Statistical and Predictive Modeling for Finance

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Overview

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Apply regression, statistical analysis, and supervised learning to evaluate financial performance and predict risk. In this course, you’ll build the quantitative skills used by financial analysts to interpret data and support investment and lending decisions. You’ll begin by calculating and interpreting alpha and beta using regression analysis. Then, you’ll examine the assumptions behind linear regression and test model reliability using residual analysis. You’ll apply descriptive statistics to summarize datasets and design A/B tests to measure financial impact. Finally, you’ll build supervised learning models, including decision trees, to predict financial risk and evaluate model accuracy. What makes this course unique is its focus on applied finance scenarios. Instead of abstract statistics, you’ll work with financial use cases such as portfolio measurement and credit risk classification. The course concludes with a portfolio-ready project where you evaluate credit risk models and recommend a lending strategy using data-driven insights.

Syllabus

Interpret Alpha & Beta with Regression: Understanding Alpha and Beta in Portfolio Measurement

You will explain how alpha and beta measure portfolio performance and risk relative to the market. You’ll explore how these metrics separate market influence from manager skill and support risk-adjusted evaluation.

Interpret Alpha & Beta with Regression: Applying Regression to Interpret Portfolio Risk and Return

You will apply regression techniques to calculate and interpret a stock's beta. You’ll translate statistical output into practical investment insights and communicate findings clearly.

Regression: Identify Assumptions & Apply Models:Understanding Regression Assumptions: The Hidden Rules Behind Reliable Models

You will recognize the key assumptions underlying classical linear regression and understand why they matter for financial modeling reliability. You’ll explore how violations can affect forecast accuracy and credibility.

Regression: Identify Assumptions & Apply Models:Applying OLS and Diagnosing Residuals in RStudio

You will apply an OLS regression model and plot residuals to identify heteroscedasticity. You’ll interpret diagnostic outputs and assess whether your model meets statistical standards.

Uncover Data's True Story: Statistics Unveiled: Understanding Measures of Central Tendency

You will understand key measures of central tendency and determine when the mean or median is more appropriate, especially with skewed financial data. You’ll interpret summary statistics to support sound decision-making.

Uncover Data's True Story: Statistics Unveiled: Describing Data Like a Pro: From Numbers to Narratives

You will apply descriptive statistics to summarize key features of a dataset. You’ll calculate, visualize, and communicate data patterns clearly for professional audiences.

Design A/B Tests for Financial Impact: Hypotheses in Finance

You will explain the difference between a null and an alternative hypothesis and understand their role in financial experimentation. You’ll connect hypothesis testing logic to risk-adjusted performance evaluation.

Design A/B Tests for Financial Impact: A/B Tests for Sharpe Ratio Impact

You will apply A/B testing principles to design an experiment measuring an algorithm’s impact on the Sharpe ratio. You’ll structure test plans that distinguish true improvement from random variation.

Predictive Models for Financial Risk: The Predictive Modeling Workflow

You will describe the standard workflow for developing and evaluating supervised learning models, from defining the predictive question to validating results. You’ll understand how structured workflows improve transparency and trust.

Predictive Models for Financial Risk: Building and Evaluating a Financial Risk Classifier

You will apply a decision tree model to predict a categorical outcome and report its accuracy. You’ll interpret model performance metrics and communicate findings in clear business language.

Project: Evaluate Credit Risk Models and Recommend a Lending Strategy

In this project, you will evaluate two predictive credit risk models—a logistic regression model and a decision tree classifier—using provided statistical outputs and performance metrics. You will interpret regression coefficients, assess statistical significance, evaluate model assumptions, and compare classification performance using accuracy, precision, and recall. You will also analyze confusion matrix results and interpret pilot A/B testing outcomes. Based on your analysis, you will recommend a lending strategy that balances predictive performance, financial risk exposure, and business priorities. This project simulates a real credit risk evaluation task performed by entry-level financial and risk analysts.