THE UNIVERSITY OF MANCHESTER PARTICULARS OF APPOINTMENT FACULTY OF ENGINEERING & PHYSICAL SCIENCES SCHOOL OF COMPUTER SCIENCES

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1 THE UNIVERSITY OF MANCHESTER PARTICULARS OF APPOINTMENT FACULTY OF ENGINEERING & PHYSICAL SCIENCES SCHOOL OF COMPUTER SCIENCES Marie Curie ITN Early Stage Researchers in Data Science and Finance (2 posts) Vacancy ref: EPS Starting salary: Available: Probation: Based at: Responsible to: BACKGROUND Marie Curie Fellow scale (H1 H4) 27,741 to 36,153 per annum (dependent on family circumstances) Please note these posts involve concurrent PhD study for 3 years. Immediately for 36 months 9 months The University of Manchester Professor John Keane Professor of Data Engineering (Computing Science) Dr Xiao-jun Zeng Senior Lecturer This is an opportunity to join the EC Horizon 2020 funded Marie Skłodowska-Curie Innovative Training Network (ITN) Big Data Finance: Training for Big Data in Financial Research and Risk Management. These salaried positions involve concurrent post-doctoral study for 3 years, resulting in a PhD from The University of Manchester. The two posts in Data Science and Finance are based in the School of Computer Science, a centre of excellence in research and education which has been at the forefront of computing for over 40 years, since our foundation as the UK s first specialist university computing department. We continue to build on our history of ground-breaking achievements in computer science, and were rated in the top 5% for research quality in the 2014 UK government review of research (Research Excellence Framework - REF). Big Data Finance provides doctoral training in sophisticated data-driven risk management and research at the crossroads of Finance and Big Data for 13 researchers. The main objectives are i) to meet an increasing commercial demand for well-trained researchers experienced in both Big Data techniques and Finance and ii) to develop and implement new quantitative and econometric methods for empirical finance and risk management with large and complex datasets. To achieve the objectives, the emphasis is put on exploiting big data techniques to manage and use datasets that are too large and complex to process with conventional methods. For more information, please visit: Finance is clearly a European sector that could greatly benefit from the use of Big Data. People in modern societies are leaving behind a vast amount of data that can be analysed and exploited in new and unprecedented ways to understand and model financial markets for better risk management. These data sets of high-volume, high-velocity, and high-variety information, directly relevant to the financial sector, arise from various sources. The sources include social media and news services being heterogeneous and unstructured, and electronic financial markets that generate terabytes of structured ultra-high-frequency limit order book data each day. This data

2 could provide valuable information to validate financial strategies, manage risks, and make decisions. Moreover, since computers are now directly involved in transactions, financial risk management and analysis with complex and large data would benefit from being partly automatized. However, to exploit this potential, banks and other financial institutions must be able to handle and process massive heterogeneous data sets in a fast and robust manner. Unfortunately, experts with an understanding of both finance and suitable Big Data techniques and ready to rise to this challenge are scarce. Immediate actions are required not to miss these opportunities. Compared to the USA, Europe is still at an early stage of adopting Big Data technologies and services. You will participate in challenging and exciting projects at the forefront of research in data science for finance. You will benefit from several network-wide schools, workshops and specific training courses plus opportunities for secondments to partner institutions. Applications, including a CV and indicating the job reference EPS-07210, should be made online. We are looking for bright and motivated individuals with the will to excel in a highly competitive environment. Applicants should have an excellent undergraduate degree (2:1 or above) or MSc ideally in Computer Science or a closely related discipline. Applicants must also be eligible to be appointed as an Early Stage Researcher in the UK. Please note there are strict *eligibility requirements* which apply to all Marie Skłodowska- Curie Early Stage Researchers. At the time of the appointment, applicants should not hold a PhD or have more than 4 years research experience after Undergraduate/Masters graduation and should not have resided in the UK for more than 12 months in the last 3 years immediately before appointment. JOB DESCRIPTION Main responsibilities These job openings cover 3-year research positions in the frame of a 3-year PhD doctoral programme, resulting in a PhD degree awarded by The University of Manchester under the supervision of Professor John Keane and Dr Xiao-jun Zeng. The doctoral research may include periodic research secondments hosted by another partner institution. Duties expected of the role are: To perform original research under the supervision of an academic advisor in the area of data science and finance. To participate in the activities of the Big Data Finance Innovative Training Network: attending training workshops, collaborating with network partner and undertaking periodic secondment placement at partner institutions. To register for a PhD with the School of Computer Science at the University of Manchester and follow the associated training programmes to attain the PhD within the allowed timeframe. To produce written outputs (including publications in leading outlets) as required during this time and contribute to engagement and dissemination of the project s findings. To present regular progress reports within the supervisory requirements laid down by the University of Manchester.

3 Project Objectives Post 1: Distributed and Real-Time Machine Learning for Financial Data Analysis (Keane, Zeng) Objectives: Big data has both high volume and high velocity one way this manifests is as silos of in-situ data representing departments in banks that are very difficult to move and integrate to obtain a single coherent customer view. Further, the ability to perform data analytics dynamically and in near real-time of rapidly changing customer and market data is increasingly critical for competitiveness. By considering the distributed nature of financial data storage and the velocity of financial markets, the objective of this RP is to develop distributed and real-time machine learning methods to identify decentralised and dynamic models for financial analysis, prediction, and risk management. This project will develop (i) methods to identify cross-effects between different data resources, regions, sectors, and markets, (ii) distributed versions of methods to identify decentralised models that include individual local model components learned from local resources and cross-impact model components learned from data resources in other regions/sectors/markets, and (iii) realtime learning methods to update decentralised models and address financial market velocity. Expected Results: Based on the distributed and cloud computing infrastructure, this approach should address the weakness of existing data-centralised and off-line machine learning methods, which fail to consider the cost of data transportation, storage, and fast time-varying characteristics of financial markets. The originality of this approach is its dynamic integration, by distributed and real-time mining, to maximise the effectiveness and efficiency of big data analysis. Post 2: Identify Financial Market Mood Indexes (Zeng, Keane) Objectives: The financial mood and confidence indexes should be more rapid, comprehensive, and cost-effective than existing survey-based indexes. Based on social media feeds, web search terms and query volumes, and online financial news/blogs, this project will (i) develop big text and data mining algorithms to identify real-time financial market mood and confidence indexes; (ii) develop visualisation technique to allow identified mood and confidence indexes to be openly accessible to both financial institutes and public. The innovation is that this will be the first realtime text mining technique to model dynamic behaviours of financial mood and confidence indexes. This project is partly based on the recent paper (Bollen, J., H. Mao, X. Zeng, 2011), which used twitter mood to predict stock prices. Expected Results: We expect to show that online identified indexes are more reliable, accurate and informative by comparing correlations and prediction accuracies to financial market movement. The correlations and prediction analysis should provide earlier insights/ warnings of market movements, risks, and crises for financial markets. Visualisation and corresponding index data can assist financial organisations and policy makers in market analysis and risk assessment. For each index, the corresponding web data resources (e.g. market and business news) and blogs and social media data (e.g. twitter) will be identified and retrieved in real-time. Moreover, real-time big data/text mining methods will be designed and implemented to identify financial market mood and confidence indexes. Visualisation to display real-time series of mood and confidence indexes and make accessible to financial institutes and the public will be developed. Both research projects are expected to lead to publications in leading journals. In addition to journal submissions, a PhD is expected to be submitted/completed (in Data Science/Finance) in three years.

4 PERSON SPECIFICATION Essential At least a 2.1 UK Bachelor degree (or international equivalent) or Master s degree ideally with distinction (or internationally equivalent) in Computer Science (with a data science focus). Applicants must not hold a PhD degree and must have less than 4 years research experience. Excellent skills in data science and analytic methods (both posts). Excellent skills in text mining (of particular relevance to Post 2). Excellent analysis skills and an analytical mind-set, as well as excellent communication skills (written and oral). Excellent software engineering skills. Experience of report writing / authoring publications. Ability to work independently and as a member of a research team. Ability to review and engage with interdisciplinary studies. English language proficiency: If you have not graduated from a UK institution or country where English is the first language then you will need an IELTS score of 7 (writing of 6.5 or above) or TOEFL 100+ (internet-based) or Cambridge Proficiency Grade `C' (minimum). Applicants must also satisfy the eligibility requirements for an Early Stage Researcher under then EC Horizon 2020 Innovative Training Network Programme; in particular they should be eligible to be appointed as an Early Stage Researcher in the UK this means: o To have less than four years research experience after Undergraduate/Masters graduation (this is cumulative research experience and does not include management/industrial or other work experience). o To not hold a PhD degree (PhD candidates under 4 years of registration and before completion may apply). Desirable o To not have resided or carried out main research activity in the UK for more than 12 months in the three years immediately prior to their recruitment. Experience in financial data processing. In addition to The University of Manchester online application please also submit the following documents to Professor John Keane by to john.keane@manchester.ac.uk by the application deadline: Degree transcripts including the grading scheme. English language test certificate if applicable. The Marie Curie Eligibility Form (Annex 1 of this document). A personal statement demonstrating your commitment to the post / research project highlighting relevant qualifications and experience and stating the project reference.

5 Annex 1: Eligibility Form Marie Curie Fellowship Horizon 2020 Innovative Training Network Big Data Finance. This form is to be completed by all applicants and returned by (preferred) or post to the contact above. Surname/Family name: First/Given name(s) Dual Nationality: If holding more than one nationality, please give details of nationalities held: Gender: Male Female Residence: Please indicate the country(ies) of residence and the main activity (work, studies etc) during the five years prior to the planned start date of the project (1 st November 2015). Please include details of the dates you were awarded your degree(s). Country Activity Start date Completion date Research experience: Please indicate research training and research activities from the date when your first degree was awarded. This does not include periods of employment if research training is not present.

6 Research programme training Brief description of activity Start date Completion date Language: Please provide details of any languages you are proficient in and qualification achieved. Language Qualification 1 st language Other language Other language Family Status: Married or living as married Single Number of dependent children Have you applied previously for or been awarded any other type of Marie Curie fellowship? Yes/No If yes, please give details: Signature Date