Greedy Online Classification of Persistent Market States
Using Realized Intraday Volatility Features
A webinar by
Tuesday, June 16, 2020 6:00pm EDT
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Abstract
In many financial applications it is important to classify time series data without any latency while maintaining persistence in the identified states. We propose a greedy online classifier that contem- poraneously determines which hidden state a new observation belongs to without the need to parse historical observations and without compromising persistence. Our classifier is based on the idea of clustering temporal features while explicitly penalizing jumps between states by a fixed-cost reg- ularization term that can be calibrated to achieve a desired level of persistence. Through a series of return simulations, we show that in most settings our new classifier remarkably obtains a higher accuracy than the correctly specified maximum likelihood estimator. We illustrate that the new classifier is more robust to misspecification and yields state sequences that are significantly more persistent both in and out of sample. We demonstrate how classification accuracy can be further improved by including features that are based on intraday data. Finally, we apply the new classifier to estimate persistent states of the S&P 500 index.
This is joint work with Peter Nystrup and Erik Lindstrom.
Biography
Petter Kolm is the Director of the Mathematics in Finance Master’s Program and Clinical Professor at the Courant Institute of Mathematical Sciences, New York University. Previously, Petter worked at Goldman Sachs Asset Management where his responsibilities included researching and developing new quantitative investment strategies. Petter has coauthored many articles and books on quantitative finance and financial data science and financial data science, serves on several editorial boards for academic journals, professional associations, and company advisory boards. He holds a Ph.D. in Mathematics from Yale, an M.Phil. in Applied Mathematics from the Royal Institute of Technology, and an M.S. in Mathematics from ETH Zurich.
Petter's work and research interests include alternative data, data science, econometrics, forecasting models, high frequency trading, machine learning, portfolio optimization w/ transaction costs and taxes, quantitative and systematic trading, risk management, robo-advisory and investing, smart beta strategies, transaction costs, and tax-aware investing.
Acknowledgments
Special thanks to the Fordham University Gabelli School of Business for hosting and sponsoring the seminar series this fall.
About the Series
The IAQF's Thalesians Seminar Series is a joint effort on the part of the IAQF (www.iaqf.org) and the Thalesians (www.thalesians.com). The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion.
