Stock Price Prediction
25 papers with code • 1 benchmarks • 2 datasets
Stock Price Prediction is the task of forecasting future stock prices based on historical data and various market indicators. It involves using statistical models and machine learning algorithms to analyze financial data and make predictions about the future performance of a stock. The goal of stock price prediction is to help investors make informed investment decisions by providing a forecast of future stock prices.
EDT
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
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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.
Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models
In this work, we propose an approach of hybrid modeling for stock price prediction building different machine learning and deep learning-based models.
Automatic Relevance Determination in Nonnegative Matrix Factorization with the β-Divergence
This paper addresses the estimation of the latent dimensionality in nonnegative matrix factorization (NMF) with the \beta-divergence.
Neural networks for stock price prediction
Due to the extremely volatile nature of financial markets, it is commonly accepted that stock price prediction is a task full of challenge.
FactorVAE: A Probabilistic Dynamic Factor Model Based on Variational Autoencoder for Predicting Cross-Sectional Stock Returns
Yaotian-Liu/FactorVAE
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AAAI Conference on Artificial Intelligence 2022
As an asset pricing model in economics and finance, factor model has been widely used in quantitative investment.
Artificial Counselor System for Stock Investment
This paper proposes a novel trading system which plays the role of an artificial counselor for stock investment.
Stock Price Prediction Based on Natural Language Processing
thlzm/codeforpaper
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Complexity 2022
The keywords used in traditional stock price prediction are mainly based on literature and experience.
Context-aware Frame-Semantic Role Labeling
Frame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis.
Stock Price Prediction via Discovering Multi-Frequency Trading Patterns
microsoft/qlib
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Then the future stock prices are predicted as a nonlinear mapping of the combination of these components in an Inverse Fourier Transform (IFT) fashion.
Predicting the Effects of News Sentiments on the Stock Market
nicklamonica/stock-sentiment-analysis
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Stock market forecasting is very important in the planning of business activities.
