A consistent refrain from employers is the growing need for managers with a solid understanding of both finance and the impact of data science and machine learning. With the rapid advance of fintech and other developments, this demand continues to grow. Smith’s MFIT program will fill this skills gap.

Essential to survival in the digital age is an organization’s ability to discover new technology-enabled opportunities and deliver solutions that realize benefits. As technology becomes more pervasive, the intersection between IT and traditional business functions has blurred ushering in new practices in digital strategy, design, development and implementation. This course introduces students to modern practices in designing digital innovation that incorporates foundational concepts inherent in design and systems thinking. By taking this course, students will develop the critical thinking skills needed to reimagine business models and deliver solutions that make an impact.

This course will explore why a range of fintech innovations (e.g., token-based economy) have struggled to prove their viability. Several reasons will be discussed including digital shifts being approached as technology solutions (i.e., cryptocurrencies) as opposed to a digital business transformation initiatives, and the challenges of integrating department-specific digital initiatives so that new products and services give stakeholders (i.e., customers, employees) what they want and need. This course will culminate in a “Pitch Day” for the MFIT students.

In this course you will learn how technology has changed how financial markets and institutions function. Learn key concepts for evaluating electronic market quality, how trading algorithms work, and the hype around high frequency trading and dark markets. All of these aspects are changing the skills required to be successful in digital capital markets. We will also cover how finance has changed and is changing, the skills required to be successful and what the future may bring. Where possible we will link new phenomena to classical finance theory and highlight where predictions and reality have diverged.

Gone are the days of the portfolio managers pouring over the annual reports of companies to make investment decisions. Today, algorithms and computers are making investment decisions. This has changed how the market for advice works, how clients access expertise and the role of financial professionals in the investments industry. In this class, we will cover how data science and machine learning algorithms are changing how fundamental research is performed. We will also cover the recent rise of robo-advisors, and automated advice, and understand the role of other types of wealth-technology.

This course provides a comprehensive overview of derivatives and the markets in which they are traded. We implement the manufacturing process underpinning linear as well as non-linear instruments and, in this process, uncover the key relationships employed by market participants to value them. Furthermore, we explore how derivatives are used by financial institutions, as well as by non-financial firms, to manage unwanted risk exposures and/or to enhance investment yields.

The course covers plain-vanilla derivatives (e.g. futures, forwards, FRAs, swaps, and options) as well as more recent innovations, such as exotic options and credit derivatives. We also explore best practices in enterprise-wide market and credit risk management, as well as recent developments in the regulatory environment surrounding the derivatives marketplace.

The course will establish a foundation of statistical modelling techniques to be immediately useful for analysis and to provide a foundation for more advanced material studied throughout the program. Topics will include data types, random numbers, probability models, hypothesis testing and statistical inference, and a thorough grounding in simple and multiple regression. The course is designed to ensure that all students, regardless of background or experience, are proficient in the use and application of a variety of statistical methods.

This course will focus on the data management techniques frequently used as a precursor to analysis with ‘real world data’. Topics include database structures, SQL, data cleaning, merging and filtering, detecting and correcting errors. The course will also cover the application of visualization to developing and telling stories with data with data visualization techniques.

The course will combine three key elements: analytics techniques, business applications, and basic coding/programming (in R, one of the leading open-source tools for analyzing data that you will be able to use in your jobs.) The emphasis will be not on the technicalities or theory, but rather on applications to various business cases. Basic familiarity with R is required, but for most classes you will receive a starter code, by running and modifying which you will learn analytics techniques and coding principles, and which you will also be able to use in your jobs. Because of that, much of the course will be in a form of a "hands-on" workshop; students are be expected to bring your laptop to class (with all the necessary software tools installed) and actively participate in the learning process.

The course will cover 2 major topics within the domain of predictive analytics: “predicting quantities” and “predicting events”. Within the “quantities” part we will focus on linear models, variable selection and regularizations, as well as on time-series analyses. Within the “events” part we will focus on generalized linear models (logistic regression) and get an introduction to supervised machine learning (CART, random forest, boosting, and neural networks).

Technology in banking will cover the financial system and banking topics with a focus in technology induced changes. Open-banking that allows for the free flow of financial data in a manner that is controlled by the user, and not the financial institution will change how banks seek deposits, manage investments, and make loans. The impact of machine learning algorithm on lending and credit-scoring will be explored.

Distributed ledger technologies, like Blockchain and Ethereum, have changed how people view and transfer funds and how economics of the future will function. Central-bank issued digital currencies, Bitcoin and other crypto-currencies and tokens are changing the way that firms acquire funding. The course will cover the technology and economics driving these changes including consensus mechanism, use case and other application. Students will be required to create their own smart contract on the Ethereum platform as a final course deliverable. These and other topics will be explored in depth during this course.

The financial statement analysis component will provide students with a detailed discussion of the key accounting principles used in constructing the income statement, the balance sheet and the statement of cash flows, as well as covering financial ratio analysis. The emphasis will be on using this information to interpret and make adjustments to reported financial figures for use in financial analysis of companies. Topics include, but are not limited to a discussion of international financial reporting standards (IFRS) vs U.S. GAAP; a discussion of how the major components of the three financial statements are determined, including a discussion of the various accounting choices that are available to a reporting entity; estimation and interpretation of key financial ratios that can be used to assess a company’s financial health; and, a review of earnings quality issues that arise from various accounting choices.

The core focus of the course is on ratio analysis, including the adjustments that an analyst must make to financial statements to data presented for inventories, fixed assets, deferred taxes, bonds, and leases. The corporate finance component of MFIN 821 will introduce students to the basics of corporate finance including a “brief” review of financial statement analysis, and a detailed discussion of financial forecasting, the cost of capital, and capital budgeting. The emphasis will be on using this information to examine company decisions and assess their future growth prospects and risks.

Whether we like it or not, as individuals and managers we evolve within various markets, and are subject to their rules. But how do world events shape our markets, decisions, and outcome predictions? This is the question we will address in our first theme, "Understanding Markets".

These markets are themselves part of the economy as a whole – Canadian and global. This economy goes through booms and busts over time – the business cycle – and these fluctuations have an important impact on prices, national output, unemployment, and indeed affect our daily lives. In our second theme, "Understanding the Economy", we will learn the tools necessary to understand business cycles and governments’ policy responses; and to form educated opinions about what we read on the subject in the news.

But beyond understanding markets and the economy, today’s managers face a myriad of economic issues when formulating strategies. Should we enter this market? Should we exit that one? What price should we charge? How much should we produce? Should we try and “deter” entry by a rival, or should we “accommodate”? In some markets — monopolistic markets — our ability to affect outcomes is strong. How can we use this market power to our advantage? Other markets are characterized by the fact that our actions affect, and are affected by, rivals’ actions and require a whole new set of tools — game theoretic tools. How can we use game theory to analyze these situations? These and other fundamental questions are addressed in our third theme, "Decision-Making in Market Environments".

The primary pedagogical objectives of the course are for students a) to familiarize themselves with important economic concepts, and b) to learn to use them as “tools” for efficient decision-making in business and other environments.

This course discusses the entire portfolio management process, from setting investment policies to assessing performance. Portfolio theory will be extended to cover topics including investment planning, practical estimation of models, asset class properties, asset allocation, portfolio management risks, and performance measurement. Application of these topics to both individual and institutional portfolios will be discussed. The class will take a “hands-on” approach with students having the opportunity to work with financial data, apply various estimation techniques, and evaluate actual portfolio performance.

This course explores the profound implications of AI on business and society. The ethical and policy issues linked with the application of AI in business are covered in-depth, including such issues as overcoming the job displacement due to AI by job creation, ensuring the public good as AI pervades the new economy, and balancing privacy and transparency in AI related endeavors.

This course covers machine learning (ML) and AI methods and their implementation in state-of-the-art tools and packages. Through lectures, case studies, and readings, students will gain a broad understanding of ML concepts, and will understand how to use ML as a way of thinking. Students will also develop a proficiency in building ML and AI applications — using modern tools and packages to solve real-world business problems. This course will include an introduction to machine learning, data mining, and statistical pattern recognition.

Technical topics will include: supervised learning e.g., decision trees, Naïve Bayes, support vector machines, neural networks; unsupervised learning e.g., clustering, dimensionality reduction, recommender systems; best practices in building machine learning applications e.g., bias/variance theory, model evaluation, feature engineering and selection, hybrid models; and the practical implementation of ML applications using modern tools and packages e.g., R, Python, and cloud-based services.

Experiential Learning

Experiential learning, or “learning by doing” is one of the most effective ways to learn. Students in Smith’s Master of Financial Innovation & Technology program not only master the theoretical concepts but learn how to apply these concepts to real-life situations.

Students in the Master of Financial Innovation & Technology program have the opportunity to participate in the Queen’s University Alternative Assets Fund (QUAAF). It is the only student run hedge fund in North America and is comprised of an Executive Committee and teams of Analysts. They are supported by an Advisory Committee of industry professionals. This fund has been seeded with contributions from alumni and friends of Queen’s, and all proceeds contribute to the maintenance and expansion of the fund.

Live Data Sets & Technical Training

The program will also include access to finance specific data provided by industry. The data will allow students to work on current and relevant financial issues using state-of-the-art data science techniques.

MFIT students have access to LinkedIn Learning, previously known as LinkedIn Learning is an online learning platform that offers training on topics such as software, technology, business and creative skills. Containing the full library, LinkedIn Learning provides thousands of current, high-quality courses.

Meet the Experts

Courses are taught by experts and range from importing data and data visualization to machine learning, deep learning and more.

View Faculty & Instructors
Double Degree Option

You can apply Master of Financial Innovation & Technology credits to a number of other degree programs at Smith School of Business to fast-track to a double degree at a reduced fee. Other programs include the Full-time MBA, Master of Finance - Toronto, Master of Management Analytics, Global Master of Management Analytics, and the Master of Management in Artificial Intelligence.

Contact an advisor to learn more.

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