About the Project
Developing and evaluating new and emerging artificial intelligence (AI) methods to extract useful information from unstructured datasets, namely earnings call transcripts.
University funded project
Fee waiver at Home/EU rate and annual stipend
*Whilst open to International candidates, please note that this scholarship covers Home/EU/RUK Fee rate only
All applications should be submitted by 31/07/2020. We advise applications to be submitted as soon as possible as we will start conducting Skype and/or Zoom interviews for shortlisted applicants immediately.
42 months from October 2020
Candidates are required to have:
• An excellent undergraduate degree with Honours in a relevant business, computing or social science subject
• A Master’s degree (or equivalent) will be strongly preferred
• Some exposure to programming languages such as R and/or Python
• Excellent analytical skills and a demonstrable aptitude to undertake research and develop into an independent researcher.
• Prior knowledge and/or willingness to learn advanced Data Science, Machine Learning and Artificial Intelligence approaches as well as the ability to conduct research in an interdisciplinary domain of Data Science and FinTech
• Candidates who are not native English speakers will be required to provide evidence for their English skills (such as by IELTS or similar tests that are approved by UKVI, or a degree completed in an English-speaking country).
Earnings calls have become an established medium through which firms can disseminate value-relevant information to the market, thus alleviating potential information asymmetry between managers and shareholders. Previous literature identifies the dialogue between managers and analysts during these calls as a potentially rich information source for investors (McKay Price et al., 2012) and, given that participants involved in these dialogues typically use natural language (Core, 2001), there exists great potential for us to gain a better understanding of the nature and content of earnings calls through the application of Natural Language Processing (NLP) techniques. Furthermore, through applied NLP it may be possible to identify changing themes within earnings calls over time.
Audio recordings of earnings calls also offer a dataset with which researchers can examine the extent to which the manner with which information is conveyed (e.g. pace, repetition, and hesitation) has a material impact upon the market response to earnings calls. Evidently, there is a great deal of scope in this project and as such we expect that the successful individual will possess an ability to identify and pursue potentially exciting avenues for research in this area.
In this collaborative project, you will be supervised by Dr James Bowden (Accounting & Finance) and Dr Yashar Moshfeghi (Computer Information Sciences). The cross-disciplinary nature of this funded PhD will allow the chosen individual to apply advanced NLP methods developed in Computer Science to the wealth of value-relevant company information that exists in unstructured formats. We expect that the finished work will be of interest to both academics and practitioners in the area of financial technology (Fintech).
Centre for Doctoral Training
The University of Strathclyde is delighted to announce the establishment of the Centre for Doctoral Training (CDT) in FinTech. The CDT aims to train the next generation of researchers who will design, develop and test novel, advanced and generalisable Artificial Intelligence and Data Science approaches and by doing so, will provide answers to substantial questions relating to the study of FinTech.
The CDT is a multidisciplinary endeavour bringing together faculty from Accounting & Finance (Dr James Bowden, Daniel Broby, Dr Devraj Basu, Dr Andrea Coulson, Prof Lesley Walls), Management Science (Dr Viktor Dorfler), Marketing (Dr Matthew Alexander), Computer and Information Sciences (Dr Yashar Moshfeghi, Dr Martin Goodfellow, Prof Crawford Revie), Mathematics and Statistics (Michael Grinfeld), Design, Manufacturing and Engineering Management (Dr Kepa Mendabil), and Humanities and Social Sciences (Prof Daniela Sime).
The PhD students will be registered to the PhD programmes in Business, or Computer and Information Science depending on the requirements of their project (see details below). As well as the training provided in their registered degree programme, the PhD students will be able to attend to a selection of the classes from the Artificial Intelligence & Applications, Advanced Computer Science with Big Data, and/or Finance and Financial Technology courses depending on their background, training needs and research projects.
Successful applicants will also be a part of a vibrant and rapidly emerging research group on data science for FinTech consisting of a number of PhD students, research assistants, postdoctoral researchers, primary supervisors as well as other associated faculty. The research group will provide specialist customised training to the PhD students on data science. Furthermore, the research group has a number of large-scale active research projects and upcoming proposals, which will provide hands-on training.
• One studentship covering home/EU fees and a tax-free stipend circa £15,384 per annum for 42 months. International applicants will need to cover the difference between Home/EU and International fees.
• We also welcome self-funded or externally funded applications
Dr James Bowden, Accounting & Finance, https://www.strath.ac.uk/staff/bowdenjamesmr/
Dr Yashar Moshfeghi, Computer and Information Sciences, https://www.strath.ac.uk/staff/moshfeghiyashardr/
Dr James Bowden: [Email Address Removed]
Dr Yashar Moshfeghi: [Email Address Removed]
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