Application of Control Theory in Quantitative Finance?

Question

I have recently completed an MSc in Control Systems from a top university. It seems to me that control theory must have an application within quantitative finance. I would like to apply my degree within finance, but I want to be sure that it is relevant to the role of a quantitative analyst. The topics which I have particular interest and experience in are State Space Control, Systems Identification, Model Predictive Control and Optimal Control. I imagine that effective management of finances must involve modelling of financial systems in terms of transfer functions/state space models (based on large sets of historical data). These models could then be used to predict the evolution of a market over time, and therefore optimise a given cost function such as profit, risk etc. If this kind of role exists within quantitative finance/ other areas, can you please give me more information/ ideas of job roles/ industries to research.

Answer 1

Yes, control theory has several applications in quantitative finance. Your strongest overlap would probably be in areas such as algorithmic trading, portfolio optimization, risk management, and optimal trade execution.

State-space models and Kalman filters can be used to estimate variables that cannot be directly observed, while system identification has similarities to estimating financial models from historical data. Optimal control and dynamic programming are also used for problems where a trader or portfolio manager must make a sequence of decisions while balancing return, risk, and transaction costs.

One important difference is that financial markets are much noisier and less stable than most physical control systems, so stochastic models are generally more useful than traditional deterministic transfer-function models.

For job searches, I would look at quantitative researcher, quantitative analyst, algorithmic trading, portfolio optimization, and optimal execution roles. Your control-systems background should be relevant, particularly if supplemented with probability, statistics, stochastic calculus, and programming.