FAST: Financial News and Tweet Based Time Aware Network for Stock Trading
42630Authors
nan
Abstract
AbstractDesigning profitable trading strategies is complex as stock movements are highly stochastic; the market is influenced by large volumes of noisy data across diverse information sources like news and social media. Prior work mostly treats stock movement prediction as a regression or classification task and is not directly optimized towards profit-making. Further, they do not model the fine-grain temporal irregularities in the release of vast volumes of text that the market responds to quickly. Building on these limitations, we propose a novel hierarchical, learning to rank approach that uses textual data to make time-aware predictions for ranking stocks based on expected profit. Our approach outperforms state-of-the-art methods by over 8% in terms of cumulative profit and risk-adjusted returns in trading simulations on two benchmarks: English tweets and Chinese financial news spanning two major stock indexes and four global markets. Through ablative and qualitative analyses, we build the case for our method as a tool for daily stock trading.
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Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, pages 2164–2175 April 19 - 23, 2021. ©2021 Association for Computational Linguistics 2164 FAST: Financial News and Tweet Based Time Aware Network for Stock Trading Ramit Sawhney* Netaji Subhas Institute of Technology ramits.co@nsit.net.in Arnav Wadhwa* MIDAS, IIIT Delhi arnavw96@gmail.com Shivam Agarwal Manipal Institute of Technology shivamag99@gmail.com Rajiv Ratn Shah IIIT Delhi rajivratn@iiitd.ac.in Abstract Designing profitable trading strategies is com- plex as stock movements are highly stochastic; the market is influenced by large volumes of noisy data across diverse information sources like news and social media. Prior work mostly treats stock movement prediction as a regres- sion or classification task and is not directly op- timized towards profit-making. Further, they do not model the fine-grain temporal irregular- ities in the release of vast volumes of text that the market responds to quickly. Building on these limitations, we propose a novel hierar- chical, learning to rank approach that uses tex- tual data to make time-aware predictions for ranking stocks based on expected profit. Our approach outperforms state-of-the-art methods by over 8% in terms of cumulative profit and risk-adjusted returns in trading simulations on two benchmarks: English tweets and Chinese financial news spanning two major stock in- dexes and four global markets. Through abla- tive and qualitative analyses, we build the case for our method as a tool for daily stock trading. 1 Introduction The stock market, a financial ecosystem involv- ing quantitative trading and investing, observed a market capitalization exceeding $US 60 trillion as of 2019.1 Stock trading presents lucrative op- portunities for investors to utilize the market as a platform for investing funds and maximizing prof- its. However, making profitable investment deci- sions is challenging due to the market’s volatile and rapid-changing nature (Adam et al., 2016; Foucault et al., 2016). Research at the intersection of natural language processing (NLP) and finance presents encouraging prospects in stock prediction (Jiang, *Equal contribution. 1World Federation of Exchanges: https://data. worldbank.org/indicator/CM.MKT.LCAP.CD/ Figure 1: Here, we study how Tesla’s tweets influence investors’ opinions about the company and impact its stock price trend. The first tweet shows positive opin- ions, and we observe a rise in prices. Later, the tweets made by the CEO rapidly lead to drastic price drops within minutes. Further, without a sequential context, it gets challenging to understand the tweets that follow. 2020). Conventional work forecasts future trends by modeling numerical historic stock data (Lu et al., 2009; Bao et al., 2017). However, price signals alone can not capture market surprises, news, and company announcements. Such events, often re- ported across financial news and social media, have shown to influence market dynamics (Laakkonen, 2004). As shown in Figure 1, prices immediately react to breaking news about the related company. Such reactions conform to the Efficient Market Hy- pothesis (EMH), which states that financial markets are informationally efficient and prices reflect all available information (Malkiel, 1989). The abundance of stock affecting information across news and Twitter helps investors analyze …
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Labeled Text
AbstractDesigning profitable trading strategies is complex as stock movements are highly stochastic; the market is influenced by large volumes of noisy data across diverse information sources like news and social media. Prior work mostly treats stock movement prediction as a regression or classification task and is not directly optimized towards profit-making. Further, they do not model the fine-grain temporal irregularities in the release of vast volumes of text that the market responds to quickly. Building on these limitations, we propose a novel hierarchical, learning to rank approach that uses textual data to make time-aware predictions for ranking stocks based on expected profit. Our approach outperforms state-of-the-art methods by over 8% in terms of cumulative profit and risk-adjusted returns in trading simulations on two benchmarks: English tweets and Chinese financial news spanning two major stock indexes and four global markets. Through ablative and qualitative analyses, we build the case for our method as a tool for daily stock trading.
Publication Details
- Paper ID:
- 42630
- Year:
- 2021
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan