Project for Operations Research 1 March 27, 2019 • Marks for project: 30%. • Project due date: 23:55 Friday 12 April 2019 (Week 12). 1 Project Description A file has been placed at http://jkcray.maths.ul.ie/ms4303/Projectdata. pdf containing a list of LP’s, a different one for every student in the class. The LP’s are in Standard Form, […]
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1 1. 2. 3. 4. 2 2.1 Basic Requirements Programming language: Matlab (recommended), or other languages (Python, etc.) Implement your code under Code folder, and read data from Data folder. Under Data folder, Database contains all the images to retrieve from. Final project report under name report.pdf at the project root folder. Total credits for
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COMP0040 – Assignment 2 Scaling Laws, Dependency and Causality 1 Overview This assignment is focused on (i) Scaling and Multiscaling, and (ii) Dependency and Causality. It is part of feedback for the preparation of final report. 2 Data The data set is the same as for Assignment 1. You can also include other currencies and
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Exercise sheet 3 – NLAA– Numerical Linear Algebra with Applications Instructions: Please hand in solutions to Q1, and Q2 by Friday 1pm week 11 (March 29). You should also upload to Canvas by the same deadline a single zip file containing the files nsd.m, eigspower.m. Please name your files as suggested in the question. Plagiarism
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Department of Informatics, King’s College London Pattern Recognition (6CCS3PRE/7CCSMPNN). Assignment: Support Vector Machines (SVMs) and Ensemble Methods This coursework is assessed. A type-written report needs to be submitted online through KEATS by the deadline specified on the module’s KEATS webpage. In this coursework, we consider (before Q8) a classification problem of 3 classes. A multi-class
MACHINE INTELLIGENCE LAB SESSIONS 1 & 2 Artificial Neural Networks & Fuzzy Logic Professor A. Dehghani a.dehghani@leeds.ac.uk Contents Lab session 1: Artificial Neural Networks 3 Task sheet 8 Lab session 2: Fuzzy Logic 11 Task sheet 14 Appendix 15 Online manuals: Matlab Neural Network Toolbox Matlab Fuzzy Logic Toolbox 2 School of Mechanical Engineering Machine
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Math 104B Final Project ∗ Instructor: Xu Yang General Instructions: Please write your homework papers neatly. You need to turn in both your codes and descriptions on the appropriate runs you made by following TA’s instructions. Write your own code, individually. Do not copy codes! Consider the following partial differential equation (PDE): a(x,y)(uxx +uyy)=f(x,y), Domain:
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Department of Informatics, King’s College London Pattern Recognition (6CCS3PRE/7CCSMPNN). Assignment: Support Vector Machines (SVMs) and Ensemble Methods This coursework is assessed. A type-written report needs to be submitted online through KEATS by the deadline specified on the module’s KEATS webpage. In this coursework, we consider (before Q8) a classification problem of 3 classes. A multi-class
COMP0040 – Assignment 2 Scaling Laws, Dependency and Causality 1 Overview This assignment is focused on (i) Scaling and Multiscaling, and (ii) Dependency and Causality. It is part of feedback for the preparation of final report. 2 Data The data set is the same as for Assignment 1. You can also include other currencies and
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第七届“泰迪杯”数据挖掘挑战赛—— A 题:通过机器学习优化股票多因子模型 Fama 通过分析美国市场几十年的数据发现，美国股市绝大部分可以被市值、估值以及 市场收益 3 个因子解释，并因此获得了 2013 年诺贝尔经济学奖。Fama 的工作开启了通过因 子化分析股市获取超额收益的先河，此后学术界及业界不断地寻找其他能获取超额收益的因 子及其组合和风险控制的方式。 在我国，基于财务因子(比如市盈率、市值等)及长周期的量价因子(比如月度反转、 月度成交量等)为主要因子的传统多因子模型在 A 股市场曾经获得过较为稳健的超额收益， 但是由于 A 股市场存在明显的风格切换(比如 2017 年下半年从传统的小市值风格切换到只 有极少数大市值股票上涨，而绝大部分股票下跌的风格)，传统多因子模型的稳定性及有效 性受到了较大的考验。 相比传统的线性多因子模型，机器学习算法能够通过对因子的非线性表达，捕捉到更加 精细的市场信号，获取较为稳健的超额收益。 根据2016年1月1日至2018年9月30日我国A股市场的数据(数据提取方式见附录 2)，筛选出各大类股票因子中较优的子因子。在此基础上，分析不同的机器学习算法对提升 这些因子的等权重线性模型表现的优劣，并使用“Auto-Trader 策略研究回测引擎”进行策略 回测(初始资金为 1000 万元整，手续费为双边千分之 3，每月月初调仓)。 可以从以下角度入手进行分析: (1) 利用Auto-Trader中各大类因子(见附录3)的日频数据(数据提取方式见附录4)， 分别做单因子策略研究和绩效分析，挑选出使得年化夏普比率(Sharpe ratio)最优的各个大 类的因子。 (2) 基于机器学习算法对 (1) 中挑选的因子，进行增强，利用 2016 年 1 月 1 日至 2018 年 9 月 30
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