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Likelihood-Lab / Detecting Short-selling Using Ensemble Learning

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README

Detecting Short-selling Using Ensemble Learning

介绍

软件架构

  • model code
    • CART-AdaBoost
    • LSTM-AdaBoost
  • feature construction
    • textual feature
      • Scraping-SEC-filings
      • FReader
      • features
  • data

使用说明

  • model code
    存放模型代码
    • CART-AdaBoost
    • LSTM-AdaBoost
  • feature construction
    存放特征构建代码
    • textual feature
      文本特征:可读性,信息含量,文本情绪
      • Scraping-SEC-filings
        爬取SEC EDGAR FTP site上的年报
      • FReader
        解析html格式的年报,提取所有文字内容,计算可读性
      • features
        文本情绪:
        Generic_Parser.py: 基于LM(2011)词典构建文本情绪和可读性
        LoughranMcDonald_MasterDictionary_2018.csv : LM词典
        信息含量:
        tf-idf_LSA_cos.py: TF-IDF+余弦相似度+LSA计算信息含量
        主题分布:
        LDA.py
  • data
    2009-2019 Chinese concept stocks list.csv:2009-2019年美股在市中概股
    yearly dataset.csv:年度频率的数据集,包含文本特征和财务特征
    monthly dataset.csv:月度频率的数据集,包含股价特征

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Detecting Short-selling Using Ensemble Learning
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