time users can access this course: lifetime
Access options: web streaming, mobile streaming
Certification of completion not included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: all levels

Compatibility

Internet required

Terms

Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.

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