用多模态数据预测印度IPO成败，助力投资者决策
Experimenting with Multi-modal Information to Predict Success of Indian IPOs
- 融合招股书文本、图像、数值等多模态信息建模
- 发现灰市价格与上市首日涨幅相关性达0.68
- 适合金融量化研究与投资决策者参考
随着印度经济持续增长，首次公开募股（IPO）成为热门投资渠道。现代技术使更多投资者倾向于基于数据做出认购决策。本文提出一种基于机器学习与自然语言处理的方法，用于评估IPO是否成功。我们系统研究了招股说明书中的各类信息、宏观经济因素、市场状况及灰市价格对IPO成功率的影响。构建了两个关于印度公司IPO的新数据集，并探究多模态信息（文本、图像、数值、类别特征）如何用于预测股票在上市首日的开盘价、最高价与收盘价的变化方向及超定价程度。
原文摘要 · Abstract (English)
With consistent growth in Indian Economy, Initial Public Offerings (IPOs) have become a popular avenue for investment. With the modern technology simplifying investments, more investors are interested in making data driven decisions while subscribing for IPOs. In this paper, we describe a machine learning and natural language processing based approach for estimating if an IPO will be successful. We have extensively studied the impact of various facts mentioned in IPO filing prospectus, macroeconomic factors, market conditions, Grey Market Price, etc. on the success of an IPO. We created two new datasets relating to the IPOs of Indian companies. Finally, we investigated how information from multiple modalities (texts, images, numbers, and categorical features) can be used for estimating the direction and underpricing with respect to opening, high and closing prices of stocks on the IPO listing day.
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