Stock Market Prediction
41 papers with code • 3 benchmarks • 4 datasets
Libraries
Use these libraries to find Stock Market Prediction models and implementations
StockNet
Most implemented papers
Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market
riron1206/candlestick_model
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This work explores the predictability in the stock market using Deep Convolutional Network and candlestick charts.
Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network
armelf/Financial-Algorithms
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Moreover, the results show that future stock prices can be predicted even if the training and testing procedures are done in different countries.
Event Representation Learning Enhanced with External Commonsense Knowledge
MagiaSN/CommonsenseERL_EMNLP_2019
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IJCNLP 2019
Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as script event prediction.
Improving S&P stock prediction with time series stock similarity
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.
Applications of deep learning in stock market prediction: recent progress
Hence, our motivation for this survey is to give a latest review of recent works on deep learning models for stock market prediction.
SliceNStitch: Continuous CP Decomposition of Sparse Tensor Streams
SLICENSTITCH changes the starting point of each period adaptively, based on the current time, and updates factor matrices (i. e., outputs of CP decomposition) instantly as new data arrives.
Stock price prediction using Generative Adversarial Networks
ChickenBenny/Stock-prediction-with-GAN-and-WGAN
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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.
Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading
Zhihan1996/TradeTheEvent
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Findings (ACL) 2021
In this paper, we introduce an event-driven trading strategy that predicts stock movements by detecting corporate events from news articles.
Effectiveness of Artificial Intelligence in Stock Market Prediction based on Machine Learning
finnmorrison/ML-Fall-2021-Project
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This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies.
DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation
microsoft/qlib
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To handle concept drift, previous methods first detect when/where the concept drift happens and then adapt models to fit the distribution of the latest data.
