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
Latest papers
Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels
When applied to the largest binary UCR dataset, Detach-ROCKET is able to improve test accuracy by $0. 6\%$ while reducing the number of features by $98. 9\%$.
Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction
koa-fin/dva
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The hierarchical VAE allows us to learn the complex and low-level latent variables for stock prediction, while the diffusion probabilistic model trains the predictor to handle stock price stochasticity by progressively adding random noise to the stock data.
PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance
This paper introduces PIXIU, a comprehensive framework including the first financial LLM based on fine-tuning LLaMA with instruction data, the first instruction data with 136K data samples to support the fine-tuning, and an evaluation benchmark with 5 tasks and 9 datasets.
A Multifactor Analysis Model for Stock Market Prediction
Stock Market predictions have historically been a problem tackled by different singular approaches even though markets are influenced by many different factors.
Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural Network and Variational Mode Decomposition
In the prediction and reconstruction phase, each of the IMFs is given to a separate MFRFNN for prediction, and predicted signals are summed to reconstruct the output.
MFRFNN: Multi-Functional Recurrent Fuzzy Neural Network for Chaotic Time Series Prediction
MFRFNN consists of two fuzzy neural networks with Takagi-Sugeno-Kang fuzzy rules, one is used to produce the output, and the other to determine the system’s state.
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.
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.
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.
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.
