Quantitative Analyst with R

This finance-oriented introduction to R will show you how to use data structures such as lists and data frames. This knowledge will also allow you to apply it in real-world financial situations. You'll feel confident using R to manipulate financial data.

If you liked Introduction to R Financial Management, Intermediate R for Finance is for you. This section will explain the basics of R's date system. This knowledge is crucial for the rest of the course. Next, you'll explore the worlds loops, functions and if statements. These ideas are vital for financial data scientists. We will also be discussing the apply function vectorized option to loops. All examples are finance-related. Enjoy!

Time series can be found everywhere, from server logs to high frequency financial data. The management and manipulation order observations are the basis of all time series analysis. You can accomplish this task quickly and without errors using the xts and zoo packages. This course will cover everything you need to know about xts.

If you've ever worked in economic or financial time series, you are familiar with the different data formats and periodicities available to you. It can be time-consuming and difficult to import data into R, particularly if data is from multiple sources. This course will show you how to import data both from local files and from online.

Intervals can be used to measure many phenomena that we encounter in our everyday lives, such as the movement of stock prices over time. These data types can be used to analyze time series analysis methods. This course will teach you the fundamental concepts and techniques of time-series analysis.

This course will show you how to fit ARIMA model into time series data using R. Next you will learn how to use R package astsa to fit different ARMA model into simulated data (where you will identify the correct model). Once you have mastered the basics, you will be able to apply integrated ARMA/ARIMA models to various data sets. You will learn how to validate ARIMA models and forecast time series data. Learn how to use seasonal data to fit ARIMA models. Forecasting with astsa is also possible.

Interactive exercises and access to data will allow you to improve your knowledge of time series topics. You will explore a variety of datasets about Boston, including data regarding weather and economic trends, as well as data on local sports teams.

Forecasting is the ability predict the future. Forecasting is essential in many situations. Forecasting is necessary in many situations. For instance, it is important to forecast the future demand and how staff will be staffed in call centers next week. Forecasts are required for stocking inventory and stocking plants. Forecasts are necessary for capital investments but also for routing telecommunications. Forecasting can be used to plan efficiently, no matter what time frame or conditions. This course will introduce you to R time series forecasting.

As the old saying goes, charts are worth a thousand phrases. Visualization is the most effective and powerful way to get a deeper understanding of your data. This course will provide a solid understanding of R's time series visualization capabilities. This course will help you make an informed decision about the model that you would like to use in your analysis. It will be possible to communicate your message in a beautiful, efficient way.

It is important to analyze your portfolio strategy with historical data, and then monitor its performance after it has been traded. This is the golden rule of investing. In this course, you will learn this by critically analyzing portfolio returns using the package PerformanceAnalytics. This course will teach you how portfolio weights can be calculated to achieve the best balance between risk and return. This data-driven course blends portfolio theory with the practice of R. It's illustrated with real-life examples to illustrate asset allocation problems as well as equity portfolios. The data in the tseries package includes the data from the three previous chapters. After you've completed the course, you can explore the data further.

This course extends the concepts of Introduction to Portfolio Analysis and R and explores advanced concepts in portfolio optimization. Portfolio managers and analysts must understand all aspects of portfolio optimization to be able make informed decisions. This course will show you how to use modern portfolio theory in order to create a portfolio and identify constraints and objectives. Then, it will help you solve the problem and analyze your results. The R package PortfolioAnalytics will be used to solve portfolio optimization problems with complex constraints. These problems are similar to real-world problems.

This course will show you how to use R in order to create a model that can be used to assess a fixed-interest bond and estimate its yield. This course will also teach you how to protect your bond portfolios from changes in interest rates.

Why value bonds Bonds are issued by corporations and governments and pay interest according to a schedule. They are the most widely used type of fixed-income securities. Unlike stocks, the US fixed income market has 1.5x more transactions than the US stock market. However, bonds and other fixed income instruments trade very little, which is why they are so popular. The price of a bond is not always a good indicator of its worth. When analysing and valuing bonds, analytical techniques are necessary.

This course will teach you how to model credit risks using logistic regression and decision trees in R.

The role of banks is crucial in assessing credit risk for company and personal loans. The probability of a debtor defaulting is a key component in determining credit risk. Although you will learn many other models in this course, the only two that will be used for credit scoring are logistic regression and decision trees. These models will be discussed in the context of how banks evaluate them.

Quantitative Risk Management (QRM), is the process of creating models to evaluate the risks associated financial portfolios. This is a critical task in the asset management and banking industries as well as the insurance industry. You must first gather information about the underlying risk factors that can impact portfolio value. Next, analyze their behavior. This course will show you how to use risk factor return sequences and analyze the empirical properties or "stylized fact" of these data. It will also teach you how calculate value-at-risk.

This course will cover the basics of financial trading. This course will teach you how to interpret your results both from a statistical and visual perspective.