RiskLab - Q Group Seminar [Melbourne]: Pairs Trading, Technical Analysis and Data Snooping

20 Mar 2019

Dr Ioannis Psaradellis, Lecturer in Finance at the University of St. Andrews, UK


Data61 Demonstration Lab
710 Collins Street, Docklands, Melbourne

Dear Q Group members,

You are invited to attend a seminar “Pairs Trading, Technical Analysis and Data Snooping: Mean Reversion vs Momentum” given by Dr Ioannis Psaradellis, at 5pm Wednesday 20 March 2019, at Data61 Demonstration Lab (710 Collins Street, Docklands) in Melbourne.


Please use the following link for RSVP


Agenda: Wednesday 20 March 2019

5:00pm Pre-drinks and food

5:30pm Seminar starts

6:30pm Networking


Data61 Demonstration Lab, 710 Collins Street, Docklands, a short walk from Southern Cross Station

Seminar Title: Pairs Trading, Technical Analysis and Data Snooping: Mean Reversion vs Momentum

Speaker: Dr Ioannis Psaradellis, Lecturer in Finance at the University of St. Andrews, UK

Abstract In this presentation, we utilize a sophisticated universe of 18,410 technical trading rules (TTRs) and adopt a technique that controls for “false discoveries” to evaluate the performance of statistical arbitrage investment strategies using daily data over 1990-2016 for frequently traded pairs. Hence, for the first time, the paper applies an excessive out-of-sample analysis in different subperiods across all TTRs examined. Short-term outperformance typically exceeds transaction-cost estimates, suggesting interim market inefficiencies. Strikingly, for commodity spreads, the evidence of significant predictability appears stronger. Finally, we reject the existence of a uniformly monotonic downward trend in the selection of outperforming TTRs over the years.


Ioannis holds a BSc in International and European Economics and an MSc in Financial Economics both from Athens University of Economics and Business. He also holds an MRes in Decision Making and a PhD in Quantitative Finance both from University of Liverpool, UK. He has a particular interest in exploring the predictability of quantitative methods in the empirical asset pricing context. He has worked on subjects of modelling and trading the implied volatility and on the identification of potential market price anomalies as those revealed by techniques actively implemented by hedge funds and investment banks (e.g. technical analysis, statistical arbitrage etc). His current work concentrates on revisiting multiple hypothesis frameworks adjusting for statistical biases (i.e., data snooping).


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