WQU Master in Financial Engineering

This page is my ongoing learning log. I will keep adding course updates, assignments, and reflections.

This is my first Master course i am taken since graduation from NTU in 2022 :D

The purpose is to learn the fundamentals of financial engineering and to prepare for my future career in HFT
Will update this page with my learning updates, assignments, and reflections
Bare with me throughout the upcoming journey!

Modules

This the first module of the course. It is a foundation module that covers the basics of financial markets and financial instruments. The grade components is quite simple with 7 Quizzes at the end of each week, 2 CRT assignments and 1 Final Quiz. The course is quite informative-heavy as together with the lecture notes, there are also multiple required readings in multiple formats like paper, videos or articles. Tbh, I am not remembering all of those contents completely but I do feel like I have a better understanding of the financial markets and financial instruments. The course is culminated with a quiz which I score 89%.

Week 1 Credit Risk and Financing

For this first week of the course, I learn about concepts of Time Value of Money, Credit Risk, Shorting as well as basics of Bonds just to name a few.

There is a quiz at the end of the week which i score 95%

Week 2 Return and Volatility

For this 2nd week of the course, I learn about concepts of Stock, its comparison with bonds and using Gordon Growth Model to evaluate the stock portfolio value. The basic in Blockchain and CryptoCurrency is also introduced in this week. It is interesting to see that there are some research papers discussing about variables consider to evaluate Bitcoin price.

Beside the quiz at the end of the week which i score 100%, I also have the first CRT assignment to compare between different types of investments like Certificate of Deposit(CD), Stocks, Bonds, etc. The CRT result is released aft 2 weeks and I score 96% for the assignment and 100% for the review.

Week 3 Correlation

For this 3rd week of the course, I learnt about measuring the performance of a portfolio with various type of securities. The concept of Correlation is also introduced to see the relationship between two assets. The less correlated the assets are, the more diversified the portfolio is which helps manage the risk better. Exchange Traded Funds (ETF) is also introduced as a type of investment to consider for investors. At the end of the week, I score 95% for the quiz.

Week 4 Leverage and NonLinearity

For this 4th week of the course, I learnt about the concept of Derivatives and as well as an exemplary of derivation financial product called Options. The concept of Leverage is also introduced. Some basic Option trading strategies like Straddle Trading, Hedging with Options, Arbitraging and Spread Trading. The Home Equity also be discussed in the lens of an Option. At the end of the week, I score 95% for the quiz.

Week 5 Liquidity and Regulation

For this 5th week of the course, I learnt about the concept of Securitization and an example of securitization called Mortgage-Backed Securities (MBS). Additionally, 5C's criteria also introduced for evaluating the creditworthiness of a borrower. Some formula introduce to link between the Loss Given Default (LGD) and the Recovery Rate (RR). and Potential Sale Prices of the collateral. Multiple nice required readings are given about MBS, Dodd-Frank Act and different Bonds type. At the end of the week, I score 88% for the quiz which is not good enough for me.

Week 6 Model Failure and Crises

For this 6th week of the course, I learnt about the concept of Balance Sheet as well as diving deep into the Fundamental Accounting Equation with the example of a bank. Housing Development also discussed with multiple financing options like Bond, Stock, REIT, etc. Multiple ways to pick a Stock like Fundamental Analysis, Technical Analysis, etc are also discussed together with multiple ratio like P/E, etc to evaluate the stock performance. At the end of the week, I score 100% for the quiz.

Week 7 Liquidity and Regulation

At this final week, the knowledge from the previous weeks are summarized together with some ethics discussions. At the end of the week, I score 100% for the quiz.

This is the second module of the course. Will update this card along the way as I progress through the course.

Week 1 - Fixed Income Data

In the 1st week of the course, an introduction to multiple data type like structured data, unstructured data, semi-structured data, etc is given together with the importance of timezone and different data descriptors like Tickers, ISIN, Cusip. The government bond yield curve is introduced with the example of US Treasury Yield Curve with analyze of Bull/Bear Market and Flattening/Steepening yield curve. Two fitting models are introduced to fit the yield curve: Nelson-Siegel and Spline Interpolation. Similar with the 1st module, there is a quiz at the end of the week with an additional Graded Code about the yield curve shape prediction with Pandas and Matplotlib. The Python assignment is really helpful to understand the Principal Component Analysis (PCA) and basic model fitting. I score 100% for both of the assignments.

Week 2 - Equities and CryptoCurrencies

To be honest, this week content is quite algebraic and statistical heavy. Multiple concepts of matrix like Covariance, Correlation, Eigen(Values/Vectors), Cholesky Decomposition are introduced. Different matrix types like Symmetry Metrics, Positive Definite Metrics, Semi-Definite Metrics are also introduced. Similar with the 1st week, there is a quiz at the end of the week with an additional Graded Code related to analyzing multiple aspects of the stocks like price, volume, sentiment, etc. The Graded Code assignment provide a good opportunity to see how Cholesky Decomposition helps to generate random correlated data as well as introducing multiple new metrics to evaluate the contribution effect of each stocks in portfolio to the overall portfolio performance. I score 100% for both of the assignments.

Week 3 - Working with Portfolios and Tick Data

This week reviews portfolio variance from the previous Financial Markets course, with additional hands-on calculations in Jupyter Notebook. Several metrics are introduced — Sharpe Ratio, Treynor Ratio, and Jensen's Alpha with the CAPM model — to measure how a portfolio outperforms a risk-free investment relative to market movements. Tick data is also introduced as the smallest unit of asset price movement, along with readings on cleaning high-frequency data. The week culminates with linear algebra on Singular Value Decomposition (SVD) A = UΣV^T, which breaks a matrix into three components; the middle matrix carries information about the singular values. As in Weeks 1 and 2, there is an end-of-week quiz plus a graded Python assignment on SVD — comparing storage use against the economical version and PCA explained variance. The optional benchmarking exercise looks interesting; I will revisit it when I have time :) I scored 100% on both assignments.

Week 4 - Alternative Data

This week reviews different similarities measurements like Cosine Similarity, Jaccard Similarity as well as multiple distance measurements like Euclidean Distance, Manhattan Distance, Minkowski Distance. TF-IDF is also introduced to measure the how important a word is in a document given a corpus of documents. LSA with SVD is also introduced to reduce the dimensionality of the data and to find the latent topics of the documents. Multiple readings about using social media data like Twitter, Reddit and PyTrends also introduced for analyzing the sentiment of the market. I scored 100% on the quiz and To Be Updated on the Collaborative Review Assignment.