Stock Prediction

25 papers with code • 0 benchmarks • 3 datasets

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Most implemented papers

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

Xinyi6/DP-LSTM-Differential-Privacy-inspired-LSTM-for-Stock-Prediction-Using-Financial-News 20 Dec 2019

In this paper, we propose a novel deep neural network DP-LSTM for stock price prediction, which incorporates the news articles as hidden information and integrates difference news sources through the differential privacy mechanism.

Temporal Relational Ranking for Stock Prediction

hennande/Temporal_Relational_Stock_Ranking 25 Sep 2018

Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner.

HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction

dmis-lab/hats 7 Aug 2019

Methods that use relational data for stock market prediction have been recently proposed, but they are still in their infancy.

Stock trend prediction using news sentiment analysis

nicklamonica/stock-sentiment-analysis 7 Jul 2016

The accuracy of the prediction model is more than 80% and in comparison with news random labeling with 50% of accuracy; the model has increased the accuracy by 30%.

Artificial Counselor System for Stock Investment

bghojogh/Fuzzy-Investment-Counselor Proceedings of the AAAI Conference on Artificial Intelligence 2019

This paper proposes a novel trading system which plays the role of an artificial counselor for stock investment.

Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport

microsoft/qlib 24 Jun 2021

In this paper, we propose a novel architecture, Temporal Routing Adaptor (TRA), to empower existing stock prediction models with the ability to model multiple stock trading patterns.

Enhancing Stock Movement Prediction with Adversarial Training

yuxiangalvin/Stock-Move-Prediction-with-Adversarial-Training-Replicate 13 Oct 2018

The key novelty is that we propose to employ adversarial training to improve the generalization of a neural network prediction model.

Improving S&P stock prediction with time series stock similarity

liorsidi/StockSimilarity 8 Feb 2020

Stock market prediction with forecasting algorithms is a popular topic these days where most of the forecasting algorithms train only on data collected on a particular stock.

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

start2020/Multi-GCGRU 11 May 2020

However, it is well known that an individual stock price is correlated with prices of other stocks in complex ways.

Stock price prediction using Generative Adversarial Networks

ChickenBenny/Stock-prediction-with-GAN-and-WGAN Journal of Computer Science 2021

In this paper, it 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 price and Convolutional Neural Network (CNN) as a discriminator to discriminate between the real stock price and generated stock price.