Investor sentiment improves model accuracy but complexity doesn't always boost predictive power.
problem Determining the optimal complexity of investor sentiment measures in asset pricing models.
method Comprehensive review of 71 papers from 2000-2021, analyzing various sentiment measures and models.
result Higher complexity of sentiment measures does not necessarily improve predictive power.
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.
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.
Study finds investor sentiment has a significant positive relationship with stock returns in Moroccan and Tunisian markets.
problem Investor sentiment and stock returns relationship in Moroccan and Tunisian markets.
method Used indirect measures of investor sentiment (SENT and ARMS) and Granger causality tests.
result Sentiment has a significant positive relationship with stock returns, but not the other way around.
LLMs improve financial sentiment analysis in finance.
problem Defining and measuring financial sentiment.
method Investigation of sentiment measurement methods and LLMs.
result LLMs enhance financial sentiment analysis.
SentiCite analyzes citations for sentiment and nature, improving on existing methods.
problem Identifying quality scientific work amidst many citations.
method Sentiment analysis of citations with motivation detection.
result SentiCite outperforms state-of-the-art methods with a F1-measure of 0.71.
Detects non-linear causality between social media sentiment and cryptocurrency prices.
problem Detecting causality between non-linear time series data.
method Use of transfer entropy to measure information transfer, validating against synthetic data, and applying significance tests.
result Significant non-linear causality detected, orders of magnitude greater than linear causality.
Study shows how Twitter users' sentiment levels correlate with network structure and external events.
problem Understanding how Twitter users' sentiment levels are influenced by network structure and external events.
method Used a large dataset of tweets, applied sentiment scoring algorithms, and created an agent-based model.
result People with larger communication reach use positive sentiment more often and negative sentiment less often.
Improved crude oil price forecasting using multi-dimensional LLM sentiment signals.
problem Challenges in predicting crude oil prices due to unstructured news.
method Extracted five sentiment dimensions from GPT-4o, Llama 3.2-3b, and FinBERT models on energy-sector news articles.
result Combining GPT-4o and FinBERT yields the best predictive performance for weekly WTI crude oil futures returns.
Study investor sentiment and disagreement on StockTwits during COVID-19.
problem Understanding investor beliefs and sentiment during the pandemic.
method Analysis of social media data (StockTwits) for investor messages.
result Sentiment and disagreement sharply decreased in early March 2020, followed by a reversal.
This paper discusses sentiment analysis on social media text.
problem Detecting sentiments in short text messages for applications like mental health monitoring.
method Combines concepts from Natural Language Processing and Machine Learning.
result Explains techniques used in sentiment analysis of textual data.
Dual-CLVSA predicts financial markets using both trading data and sentiment measurements.
problem Predicting financial markets with complex interactions and emotional influences.
method Hybrid convolutional LSTM-based variational sequence-to-sequence model with attention.
result Dual-CLVSA effectively fuses trading data and sentiment measurements, improving prediction performance.
New measure corrects news bias in NLP stock return forecasting.
problem Improving stock return and volatility forecasting accuracy.
method Hype-Adjusted Probability Measure, sentiment score equation.
result Significantly improved forecast accuracy for U.S. semiconductor tickers.
Study evaluates if LLMs have company-specific biases in financial sentiment analysis.
problem Evaluating if large language models exhibit company-specific biases in financial sentiment analysis.
method Comparing sentiment scores with and without company names, constructing economic models, and empirical analysis.
result LLMs show company-specific biases in sentiment analysis, impacting investor behavior and stock prices.
Paper proposes online optimization methods for quantification tasks.
problem Estimating class prevalence in various domains.
method Online stochastic algorithms for optimizing quantification-specific performance measures.
result Proves optimal convergence and significantly outperforms existing techniques.
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.
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.
This paper proposes using neural network sentiment measures to prioritize test inputs.
problem High cost of obtaining test oracle data limits testing efficacy of neural networks.
method Proposes using confidence, uncertainty, and surprise measures to prioritize test inputs.
result Sentiment measures can effectively flag inputs revealing DNN weaknesses.
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.
Financial market prediction on the basis of online sentiment tracking has drawn a lot of attention recently. However, most results in this emerging domain rely on a unique, particular combination of data sets and sentiment tracking tools. This makes it difficult to disambiguate measurement and instrument effects from f…
Study compares sentiment spillover networks from news and social media in tech companies.
problem Understanding how sentiment information flows between companies through news and social media.
method Network-based transfer entropy method to measure and compare sentiment spillover.
result News shows stronger information flow among tech companies after COVID-19.
PrivySense uses price volatility to estimate news sentiment, improving financial trading strategies.
problem Improving sentiment analysis in financial news for better trading strategies.
method Uses price volatility to estimate sentiment in financial news, comparing with traditional methods.
result Demonstrates improved accuracy in sentiment estimation from financial news.
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.
Method selects optimal source domain for better cross-domain sentiment classification.
problem Improving sentiment classification on unseen domains with limited labeled data.
method Predictive measure based on distance functions between source and target domains.
result Significant improvement in cross-domain classification error compared to random selection.
Study uses TV news to measure climate risks affecting clean energy firms.
problem Understanding how climate risks impact clean energy firms' financial stability.
method Developed climate risk measures from TV news coverage and analyzed their effects on clean energy firms' risks.
result Increased TV news coverage of climate risks correlates with higher systematic risk and lower idiosyncratic risk for clean energy firms.
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.
Defines reputation as a time-based index of mined opinions.
problem Lack of formal definitions for reputation.
method Formalizes reputation as a time-based index of mined opinions, validating against informal notions.
result Quantifies unexpressed positive sentiment.
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…
Study uses machine learning to analyze Twitter sentiments about COVID-19.
problem Examining public concerns and sentiments about COVID-19 from Twitter.
method Machine learning (Latent Dirichlet Allocation) to identify topics and sentiments.
result Identified 13 topics and categorized into five themes, revealing dominant fears and mixed feelings.
The study distills news sources to analyze stock reactions, finding sentiment has asymmetric and sector-specific effects.
problem Analyzing the influence of financial text sources on stock reactions.
method Mixed text sources from professional platforms, blogs, and message boards were distilled using different lexica to analyze sentiment variables.
result Sentiment has an asymmetric and sector-specific effect on stock reactions.
Transformer learns CoVaR from financial news, improving systemic risk forecasts.
problem Quantifying systemic financial risk using conditional Value-at-Risk (CoVaR).
method Transformer-based approach integrating financial news articles with market data.
result Transformer CoVaR improves out-of-sample forecasts and identifies market stress periods.
Transformer model predicts stock trends using technical data and sentiment analysis.
problem Lack of accurate long-term stock trend prediction using traditional models.
method Developed a Transformer-based model integrating technical stock data and sentiment analysis.
result Transformer model shows significant improvement in directional accuracy over RNNs, especially for longer sequence lengths.
News sentiment in U.S. economic newspapers has become more persistent over 45 years.
problem Understanding the temporal dynamics of U.S. economic news sentiment over time.
method Daily economic news sentiment index from 1980-2025, analyzed using sentiment indexes.
result News sentiment states have become more persistent, with longer residence times in optimistic or pessimistic regimes.
PARSEC compresses text for sentiment analysis with minimal loss in accuracy.
problem Compressing text data for sentiment analysis without losing accuracy.
method Uses Parts-of-Speech tags to compress text intelligently.
result Accurate compression is possible with minimal loss in sentiment classification accuracy.
New algorithms improve automated text sentiment analysis.
problem Automated classification of text sentiment.
method Two new Genetic Algorithms (GAs) for identifying sentiment and amplifier words in text.
result Our approach outperformed existing algorithms in sentiment analysis experiments.
Study uses Granger causality to show investor sentiment influences stock prices.
problem Understanding the relationship between investor sentiment and stock market movements.
method Applied Granger causality to analyze the relationship between close price index and sentiment score.
result Sentiment analysis shows a positive correlation with stock price movements.
Method quantifies sentiment across languages without labeled data.
problem Performing sentiment analysis across multiple languages without labeled data.
method Combining quantification methods with cross-lingual vectorial representations.
result Cross-lingual sentiment quantification achieved surprising accuracy.
Behavioral finance has become an increasingly important subfield of finance. However the main parts of behavioral finance, prospect theory included, understand financial markets through individual investment behavior. Behavioral finance thereby ignores any interaction between participants. We introduce a socio-financia…
The paper constructs financial sentiment factors using NLP for the Chinese market.
problem Evaluating sentiment in the Chinese financial market.
method Crawling news and comments, applying NLP techniques, building a finance-specific lexicon, and adjusting the sentiment factor.
result The adjusted sentimental factor has a strong correlation with the Chinese market, especially during crises.
Anonymizing company names in financial news improves trading performance, contrary to initial expectations.
problem Look-ahead and distraction biases in sentiment analysis of financial news.
method Investigated trading strategies based on original and anonymized headlines, comparing performance.
result Anonymized headlines outperform original in-sample, suggesting distraction effect is stronger.
Model quantifies market sentiment using news data.
problem Quantifying high-frequency market sentiment for economists.
method Support vector machine classifiers for sentiment analysis; stochastic volatility model for joint evolution.
result News sentiment raises the threshold of volatility reversion.
TSPRA integrates topics, sentiment, and user preference for better online review prediction and analysis.
problem Improving online review prediction and sentiment analysis accuracy.
method HDP-based model combining topics, sentiment, and user preference.
result Outperforms state-of-the-art model FLAME in rating prediction and sentiment analysis.
Enhanced portfolio selection using sentiment data and LSTM.
problem Improving portfolio selection through sentiment analysis and price prediction.
method Semantic Attention Model for sentiment prediction, LSTM for price prediction, mean-variance strategy for portfolio optimization.
result Sentiment-aware portfolio strategies outperform non-sentiment aware models on average.
Dual-decoder model generates responses with targeted sentiment.
problem Generating human-like responses with specific sentiment.
method Simple dual-decoder model with two sentiment decoders connected to one encoder.
result Significant performance gain in sentiment accuracy and word diversity.
Sentiment analysis of DAX40 stocks improves performance by 5.38% annually.
problem Creating a more responsive stock market index using sentiment analysis.
method Extract sentiment from news articles, adjust index weights based on sentiment, compare performance to existing indices.
result Sentiment index outperforms DAX40 by 7.51% annually, adjusted for costs.
Study enhances cryptocurrency sentiment analysis using TikTok and Twitter data.
problem Lack of comprehensive sentiment analysis in cryptocurrency markets.
method Multimodal analysis of TikTok and Twitter data using large language models.
result TikTok's video sentiment influences speculative assets and short-term trends.
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.
Study uses ML to reconstruct stock market sentiment from trading data.
problem Reconstructing underlying sentiment states from stock price behavior.
method Applied Hidden Markov Models and Recurrent Neural Networks.
result Recovered sentiment states from observed stock price behavior.