Workshop on Risk Management and Predictive Analytics in Financial and Actuarial Mathematics, January 2025

This workshop, supported by the London Mathematical Society, aims to bring together researchers at all career stages who are interested in finance and actuarial mathematics. A particular focus will be on sentiment analysis with applications in risk assessment, financial modelling in jump detection, and predictive analytics in coherent multipopulation mortality modelling. We recognise the value of diversity in our discipline and welcome all attendees, with a special invitation to PhD students. The event is hybrid, and you can join online with Meeting ID: 322 126 705 504 (Passcode: Jb3MH6i4).

Speakers

• Jia Shao (University of Birmingham)
• Paresh Date (Brunel University London)
• Bo Wang (University of Leicester)
• Jing Chen (Cardiff University)

Schedule

13:30 - 14:15, Jia Shao, Leveraging Sentiment Analysis to Improve Customer Satisfaction in UK Banks
14:15 - 15:00, Paresh Date, ESG news-enhanced volatility prediction
15:00 - 15:30, Coffee Break
15:30 - 16:15, Bo Wang, Multipopulation mortality modelling and forecasting: the weighted multivariate functional principal component approaches
16:15 - 17:00, Maggie Chen, A new method for jump detection: analysis of jumps in S&P 500 financial index
17:00 – 18:00, Social Reception to celebrate the lectureship

Abstracts

Leveraging sentiment analysis via text mining to improve customer satisfaction in UK banks (Shao)

Abstract: This study examines the role of online customer reviews through text mining and
sentiment analysis to improve customer satisfaction across various services within the UK
banking sector. Additionally, the study analyses sentiment trends over a five-year period.
Both positive and negative sentiments provide valuable insights. The results indicate a high
prevalence of negative sentiments related to customer service and communication, with
HSBC and Santander receiving 90.8% and 89.7% negative feedback, respectively, compared
to Tesco Bank’s 66.8%. Key areas for improvement include HSBC’s credit card services and
call center efficiency, which experienced increased negative feedback during the COVID-19
pandemic. The findings also demonstrate that DistilBERT excelled in categorizing reviews,
while the SVM model, when combined with customer ratings, achieved 96% accuracy in
sentiment analysis. Online customer reviews become more informative when categorised by
service sector. To enhance customer satisfaction, bank managers should pay attention to both
positive and negative reviews, and track trends over time.

ESG news-enhanced volatility prediction (Date)

Abstract: We study predictive ability of news for stock price crashes for a set of governance
failure events across world markets, over the past 15 years. From a database which provides a
number of open-source news items daily for each company tagged as positive, negative or
neutral, we construct a simple and easily explainable extension of GARCH model which uses
positive and negative news sentiment as exogenous inputs. We demonstrate that this
significantly enhances ability to predict an increase in volatility due to governance failure at a
company. The broad objectives of the work, which is still ongoing, are (i)  to provide hard
quantitative evidence for good corporate behaviour (as evidenced in a large number of
positive news items) generally leading to good stock price performance (as evidenced by
'buy' or related investment signal); (ii) to combine E, S and G sentiment scores for
standardised ESG reporting, with a peer-reviewed and open source methodology.

Coherent multipopulation mortality modelling and forecasting: a multivariate functional principal component approach (Wang)

Abstract: Human mortality patterns and trajectories in closely related populations are likely
linked together and share similarities. It is always desirable to model them simultaneously
while taking their heterogeneity into account. When a mortality model is applied to each
population separately, they tend to result in divergent forecasts of life expectancy in the long
term. We introduce a method for joint and coherent mortality modelling and forecasting of
multiple subpopulations using the multivariate functional principal component analysis
techniques, which ensures the non-divergent forecasting in the long run when several
subpopulation groups have similar socio-economic conditions or common biological
characteristics. We demonstrate the proposed methods by using sex-specific mortality data,
and the forecast performances are compared with several existing models, including the
independent functional data model and the Product-Ratio model.

A new method for jump detection: analysis of jumps in S&P 500 financial index (Chen)

Abstract: Jump detection methods in finance have evolved over the past few decades due to
growing empirical evidence of their impact on financial returns and volatility. Also, the
frequency of jump occurrence has increased with the advent of high-frequency finance. Risk
assessment, portfolio re-balancing, flash crashes and other ramifications may be triggered by
jumps. Most methods of jump detection treat a jump as a singular, random and isolated shock
event standing out from the rest of the time series. They were not designed to capture series
of jumps that relate to contagious behaviour, in which the occurrence of jumps increases the
probability of further jumps soon after, with jumps tending to occur in clusters. We advance
the median method of jump detection that is designed to work well in contagious situations,
accounting accurately for both singular jumps and consecutive jumps. This judges the size of
individual returns with a measure of local volatility based on running medians of absolute
returns. We use this method to study the S&P 500 financial index, and compare its
performance with well-known existing jump-detection methods.

Participation

Participation is open to all university-level staff and students. All participants of the workshop are expected to adhere to the School of Mathematics Code of Conduct. The School of Mathematics has established a programme to offer funded child-care services to visiting researchers. To take advantage of this opportunity, it is recommended that you contact the organiser at the earliest convenience to ensure proper arrangements are made.

Funding

We acknowledge funding from the London Mathematical Society via Celebrating New Appointments (Scheme 9), and School of Mathematics, University of Birmingham.