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.
Study uses machine learning to predict stock prices, finds Kalman filter works well for low-volatility stocks.
problem Predicting stock prices using machine learning.
method Applied recursive machine learning techniques including linear Kalman filters and LSTM architectures to historical stock prices.
result Simple linear Kalman filter performs well for low-volatility stocks, while LSTM architectures outperform for high-volatility stocks.
This study optimizes stock portfolios using LSTM for historical data analysis.
problem Optimizing stock portfolios with predicted future prices and risks.
method Historical stock price data from Indian market sectors, LSTM model for prediction.
result LSTM model predicts high returns and low risks for optimized portfolios.
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.
Study predicts stock prices using historical data and sentiment analysis.
problem Predicting future stock movements in Indian markets.
method Used LSTM and Random Forest models with historical prices and sentiment data.
result Predicted stock prices of 4 major Indian companies with improved accuracy.
Analyzes how rough volatility affects stock pricing and risk premium.
problem Impact of non-deterministic volatility risk on stock pricing.
method Rough volatility model under historical measure, analysis of stochastic volatility risk.
result Impact of non-deterministic volatility risk on pricing is significant.
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.
AI models predict stock trends using historical data and public sentiment.
problem Improving stock market prediction accuracy using AI.
method Employed regression and classification ML algorithms for technical and fundamental analysis respectively.
result Median performance suggests AI is not yet superior to stock markets.
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.
The importance of considering the volumes to analyze stock prices movements can be considered as a well-accepted practice in the financial area. However, when we look at the scientific production in this field, we still cannot find a unified model that includes volume and price variations for stock assessment purposes.…
This research predicts stock market movements using Vision-Language models.
problem Predicting future stock market direction using historical data.
method Utilizing image and byte-based representations of stock data processed with Vision-Language models.
result The proposed approach significantly outperforms deep learning baselines.
Hybrid model combines PCA and RNN for better aerospace stock price prediction.
problem Challenges in predicting stock prices of aerospace companies due to market uncertainty and complexity.
method Combination of Principal Component Analysis (PCA) and Recurrent Neural Networks (RNN).
result PCA improves both accuracy and efficiency of stock price prediction.
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.
Proposes a multi-modal attention network for better stock price prediction.
problem Predicting future stock movements using historical records and social media.
method Extracts semantic information from social media, estimates credibility, and integrates with numeric features.
result Significantly improved prediction accuracy and trading profits compared to previous methods.
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.
Predict stock price movements using financial data and news articles with LLMs.
problem Predicting stock price movements using financial data and news articles.
method Combining financial data and news articles, employing pre-trained LLMs, and using retrieval augmentation techniques.
result Predicted stock price movements with a weighted F1-score of 58.5% and 59.1%.
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.
Contextualizing financial news improves stock price predictions.
problem Predicting stock prices from financial news requires understanding historical context.
method Proposed a method using a large language model for main articles and a small model for historical context.
result Historical context significantly improves model performance across methods and time horizons.
Nostradamus links climate and stock market performance.
problem Understanding the impact of climate on stock prices.
method Analyzing historical data, climate indicators, and natural disasters.
result Significant correlation between climate and stock price fluctuations.
In this work we use Recurrent Neural Networks and Multilayer Perceptrons to predict NYSE, NASDAQ and AMEX stock prices from historical data. We experiment with different architectures and compare data normalization techniques. Then, we leverage those findings to question the efficient-market hypothesis through a formal…
A new GNN model predicts stock trends by learning historical and future correlations.
problem Limited improvement in stock trend prediction models due to ignoring future patterns.
method DishFT-GNN framework that trains a teacher and student model to capture historical and future data correlations.
result State-of-the-art performance on real-world datasets.
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…
Stock correlations is crucial to asset pricing, investor decision-making, and financial risk regulations. However, microscopic explanation based on agent-based modeling is still lacking. We here propose a model derived from minority game for modeling stock correlations, in which an agent's expected return for one stock…
Paper develops a new similarity metric for predicting stock market returns.
problem Predicting stock returns is challenging due to market stochasticity and various influencing factors.
method Case-based reasoning approach using historical pricing data and a novel similarity metric.
result Demonstrates the benefits of the novel similarity metric in predicting stock market returns.
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 compares advanced deep learning models for Indian stock price forecasting.
problem Complexity of stock price forecasting due to numerous influencing factors.
method Utilizes historical data from national banks in India, combines deep learning models and sentiment analysis.
result Achieved higher accuracy in stock price forecasting compared to traditional methods.
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.
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.
Study finds varying market efficiency in prewar and wartime Japanese stock market.
problem Measuring market efficiency in prewar and wartime Japanese stock market.
method Using a new market capitalization-weighted stock price index, the study examines market efficiency over time and historical events.
result The adaptive market hypothesis is supported in the prewar and wartime Japanese stock market, with efficiency varying over time and with historical events.
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.
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 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.
The paper proposes machine learning models for option pricing without using historical or implied volatility.
problem Capturing option pricing without traditional volatility inputs.
method Three supervised machine learning approaches using data from multiple assets.
result Trained models outperform or match Black-Scholes formula for option pricing.
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 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. A classification of companies into sectors of the economy is important for macroeconomic analysis and for investments into the sector-specific financial indices and exchange traded funds (ETFs). Major industrial classification systems and financial indices have historically been based on expert opinion and developed ma…
Proposes neural model for stock embeddings to capture nuanced asset correlations.
problem Lack of research on modelling financial asset correlations.
method Neural model using historical returns data to learn nuanced relationships.
result Outperforms benchmarks in two real-world financial analytics tasks.
ChatGPT struggles in predicting stock movements, underperforming traditional methods.
problem Predicting stock market movements using ChatGPT.
method Zero-shot analysis of ChatGPT's multimodal stock prediction capabilities.
result ChatGPT underperforms traditional methods and state-of-the-art models in predicting stock movements.
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.
This study improves stock price forecasting by analyzing daily news sentiment.
problem Improving stock price forecasting accuracy using news sentiment.
method Data collection, preprocessing, and sentiment analysis of NITY50 stocks' news.
result LSTM models with sentiment scores outperform without them in forecasting stock prices.
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.
This paper studies how social media posts, especially by executives, affect stock prices.
problem Predicting stock market movements using social media data.
method Integrated sentiment analysis of Twitter and Reddit posts with historical stock data using time series models and deep learning.
result Improvements in stock price prediction when social media data, especially executive posts, are included.
This paper presents deep learning models for NIFTY 50 stock price prediction.
problem Accurately predicting stock prices using historical data.
method Used CNN and LSTM-based deep learning models on NIFTY 50 historical data.
result Univariate encoder-decoder convolutional LSTM model is the most accurate.
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.
A flexible calendar rebalancing approach for Indian stock portfolios.
problem Optimizing stock portfolio performance in the Indian stock market.
method Calendar rebalancing of sector-specific portfolios based on historical stock prices.
result The proposed calendar rebalancing approach improves portfolio performance over the test period.
Deep learning model predicts stock price movements based on historical data.
problem Inefficiencies in stock market leading to potential returns above market.
method Inspired by human traders, uses Resnet's skip connections and logits.
result Significant outperformance on both Korean and US stock markets.
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.
We devise an optimal allocation strategy for the execution of a predefined number of stocks in a given time frame using the technique of discrete-time Stochastic Control Theory for a defined market model. This market structure allows an instant execution of the market orders and has been analyzed based on the assumptio…