LUMS Inaugural Lecture: Professor Sandra Nolte

Thu Oct 31 2024 at 04:00 pm to 05:00 pm UTC+00:00

Online | Online

Lancaster University Management School
Publisher/HostLancaster University Management School
LUMS Inaugural Lecture: Professor Sandra Nolte
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Lancaster University Management School invites you to celebrate the careers of our newest Professors.
About this Event

About this event

Lancaster University Management School is excited to invite you to our Inaugural Lecture Series, a special set of public lectures that celebrates the careers of our newest Professors. Each lecture will feature a brief presentation by one of our Professors, offering an overview of their research and a glimpse into their personal academic journey.

The lectures will be held online throughout the academic year, allowing us to make them accessible to a broad audience, including staff, students, and the wider public. Each session will last approximately 60 minutes, including time for both the presentation and a Q&A session.

An inaugural lecture is a distinguished academic event, marking the promotion of a colleague to professorship—a significant milestone in their career. These events provide a unique opportunity for our newly appointed Professors to share their pioneering research, highlight their achievements in research, innovation, engagement, and teaching, and showcase the societal impact of their work.

The series is free of charge and open to everyone. Whether you are an expert in the field or simply curious, we warmly welcome all staff, students, their guests, and members of the local community to join us in celebrating these academic milestones.

About the speaker

Professor Nolte's research interests lie in the areas of empirical finance, applied econometrics and behavioral finance. She focuses on the analysis of individual trading behaviour, the relationship between order flows and price changes, and the information content of high-frequency sentiment indicators. She is also interested in the analysis of qualitative survey data applied to problems of nonlinear forecasting methods in the presence of misclassification, the learning behaviour of individual forecasters, their degree of rationality and their reaction to news sentiment.

Please note you will be sent a TEAMS link to join the session following registration.

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