光華講壇——社會名流與企業家論壇第6682期

主題：全球勝任力講座（第23期）Deep Learning in Quantitative Finance 金融智能：量化金融中的深度學習

主講人：Imperial College London   Panos Parpas

主持人：特拉華數據科學學院 余欣

時間：11月24日 21:00

舉辦地點：https://us06web.zoom.us/j/81921528531 會議號: 819 2152 8531

主辦單位：特拉華數據科學學院 科研處

主講人簡介：

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博士現任倫敦帝國理工學院計算系教授。在加入帝國理工學院之前，Panos Parpas是麻省理工學院的博士后(2009-2011)。在此之前，他是瑞士信貸(credit suisse)的量化研究員(2007-2009)。教授研究興趣聚焦大規模優化問題的算法開發和分析、應用中出現的大型模型結構。他的研究發表在SIAM Journal of Optimization, SIAM Journal of Scientific computing, SIAM Journal on Mathematical Finance等頂級國際學術期刊上。教授同時是兩家期刊的副主編，并于2019年和2021年獲得摩根大通學院獎。

內容簡介：

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.

在大數據和AI時代的金融行業，以量化交易、風險控制與管理、AI顧問為代表的智能金融創新方興未艾，創新成果的獲得離不開實用編程軟件的開發。在許多機器學習算法編程語言中，Matlab、C++和Python是使用最廣泛的。在本次講座中將有機會了解基本模型和數學理論，了解金錢的時間價值、基于套利定價理論的價格衍生品、在風險與回報之間進行權衡以優化設計投資策略、以及使用有效的數值方法求解優化模型并模擬隨機過程。