Hybrid model predicts stock prices using online forum sentiments and popularity.
problem Predicting stock prices accurately considering investor sentiment.
method XLNET for sentiment analysis, BiLSTM-highway model integration, combining post popularity.
result Hybrid model outperforms traditional methods in stock price prediction.
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.
Deep learning models predict stock prices with high accuracy.
problem Accurate prediction of future stock prices in an efficient market.
method Robust deep learning models using historical stock data.
result Models achieve high precision in predicting stock prices.
In this paper, we address one of the main puzzles in finance observed in the stock market by proponents of behavioral finance: the stock predictability puzzle. We offer a statistical model within the context of rational finance which can be used without relying on behavioral finance assumptions to model the predictabil…
Quantum algorithms improve stock price prediction accuracy.
problem Improving stock price prediction accuracy using quantum techniques.
method Extracted stock price indicators, used QA and PCA for feature selection and dimensionality reduction, trained QSVM for binary classification.
result Quantum Support Vector Machine (QSVM) outperformed classical models in stock price prediction accuracy.
Paper compares stock price prediction models using Heston and Geometric Brownian Motion.
problem Predicting stock prices accurately.
method Developed Heston and Geometric Brownian Motion models using Ito's lemma and Euler-Maruyama methods.
result Models outperform statistical indicators in predicting stock prices.
Deep learning predicts cross-sectional stock prices for practical investment.
problem Predicting stock prices using cross-sectional factors.
method Deep learning model for daily stock price prediction.
result Profitable investment framework demonstrated in Japanese stock market.
Deep learning LSTM predicts stock prices for portfolio design in Indian sectors.
problem Predicting stock prices in Indian stock market.
method Long Short-Term Memory (LSTM) model for historical stock price prediction.
result Efficacy of LSTM model in predicting stock prices and informing investment decisions.
Transformer model predicts stock prices in Bangladesh's stock market.
problem Predicting volatile stock prices in the Bangladesh stock market.
method Transformer model applied to time series data for stock price prediction.
result Transformer model shows promising results in predicting stock price movements.
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.
Paper proposes integrating wavelet transform, channel attention, and LSTM for better stock price prediction.
problem Inherently difficult stock price prediction due to low signal-to-noise ratio.
method Wavelet transform convolution, channel attention, and LSTM integration.
result Robust performance in post-pandemic market conditions.
This paper predicts stock prices using LLMs and news embeddings.
problem Predicting stock prices with high accuracy and relevance.
method Integrates LLMs with stock name embeddings and attention mechanisms for news filtering.
result Reduces MAE by 7.11% compared to baseline.
The paper uses LSTM to predict stock prices and analyzes sector profitability.
problem Predicting future stock prices in a volatile market.
method LSTM architecture for predicting stock prices from historical data.
result The model accurately predicts stock prices and analyzes sector profitability.
LSTM model predicts stock prices for optimized portfolios.
problem Accurate stock price prediction for optimized portfolio design.
method Past stock prices from 2016-2020, LSTM model for prediction.
result High accuracy of LSTM model in predicting stock returns.
Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most ch…
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.
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.
The paper defines the time function of stock prices using a mathematical model.
problem Understanding the movement and predictability of stock prices over time.
method Empirical evidence and mathematical modeling of white noise.
result Derives auto-correlation function, displacement formula, and power spectral density of stock price movement.
The paper uses LSTM to predict stock prices and optimize portfolio weights.
problem Accurate prediction of stock prices and designing optimized portfolios.
method Built sector-wise portfolios and an LSTM model for stock price prediction.
result The LSTM model accurately predicts stock prices with high accuracy.
Model predicts stock price changes and forecasts using tokenized data.
problem Challenges in stock price forecasting and prediction due to dynamic data and statistical differences.
method Introduces PCIE model with tokenization to handle both forecasting and prediction.
result PCIE model outperforms state-of-the-art models in forecast and prediction tasks.
StockTime predicts stock prices more accurately using LLMs and time series data.
problem Challenges in integrating time series data and natural language for stock price prediction.
method StockTime is a specialized LLM architecture that integrates textual and time series data to predict stock prices.
result StockTime outperforms recent LLMs in predicting stock prices with more accuracy.
Paper predicts stock prices using ML and human intelligence.
problem Uncertainty in stock price prediction.
method ML models (LSTM, ARIMA, CNN-LSTM, GRU, LSTM-GRU) augmented with Superforecasters predictions.
result Superforecasters' predictions improve stock price prediction accuracy.
This study improves stock price prediction by incorporating anticipated macroeconomic policy changes.
problem Improving accuracy in stock price prediction.
method Incorporates future expected macroeconomic policy changes and historical stock prices.
result Our method outperforms conventional approaches with an RMSE of 1.61 compared to 1.75.
Study compares LSTM models with sentiment analysis for stock price prediction.
problem Efficient stock price prediction models using LSTM with sentiment analysis.
method Various types of LSTM models combined with sentiment analysis.
result Identifies the most effective model for short and long-term stock price prediction.
Deep learning models predict stock prices with high accuracy and speed.
problem Precise prediction of stock prices in an efficient market.
method Design and training of ten deep learning regression models.
result Models achieve high accuracy in forecasting stock prices of an auto sector company.
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.
This paper predicts stock prices during unusual events like the pandemic.
problem Lack of models to predict stock price changes during catastrophic events.
method ARIMA, LSTM, sentiment analysis models trained on historical data.
result Achieved 98% prediction accuracy for stock prices during anomalous circumstances.
Diffusion-VAE tackles multi-step stock price prediction with stochastic noise.
problem Challenges in multi-step stock price prediction due to stochasticity and target price sequence.
method Combines hierarchical VAE and diffusion probabilistic techniques for seq2seq stock prediction.
result D-Va model outperforms state-of-the-art solutions in prediction accuracy and variance.
Stock prediction is a topic undergoing intense study for many years. Finance experts and mathematicians have been working on a way to predict the future stock price so as to decide to buy the stock or sell it to make profit. Stock experts or economists, usually analyze on the previous stock values using technical indic…
The study evaluates nine machine learning regressors for predicting NASDAQ stock opening prices.
problem Predicting stock market opening prices for profitable trading strategies.
method Nine different machine learning regressors were applied to NASDAQ stock market data.
result The study found that certain regressors outperform others in predicting stock opening prices.
FinALBERT predicts stock prices using labelled Stocktwits data.
problem Efficient stock price prediction with limited labelled datasets.
method FinALBERT is an ALBERT-based model trained on labelled Stocktwits data for financial text classification.
result FinALBERT achieves optimal results in predicting stock price changes.
This paper compares LSTM, GRU, and Transformer models for stock price prediction.
problem Improving stock price prediction accuracy in fast-paced financial markets.
method Training models on Tesla stock data from 2015 to 2024, comparing LSTM, GRU, and Transformer.
result LSTM model achieved 94% accuracy in predicting stock prices.
Survey of methods to incorporate external knowledge into stock price prediction.
problem Challenges in predicting stock prices due to market volatility and non-linearity.
method Survey of methods for acquiring and incorporating external knowledge into stock price prediction models.
result Systematic synthesis of previous studies on external knowledge types and their application in stock price prediction.
Combining various data types predicts S&P 500 stock prices with high accuracy.
problem Predicting S&P 500 stock prices with high accuracy.
method Combined technical, fundamental, and text data with machine learning models like Random Forest and LSTM.
result Achieved 66.18% accuracy in S&P 500 index prediction and 62.09% in individual stock prediction.
Taureau uses Twitter sentiment analysis to predict stock market movement.
problem Predicting stock market movement using public opinion on Twitter.
method Obtained historical tweets, filtered and labeled, generated word embeddings, assessed sentiment scores, correlated with stock price movement, designed and evaluated predictive model.
result Taureau can predict stock price movement from lagged sentiment scores.
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.
Paper uses CNN to predict stock price movement as an image classification problem.
problem Predicting stock price movement using machine learning.
method CNN-based model for classifying stock price movement based on the first hour of trading.
result The algorithm effectively separated between stock price movement classes and outperformed other strategies.
This paper proposed a method for stock prediction. In terms of feature extraction, we extract the features of stock-related news besides stock prices. We first select some seed words based on experience which are the symbols of good news and bad news. Then we propose an optimization method and calculate the positive po…
ReVol normalizes stock price features to mitigate distribution shifts, improving prediction accuracy.
problem Distribution shifts in stock price data hinder accurate prediction.
method ReVol uses normalization, attention-based estimation, and geometric Brownian motion.
result ReVol achieves an average improvement of more than 0.03 in IC and over 0.7 in SR.
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.
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.
Advanced ML/DL models predict stock prices using technical analysis.
problem Accurately predicting stock prices in a complex market.
method Use of deep learning models for stock price prediction.
result Deep learning models can predict stock prices with high accuracy.
Paper proposes a model to predict stock prices using historical and sentiment data.
problem Improving accuracy in predicting stock prices.
method Integrates historical and sentiment data to predict stock prices using LSTM.
result Improved accuracy in predicting stock prices.
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. Study shows Twitter sentiments predict stock price fluctuations.
problem Predicting stock prices using public opinions.
method Time series analysis and natural language processing with LSTM model.
result Positive, negative, and subjective sentiments correlate with stock price changes.
We propose that predictability is a prerequisite for profitability on financial markets. We look at ways to measure predictability of price changes using information theoretic approach and employ them on all historical data available for NYSE 100 stocks. This allows us to determine whether frequency of sampling price c…
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.
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.