Discover @Lhierarch Open Source projects

Xinyu Zhang (@Lhierarch)

Lhierarch

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Big-Data-in-Finance-Asset-Management

We then move to asset management, in which the use of large unstructured datasets and new techniques is generating significant innovation. We begin with a review of standard techniques used for portfolio construction in time-series and cross-section, and then consider new techniques used to predict variation in asset prices in the time series and cross-section. These techniques are useful, for example, in quant hedge fund portfolio construction. We then recognize that asset managers need to consider their liability structure as well as their asset structure. This implies a deeper understanding of ultimate end-investors’ decision making models and learning techniques. Insights from this portion of the course are increasingly proving to be a critical input into roboadvising strategies.

Big-data-in-Finance-Credit-Analytics-Part

This is the first part assignemnt from course Big Data in Finance I, ICBS. Professor of Financial Economics: Tarun Ramadorai. In the credit analytics part of the module we will consider how a range of techniques (including machine learning) can be used to predict default in both corporate (firms) and retail (credit cards and peer to peer lending) credit markets. We then turn to perhaps the largest retail credit market – the mortgage market – and examine theoretical, and primarily empirical, models of mortgage choice, mortgage refinancing, and mortgage default. Our attempt will be to keep up with the rapid pace of innovation in B2C lending markets in this part of the module.

Structured-Credit-Equity-Products

This is the Course project of BUSI97139-Structured Credit & Equity Products. Supervisor: Dr. Jeffery Hurley In this project, I developed a 3D simulation of a UAV show over a football field, using OpenGL and MPI. Specifically, I used 16 threads to control each UAV respectively, which are capable of real-time communication with each other. Please refer to "Final Project.pdf" for full description.