Financial Markets
Students are introduced to the markets, products, participants, and regulations of professional finance in this pilot course for the MScFE Program. We’ll talk about trading, financing, brokering, pricing, hedging, optimizing, and risk management, among other financial market operations. Students compile a list of essential factors that have an impact on the financial business throughout the semester. Web programs that demonstrate these ideas can be interacted with by students. Students will have a strong basis on which to create computational and mathematical methods to develop financial engineering models by having an understanding of the asset classes, activities, and significant elements of the financial environment. A financial background is not necessary.
Deep Learning for Finance
In Deep Learning for Finance, students will further investigate neural networks, directly expanding on their knowledge from Machine Learning. By using TensorFlow and Python, students will become experts in creating and training neural networks, which they can then use to analyze real-world financial scenarios. They’ll carry out regularization by adding more tools to their toolkits. In this course, students will learn how to adjust hyperparameters using a variety of algorithms, including stochastic, Bayesian, and classical approaches. We’ll talk about a variety of neural network topologies, including recurrent neural networks (RNNs), long short-term memory (LSTMs), convolutional neural networks (CNNs), and gated recurring units (GRUs). The construction of these neural networks from the ground up will be demonstrated using financial examples such stock price prediction, factor identification for investments, and back-testing trading techniques.
Portfolio Management
