Python and Machine-Learning for Asset Management with Alternative Data Sets

所在平台: Coursera

课程主页: https://www.coursera.org/learn/machine-learning-asset-management-alternative-data

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课程简介

课程总结：Python与机器学习在资产管理中的应用（使用替代数据集） 本课程旨在应对市场和会计数据过度利用所造成的投资组合集中、表现平平及系统性风险问题，通过结合替代数据，以帮助金融机构寻找竞争优势。课程内容涵盖替代数据的核心概念、最新研究成果及实际投资组合案例，重点在于实践与理论的结合。 课程适合那些希望在金融市场担任数据科学家、提升金融分析技能或对大数据及其前沿技术感兴趣的学员。学习者需具备Python编程、投资理论和统计学的基础。课程不仅传授在金融市场应用的新数据和研究技术，还帮助增强数据科学与Python技能。 课程大纲： 1. **消费模块**：介绍基于消费的替代数据，包括线上线下消费者购买活动和行为数据的聚合，利用地理位置、交易数据及社交媒体互动等信息，提前了解企业表现，并为投资和风险管理提供优势。 2. **金融文本分析**：通过文本挖掘展示如何从数据获取到获取金融市场见解，包括经典文本挖掘方法，如文本向量化、停用词处理及TF-IDF技术。学生将学习文本的数学表示及降噪。 3. **处理公司财务申报**：将文本挖掘应用于10-K和13-F财务文件，探讨如何利用Python分析这些大文件，自动提取数据并定义相关指标，分析公司财务文档之间的相似性。 4. **使用媒体衍生数据**：模块最后探讨情感分析与网络分析，包括通过社交媒体理解公众对公司的看法及公司内部的前景看法。同时，介绍如何将推文数据转化为网络表示，分析公司间的关联。 本课程重视实践指导，与学员互动，结合实际操作，帮助学生在金融领域获得宝贵的技能和经验。

课程大纲

Name:Consumption

Description:The consumption module introduces students to the basics of consumption-based alternative data. By aggregating online and offline consumer purchase activity and behavioral datasets including geolocation data (e.g., cell locations, satellite imagery etc.), transaction data (e.g., credit card transaction logs and point of sale data), as well as consumer interaction with brands and products on social media, researchers can learn about company performance ahead of official company earning announcements. Such information may be extremely useful and can provide investment and risk management advantages. This module reviews the theoretical aspects of various consumption datasets, and provides practical demonstrations of relevant data analytics.

Name:Textual Analysis for Financial Applications

Description:Module 2 is an introduction to text mining as well as a demonstration of how to get from data retrieval (web scraping) to financial market insights. Some of the classic text mining methodologies are covered such as vectorization of text (the bag of words approach), stop words for filtering, and term frequency-inverse document frequency (TF-IDF). Students will learn how text can be mathematically represented, and regularized/filtered to reduce noise. Measures of text-similarity will be covered in theoretical and practice sessions. Lab sessions go through examples of web scraping data, regularizing with the described techniques and finally, insights will be derived from the textual data.

Name:Processing Corporate Filings

Description:Module 3 is a practical extension of the text mining lessons to 10-K and 13-F, two of the most commonly researched corporate filings. This type of data can be extremely daunting when used by individual analysts due to the sheer size of the documents, but module 3 describes the methodologies for quantitatively analyzing these documents with Python code. Both the 10-K and 13-F documents are worked through, and within the lab sessions it is demonstrated how one can automatically pull this kind of data as well as define metrics around them. We investigate implementations of research in this field around similarity of given companies 10-K statements over time as well as similarity between fund holdings from the 13-F in the lab.

Name:Using Media-Derived Data

Description:The final module introduces both sentiment analysis in the context of textual data as well as network analysis in the context of connectivity of firms. Sentiment analysis is an avenue of potentially fruitful information that when done correctly can display what a general population might believe about a company (through for example social media) or even whether the company itself is positive or negative on future outlook (through analysis of tone in corporate filings). Network analysis, as shown in the research of course instructors and his colleagues, can be used to accurately capture how a financial network is oriented and what companies might perform well because of other firm’s mentioning them as a threat. The lab session of this module extends the corporate filings analysis to examine sentiment while also introducing a set of tweets which are then transformed into a network representation.

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