Transformer predicts Ethereum prices using cross-currency correlation and sentiment analysis.
problem Predicting Ethereum cryptocurrency prices with limited data.
method Transformer-based neural network with cross-currency correlation and sentiment analysis.
result Transformer model outperforms other models on some parameters.
In this paper, we design an integrated algorithm to evaluate the sentiment of Chinese market. Firstly, with the help of the web browser automation, we crawl a lot of news and comments from several influential financial websites automatically. Secondly, we use techniques of Natural Language Processing(NLP) under Chinese…
Study uses sentiment analysis to predict implied volatility surface, improving prediction accuracy.
problem Improving prediction accuracy of implied volatility surface.
method Constructed daily high-frequency sentiment data, used VAR method, deep learning (BERT, LSTM), FFT, EMD for sentiment decomposition.
result High-frequency sentiment correlates with ATM options' implied volatility, low-frequency with DOTM options.
Predicting market volatility from financial news and tweets.
problem Quantifying future volatility and returns in financial modeling.
method Topic modeling and sentiment analysis of financial news and tweets.
result Positive sentiment in tweets is negatively correlated with market volatility.
ChatGPT predicts stock market movements based on Bloomberg headlines, showing a positive correlation over short to medium terms.
problem Predicting stock market movements using news headlines.
method Used a two-stage prompt approach with a dataset of Bloomberg market summaries from 2010 to 2023.
result ChatGPT's sentiment scores correlate positively with future equity market returns over short to medium terms, with a negative correlation over longer horizons.
The paper analyzes how news sentiment of companies can affect market movements.
problem Understanding how news sentiment impacts market performance and volatility.
method Applied NLP techniques to analyze news sentiment of 87 companies over 7 years.
result Strong media sentiment towards one company can indicate significant changes in sentiment towards related companies.
This paper proposes a new HDP based online review rating regression model named Topic-Sentiment-Preference Regression Analysis (TSPRA). TSPRA combines topics (i.e. product aspects), word sentiment and user preference as regression factors, and is able to perform topic clustering, review rating prediction, sentiment ana…
The study improves sentiment analysis of 10-K filings, revealing aggregation effects on accuracy and correlation with market outcomes.
problem Lack of sentiment analysis for 10-K filings, particularly for risk disclosures.
method Supervised lexicon-learning approach applied to 10-K filings and Item 1A risk-factor sections, trained against return and volatility labels at different levels of aggregation.
result Sentiment analysis of Item 1A sections performs better at the individual-firm level, while full-filing text is more accurate at sector and portfolio levels.
Paper proposes ICCN to learn correlations between text, audio, and video for multimodal sentiment analysis.
problem Improving multimodal sentiment analysis by learning hidden correlations between text and audio/video features.
method Interaction Canonical Correlation Network (ICCN) using deep canonical correlation analysis (DCCA).
result Empirical results confirm the effectiveness of ICCN in capturing useful information from all three views.
A new Twitter sentiment model predicts stock market trends with high accuracy.
problem Real-time prediction of future stock market prices.
method Baseline correlation approach using polynomial regression, classification, and lexicon-based sentiment analysis.
result Predicts stock market trends with 67.22% accuracy, up to 15 time samples in advance.
Study finds Twitter activity correlates with stock volatility but not sentiment.
problem Understanding the impact of social media on stock market dynamics.
method Collected and analyzed tweets from Twitter and Reddit, examining their sentiment and correlation with stock volatility.
result Twitter activity correlates with stock volatility but not sentiment.
BondBERT improves sentiment analysis for bond markets.
problem Misleading sentiment models for bond markets due to domain-specific dynamics.
method Fine-tuned transformer model on bond-specific news.
result BondBERT produces positive correlations with bond returns and higher forecasting accuracy.
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.
The study analyzes sentiment of European tweets during the pandemic.
problem Understanding public sentiment during the COVID-19 pandemic.
method Cross-language sentiment analysis of multilingual tweets using neural networks and sentence embeddings.
result Sentiment analysis reveals that lockdown announcements correlate with a deterioration of mood, which recovers quickly.
We study the dependency and causality structure of the cryptocurrency market investigating collective movements of both prices and social sentiment related to almost two thousand cryptocurrencies traded during the first six months of 2018. This is the first study of the whole cryptocurrency market structure. It introdu…
Study uses topic modeling and sentiment analysis to uncover hedge fund performance insights.
problem Hedge fund opacity and limited disclosure make them hard to analyze.
method Applied topic modeling and sentiment analysis to hedge fund documents using DistilBERT and Top2Vec.
result Automated topic modeling and sentiment analysis can predict hedge fund performance.
New method reconstructs stable sentiment signals from sparse news data.
problem Transforming raw news sentiment outputs into reliable temporal series.
method Modular three-stage pipeline: aggregation, gap filling, and smoothing.
result Three-week lead-lag pattern between reconstructed sentiment and stock prices.
This paper optimizes portfolios using TDA and financial news sentiment.
problem Effective portfolio diversification through understanding asset similarity.
method Integrates TDA with FinBERT sentiment scores for dynamic rebalancing.
result Outperforms traditional methods in returns and risk-adjusted performance.
GIFsentiment predicts stock market returns and investor sentiment from social media GIFs.
problem Understanding investor sentiment in the stock market.
method Constructing a sentiment index from social media GIFs and analyzing its correlation with market returns and volume.
result GIFsentiment positively predicts stock market returns and negatively predicts returns for up to four weeks.
Study uncovers financial trends from cross-lingual news data.
problem Understanding financial dynamics across diverse global economies.
method Sentiment analysis, NER, and semantic textual similarity for news articles.
result Meaningful correlation between stock price movements and cross-linguistic news sentiments.
SentARL uses sentiment features to improve trading profits.
problem Improving profit stability in single-asset trading.
method Sentiment-Aware Reinforcement Learning (SentARL) system.
result SentARL consistently outperforms baselines across multiple assets and conditions.
Analyzes stock trends and e-commerce user behavior using Twitter data.
problem Understanding the relationship between stock prices, stock news, and e-commerce user behavior.
method Cross-domain analysis using Hadoop, Hive, and Tableau on three datasets.
result Identified correlations between stock sentiment, stock trends, and e-commerce user behavior.
Study proposes a machine learning method to predict stock price crashes based on investor sentiment.
problem Predicting stock price crashes due to investor sentiment.
method Minimum covariance determinant methodology and cross-sectional regression analysis.
result The proposed method effectively captures stock price crash risk and is robust across different firm sizes.
Transformer models improve financial sentiment measurement.
problem Capturing nuanced sentiment from financial news articles.
method Transformer-based language models for sentiment classification and aggregation.
result Transformer models outperform traditional dictionary-based methods in sentiment classification.
Enhances price sentiment index using survey comments.
problem Improving accuracy in price sentiment analysis.
method Classified comments from Economy Watchers Survey using LLMs.
result Higher correlation with existing indices.
Study uses AI to analyze emojis for predicting cryptocurrency market trends.
problem Predicting cryptocurrency market trends using social media sentiment.
method Fine-tuned transformer-based BERT model for multimodal sentiment analysis of emojis.
result Emoji sentiment analysis outperforms text-only sentiment analysis in predicting market trends.
This paper uses deep learning to analyze sentiment in financial forums and improve stock market prediction.
problem Improving stock market prediction accuracy through sentiment analysis.
method Crawling financial forum data, training BERT model on financial corpus, and using maximum information coefficient.
result Sentiment features from financial text can reflect stock market fluctuations and improve prediction accuracy.
Study shows how financial report sentiment impacts bank profitability.
problem Understanding causal effects of financial report sentiment on bank profitability.
method Causal forest machine learning methodology, FinancialBERT sentiment scores, SHAP analysis, comprehensive dataset.
result Statistically significant causal associations between balance sheet and expense management variables and profitability.
Understanding customer sentiments is of paramount importance in marketing strategies today. Not only will it give companies an insight as to how customers perceive their products and/or services, but it will also give them an idea on how to improve their offers. This paper attempts to understand the correlation of diff…
Study shows negative war news correlates with increased stock market volatility.
problem Understanding the impact of geopolitical events on financial markets.
method Used BERT model for sentiment analysis and GARCH model for volatility forecasting.
result Negative news sentiment during geopolitical crises is associated with increased stock market volatility.
Hybrid engine analyzes news sentiment for markets in real-time.
problem Real-time market analysis of news sentiment.
method Three-way ensemble learning combining financial lexicon, adaptive TF-IDF clustering, and auto-calibrated weighting.
result Adaptive statistical clustering learner improves adaptability to market changes.
We introduce and treat rigorously a new multi-agent model of the continuous double auction or in other words the order book (OB). It is designed to explain collective behaviour of the market when new information affecting the market arrives. The novel feature of the model is two additional slow changing parameters, the…
A new framework predicts stock movements using news sentiment and relational data.
problem Predicting stock prices from textual information is challenging due to market uncertainty and natural language complexity.
method Multi-Graph Recurrent Network (MGRN) combining textual sentiment from financial news and relational data.
result The model outperforms benchmarks in predicting stock movements.
We propose a novel approach to sentiment data filtering for a portfolio of assets. In our framework, a dynamic factor model drives the evolution of the observed sentiment and allows to identify two distinct components: a long-term component, modeled as a random walk, and a short-term component driven by a stationary VA…
The paper predicts Bitcoin prices using machine learning and sentiment analysis.
problem Predicting the future price of Bitcoin in USD.
method Applied supervised machine learning and sentiment analysis to Twitter and Reddit data.
result LSTM models with multi-feature analysis outperformed ARIMA models in predicting Bitcoin prices.
To understand the relationship between news sentiment and company stock price movements, and to better understand connectivity among companies, we define an algorithm for measuring sentiment-based network risk. The algorithm ranks companies in networks of co-occurrences, and measures sentiment-based risk, by calculatin…
Cryptocurrency forecasting model considers macro, sentiment, and technical indicators.
problem High price volatility in cryptocurrency markets.
method Dual-prediction mechanism incorporating macroeconomic fluctuations, technical indicators, and individual cryptocurrency price changes.
result The proposed model outperforms ten comparison methods in short-term cryptocurrency forecasting.
This research aims to identify how Bitcoin-related news publications and online discourse are expressed in Bitcoin exchange movements of price and volume. Being inherently digital, all Bitcoin-related fundamental data (from exchanges, as well as transactional data directly from the blockchain) is available online, some…
Traditional stock market prediction approaches commonly utilize the historical price-related data of the stocks to forecast their future trends. As the Web information grows, recently some works try to explore financial news to improve the prediction. Effective indicators, e.g., the events related to the stocks and the…
Quantitative model predicts Sri Lankan stock market using NLP, clustering, and time-series forecasting.
problem Predicting economic regimes and market signals in Sri Lankan stock indices.
method Integrates NLP, clustering, and time-series forecasting; uses FinBERT for sentiment analysis, UMAP/HDBSCAN for clustering, and GRU/LSTM for forecasting.
result GRU model achieves 80.1% R-squared for daily closing price forecasts.
We investigate the trading behavior of a large set of single investors trading the highly liquid Nokia stock over the period 2003-2008 with the aim of determining the relative role of endogenous and exogenous factors that may affect their behavior. As endogenous factors we consider returns and volatility, whereas the e…
This paper learns multi-modal embeddings from text, audio, and video views/modes of data in order to improve upon down-stream sentiment classification. The experimental framework also allows investigation of the relative contributions of the individual views in the final multi-modal embedding. Individual features deriv…
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.
In this paper, we analyze Twitter signals as a medium for user sentiment to predict the price fluctuations of a small-cap alternative cryptocurrency called \emph{ZClassic}. We extracted tweets on an hourly basis for a period of 3.5 weeks, classifying each tweet as positive, neutral, or negative. We then compiled these …
GRU-PFG model extracts inter-stock correlations from stock factors using graph neural networks.
problem Limited effectiveness of models relying solely on stock factors for capturing stock correlations.
method Project stock factors into a graph and use graph neural networks to extract inter-stock correlations.
result Achieves better prediction results than models relying solely on stock factors and comparable to second category models.
Study predicts cryptocurrency price movements using Twitter sentiment analysis.
problem Predicting short-term price movements of cryptocurrencies.
method Conditional examination of return and excess return rates following tweet publication.
result Statistically significant increases in return rates within the first three minutes after tweet publication.
This paper combines a node transformer with BERT sentiment analysis for more accurate stock market predictions.
problem Challenges in predicting stock markets due to noise, non-stationarity, and behavioral dynamics.
method Integrates a node transformer architecture with BERT sentiment analysis to forecast stock prices.
result The integrated model reduces prediction error by 10% overall and 25% during earnings announcements.
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.