Stock Market Prediction
41 papers with code • 3 benchmarks • 4 datasets
Libraries
Use these libraries to find Stock Market Prediction models and implementations
StockNet
Latest papers
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.
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.
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.
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.
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.
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance
AI4Finance-Foundation/FinRL
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In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies.
Qlib: An AI-oriented Quantitative Investment Platform
microsoft/qlib
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Quantitative investment aims to maximize the return and minimize the risk in a sequential trading period over a set of financial instruments.
SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis
PaddlePaddle/PaddleNLP
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ACL 2020
In particular, the prediction of aspect-sentiment pairs is converted into multi-label classification, aiming to capture the dependency between words in a pair.
Revisiting Pre-Trained Models for Chinese Natural Language Processing
ymcui/Chinese-BERT-wwm
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Findings of the Association for Computational Linguistics 2020
Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further improve the performance of the pre-trained language models.
Forecasting directional movements of stock prices for intraday trading using LSTM and random forests
pushpendughosh/Stock-market-forecasting
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Hence we outperform the single-feature setting in Fischer & Krauss (2018) and Krauss et al. (2017) consisting only of the daily returns with respect to the closing prices, having corresponding daily returns of 0. 41% and of 0. 39% with respect to LSTM and random forests, respectively.
