Quantitative Methods for Finance

John Molson School of Business

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John Molson School of Business

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About the course

This introductory course on quantitative methods presents statistical concepts and techniques that are essential in the financial industry.

This introductory course on quantitative methods presents statistical concepts and techniques that are essential in the financial industry. The first part of the course focuses on tools for describing and estimating risk, including calculating the time value of money and descriptive statistics. Probability theory and distributions are then introduced as techniques to describe the behaviour of random variables, and this leads to estimation techniques, hypothesis testing, and technical analysis as methods used to help make investment decisions.

Modules

Expert advice and in-depth features

NOTE: The modular nature of this program allows different learners to tailor their courses to their needs. You may choose to take one course (for interest, or for a personal need), a series of courses (for career advancement, for example), or a series of modules (for professional certification preparedness in a particular field).

Module 1: The Time Value of Money Credit Hours: 2

In this module, we will cover the fundamental concepts used in time value of money applications such as simple interest and compound interest, cover the use of timelines in analyzing and understanding cash‐flows, and introduce the concepts of intra‐year compounding, annuities due, and perpetuities.

Module 2: Discounted Cash Flow Applications Credit Hours: 2

In this module, you will learn how to apply discounted cash flow (DCF) to estimate project performance, including Net Present Value (NPV) and the Internal Rate of Return (IRR). You will also learn how DCF can help financial analysis value stocks, bonds or other investments that generate cash flows.

Module 3: Statistical Concepts and Market Returns Credit Hours: 2

In this module, you will learn the basic tools and terminology associated with descriptive statistics. This will include calculating and interpreting measures of central tendency, variability, and position. You will also be introduced to the basic notions for displaying data via distributions including kurtosis and skewness.

Module 4: Probability Concepts Credit Hours: 3

This module focuses on the fundamental concepts underlying probability calculations, as well as their basic practical interpretations as a prerequisite for the later modules. The goal of this module is, therefore, to provide you with the basic tools you will need to identify unique scenarios and select or create the appropriate technique to carry out an effective probability calculation.

Module 5: Common Probability Distributions Credit Hours: 4

This module expands on the fundamental concepts of probability theory by placing you in practical situations to make informed investment decisions. In particular, you will learn how probability distributions, such as the normal distribution, are used in financial decision making. You will also be introduced to specific calculations and associated baselines devised by experts in the field to apply to authentic scenarios.

Module 6: Sampling and Estimation Credit Hours: 2

Statistics is not an exact science. After all, the whole point of statistics is to make generalizations about the population under study using a portion of the population’s data. Therefore the notions of sampling error, bias, estimators, degrees of freedom, and the central limit theorem become essential to understand if you are to design, develop, and carry out any study that involves the collection of data. This module introduces you to these concepts, along with their practical use in confidence intervals and with the t‐distribution.

Module 7: Hypothesis Testing Credit Hours: 3

The backbone of inferential statistics is the hypothesis test. In this module, you will learn how to properly initiate, carry out, and interpret the results of hypothesis tests. Throughout this module you will be introduced to various types of hypothesis tests such a sample being compared to its population, determining if a difference exists between two independent samples, the treatment effect on a given sample, and the difference in the variation of two samples

Module 8: Technical Analysis Credit Hours: 2

Some analysts believe that the truth lies not in calculations, but rather in patterns. In this module, you will learn how technical analysts study charts in order to identify patterns that signify an impending shift in stock prices, and an opportunity to take advantage of it! You will be introduced to common chart patterns, technical analysis indicators, and the importance of understanding the underlying notion of cycles in stock market prices.

Experts

Reena Atanasiadis

Bringing over two decades of wealth management and finance experience to the classroom, Reena Atanasiadis is Director of the John Molson School of Business’s (JMSB) MBA in InvestmentManagement, the world’s first MBA program fully integrating the Chartered Financial Analyst® Candidate Body of Know...

Patrick Devey

Dr. Patrick Devey is the Dean of the Centre for Continuing and Online Learning at Algonquin College (Ottawa, Ontario). He has over 15 years of professional experience in the leadership and management of quality learning and training experiences for students and clients in higher education, corpor...

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Quantitative Methods for Finance at John Molson School of Business

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