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Application of Statistical Learning on Analysis of Emerging Market Equity I...

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Imperial College London

Data Science Institute

William Penney Lab

London

SW7 2AZ

United Kingdom

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Speaker Biography

Dr. Mostafa Mostafavi is a Senior Quantitative Lead at Evalueserve in London, part time Researcher at Imperial College London and Director and Founder of Meybod Analytics, a company which provides education and research in the field of application of statistical learning in industries such as Health Care, Engineering, Finance and Telecommunication. He has worked as a Vice President for Quantitative Risk and Capital Strategy at Credit Suisse in London, Model Risk Audit Manager at HSBC in London, Quant for Asset Liability Management at Barclays Capital in London, Quantitative Analyst for Credit Derivatives at Commerzbank in London, Quantitative Developer at International asset management in London and part time lecturer at UEL in London. His education includes BSc. in Communication Engineering from Sharif University of Technology in Tehran, MSc. in Communication Engineering from King's College London, MSc. in Mathematics and Finance from Imperial College London, PhD in Communication Engineering from University of Surrey and PhD in Application of Statistical Learning in Finance at Imperial College London.

Talk Abstract

Analysis and understanding of the relationships between financial market equity indices has several applications in financial market decision making, investment strategy and risk management. According to the research developed in this field, the techniques for the equity market analysis problems can be classified in to two main area of Econometric Models and Statistical Learning Models. There exist several assumptions in Econometric models which can degrade the quality of analysis. Nevertheless, the development of statistical learning algorithms has made it possible to tackle computationally demanding mathematical models for analysis of the indices. In this talk we firstly introduce the statistical learning methods such as Support Vector Regression which are used in this analysis. We will then investigate the problem of analysis of the relationship between movements of the emerging market equity indices and other indices using support vector regression. We will show that the emerging market equity indices movement can be described by movement of the other equity indices using support vector regression.

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Imperial College London

Data Science Institute

William Penney Lab

London

SW7 2AZ

United Kingdom

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