time-series

Stock Prediction

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in stock-prediction

Trend

Dataset

Best Model

Actions

No benchmarks available.

Libraries

i

Use these libraries to find stock-prediction models and implementations

Datasets

stocknet

Astock

EDT

Dhaka Stock Exchange Historical Data

Subtasks

Text-Based Stock Prediction Event-Driven Trading PAIR TRADING

Most implemented papers

DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News

Xiao-Yang Liu, Hongyang Yang, Xinyi Li, Yinchuan Li, Liuqing Yang•Thu Dec 19 2019

A novel deep neural network DP-LSTM is proposed for stock price prediction, which incorporates the news articles as hidden information and integrates difference news sources through the differential privacy mechanism.

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Temporal Relational Ranking for Stock Prediction

Xiangnan He, Tat-Seng Chua, Fuli Feng, Yiqun Liu, Xiang Wang, Cheng Luo•Mon Sep 24 2018

A new deep learning solution, named Relational Stock Ranking (RSR), named Temporal Graph Convolution, which jointly models the temporal evolution and relation network of stocks and outperforms state-of-the-art stock prediction solutions.

481 0

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Artificial Counselor System for Stock Investment

Mark Crowley, Benyamin Ghojogh, Hadi NekoeiQachkanloo, Ali Saheb Pasand•Sat Mar 02 2019

In this paper, the stock future prices are predicted using Support Vector Regression and the predicted prices are used to recommend which portions of the budget an investor should invest in different existing stocks to have an optimum expected profit considering their level of risk tolerance.

26 0

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HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction

Jinkyu Kim, Chan Ho So, Jaewoo Kang, Raehyun Kim, Minbyul Jeong, Sanghoon Lee•Tue Aug 06 2019

A hierarchical attention network for stock prediction (HATS) which uses relational data for stock market prediction and selectively aggregates information on different relation types and adds the information to the representations of each company.

156 0

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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 0

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Improving S&P stock prediction with time series stock similarity

Lior Sidi•Fri Feb 07 2020

This paper enriched the stock data with related stocks just as a professional trader would have done to improve the stock prediction models and found co-integration similarity to have the best improvement on the prediction model.

1 0

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Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction

Jiexia Ye, Juanjuan Zhao, Kejiang Ye, Chengzhong Xu•Sun May 10 2020

A deep learning framework, called Multi-GCGRU, which comprises graph convolutional network (GCN) and gated recurrent unit (GRU) to predict stock movement, which is feasible to incorporate more effective stock relationships containing expert knowledge, as well as learn data-driven relationship.

62 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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Stock price prediction using Generative Adversarial Networks

Chen Chen, Amir Jafari, Hung-Yang Lin, Gaofeng Huang•Thu Apr 01 2021

This paper proposes a stock prediction model using Generative Adversarial Network (GAN) with Gated Recurrent Units (GRU) used as a generator that inputs historical stock price and generates future stock prices and Convolutional Neural Network (CNN) as a discriminator to discriminate between the real stockprice and generated stock price.

40 0

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Enhancing Stock Movement Prediction with Adversarial Training

Xiangnan He, Tat-Seng Chua, Fuli Feng, Maosong Sun, Huimin Chen, Ji Ding•Fri Oct 12 2018

A new machine learning solution for stock movement prediction, which aims to predict whether the price of a stock will be up or down in the near future, is added to simulate the stochasticity of price variable, and train the model to work well under small yet intentional perturbations.

274 0

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