time-series

Stock Trend Prediction

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in stock-trend-prediction

Trend

Dataset

Best Model

Actions

FI-2010

Libraries

i

Use these libraries to find stock-trend-prediction models and implementations

Datasets

Astock

EDT

Subtasks

Stock Market Prediction

Most implemented papers

Stock trend prediction using news sentiment analysis

J. Kalyani, Prof. H. N. Bharathi, Prof. Rao Jyothi•Wed Jun 29 2016

This project is about taking non quantifiable data such as financial news articles about a company and predicting its future stock trend with news sentiment classification with three different classification models which depict polarity of news articles being positive or negative.

118

Content

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Stock Movement Prediction from Tweets and Historical Prices

Yumo Xu, Shay B. Cohen•Sat Jun 30 2018

This work presents a novel deep generative model jointly exploiting text and price signals for stock movement prediction and introduces recurrent, continuous latent variables for a better treatment of stochasticity and uses neural variational inference to address the intractable posterior inference.

411 0

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Recurrent Highway Networks With Grouped Auxiliary Memory

Wei Luo, Feng Yu•Thu Dec 12 2019

This paper proposes a novel RNN architecture based on Recurrent Highway Network with Grouped Auxiliary Memory (GAM-RHN), which interconnects the RHN with a set of auxiliary memory units specifically for storing long-term information via reading and writing operations, analogous to Memory Augmented Neural Networks (MANNs).

13 0

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Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading

Zhihan Zhou, Li-Qian Ma, Han Liu•Tue May 25 2021

An event-driven trading strategy that predicts stock movements by detecting corporate events from news articles and developing an elaborately-annotated dataset EDT for corporate event detection and news-based stock prediction benchmark is introduced.

58 0

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Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model

Javen Qinfeng Shi, Lingqiao Liu, Jinan Zou, Hai Cao, Yuhao Lin, Ehsan Abbasnejad•Mon Jun 13 2022

A platform to study the NLP-aided stock auto-trading algorithms systematically and proposes a self-supervised learning strategy based on SRLP to enhance the out-of-distribution generalization performance of the system.

23 0

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AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework

Xiang Li, Zhenyu Li, Chen Shi, Yong Xu, Qing Du, Mingkui Tan, Jun Huang, Wei Lin•Mon Mar 18 2024

This work releases AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data, which has positive impact on training LLM for completing financial analysis, and benchmarks a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task.

49 0

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Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Network

Huicheng Liu•Wed Nov 14 2018

In terms of predicting directional changes in both Standard & Poor's 500 index and individual companies stock price, this technique is competitive with other state of the art approaches, demonstrating the effectiveness of recent NLP technology advances for computational finance.

31 0

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DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation

Weiqing Liu, Yingce Xia, Wendi Li, Xiao Yang, J. Bian•Mon Jan 10 2022

This paper proposes a novel method DDG-DA, that can effectively forecast the evolution of data distribution and improve the performance of models, that first train a predictor to estimate the future data distribution, then leverage it to generate training samples, and finally train models on the generated data.

74 0

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Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction

Weiqing Liu, Jiang Bian, Xuanzhe Liu, Tie-Yan Liu, Ziniu Hu•Tue Dec 05 2017

A Hybrid Attention Networks (HAN) is designed to predict the stock trend based on the sequence of recent related news, and the self-paced learning mechanism is applied to imitate the third principle.

326 0

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