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DTSTAMP:20241211T073400Z
DTSTART:20250120T133000Z
DTEND:20250120T180000Z
SUMMARY:Workshop on Risk Management and Predictive Analytics in Financial and Actuarial Mathematics, January 2025
UID:www.birmingham.ac.uk/211907
DESCRIPTION: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).\n
Speakers

Jia Shao (ºÚÁϳԹÏÍø)
Paresh Date (Brunel University London)
Bo Wang (University of Leicester)
Maggie Chen (Cardiff University)

Schedule
 13:30 - 14:15, Jia Shao, Leveraging sentiment analysis to improve customer satisfaction in UK banks\n 14:15 - 15:00, Paresh Date, ESG news-enhanced volatility prediction\n 15:00 - 15:30, Coffee Break\n 15:30 - 16:15, Bo Wang, Multipopulation mortality modelling and forecasting: the weighted multivariate functional principal component approaches\n 16:15 - 17:00, Maggie Chen, Can machine learning models better volatility forecasting? A combined method\n 17:00 - 18:00, Social reception to celebrate the lectureship\n
Abstracts
 Leveraging sentiment analysis via text mining to improve customer satisfaction in UK banks (Shao)\n
 Abstract: This study examines the role of online customer reviews through text mining and\n sentiment analysis to improve customer satisfaction across various services within the UK\n banking sector. Additionally, the study analyses sentiment trends over a five-year period.\n Both positive and negative sentiments provide valuable insights. The results indicate a high\n prevalence of negative sentiments related to customer service and communication, with\n HSBC and Santander receiving 90.8% and 89.7% negative feedback, respectively, compared\n to Tesco Bank’s 66.8%. Key areas for improvement include HSBC’s credit card services and\n call center efficiency, which experienced increased negative feedback during the COVID-19\n pandemic. The findings also demonstrate that DistilBERT excelled in categorizing reviews,\n while the SVM model, when combined with customer ratings, achieved 96% accuracy in\n sentiment analysis. Online customer reviews become more informative when categorised by\n service sector. To enhance customer satisfaction, bank managers should pay attention to both\n positive and negative reviews, and track trends over time.\n
 ESG news-enhanced volatility prediction (Date)\n
 Abstract: We study predictive ability of news for stock price crashes for a set of governance\n failure events across world markets, over the past 15 years. From a database which provides a\n number of open-source news items daily for each company tagged as positive, negative or\n neutral, we construct a simple and easily explainable extension of GARCH model which uses\n positive and negative news sentiment as exogenous inputs. We demonstrate that this\n significantly enhances ability to predict an increase in volatility due to governance failure at a\n company. The broad objectives of the work, which is still ongoing, are (i)  to provide hard\n quantitative evidence for good corporate behaviour (as evidenced in a large number of\n positive news items) generally leading to good stock price performance (as evidenced by\n 'buy' or related investment signal); (ii) to combine E, S and G sentiment scores for\n standardised ESG reporting, with a peer-reviewed and open source methodology.\n
 Coherent multipopulation mortality modelling and forecasting: a multivariate functional principal component approach (Wang)\n
 Abstract: Human mortality patterns and trajectories in closely related populations are likely\n linked together and share similarities. It is always desirable to model them simultaneously\n while taking their heterogeneity into account. When a mortality model is applied to each\n population separately, they tend to result in divergent forecasts of life expectancy in the long\n term. We introduce a method for joint and coherent mortality modelling and forecasting of\n multiple subpopulations using the multivariate functional principal component analysis\n techniques, which ensures the non-divergent forecasting in the long run when several\n subpopulation groups have similar socio-economic conditions or common biological\n characteristics. We demonstrate the proposed methods by using sex-specific mortality data,\n and the forecast performances are compared with several existing models, including the\n independent functional data model and the Product-Ratio model.\n
 Can machine learning models better volatility forecasting? A combined method (Chen)\n
 Abstract: Volatility forecasting for Bitcoin constantly gains attention due to the increased in- vestment interest and the high riskiness of cryptocurrencies. The traditional forecast- ing models, such as the GARCH family models, are widely adopted. However, there should be careful consideration whether whether they can capture extreme market shocks and associated extreme risk (e.g. contagion). Hence, we fit several GARCH models and the EGARCH shows the best goodness of fit. We further take its his- torical volatility observations for an automated forecasting solution,using the Long short-term memory (LSTM) neural network to take predictions. Our results show clear improvement in volatility forecasting regarding both the model’s in-sample and out-of-sample accuracy. More importantly, the LSTM can optimize information intake through the short- and long-memory states. Overall, our new LSTM neural network model is more robust in reflecting to market shocks and regime changes.\n
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.\n
Funding
 We acknowledge funding from the London Mathematical Society via Celebrating New Appointments (Scheme 9), and School of Mathematics, ºÚÁϳԹÏÍø.\n
LOCATION:B16, Watson Building (R15 on Campus Map)
STATUS:CONFIRMED
TRANSP:OPAQUE
CLASS:PUBLIC
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