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.
This study introduces a new GAS blending ensemble model for Bitcoin price prediction.
problem Predicting Bitcoin price fluctuations in the cryptocurrency market.
method Integrates advanced ensemble learning methods, feature selection algorithms, and sentiment analysis.
result The GAS model demonstrates excellent performance in daily Bitcoin trend prediction.
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.
Study evaluates LLMs for predicting Chinese stock movements using financial news sentiments.
problem Evaluating LLMs' ability to predict stock price movements using financial news sentiments.
method Standardized experimental procedure with three LLMs, each with unique performance enhancement methods.
result Developed quantitative trading strategies and conducted back-tests to assess LLMs' performance.
Paper introduces metrics to assess and control nuisance factors in sentiment analysis.
problem Challenges in learning invariant representations for sentiment analysis due to entangled nuisance factors.
method Developed two generalization metrics and a data filtering approach to control nuisance factors.
result Simple text classification baseline can be badly affected by product ID in sentiment analysis.
A new model disentangles long-term and short-term sentiment components in stock returns.
problem Identifying distinct components of sentiment data in stock markets.
method Dynamic factor model with random walk and stationary VAR(1) components, estimated via Kalman filtering and EM.
result The long-term sentiment component co-integrates with market principal factor, while the short-term captures market swings.
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.
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.
Study examines how social media sentiment impacts biotech stocks.
problem Understanding the impact of social media on biotech stock prices.
method VADER sentiment analysis, ARIMA, and VAR models were used to forecast stock market performance.
result Complex interplay between tweet sentiment and stock market performance was identified.
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…
Dynamic sentiment analysis improves stock trading strategies.
problem Improving stock trading performance through dynamic agent switching.
method Combining sentiment analysis with deep reinforcement learning and dynamic agent switching.
result Dynamic sentiment-based trading strategy outperforms traditional methods.
In recent literature it is claimed that BitCoin price behaves more likely to a volatile stock asset than a currency and that changes in its price are influenced by sentiment about the BitCoin system itself; in Kristoufek [10] the author analyses transaction based as well as popularity based potential drivers of the Bit…
FNSPID dataset integrates financial news and stock prices for improved market predictions.
problem Lack of comprehensive datasets combining quantitative and qualitative financial data.
method Developed a large-scale dataset (FNSPID) with 29.7M stock prices and 15.7M financial news records.
result FNSPID significantly boosts market prediction accuracy and sentiment analysis.
Study uses LLMs to categorize financial tweets, revealing useful sentiment signals.
problem Discovering meaningful sentiment signals from unstructured financial social media data.
method Leveraged LLMs to automatically label financial tweets with event categories and aligned with returns.
result Certain event labels consistently yield negative alpha, with statistically significant Sharpe ratios and information coefficients.
Improved model for analyzing topics, sentiments, and user preferences in online reviews.
problem Inefficient processing of large-scale online review datasets.
method Developed variational inference models (vTSPRA, svTSPRA, ovTSPRA) for faster and more efficient processing of large datasets.
result The new models (svTSPRA, ovTSPRA) achieve better performance and faster convergence compared to the original TSPRA model.
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…
Study shows how sentiment shocks affect equity markets, revealing asymmetries and state-dependent effects.
problem Understanding how sentiment shocks propagate through equity markets and their impact on different investor groups.
method Used four independent proxies with sign-aligned kappa-rho parameters, calibrated a structural model to link sentiment to returns.
result A one standard deviation sentiment shock has a 1.06 basis point impact, with effects amplified over 11.2 months and concentrated in retail-tilted stocks.
Quantformer uses transformer to predict stock returns, outperforming traditional strategies.
problem Predicting stock returns in a dynamic financial market.
method Transfer learning from sentiment analysis to build investment factors using a transformer-based neural network.
result Quantformer outperforms other 100-factor-based quantitative strategies in predicting stock trends.
Deep learning models predict S&P500 option hedge ratios.
problem Optimizing hedging strategies for S&P500 index options.
method Feedforward neural network with time to maturity, delta, and sentiment variables.
result Deep learning model outperforms traditional hedging methods.
Study analyzes global public sentiment on DeFi from 2012-2022.
problem Global public sentiment on DeFi is understudied.
method Sentiment analysis, spatial econometrics, clustering, topic modeling.
result Economic development significantly influences DeFi engagement, especially after 2015.
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.
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.
Predict stock trends using news sentiment and technical indicators in Spark.
problem Predicting the stock market trend is challenging due to multiple influencing factors.
method Created a machine learning classification problem with features from technical indicators and news sentiment scores.
result Random Forest model achieved 63.58% test accuracy in Spark.
Deep model improves option pricing for CSI 300 index with sentiment and volatility features.
problem Challenges in real market option pricing, especially with constant volatility assumption.
method Deep Forward-Backward Stochastic Differential Equation (FBSDE) framework with dual-network architecture.
result Significant reduction in MAE and MAPE compared to BSM model.
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.
A new method reduces redundancy in multimodal data for improved inference.
problem Understanding and optimizing the contribution of each modality in multimodal tasks.
method Modality-based Tensor Factorization (MRRF) for multimodal fusion.
result Improves multimodal inference tasks by 1% to 4% compared to state-of-the-art.
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.
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.
Crypto simulations show HODL strategy loads risk onto most investors, with macro-sentiment affecting returns.
problem Understanding real risk-return trade-offs and factors affecting crypto returns.
method Two independent analyses: 480 million Monte Carlo simulations and Bayesian multi-horizon local projection framework.
result HODL strategy exposes most investors to extreme downside risk, and macro-sentiment conditions are dominant indicators for future outcomes.
Hybrid AI system combines technical, sentiment analysis for adaptive equity trading.
problem Traditional trading strategies fail during high volatility and regime shifts.
method Combines trend-following, mean-reversion, sentiment analysis, machine learning, and market regime filtering.
result Hybrid model achieved 135.49% return on investment over 24 months.
Gradual ML improves ALSA with less manual labeling.
problem Lack of high-quality labeled data for ALSA.
method Gradual machine learning for automatic labeling of ALSA tasks.
result Performance surpasses unsupervised and supervised alternatives.
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.
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.
This paper fine-tunes BERT for stock market sentiment analysis and improves trading performance.
problem Improving trading performance in non-strongly efficient markets.
method Fine-tuning BERT on annotated data, combining with Alpha191 model for regression and prediction.
result Emotional factors significantly improve trading performance, increasing return rates by 73.8% compared to baseline.
We propose a model for the credit markets in which the random default times of bonds are assumed to be given as functions of one or more independent "market factors". Market participants are assumed to have partial information about each of the market factors, represented by the values of a set of market factor informa…
Paper uses NLP and IRT to score ESG factors from news articles.
problem Lack of precise ESG metrics in finance.
method Combines NLP and IRT models on ESG-related news data.
result Method offers more precise ESG metrics with temporal dynamics.
Computer science scans LLMs to understand and manipulate their economic forecasts.
problem Understanding and controlling the reasoning of large language models in economics.
method Brain scanning techniques applied to LLMs to identify and manipulate underlying concepts.
result LLMs can be steered to generate forecasts with specific biases, allowing for correction or simulation.
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.
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.
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.
Using a time-varying approach, this paper examines the dynamics of volatility in the REIT sector. The results highlight the attractiveness and suitability of using GARCH based approaches in the modeling of daily REIT volatility. The paper examines the influencing factors on REIT volatility, documenting the return and v…
Can textual data be compressed intelligently without losing accuracy in evaluating sentiment? In this study, we propose a novel evolutionary compression algorithm, PARSEC (PARts-of-Speech for sEntiment Compression), which makes use of Parts-of-Speech tags to compress text in a way that sacrifices minimal classification…
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.
Estimates crypto risk premia using hidden factors and finds significant integration with traditional markets.
problem Estimating risk premia in cryptocurrency returns.
method Giglio-Xiu (2021) three-pass approach, controlling for latent factors and non-tradable state variables.
result Latent factors significantly impact crypto returns, highlighting the importance of controlling for unobserved risks.
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.
The ability to identify sentiment in text, referred to as sentiment analysis, is one which is natural to adult humans. This task is, however, not one which a computer can perform by default. Identifying sentiments in an automated, algorithmic manner will be a useful capability for business and research in their search …
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.
Study improves retail demand forecasting by integrating macroeconomic data.
problem Lack of accurate demand forecasting due to incomplete data.
method Enriched time series data with macroeconomic variables; compared regression and machine learning models.
result Improved accuracy in predicting retail demand through comprehensive data integration.