[Advance Notice] The Energy Strategy Parallel Session of "Mangshan Forum&q
On May 30, 2018, Academy of Chinese Energy Strategy will hold the Energy Strategy Parallel Session of "Mangshan Forum" in 306 lecture hall of the International Exchange Center. Three domestic and foreign experts from the fields of new energy development and energy finance were invited to share their cutting-edge academic views for us. Professor Qi Zhang will be the host of the session.
There is a brief introduction of our lectures:
1. Dr. Xi Lu
Time: 8:30-09:30
Topic: Enhance Utilization of Wind Power in China through Spatial Dispersion and Synergistic Effects in Multiple Systems
Guest bio:
Dr. Xi Lu, associate professor, doctoral supervisor of Tsinghua University. In 2015, he was selected into the Young overseas high-level talents introduction plan.
2. Donald D. Ripple
Time: 09:30-10:30
Topic: Shanghai International Energy Exchange Crude Oil Futures Contract: How does it fit into the world crude oil trading system?
Topic Introduction:
The Shanghai International Energy Exchange (INE) launched its crude oil futures contract on March 26, 2018. This contract differs in many respects from other crude oil futures contracts traded around the world. The presentation will compare and contrast the Shanghai contract with the New York Mercantile Exchange (NYMEX) and Intercontinental Exchange (ICE) contracts, with some mention of the Dubai Mercantile Exchange (DME) contract. Prof. Ronald D. Ripple will discuss issues around what traditionally makes for a successful contract, and, employing the limited data available, I will present some initial findings regarding the direction of flow of international pricing signals. I will also provide a close examination of the initial trading patterns. Overall, the aim is to discuss whether this new contract will challenge the role of other existing contracts, or will it rather fill a void and complement the other contracts in the role of market price risk mitigation.
Guest bio:
Dr. Ronald D. Ripple is the Mervin Bovaird Professor of Energy Business and Finance in the School of Energy Economics, Policy, and Commerce in the Collins College of Business at The University of Tulsa. Ron took up his current position lecturing in the TU Master of Energy Business Program in 2013 after spending over fourteen years in Australia, with another year in Hong Kong. Dr. Ripple has studied oil and natural gas markets for over 37 years, getting his start in the Office of the Governor of Alaska, followed by a stint of consulting with Economic Insight, Inc. and research at the East-West Center. He wrote his PhD dissertation on Alaska North Slope natural gas and authored a chapter on the Geopolitics of Australia Natural Gas Development for the joint Harvard-Rice Geopolitics of Natural Gas Study. Ron has published numerous peer-reviewed journal articles, trade press articles, and reports, typically focusing on oil and natural gas markets and the financial derivatives markets that support them. Ron was awarded the Mayo Research Excellence Award, from the Collins College of Business, 2015-2016. Ron is also the International Association for Energy Economics (IAEE) VP for Conferences.
3. Dr. Liqiang Su
Time: 10:30-11:30
Topic: Crude Oil Commodity Forecast – A Deep Machine Learning Approach
Topic Introduction:
Crude Oil Future (WTI and Brent) is the most active traded commodity in the world. Just recently on March 23rd, 2018, Shanghai International Energy Exchange listed Chinese first crude oil commodity future contract. Forecast of commodity trading price is very difficult task in the investment communities. Nevertheless, our team utilizes the deep machine learning work and delivered the short term forecast on WTI and then INE crude oil price. The deep machine learning model on crude oil analysis consists of two main parts. The first is the machine learning on time series data by using RNN (Recurrent Neural Network) on historic energy commodity price (e.g. WTI, Brent, gasoline, natural gas, US Dollar Index and etc.). The other part is machine learning model on economic and fundamental data on long term and short term crude oil supply/demand. The forecast result shows accuracy score of 0.72 and learning score of 0.
Guest bio:
Dr. Liqiang Su had been working on investment bank industry for more than 20 years. Currently, Dr. Su works at XCC (鑫長城) company, a financial start up company as Chief Technology Officer. Prior to this role, Dr Su had worked in machine learning and big data analysis projects as Executive Director at JPMorgan for 4 years. Before that he also worked various high frequency trading hedge funds, and especially had been working in trade optimization and equity derivative team in Morgan Stanley for about 15 years. Liqiang Su got his Ph. D (Geophysics) from Yale University in 1996. His undergraduate was from Peking University in 1986.
After the lectures there will be a discussion part of our session.
Welcome to join us！