Periklis Brakatsoulas M.Sc.


Position: Ph.D. Candidate
Field of interest: Behavioral Economics, Econometrics, Asset Pricing, Algorithmic Trading
Membership: Macroeconomics and Econometrics, PhD Candidates


Email: 41517993 [AT] fsv [DOT] cuni [DOT] cz
Phone: +420 702 154 480
Personal web pages:

More information

PhD study

Tutor: PhDr. Jiří Kukačka Ph.D.

Studying from: 2018
PhDr examination:
Final exam:
Dissertation Proposal defence:
Dissertation defence:

Current work:

Dissertation topic:
Filtering Momentum Life Cycles, Price Acceleration Signals and Trend Reversals for Stocks, Credit Derivatives and Bonds

Disertation abstract:
Recent empirical research shows a growing interest in investment decision-making under market anomalies that contradict the rational paradigm. Momentum is undoubtedly one of the most robust anomalies in the empirical asset pricing research and remains surprisingly lucrative ever since first documented. Although predominantly phenomena identified across equities, momentum premia are now evident across various asset classes. Yet few many attempts are made so far to provide traders a diversified portfolio of strategies across different assets and markets. Moreover, literature focuses on patterns from past returns rather than mechanisms to signal future price directions prior momentum runs. The aim of this paper is to develop a diversified portfolio approach to price distortion signals using daily position data on stocks, credit derivatives and bonds. An algorithm allocates assets periodically and new investment tactics take over upon price momentum signals and across different ranking groups. We focus on momentum life cycles, trend reversals and price acceleration signals. The main effort here concentrates on the density, time span and maturity of momentum phenomena to identify consistent patterns over time and measure the predictive power of buy-sell signals generated by these anomalies. To tackle this, we propose a two-stage modelling process. First, we generate forecasts on core macroeconomic drivers. Secondly, satellite models generate market risk forecasts using the core driver projections generated at the first stage as input. Moreover, using a combination of the ARFIMA and FIGARCH models, we examine the dependence of consecutive observations across time and portfolio assets since long memory behavior in volatilities of one market appears to trigger persistent volatility patterns across other markets. We believe that this is the first work that employs evidence of volatility transmissions among derivatives, equities and bonds to identify momentum life cycle patterns.

Optional courses:



Sep 12-Mar 14: MSC Behavioral Economics, at Nottingham University, School of Economics - London, UK
Sep 11-Sep 12: MSC Applied Economics, at University of Thessaly, Economics Department - Volos, Greece
Sep 03-Sep 07: BSC History, at Democritus University of Thrace, Department of Social Sciences - Komotini, Greece

Job history

Jul 17-Present: ECONOMIST at Moody’s Analytics - Prague, Czech Republic
Jul 15–Jul 17: ECONOMETRICIAN at TESLA (Europe) Ltd - London, UK
Nov 14-May 15: JUNIOR ANALYST at Athena Infonomics - Chennai, India
Apr 14-Sep 14: TRAINEE DATA ANALYST at United Nations Development Programme - Addis Ababa, Ethiopia

Topics for supervision


CV 2019


McKinsey & Company
Moneta Money Bank