Sentiment analysis from news and social media predicts forex market movements.
problem Forecasting forex market movements using sentiment analysis.
method Lexicon-based analysis and Naive Bayes machine learning.
result Sentiment analysis is effective in predicting forex market movements.
We propose a new indicator for technical analysis. The indicator emphasizes maximums and minimums in price series with inherent smoothing and has a potential to be useful in both mechanical trading rules and chart pattern analysis.
Quantum analysis tags news for sentiment and entities.
problem Identifying bias in news reporting.
method Continuous data collection, NER and sentiment analysis.
result A corpus of tagged news articles for public use.
Enhanced stock market strategy using stress index and financial news sentiment analysis.
problem Improving risk assessment and prediction in equity markets.
method Combines financial stress indicator with sentiment analysis of financial news.
result Improved performance with higher Sharpe ratio and reduced drawdowns.
Study compares LLMs vs classical models for financial sentiment analysis.
problem Improving sentiment analysis in financial market news.
method Comparative analysis of LLMs and classical models.
result LLMs outperform classical models in sentiment analysis of financial news.
Data describing historical economic growth are analysed. Included in the analysis is the world and regional economic growth. The analysis demonstrates that historical economic growth had a natural tendency to follow hyperbolic distributions. Parameters describing hyperbolic distributions have been determined. A search …
New method uses topological data analysis to study stock market crashes.
problem Characterizing and predicting stock market crashes.
method Topological data analysis, persistence landscape, dynamic time series analysis.
result Demonstrates effectiveness of new method for Flash Crash characterization and prediction.
New robust discriminant analysis for non-Gaussian data.
problem Classical discriminant analysis struggles with non-Gaussian distributions and contaminated datasets.
method Each data point follows its own ES distribution with arbitrary scale, leading to robust classification.
result Maximum-likelihood estimation and classification are simple, fast, and robust.
Two new PCA variants improve financial data analysis.
problem Numerical instability and nonstationarity in PCA for finance.
method Iterated and exponentially weighted moving PCA variants using Ogita-Aishima iteration.
result Improved stability and adaptability in financial data analysis.
New tool helps analyze complex financial data.
problem Difficulty in comprehending high-dimensional financial data.
method Topological Data Analysis Ball Mapper algorithm.
result Shows new way to see detail in financial data.
New method simplifies data analysis.
problem Complex data analysis challenges.
method Innovative algorithm for data simplification.
result Significant reduction in analysis time.
This paper deals with various topics in analysis on hyperbolic spaces. It surveys some recent progress in non-Euclidean Fourier Analysis and proves some new results for the geodesic Radon transform on hyperbolic spaces.
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.
FinGPT uses LLMs for real-time market sentiment analysis.
problem Real-time market sentiment analysis for trading.
method Synthesizes financial news and social media data, integrates with technical indicators, uses FinGPT for sentiment analysis.
result Generates actionable trading signals using LLMs.
We present two alternative ways to apply PAC-Bayesian analysis to sequences of dependent random variables. The first is based on a new lemma that enables to bound expectations of convex functions of certain dependent random variables by expectations of the same functions of independent Bernoulli random variables. This …
Traditional statistical analysis requires that the analysis process and data are independent. By contrast, the new field of adaptive data analysis hopes to understand and provide algorithms and accuracy guarantees for research as it is commonly performed in practice, as an iterative process of interacting repeatedly wi…
New formula classifies product reviews into higher and lower ratings based on sentiment analysis.
problem Lack of research on using sentiment analysis for classifying text into ratings.
method Redefined sentiment proportions as a triangle structure to derive variables for classifying text into higher and lower ratings.
result Proved a dependence exists between sentiments and ratings.
FinEAS models financial sentiment using BERT embeddings.
problem Financial sentiment analysis in markets.
method Supervised fine-tuning of BERT embeddings for financial texts.
result FinEAS outperforms vanilla BERT, LSTM, and FinBERT.
This study evaluates LLMs for sentiment analysis in stock price prediction.
problem Improving stock price prediction accuracy using LLMs for news sentiment analysis.
method Compared 3 LLMs (DeBERTa, RoBERTa, FinBERT) for sentiment-driven stock prediction.
result DeBERTa outperforms other models with 75% accuracy, and ensemble model increases accuracy to 80%.
Improved regression analysis using Padé approximants with new residuals and regularization.
problem Improving regression analysis with Padé approximants for accuracy and avoiding overfitting.
method New residuals in least squares method, system of linear equations for rational functions, Tikhonov regularization.
result Demonstrated efficiency in practical cases from physics and reliability theory.
New risk measure uses Fourier analysis of stock prices.
problem Identifying speculative behavior in financial products.
method Fourier analysis applied to stock price changes.
result Speculative behavior indicated by disproportionate price changes within one week.
With developing of computation tools in the last years, data analysis methods to find insightful information are becoming more common among industries and researchers. This paper is the first part of the times series analysis of New England electricity price and demand to find anomaly in the data. In this paper time-se…
Survival analysis models research reproducibility, offering new insights.
problem Reproducibility crisis in machine learning research.
method Survival analysis to model reproducibility as a continuous process.
result Survival analysis provides deeper insights into research reproducibility.
These are the proceedings of the workshop "Math in the Black Forest", which brought together researchers in shape analysis to discuss promising new directions. Shape analysis is an inter-disciplinary area of research with theoretical foundations in infinite-dimensional Riemannian geometry, geometric statistics, and geo…
New RL algorithms correct bias in dynamic data analysis.
problem Dynamic data generation and analysis create endogeneity issues.
method Instrument variable (IV)-based reinforcement learning (RL) algorithms.
result Established theoretical properties of IV-RL algorithms.
Correlated component analysis as proposed by Dmochowski et al. (2012) is a tool for investigating brain process similarity in the responses to multiple views of a given stimulus. Correlated components are identified under the assumption that the involved spatial networks are identical. Here we propose a hierarchical pr…
New method for analyzing multiple longitudinal data processes.
problem Exploring associations between multiple random processes observed jointly.
method Functional Generalized Canonical Correlation Analysis (FGCCA) based on multiblock Regularized Generalized Canonical Correlation Analysis (RGCCA).
result FGCCA framework is robust to sparsely and irregularly observed data.
The increasing availability of large but noisy data sets with a large number of heterogeneous variables leads to the increasing interest in the automation of common tasks for data analysis. The most time-consuming part of this process is the Exploratory Data Analysis, crucial for better domain understanding, data clean…
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.
Machine learning enhances cosmology through new tools and data analysis.
problem Leveraging machine learning in cosmology for better understanding of the universe.
method Development of new computational tools, data analysis techniques, and community building.
result Substantial potential in cosmology remains untapped with machine learning.
Symbolic data analysis (SDA) is an emerging area of statistics concerned with understanding and modelling data that takes distributional form (i.e. symbols), such as random lists, intervals and histograms. It was developed under the premise that the statistical unit of interest is the symbol, and that inference is requ…
New framework improves classification accuracy using Pillai's trace and ULDA.
problem Traditional LDA's limitations in noise sensitivity and non-invertible matrices.
method Integrates Pillai's trace with ULDA for a unified classifier.
result Effective control of Type I error rates and improved classification accuracy.
Data science enhances knot theory by analyzing invariant relations.
problem Understanding the complex relations between knot invariants.
method Topological data analysis applied to knot theory.
result New insights into long-standing conjectures about knots.
News explains most overnight stock market gains.
problem Understanding why overnight stock market gains are common.
method Supervised topic analysis of 2.4 million news articles.
result Time variation in news topics and their responses explain overnight returns.
New benchmark for EEG-eye movement reconstruction from functional data.
problem Reconstructing eye movements from EEG data.
method Functional neural networks and open challenges for evaluation.
result Baseline results for consumer-grade and research-grade hardware.
New method uses contours of segmented images for X-ray classification.
problem Classifying X-ray images of segmented radiography.
method Develops a new approach for image analysis of multivariate planar curves, addressing alignment issues.
result Demonstrates the robustness and appeal of the proposed method through detection of cardiomegaly and numerical experiments.
New framework for Adam-type algorithms with constant β1, improving regret analysis.
problem Theoretical vs. practical use of Adam and variants with constant β1.
method Proposed a novel framework to derive optimal, data-dependent regret bounds with constant β1.
result Optimal, data-dependent regret bounds with constant β1 are achievable without further assumptions.
New L0 norm added to TDA for market analysis.
problem Improving TDA tools for market prediction.
method Defined and applied L0 norm in TDA for four markets.
result Enhanced TDA tools for market analysis.
Techniques for understanding the functioning of complex machine learning models are becoming increasingly popular, not only to improve the validation process, but also to extract new insights about the data via exploratory analysis. Though a large class of such tools currently exists, most assume that predictions are p…
New TDA approach using Finsler metrics.
problem Traditional TDA concepts and methods.
method Introducing Finsler metrics for TDA.
result Relevance of Finsler metrics to TDA.
A new classification rule for FDA improves classification performance by accounting for unequal covariance matrices.
problem Unequal covariance matrices in practical situations affect the performance of FDA and its variants.
method Proposes a novel classification rule for FDA that accounts for unequal covariance matrices, applicable to many FDA variants.
result The new classification rule improves classification performance compared to original FDA and variants.
Analyzes news graphs to predict financial market dislocations.
problem Predicting financial market dislocations using news content.
method Extracts entities from news articles, aggregates them into graphs, applies network analysis, and uses sentiment analysis.
result Identifies high entropy in news graphs correlates with financial market dislocations.
New algorithm improves efficiency of Bayesian Causal Forest for subgroup analysis.
problem Estimating heterogeneous effects in subgroup analysis.
method Developed a novel algorithm for fitting Bayesian Causal Forest (BCF) model, more efficient than Gibbs sampler.
result New algorithm improves posterior exploration and coverage of interval estimates.
New theoretical tools simplify kernel-based tests analysis.
problem Asymptotic behavior of kernel-based tests in various scenarios.
method Avoids complex expansions and limit theorems, works directly with Hilbert spaces random functionals.
result Framework leads to simpler analysis with minimal regularity conditions.
New method combines FMEA and Bayesian Network for root cause analysis in lithium-ion battery production.
problem Complex cause-effect relationships in lithium-ion battery production.
method Combining FMEA with Bayesian Network to detect and resolve inconsistencies.
result Holistic method builds large-scale cross-process Bayesian Failure Network for root cause analysis.
MarketSenseAI uses LLMs to improve stock analysis and outperforms benchmarks.
problem Improving accuracy in stock analysis and selection.
method Combining LLMs with SEC filings, earnings calls, and macroeconomic reports.
result Significant improvement in fundamental analysis accuracy and outperformance of benchmarks.
Study combines sentiment analysis with traditional models for better S&P 500 trading.
problem Improving trading performance in volatile markets.
method Sentiment analysis from financial news, GPT-2, FinBERT, combined with technical indicators and time-series models.
result Combining sentiment-driven insights with traditional models improves trading performance.
Matrix factorizations and their extensions to tensor factorizations and decompositions have become prominent techniques for linear and multilinear blind source separation (BSS), especially multiway Independent Component Analysis (ICA), NonnegativeMatrix and Tensor Factorization (NMF/NTF), Smooth Component Analysis (Smo…