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Stock Market Prediction

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in stock-market-prediction-1

Trend

Dataset

Best Model

Actions

Astock

stocknet

S&P 500

Libraries

i

Use these libraries to find stock-market-prediction-1 models and implementations

huggingface/transformers

2 papers 124,196

Datasets

stocknet

Astock

EDT

Dhaka Stock Exchange Historical Data

Subtasks

Stock Price Prediction Stock Prediction Stock Trend Prediction

Most implemented papers

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Jacob Devlin, Kenton Lee, Kristina Toutanova, Ming-Wei Chang•Mon Dec 31 2018

A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.

109344

Content

kaushaltrivedi/fast-bert

2 papers 1,846

utterworks/fast-bert

2 papers 1,846

Tencent/TurboTransformers

2 papers 1,439

IndicoDataSolutions/finetune

2 papers 695

awslabs/mlm-scoring

2 papers 330

Karthik-Bhaskar/Context-Based-Quest…

2 papers 39

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RoBERTa: A Robustly Optimized BERT Pretraining Approach

Danqi Chen, Omer Levy, Luke Zettlemoyer, Veselin Stoyanov, M. Lewis, Myle Ott, Naman Goyal, Mandar Joshi, Jingfei Du, Yinhan Liu•Thu Jul 25 2019

It is found that BERT was significantly undertrained, and can match or exceed the performance of every model published after it, and the best model achieves state-of-the-art results on GLUE, RACE and SQuAD.

28280 0

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SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis

Bolei He, Xinyan Xiao, Hua Wu, Haifeng Wang, Hao Tian, Can Gao, Hao Liu, Feng Wu•Mon May 11 2020

Sentiment Knowledge Enhanced Pre-training (SKEP) is introduced in order to learn a unified sentiment representation for multiple sentiment analysis tasks, and significantly outperforms strong pre-training baseline, and achieves new state-of-the-art results on most of the test datasets.

270 0

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Sentiment analysis of Twitter data for predicting stock market movements

Venkata Sasank Pagolu, Kamala Challa, G. Panda, B. Majhi•Fri Sep 30 2016

A strong correlation exists between the rise and falls in stock prices with the public sentiments in tweets, and this work has applied sentiment analysis and supervised machine learning principles to the tweets extracted from Twitter and analyzed the correlation between stock market movements of a company and sentiments in tweet.

399 0

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Revisiting Pre-Trained Models for Chinese Natural Language Processing

Ting Liu, Bing Qin, Shijin Wang, Wanxiang Che, Yiming Cui, Guoping Hu•Tue Mar 31 2020

Experimental results show that MacBERT could achieve state-of-the-art performances on many NLP tasks, and it is proposed that this model improves upon RoBERTa in several ways, especially the masking strategy that adopts MLM as correction (Mac).

806 0

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FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance

Hongyang Yang, Chris Wang, Liuqing Yang, Xiao-Yang Liu, Qian Chen, Runjia Zhang, Bowen Xiao•Wed Nov 18 2020

A DRL library FinRL is introduced that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies and to compare with existing schemes easily.

160 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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Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model

Hyeong Kyu Choi•Sat Aug 04 2018

This work applies LSTM recurrent neural networks (RNN) in predicting the stock price correlation coefficient of two individual stocks using the ARIMA-LSTM hybrid model, which turned out superior to all other financial models by a significant scale.

56 0

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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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Twitter mood predicts the stock market

J. Bollen, Huina Mao, Xiao-Jun Zeng•Wed Oct 13 2010

This work investigates whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time and indicates that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others.

5306 0

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