In this crash course we will cover the core concepts behind quantitative portfolio management. For some sections I have attached sources for further study. These are not required, but useful if you want to expand your knowledge on a specific topic. In the future, I will create more crash courses on foundational quant topics.
This post will be paywalled in 2 weeks
Why Active Portfolio Management?
Active portfolio management is the art and science of generating investment alpha. Beta is a benchmark stream of investment returns often sourced through composite index-tracking ETFs.
To generate alpha is to:
Form hypotheses about information not reflected in asset prices
Incorporate that information into assets through the act of buying or selling
Profit when (and if) markets validate your hypothesis
The existence of active portfolio managers (hedge funds, asset managers, proprietary traders, family offices) is a fundamental consequence of markets. Let us start with the assumption that there are no active managers. The following market states arise:
Markets are inefficient in the absence of rational actors
These inefficiencies attract rational actors to manage portfolios
Inefficiencies become increasingly scarce pushing some rational actors out of the market
New inefficiencies emerge from this exodus
A fresh wave of rational actors enter the market
The efficient market hypothesis claims all available information is reflected in asset prices at all times. Given such assumptions, alpha is impossible to generate.
It’s safe to say that few people are able to generate alpha, and even fewer can generate alpha for years and decades.
Why Quantitative Portfolio Management?
I believe (as do many quants) that alphas can be generated by carefully applying tools from mathematics, applied science, and programming to uncover hidden phenomena within markets.
Markets adapt, and yesterday’s alpha quickly becomes today’s beta. There are hoards of intelligent, ambitious, and capitally equipped players who want to find alphas before you do. Yet, honing skills in information discovery, analysis, and execution have proven to be promising methods for generating competitive advantage.
The task of portfolio management is more complex than finding alpha. It entails managing risk, attributing returns, battling transaction costs, and executing optimally. The synthesis of these problems formulate the task of quantitative portfolio management.
Mathematics, applied science, and programming are the bread and butter of a quant. These are the tools he/she ruthlessly hones in order to see what others do not, and trade in ways others cannot. The quant is a mental athlete battling within an arena of randomness.