Stock Price Prediction
26 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.
Astock
Most implemented papers
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.
Particle Filter Recurrent Neural Networks
Yusufma03/pfrnns
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Recurrent neural networks (RNNs) have been extraordinarily successful for prediction with sequential data.
A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing
Based on the data of 2015 to 2017, we build various predictive models using machine learning, and then use those models to predict the closing value of NIFTY 50 for the period January 2018 till June 2019 with a prediction horizon of one week.
Deep Stock Predictions
Forecasting stock prices can be interpreted as a time series prediction problem, for which Long Short Term Memory (LSTM) neural networks are often used due to their architecture specifically built to solve such problems.
Trader-Company Method: A Metaheuristic for Interpretable Stock Price Prediction
We show the effectiveness of our method by conducting experiments on real market data.
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.
S&P 500 Stock Price Prediction Using Technical, Fundamental and Text Data
Shanlearning/SP-500-Stock-Prediction
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We summarized both common and novel predictive models used for stock price prediction and combined them with technical indices, fundamental characteristics and text-based sentiment data to predict S&P stock prices.
Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model
jinanzou/astock
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In addition, we propose a self-supervised learning strategy based on SRLP to enhance the out-of-distribution generalization performance of our system.
