| Challenge: | a novel deep generative model exploits text and price signals to make stochastic stock movement predictions. |
| Approach: | They propose a deep generative model exploiting text and price signals to solve this problem. |
| Outcome: | The proposed model exploits text and price signals to make temporally-dependent predictions from chaotic data. |
Similar Papers
Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models that predict stock movements are based on time series and technical analysis, but price signals alone fail to capture market surprises and impacts of sudden unexpected events. |
| Approach: | They propose a model that integrates chaotic temporal signals from financial data and social media to create hierarchical temporal networks. |
| Outcome: | The proposed model can be used to forecast stock movements on real-world S&P 500 index data and English tweets. |
FAST: Financial News and Tweet Based Time Aware Network for Stock Trading (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing methods for stock movement prediction are limited and do not account for the fine-grain temporal irregularities in the release of large volumes of text. |
| Approach: | They propose a hierarchical, learning to rank approach that uses textual data to make time-aware predictions for ranking stocks based on expected profit. |
| Outcome: | The proposed method outperforms state-of-the-art methods by over 8% in terms of cumulative profit and risk-adjusted returns on two benchmarks: English tweets and Chinese financial news spanning two major stock indexes and four global markets. |
Causality-Guided Multi-Memory Interaction Network for Multivariate Stock Price Movement Prediction (2023.acl-long)
Copied to clipboard
| Challenge: | Existing models for stock price movement prediction use auxiliary data, but we assume other stocks should be utilized as auxiliary information to enhance performance. |
| Approach: | They propose a Causality-guided multi-memory interaction network for stock movement prediction which transforms basic attention into Causal Attention by calculating transfer entropy between multivariate stocks. |
| Outcome: | The proposed model outperforms existing models on three real-world datasets from the U.S. and Chinese markets. |
Incorporating Fine-grained Events in Stock Movement Prediction (D19-51)
Copied to clipboard
| Challenge: | Existing studies mainly adopt coarse-grained events, which loses the specific semantic information of diverse event types. |
| Approach: | They propose to use a finance event dictionary to extract fine-grained events from finance news to train a neural model that uses the extracted events as the distant supervised label to train stock prediction. |
| Outcome: | The proposed method outperforms baselines and has good generalizability. |
Quantitative Day Trading from Natural Language using Reinforcement Learning (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches to stock prediction are not optimized to make profitable investment decisions. |
| Approach: | They propose a deep reinforcement learning approach that makes time-aware decisions to trade stocks while optimizing profit using textual data. |
| Outcome: | The proposed method outperforms state-of-the-art in terms of risk-adjusted returns on two benchmarks: Tweets (English) and financial news (Chinese) |
VolTAGE: Volatility Forecasting via Text Audio Fusion with Graph Convolution Networks for Earnings Calls (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to stock volatility forecasting ignore correlations between stocks. |
| Approach: | They propose to combine vocal cues with verbal and financial cue data to create a multimodal stock volatility prediction model that accounts for stock interdependence via graph convolutions. |
| Outcome: | The proposed model outperforms existing methods showing that it can predict volatility using multimodal learning. |
Event-Driven Learning of Systematic Behaviours in Stock Markets (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Using financial news, we can predict stock market behaviours by extracting financial events from the news and ranking the importance of the events. |
| Approach: | They propose to combine open information extraction and neural co-reference resolution to extract financial events from news streams and extend hierarchical attention networks that include attentions on event, news and temporal levels. |
| Outcome: | The proposed method achieves significantly better accuracies and higher simulated annualized returns than state-of-the-art models when being applied to predicting Standard&Poor 500, Dow Jones, Nasdaq indices and 10 individual stocks. |
Stock Embeddings Acquired from News Articles and Price History, and an Application to Portfolio Optimization (2020.acl-main)
Copied to clipboard
| Challenge: | Recent studies have shown that news articles can be leveraged to improve price prediction. |
| Approach: | They propose a method to encode the influence of news articles through a vector representation of stocks . they use a deep learning framework to acquire the vector representation using news articles and price history . |
| Outcome: | The proposed method can be applied to other financial problems besides price prediction. |
LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent work has focused on the performance of Large Language Models (LLMs) but the finance sector is relying on time-series data for complex forecasting tasks. |
| Approach: | They propose a framework that employs Sequential Knowledge-Guided Prompting to identify factors that influence stock movements using LLMs. |
| Outcome: | The proposed framework outperforms existing methods and is effective in time-series forecasting. |
DocFin: Multimodal Financial Prediction and Bias Mitigation using Semi-structured Documents (2022.findings-emnlp)
Copied to clipboard
Puneet Mathur, Mihir Goyal, Ramit Sawhney, Ritik Mathur, Jochen Leidner, Franck Dernoncourt, Dinesh Manocha
| Challenge: | Existing research focuses on textual and audio modalities of financial disclosures but ignores the rich tabular data available in financial reports. |
| Approach: | They propose to combine tabular financial data with text transcripts and audio recordings to improve stock volatility and price movement prediction by 5-12% and reduce gender bias by over 30%. |
| Outcome: | The combined data improves stock volatility and price movement prediction by 5-12% and reduces gender bias caused due to audio-based neural networks by over 30%. |