One of the most exciting developments in investing in the last couple of years is the convergence of the quants and fundamental processes. This is called quantamental investing in the popular press, as a combination of the words quant and fundamental. But don’t let that goofy name fool you. It’s more than just a fad or a marketing term. This quantamental convergence of fundamental and quant is significant and growing. The idea is that the advent of AI methods, and the exponential growth of data generated by the real economy are now allowing quants to use inputs that have historically been exclusively in the domain of fundamental investors. On the other hand, the growing acceptance of the value of data science, and the observable successive quants, has led fundamental investors to embrace data-driven research methods and some aspects of the quant workflow, like the use of risk models. This is an exciting time. Let me give you a concrete example of this convergence. A part of the investment process that has been historically solely in the domain of fundamental researchers is text analysis, listening to conference calls, reading corporate filings, news, et cetera. There are now effective computational methods to get intelligence from this text information in a systematic way. Note that in term two, we will cover traditional natural language processing techniques, as well as deep learning for NLP. I’m sure you are very excited about that. But please be patient, let’s finish term one first.