We are extremely excited to be going back to in-person seminars—with our first on-site event since 2020!—with the first of Quantess’ roster of events for the upcoming academic year. Dr. Lavinia Rognone (AMBS) will be speaking about financial noise-timing strategy at BlackRock.

Please come and hear this fascinating talk and network with some of our colleagues afterwards. Thank you so much to BlackRock for hosting us. (We’ll update details of the room address on the day when people check in.)* We are happy to be back!

** Please note that there are limited spaces!

Title: “The economic value of financial noise timing”

Abstract: We propose a dynamic noise-timing strategy which exploits the temporary dependence in noise traders’ beliefs. Decomposing prices of the portfolio assets (stocks, bonds, gold, and cryptocurrencies) into permanent and noise components using a Kalman filter, we assess the economic value of a dynamic investment strategy which times the noise component. Our results show that investors would be willing to pay a positive annual performance fee to switch from an ex-ante static and from a volatility-timing strategies to a noise-timing strategy. Our findings are robust to comparisons with other benchmark strategies and different periods of heightened volatility, including the COVID-19 period. This paper is co-authored (in order) with Sarah S. Zhang, Stuart Hyde, and Ying Chen.

Speaker Bio

Lavinia Rognone is a Research Associate (Postdoc) in Financial Technologies (FinTech) at Alliance Manchester Business School (AMBS), University of Manchester. Her research explores the implications that changes in climate have on financial markets and how financial innovations affect the economy as we know. She holds a Ph.D. in Finance from AMBS under the supervision of Prof Stuart Hyde and Dr S. Sarah Zhang and was a visiting researcher at the National University of Singapore, hosted by the department of Mathematics and the Risk Management Institute.

As external positions, she served as an Economist at the European Central Bank (ECB) in the Monetary Policy Division and joined as an Expert the Climate Change ECB group. Beside the policy-related work, she mainly carried academic research on a climate finance paper with machine learning and text-analysis applications and built the textual analysis infrastructure now used by the ECB division. More importantly, she designed and constructed two climate risk indicators for physical and transition risks which find applications to both risk and portfolio management issues, among others, being of interest for researchers, practitioners, and policy makers and currently used by the ECB and other National Banks for policy studies, as well as for research by academics.