BAFN310 - Artificial Intelligence and Machine Learning in Finance

Year

Credit points

Campus offering

Prerequisites

BAFN200 Principles of Finance

Unit rationale, description and aim

This unit is the foundation for understanding the application of artificial intelligence (AI) and machine learning (ML) in finance. Students need to understand the AI concepts, ML algorithms such as decision trees, random forests, support vector machines, neural networks, and bagging and boosting ensemble techniques. Students need to compare and contrast different ML algorithms in terms of accuracy, linearity and number of parameters. Students will further develop problem solving skills in predicting both socially responsible investment (SRI) and conventional (non-SRI) stock prices and in applying the stewardship of scarce resources. Students will be able to apply AI and ML to real-life cases. The unit aims at providing students with the necessary knowledge and skills needed to apply AI and ML for a financial analyst career.

Learning outcomes

To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.

Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.

Explore the graduate capabilities.

On successful completion of this unit, students should be able to:

LO1 - Demonstrate the understanding of artificial intelligence (AI) and machine learning (ML) in the finance industry (GA4, GA5) 

LO2 - Identify and analyse the various kinds of ML techniques for the finance industry (GA5, GA6) 

LO3 - Assess the application of AI and ML algorithms in an ethical perspective (i.e., respecting ethical principles and values) (GA3, GA5)

LO4 - Apply ML algorithms to predict finance variables (e.g., stock price, oil price) (GA5, GA8)

LO5 - Evaluate the machine learning algorithms and assess how machine learning algorithms can contribute to the better stewardship of scarce resources in terms of a cost-benefit analysis. (GA2, GA5)

Graduate attributes

GA2 - recognise their responsibility to the common good, the environment and society 

GA3 - apply ethical perspectives in informed decision making

GA4 - think critically and reflectively 

GA5 - demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession 

GA6 - solve problems in a variety of settings taking local and international perspectives into account

GA8 - locate, organise, analyse, synthesise and evaluate information 

Content

Topics will include:

• Introduction of AI
• Application of AI in the finance industry
• Types of ML: supervised learning techniques, unsupervised learning techniques and reinforcement learning techniques
• ML algorithms: decision trees, random forests, neural networks. bagging and boosting ensemble techniques
• Application of ML algorithms for stock price prediction
• Application of AI and ML for the allocation of scare resources (e.g., cost savings on front office (conversational banking), middle office (fraud detection and risk management) and back office (underwriting) of the banking industry by using AI)

Learning and teaching strategy and rationale

ACU’s teaching policy focuses on learning outcomes for students. Our teaching aims to engage students as active participants in the learning process while acknowledging that all learning must involve a complex interplay of active and receptive processes, the constructing of meaning for oneself, and learning from others. ACU promotes and facilitates learning that is autonomous and self-motivated, is characterised by the individual taking satisfaction in the mastering of content and skills and is critical, looking beneath the surface level of information for the meaning and significance of what is being studied.

The schedule of the workshop is designed in such a way that students can achieve intended learning outcomes sequentially. Teaching and learning activities will apply the experiential learning model, which encourages students to apply higher order thinking. The unit ensures that learning activities involve real-world scenarios that in turn assist with ‘real-world’ preparedness. The unit also uses a scaffolding technique that builds a student’s skills and prepares them for the next phase of the learning process.

This unit is structured with required upfront preparation before workshops, most students report that they spend an average of one hour preparing before the workshop and one or more hours after the workshop practicing and revising what was covered. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for you to prepare and revise. It is up to individual students to ensure that the out of class study is adequate for the optimal learning outcomes and successes.

Mode of delivery: This unit is offered in different modes. These are: “Attendance” mode, “Blended” mode and “Online” mode. This unit is offered in three modes to cater to the learning needs and preferences of a range of participants and maximise effective participation for isolated and/or marginalised groups.

Attendance Mode

In a weekly attendance mode, students will require face-to-face attendance in specific physical location/s. Students will have face-to-face interactions with lecturer(s) to further their achievement of the learning outcomes. This unit is structured with required upfront preparation before workshops, most students report that they spend an average of one hour preparing before the workshop and one or more hours after the workshop practicing and revising what was covered. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for you to prepare and revise.

Blended Mode

In a blended mode, students will require intermittent face-to-face attendance determined by the School. Students will have face-to-face interactions with lecturer(s) to further their achievement of the learning outcomes. This unit is structured with required upfront preparation before workshops. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for you to prepare and revise. 

Online Mode

In an online mode, students are given the opportunity to attend facilitated synchronous online seminar classes with other students and participate in the construction and synthesis of knowledge, while developing their knowledge. Students are required to participate in a series of online interactive workshops which include activities, knowledge checks, discussion and interactive sessions. This approach allows flexibility for students and facilitates learning and participation for students with a preference for virtual learning.

Assessment strategy and rationale

Assessments are used primarily to foster learning. ACU adopts a constructivist approach to learning which seeks alignment between the fundamental purpose of each unit, the learning outcomes, teaching and learning strategy, assessment and the learning environment. In order to pass this unit, students are required to achieve an overall score of at least 50% and attempt all assessment items. Using constructive alignment, the assessment tasks are designed for students to demonstrate their achievement of each learning outcome. 

Assessments are the same regardless of whether teaching mode is attendance, blended, or online. This is indicated in overview of assessment table below.

Overview of assessments

Representative texts and references

BEUTEL, J., LIST, S. & VON SCHWEINITZ, G. 2019. Does machine learning help us predict banking crises? Journal of financial stability, 45, 100693.

CARMONA, P., CLIMENT, F. & MOMPARLER, A. 2019. Predicting failure in the U.S. banking sector: An extreme gradient boosting approach. International review of economics & finance, 61, 304-323.

GHODDUSI, H., CREAMER, G. G. & RAFIZADEH, N. 2019. Machine learning in energy economics and finance: A review. Energy Economics, 81, 709-727.

GOGAS, P. & PAPADIMITRIOU, T. 2021. Machine Learning in Economics and Finance. Computational economics, 57, 1-4.

GUIDA, T. 2019. Big Data and Machine Learning in Quantitative Investment, Newark, UNITED KINGDOM, John Wiley & Sons, Incorporated. Available from: ProQuest Ebook Central.

HULL, J. 2020. Machine learning and finance. Journal of Risk Management in Financial Institutions, 13, 104-105.

JAGTIANI, J. & LEMIEUX, C. 2019. The roles of alternative data and machine learning in fintech lending: Evidence from the Lending Club consumer platform. Financial management, 48, 1009-1029.

LOPEZ DE PRADO, M. 2018. Advances in Financial Machine Learning, Newark, UNITED STATES, John Wiley & Sons, Incorporated.

POLYZOS, S., SAMITAS, A. & KATSAITI, M.-S. 2020. Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability. International review of financial analysis, 72.

SARLIN, P. & BJÖRK, K.-M. 2017. Machine learning in finance—Guest editorial. Neurocomputing, 264, 1.

YU, L., HUANG, X. & YIN, H. 2020. Can machine learning paradigm improve attribute noise problem in credit risk classification? International review of economics & finance, 70, 440-455.