My PhD thesis was completed in 2018 and was based on applying time series clustering techniques to fixed-income investments (corporate bonds). My hypothesis was that if one could effectively cluster bonds based on how they reacted to changes in the macro-economic and financial environment that it could be possible to use the results of these clusters as the basis of a portfolio management methodology which would be superior to the portfolio management techniques currently in use (which are largely based on diversification across a variety of dimensions such as industry and geography.
In its essence, my hypothesis is that different financial investments react differently to changes in the macro-economic or financial environment, which I modeled with the VIX index (a measure of implied volatility of S&P 500 stock options) and the TED Spread (an indicator of the perceived health of the banking system). In different ways, both of these indices are believed to be reflective of the level of risk (or volatility or fear) in the global macro financial systems. My belief is that some “clusters” of investments do badly in times of high risk and others do well due to a “flight to quality” effect and that if a portfolio manager can base his portfolio selections to diversify according to these clusters that the resulting portfolio will exhibit superior performance based on traditional portfolio metrics of maximizing return while minimizing variance (risk).
Primary outputs of my thesis included the following:
- A high-level overview/literature survey of the field of time series clustering (Chapter 2 of thesis)
- An extensive Matlab-based programming environment to implement and evaluate various clustering approaches (Chapters 3 and 4 of thesis)
- The primary theoretical contribution of my thesis was an extension of model-based clustering approaches to allow the specification of candidate generative models using state space and Kalman filtering techniques (thesis chapter 5.3 and my 2018 paper published for the AETA conference.
I would like to extend my thesis work in the following areas:
- Expand Chapter 2 of my thesis into either a survey paper or a tutorial paper and submit it for publication to the ACM Computing Surveys journal.
- Update my Matlab-based evaluation system to be more general purpose and more robust, perhaps re-coding it in Python or R. When complete, I would like to publish this evaluation system for use by other researchers.
- Refresh and improve the data used for evaluating various clustering approaches. The current dataset contains US bond price histories for all US issues from approximately 2002 through 2010. I would like to refresh this data and to consider adding additional explanatory time series data and additional meta-data describing the individual bond issuers and issues.
- The initial results of a new clustering algorithm developed under this thesis appeared to be promising (Chapter 6 of thesis). I would like to continue to refine and evaluate this modeling approach to determine if it can indeed yield superior results in the composition and management of investment portfolios.