Mathematics and Programming for Finance

Singapore University of Social Sciences

Mathematics and Programming for Finance (FIN311)

Synopsis

A finance graduate will not only be challenged with the analysis and interpretation of immense amounts of financial data and appreciation of financial information, he/she will also be requested to make sense of mathematical models used in both academic and industrial settingsin the areas of risk management as well as trading and investment analyses. FinTech adds a further dimension to the current new requirements.FIN311 Mathematics and Programming for Finance aims to equip the finance graduates with suitable mathematical and programming skills to measure data, to read, understand, apply and implement models, so as to provide solutions to problems that arise from both traditionalfinance as well as FinTech.

Level: 3
Credit Units: 5
Presentation Pattern: Every semester

Topics

Data and Measurements
Data Patterns and Modality
Statistical Models
Game Theoretic Models
Programming with Python
Programming with R
Data Structures
Analysis of Complexity of Algorithms
Symmetric and Asymmetric Cryptography
Digital Signatures and Hash Functions
Achieving Consensus on Blockchains
Blockchain Stability under Attacks

Learning Outcome

Formulate models to understand complex reality in the abstract data.
Compute with models either to calibrate them to data or to draw conclusions from them.
Develop computer programs to implement the models.
Design data structures to extract and store information for applications.
Appraise the efficiency and complexity of data structures.
Examine the privacy and authenticity of data in cryptographic protocols.
Compute with random and hash functions and understand their significance in applications.
Analyse interactions on blockchains in game theory settings.
Calculate probabilities, utilities and other values in cryptocurrency systems and appreciate their security significance.
Use a computing tool (e.g. Excel/Google Spreadsheets, Python, R) for financial calculations.
Experiment with a financial information system (e.g. Reuters Eikon or the Internet) for obtaining market data and information as well as harnessing well-documented API/library/models to make inferencing more expedient. Handle data that arise from cryptocurrency systems.