Hybrid Deep Sequential Modeling for Social Text-Driven Stock Prediction

Huizhe Wu · Wei Zhang · Weiwei Shen · Jun Wang

60 citations0/0 files storedACM International Conference on Information and Knowledge Management (CIKM)

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Stock Market Forecasting Methods Financial Markets and Investment Strategies Time Series Analysis and Forecasting

Abstract

In addition to only considering stocks' price series, utilizing short and instant texts from social medias like Twitter has potential to yield better stock market prediction. While some previous approaches have explored this direction, their results are still far from satisfactory due to their reliance on performance of sentiment analysis and limited capabilities of learning direct relations between target stock trends and their daily social texts. To bridge this gap, we propose a novel Cross-modal attention based Hybrid Recurrent Neural Network (CH-RNN), which is inspired by the recent proposed DA-RNN model. Specifically, CH-RNN consists of two essential modules. One adopts DA-RNN to gain stock trend representations for different stocks. The other utilizes recurrent neural network to model daily aggregated social texts. These two modules interact seamlessly by the following two manners: 1) daily representations of target stock trends from the first module are leveraged to select trend-related social texts through a cross-modal attention mechanism, and 2) representations of text sequences and trend series are further integrated. The comprehensive experiments on the real dataset we build demonstrate the effectiveness of CH-RNN and benefit of considering social texts.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 60 citations exist upstream.

References

1985Does the Stock Market Overreact?1969Fitting autoregressive models for prediction 2017A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction 2011Comparing Twitter and Traditional Media Using Topic Models 2010You are where you tweet 2015Deep learning for event-driven stock prediction 2009Predicting risk from financial reports with regression 1996Excess Volatility and Predictability of Stock Prices in Autoregressive Dividend Models with Learning

Cited by

2023Hybrid Information Mixing Module for Stock Movement Prediction 2021FAST: Financial News and Tweet Based Time Aware Network for Stock Trading 2022Accurate Stock Movement Prediction with Self-supervised Learning from Sparse Noisy Tweets 2023Incorporating stock prices and text for stock movement prediction based on information fusion 2020Multi-Element Hierarchical Attention Capsule Network for Stock Prediction 2021Stock Trend Prediction with Multi-granularity Data

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.