光华讲坛——社会名流与企业家论坛第6682期

主题：全球胜任力讲座（第23期）Deep Learning in Quantitative Finance 金融智能：量化金融中的深度学习

主讲人：Imperial College London   Panos Parpas

主持人：特拉华数据科学学院 余欣

时间：11月24日 21:00

举办地点：https://us06web.zoom.us/j/81921528531 会议号: 819 2152 8531

主办单位：特拉华数据科学学院 科研处

主讲人简介：

Dr. Panos Parpas is a Professor of Computational Optimisation at the Department of Computing, Imperial College London. Before joining Imperial College, he was a postdoctoral fellow at MIT (2009-2011). Before that, he was a quantitative associate at Credit-Suisse (2007-2009).  He is interested in the development and analysis of algorithms for large scale optimisation problems. He is also interested in exploiting the structure of large scale models arising in applications.  His research has been published in leading journals such as SIAM Journal of Optimization, SIAM Journal of Scientific computing, SIAM Journal on Mathematical Finance among others. He has presented his research in several meetings, conferences and seminars and is frequently involved in the organisation of specialist workshops and meetings. He is an associate editor for two journals and was awarded a JP Morgan Faculty Award in 2019 and 2021.

Panos Parpas博士现任伦敦帝国理工学院计算系教授。在加入帝国理工学院之前，Panos Parpas是麻省理工学院的博士后(2009-2011)。在此之前，他是瑞士信贷(credit suisse)的量化研究员(2007-2009)。教授研究兴趣聚焦大规模优化问题的算法开发和分析、应用中出现的大型模型结构。他的研究发表在SIAM Journal of Optimization, SIAM Journal of Scientific computing, SIAM Journal on Mathematical Finance等顶级国际学术期刊上。教授同时是两家期刊的副主编，并于2019年和2021年获得摩根大通学院奖。

内容简介：

In the financial industry in the era of big data, financial innovations represented by quantitative trading, risk control and management, and robo-advisors are surging, and the gain of innovative results is inseparable from the development of practical programming software. Among many programming languages, Matlab, C++, and Python are the most widely used. In this seminar you will have the opportunity to be introduced to the fundamental models and mathematical theories. In particular, in this module you will have the opportunity to learn to understand the time value of money, price derivatives using arbitrage pricing theory, optimally design investment strategies that trade-off risk with rewards, and use efficient numerical methods to solve optimisation models and simulate stochastic processes.

在大数据和AI时代的金融行业，以量化交易、风险控制与管理、AI顾问为代表的智能金融创新方兴未艾，创新成果的获得离不开实用编程软件的开发。在许多机器学习算法编程语言中，Matlab、C++和Python是使用最广泛的。在本次讲座中将有机会了解基本模型和数学理论，了解金钱的时间价值、基于套利定价理论的价格衍生品、在风险与回报之间进行权衡以优化设计投资策略、以及使用有效的数值方法求解优化模型并模拟随机过程。