Dr Ma, Shuai (Martin) PhD, Computational Finance Centre for Computational Finance and Economic Agents (CCFEA)

Martin started his part-time PhD at the Centre for Computational Finance & Economic Agents, University of Essex in 2015. He was part of the Directional Changes Project. In his PhD research, he attempted to track market dynamics using Directional Change based indicators.

He passed his PhD in March 2022. Martin was examined by Dr Raju Chinthalapati (External Examiner, Goldsmiths, University of London) and Dr Michael Kampouridis (Internal Examiner).

It's been a long journey, but Martin did it while he was on a demanding investment bank job (Everbright Securities Co. Ltd, China), swirling hundreds of million dollars around China. He even had time to organize a glorious wedding which featured a horse-pulled carriage through the High Street of Colchester (no, it is not an everyday scene)!

The title of Martin's PhD thesis is "Tracking and Nowcasting Directional Changes in the Forex Market". Martin pioneered research in Nowcasting Directional Change: Directional changes are only confirmed in hindsight. The question is whether it is possible to recognize that a trend has ended in the market before directional change is confirmed. By using a novel indicators (which was first discovered by Dr Raju, unpublished), Martin demonstrated that Nowcasting directional change is possible.

Abstract, PhD thesis

Price changes in financial markets are typically summarized as time series (TS). Directional Change (DC) is an alternative, data-driven way to sample data points. The main objective of this thesis is to find new ways to extract new, useful information from the market. This is broken down into three directions: (1) to summarize price changes with DC, one must first determine the threshold to be used. We ask: could a threshold be too big or too small? If so, how could we determine the range of usable thresholds? (2) Could DC indicators extract volatility information from the market that is not observable under TS? (3) In DC, the start of a new trend is only confirmed in hindsight – to be precise, at the DC Confirmation (DCC) point when the price has reversed by the threshold specified. Could we detect that a new trend has begun before the DCC point? This is known as a nowcasting problem.

This thesis has made three contributions. Firstly, we have created a guideline to determine the range of useable thresholds under DC. This supports the research that follows. Secondly, we have demonstrated how DC indicators could complement TS in tracking the market for volatility information. Thirdly, we have introduced new DC indicators; by using these indicators, we have proposed an algorithm and demonstrated how it could help us nowcast whether a new trend has begun in DC.