3 – Jonathan Larkin – What Is A Quant

In his book, The Quants, Scott Patterson really hit the nail on the head. He described Quants as those searching for the truth. The truth being the universal underlying hidden dynamics in the markets and that has always captivated me. Quants build computational models of the world. Specifically, that could be about financial instruments or markets, and Quants apply the scientific method to finance. You’ll find Quants working across the entire financial industry these days. Most people associate the job of a Quant with very well known quantitative hedge funds and it is certainly true that Quants work in that type of organization, but Quants also work in commercial banks, proprietary trading firms, asset management firms, data vendor firms, and they also work across functions. You’ll find Quants working in many aspects of a investment organization. You’ll find Quants in the research function. Those are Quants looking for predictive signals and data that can be applied downstream to a portfolio. You’ll find Quants working in risk management trying to predict the risk of a particular portfolio or the risk firm wide. You’ll find Quants working in portfolio construction, combining the signals and models produced by other Quants. You’ll also find Quants working in data vendors, data vendors who are preparing unstructured data, then to sell to the financial industry. One of the reasons that I’m so fascinated with this field is that there really is no typical day. That being said, the work product of a Quant is to create a model. That could be a model of financial time series or it could be the model of a portfolio or firm-wide risk, or it could be a model to reduce unstructured data to structured form for further use in the investment process. One thing that makes this field fascinating is that things are always changing. There’s new developments in markets. There are new datasets. There are new computational techniques. You always need to keep learning and you always need to continually up your game in order to compete. People who have achieved substantial success in other scientific disciplines are often humbled when they come to quantitative finance. The reason for this is that signals in financial data are very, very faint, and you need to combine not just a scientific approach, but you need to combine that with understanding of markets, intuition of markets, and domain knowledge about markets. For example, take Isaac Newton who experienced significant stock trading losses in his personal account and is quoted as saying “I can predict the movements of the heavens, but I cannot predict the madness of people.”

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