Quant Simplified
Medium Article Series
The Quant Simplified series aims to introduce quantitative finance concepts to the general public and aspiring financial engineers. These topics have emerged from my personal experiences, particularly during the initial stages of my career as a quant. I often found it difficult to understand some concepts in the beginning, and it didn't help that many of the terminologies sound extremely sophisticated and intimidating. My objective is to break down intricate ideas into easily comprehensible and relatable insights, which I convey through engaging storytelling, to help beginners to overcome challenges I had faced in the past.
Series: (Navigate images on the right for article summaries)
1. The many facets of Risk
2. “Stochastic” is a difficult word
3. Searching for the roots
4. When the boat rocks: trading during an index rebalancing
5. Our obsession with the future
6. Lessons learned from being the "dumbest" person in the room
Appendices (GitHub)
This article offers a recap to the concept of risk and volatility, and how the two relate. It also covers 'risk' across different topics in finance, from portfolio management, regulations, to option trading.
This article introduces the concept of stochastic processes and how to asset prices can be modelled as a stochastic process. It explains the importance of modeling asset price as a stochastic process, and how this is then applied to option pricing.
This article summarizes the different basic root searching algorithms, i.e. the bisection method, Newton-Raphson method and the Secant method using relatable analogies. It also highlights the important role of these root searching algorithms in quantitative finance applications.
This article summarizes a study on stocks impacted by index rebalancing (additions and deletions), comparing returns before and after the index rebalancing date. The phenomenon observed may be explained using behavioral finance heuristics.
This article explores an often overlooked concept of futures pricing, addresses some of the applications of futures prices and a common misconception. It offers a simple explanation to the key drivers in the forward curve structure.
This is a personal reflection of my experience being a quantitative analyst for a year. To summarize, the key of being a quantitative analyst isn't just being good at the quantitative work. Staying humble, being open to communicate, and always being conscientious of model limitation are key to being a good quant.
