Overview

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Build job-ready skills in quantitative finance by mastering statistical methods, probability, and financial modeling used in a quantitative finance and CQF course pathways. This course helps you apply mathematical finance concepts to real-world problems in quant finance, trading, and investment analysis. You will begin with rates and return concepts, including time value of money and return calculations used in financial decision-making. The course then progresses to statistical measures, where you will analyze distributions, dispersion, and correlation between asset returns. Next, you will explore probability, conditional expectations, and Bayes theorem to model uncertainty in financial markets. You will also work on portfolio mathematics, applying risk-return frameworks, covariance, and diversification techniques. In advanced modules, you will learn simulation methods such as Monte Carlo and bootstrapping, followed by estimation, hypothesis testing, and regression analysis used in quantitative analyst courses. The course concludes with big data techniques, including machine learning and data science applications in quantitative trading and investment analysis. By the end, you will: • Apply statistical and probability models in financial analysis • Build quantitative frameworks for portfolio risk and return • Use regression and hypothesis testing for financial decisions • Analyze financial data using quantitative methods This course is ideal for finance students, aspiring analysts, investment professionals, and anyone interested in alternative investments. Start building your expertise and make smarter investment decisions in evolving markets. Disclaimer: This course is an independent educational resource developed by Board Infinity and is not affiliated with, endorsed by, sponsored by, or officially associated with CFA Institute or any of its subsidiaries or affiliates. This course is not an official preparation material of CFA Institute. All trademarks, service marks, and company names mentioned are the property of their respective owners and are used for identification purposes only.

Syllabus

Time Value of Money in Finance

This module introduces the foundational concepts of interest rates, returns, and the time value of money in financial decision-making. Learners explore different return measures, compounding methods, and performance metrics used to evaluate investments. The module also examines implied returns, growth expectations, and cash flow additivity concepts applied to financial instruments.

Statistical Measures of Asset Returns

This module focuses on statistical tools used to analyze financial return data. Learners examine measures of central tendency, dispersion, and distribution shape to understand return characteristics. The module also introduces correlation analysis, probability trees, portfolio mathematics, and simulation techniques used to model investment outcomes.

Estimation and Inference

This module explores statistical estimation and hypothesis testing techniques used in financial research and analysis. Learners examine sampling methods, the central limit theorem, and estimation procedures for population parameters. The module also covers parametric and nonparametric tests used to evaluate financial hypotheses and relationships

Regression testing

This module introduces regression analysis as a tool for modeling relationships between financial variables. Learners examine the assumptions of linear regression, parameter estimation, hypothesis testing, and model evaluation techniques. The module also explores prediction methods and functional forms used in financial regression models.

Big Data Techniques

This module explores the role of big data and advanced analytical tools in modern financial analysis. Learners examine how fintech, artificial intelligence, and machine learning techniques support quantitative investment strategies. The module also introduces data science approaches such as data processing, visualization, and text analytics used in financial data analysis.