This paper uses neural networks to predict stock prices more accurately.
problem Current stock analysis methods are inaccurate.
method Dynamic neural networks to identify stock price patterns.
result Neural networks outperform traditional stock analysis methods.
This study analyzes stock trading networks to quantify price impacts based on trader positions.
problem Quantifying the immediate price impact of trades in stock markets.
method Constructed stock trading networks using k-shell decomposition to classify traders and compare different market segments. result Institutional traders have lower price impacts compared to individuals at the same positions in the trading network.
This paper predicts significant stock price changes using neural networks.
problem Predicting significant stock price changes.
method Three neural network models (MLP, CNN, LSTM) and two benchmark models (Random Forest, Relative Strength Index) were tested on 10-year daily stock price data of four major US companies.
result Neural network models significantly outperform traditional methods in predicting significant stock price changes.
Study uses neural networks to predict stock prices and tests market efficiency.
problem Predicting stock prices from historical data.
method Used Recurrent Neural Networks and Multilayer Perceptrons, compared normalization techniques.
result Found that neural networks can predict stock prices accurately and challenged the efficient-market hypothesis.
Improved stock prediction using news features and RNN.
problem Predicting stock prices with high accuracy.
method Extracted news features, optimized seed words, calculated positive polar, constructed news features, proposed RNN model.
result Our method improves stock prediction accuracy by over 5%.
GCNET predicts stock price movements using graph convolutional networks.
problem Predicting stock price movements using interrelated stocks data.
method GCNET models stock relations as an influence network, uses graph convolutional networks for prediction.
result GCNET significantly improves prediction accuracy and MCC measures.
Cross-shareholding improves stock price synchronicity in China.
problem Measuring price informativeness in Chinese stock market firms.
method Analyzing cross-shareholding networks and centrality measures.
result Cross-shareholding reduces price delay and enhances price synchronicity.
Neural networks predict stock prices better than traditional methods.
problem Predicting stock prices in volatile financial markets.
method Compared five neural network models (BP, RBF, GRNN, SVMR, LS-SVMR) on three stocks.
result BP neural network outperformed other models in accuracy.
Hybrid model predicts stock prices more accurately.
problem Efficient stock price prediction.
method Symbiotic organisms search algorithm trained feedforward neural networks.
result Outstanding predictive performance compared to other models.
Paper introduces a new indicator to predict financial extremes using stock price network degree.
problem Predicting financial market extremes during bull and bear markets.
method Constructs an indicator based on the degree of stock price network generated from time series.
result The new indicator shows strong predictive power for financial extremes, both peaks and troughs.
Neural network learns to solve Black-Scholes for stock options.
problem Stock option pricing using the Black-Scholes Equation.
method Neural Networks applied to solve the Black-Scholes Equation.
result Neural network can accurately forecast stock option prices.
AI predicts stock prices with neural networks.
problem Predicting exact stock prices at the stock market.
method Feedforward multi-layer perceptron with error backpropagation, using 5:21:21:1 architecture.
result Simple neural networks can predict stock prices effectively.
Game-theoretic model captures investor interactions for stock price forecasting.
problem Complex market dynamics driving stock price movements.
method Game-theoretic modeling of heterogeneous investor interactions in a dynamic graph structure.
result Our method outperforms state-of-the-art stock price forecasting methods.
Deep Q-Network predicts global stock market returns from chart images.
problem Predicting global stock market returns using chart images.
method Deep Q-Network with CNN approximator, trained on US stock market, tested on 31 countries.
result Artificial intelligence can predict stock prices in small markets.
Deep learning predicts stock prices using CNN and NALUs.
problem Predicting future stock prices accurately.
method Convolutional Neural Network (CNN) for feature extraction and Neural Arithmetic Logic Units (NALUs) for arithmetic operations.
result Improved accuracy in predicting stock prices.
Model earnings call transcripts for better stock price prediction.
problem Predicting future stock price movements using earnings call transcripts.
method Deep learning framework with an attention mechanism to encode text data into vectors for predicting stock price movements.
result The proposed model outperforms traditional machine learning methods in stock price prediction.
LSTM model predicts stock prices with high accuracy in stable sectors but struggles with volatile ones.
problem Predicting stock prices in emerging markets with limited data.
method Developed and evaluated an LSTM network on historical OHLCV data and technical indicators.
result Strong predictive performance (R2>0.87) for stable sectors, but challenges for volatile ones. Optimal stock price prediction model using recurrent neural networks with RMSprop optimizer.
problem Stock price prediction using neural networks.
method Comparison of fully connected, convolutional, and recurrent architectures; inclusion of three optimization techniques.
result Single layer recurrent neural network with RMSprop optimizer produces optimal results with validation and test MAE of 0.0150 and 0.0148 respectively.
Paper uses neural networks to analyze oil price impact on Iranian stock and industry indices.
problem Impact of oil price volatility on Tehran stock and industry indices.
method Feed-forward neural networks analysis of two periods: sanctions and post-sanctions.
result Neural networks predict stock and industry indices well, showing significant oil price volatility impact.
Research evaluates ANN stock price prediction system for Shanghai Stock Exchange.
problem Predicting exact stock prices in the Shanghai Stock Exchange.
method Feedforward multi-layer perceptron with error backpropagation, using 5:21:21:1 configuration with 80% training data.
result Neural networks can predict stock prices with low mean absolute percentage errors (1.95%).
Neural networks for stock price prediction often misrepresent model performance due to flawed error metrics.
problem Flawed prediction error metrics lead to unreliable model evaluations in the securities market.
method Used data from 20 stock datasets across multiple markets and evaluated with four prediction error measures.
result Prediction error value only partially reflects model accuracy and fails to represent stock price direction.
Predict stock movement by considering cross effects among stocks.
problem Challenges in predicting stock price movement due to cross effects among stocks.
method Multi-GCGRU framework combining GCN and GRU, encoding cross effects from financial domain knowledge and data-driven relationships.
result Our model outperforms other baselines in predicting stock movement.
Paper uses TCN with attention to predict UHF stock price changes.
problem Predicting discrete dynamic distribution of UHF stock price changes.
method Classified price changes, used TCN with attention mechanism.
result TCN and TCN (attention) models outperform GARCH and LSTM models.
Deep learning model forecasts stock prices for portfolio optimization.
problem Precise stock price prediction and portfolio optimization.
method LSTM network for web-scraped historical data, automated stock price forecasting.
result Model demonstrates profitability of sectors for investors.
LSTM networks improve stock price prediction accuracy.
problem Enhancing stock price forecasting accuracy.
method LSTM networks with hyperparameter tuning and feature selection.
result 53% improvement in predictive accuracy.
New approach predicts stock price synchronization using RNNs and LSTMs.
problem Forecasting synchronization of stock prices in the Indian market.
method Utilizing recurrence plots and CRQA for non-linear analysis, RNNs and LSTMs for prediction.
result Accuracy of 0.98 and F1 score of 0.83 in predicting stock price synchronization.
Algorithm measures sentiment-based network risk in companies.
problem Understanding the relationship between news sentiment and stock price movements.
method Algorithm ranks companies based on news sentiment and co-occurrences, calculating individual and aggregated risks.
result The highest quarterly risk value correlates with a higher chance of stock price decline up to 70 days later.
Predict stock prices using financial news sentiment analysis.
problem Predicting stock market trends for better investment returns.
method Deep Learning (MLP, LSTM, FinBERT-LSTM) integrating news sentiment.
result FinBERT-LSTM model predicts stock prices more accurately.
Deep learning models predict stock prices with high accuracy.
problem Accurate prediction of stock prices despite market unpredictability.
method CNN and LSTM-based deep learning models trained on historical stock data.
result Models achieve high accuracy in forecasting future stock prices.
We use insight from a model of earth tectonic plate movement to obtain a new understanding of the build up and release of stress in the price dynamics of the worlds stock exchanges. Nonlinearity enters the model due to a behavioral attribute of humans reacting disproportionately to big changes. This nonlinear response …
Adversarial training improves stock price prediction accuracy.
problem Overfitting due to static stock price features.
method Adversarial training to simulate price stochasticity.
result 3.11% relative improvement in accuracy on average.
This paper uses GAN and ERMSE to improve stock price movement prediction accuracy.
problem Predicting stock price movement direction is challenging due to complex, incomplete, and fuzzy information.
method The paper proposes a deep learning model using GAN and ERMSE to forecast stock market trends.
result The GAN model outperformed LSTM in predicting stock price movement direction with a 4.35% improvement.
Study improves stock index prediction accuracy using TPE-GRNN models.
problem Enhancing prediction of stock index prices in volatile markets.
method Gated recurrent neural networks (LSTM, GRU) combined with TPE Bayesian optimization.
result TPE-LSTM method shows lowest MAPE (best accuracy) for NIFTY 50 index prediction.
The model predicts stock price trends and opening, minimum, and maximum prices with reasonable accuracy.
problem Forecasting stock prices and trends for investment decisions.
method Improvement of a model based on the association of three LSTM neural networks.
result The model predicts stock price trends and opening, minimum, and maximum prices with reasonable accuracy.
Develops a hybrid deep learning model for stock price prediction.
problem Predicting daily stock prices in the stock market.
method Representation learning with Stock2Vec embedding and temporal convolutional layers.
result Achieves better performance on stock price prediction than benchmarks.
Hybrid model predicts stock prices using ML, DL, and NLP.
problem Improving prediction accuracy of stock price movement.
method Machine learning, deep learning, natural language processing, sentiment analysis.
result LSTM model outperforms traditional machine learning models.
Hidformer improves stock price prediction accuracy using Transformer techniques.
problem Improving stock price prediction accuracy using machine learning.
method Adapted Transformer model (Hidformer) for stock price forecasting.
result Hidformer shows promising performance in stock price prediction.
Predict stock prices using HMMs trained on fractional price changes and intraday highs/ lows.
problem Forecasting stock prices considering time dependency and volatility.
method Hidden Markov Models (HMMs) trained on fractional price changes and intraday highs/ lows.
result The MAP estimate of stock prices for the next day was produced using the trained HMM.
Hybrid LSTM-GNN model improves stock price prediction accuracy.
problem Enhancing stock price prediction accuracy using complex data.
method Combining LSTM for time series and GNN for graph-based analysis.
result Hybrid model reduces MSE by 10.6% compared to standalone LSTM.
SAMBA predicts stock returns efficiently using Mamba and graph neural networks.
problem Accurate stock price predictions for financial returns.
method SAMBA integrates Mamba architecture with graph neural networks to achieve near-linear computational complexity.
result SAMBA significantly outperforms state-of-the-art models in prediction accuracy.
Neural network predicts stock prices using technical indicators.
problem Predicting stock prices for optimal trading.
method Converted financial data into buy-sell-hold signals, trained MLP ANN model with Apache Spark.
result Neural network model performs comparably to Buy and Hold strategy.
This paper predicts stock prices using BERT for sentiment analysis and GAN for technical indicators.
problem Predicting stock prices using advanced techniques.
method BERT for sentiment analysis, GAN for technical indicators, comparison with baseline models.
result GAN outperformed baseline models in predicting stock prices.
Weak predictability of stock price movement 2 days after annual report disclosure.
problem Predicting stock price movement after annual report disclosure.
method Used various models including decision tree, logistic regression, random forest, neural network, prototypical networks; used financial indicators from EastMoney.
result Maximum accuracy and precision of stock price movement prediction is around 59.6% and 0.56 respectively, with random forest performing best.
This study compares deep learning and statistical models for stock price forecasting.
problem Accurate stock price prediction is challenging due to market volatility.
method Used deep learning (LSTM, RNN, CNN, FULL CNN) and statistical models (ARIMA, Moving Averages) on S&P 500 data.
result LSTM model showed the lowest Mean Absolute Error (MAE), indicating highest accuracy.
New neural network predicts stock price jumps using limit order book data.
problem Predicting short-term price movements in stock markets.
method Attention-based Convolutional Long Short-Term Memory network architecture.
result Attention mechanism improves jump prediction performance.
Social media features predict stock prices in China's market.
problem Lack of research on stock prediction in China's market.
method Extracted features from Xueqiu tweets, analyzed collective sentiment and perception, used nonlinear models.
result Social media features are effective in predicting stock price movements in China.
Study compares MoE and RNN models for stock price prediction across volatility profiles.
problem Improving stock price prediction accuracy across different volatility levels.
method Dynamic Mixture of Experts model combining RNN and linear models, adjusting weights through a gating network.
result MoE model outperforms individual models in reducing prediction errors.
MambaStock predicts stock prices with high accuracy using a state space model.
problem Inaccurate stock price predictions due to nonlinearity in stock market data.
method Mamba-based state space model with selection mechanism and scan module.
result MambaStock outperforms previous methods in stock price prediction accuracy.