Fine-grained event tagging system for SEC 8-K filings improves precision to 96%.
problem Coarse SEC item codes mislabel routine and significant events.
method Two-stage system tagging 8-K disclosures against a 119-event taxonomy.
result LLM judge finds precision rises to 96% with quality scores.
Large language models learn company embeddings from SEC filings.
problem Lack of a rigorous definition of company similarity.
method Pre-trained and finetuned large language models (LLMs) to learn embeddings from SEC filings.
result LLMs can reproduce GICS classifications and indicate similar financial performance.
The study analyzes trading imbalances from SEC Form 13F-HR filings to identify profitable trading opportunities.
problem Identifying profitable trading opportunities based on SEC Form 13F-HR filings.
method Statistical analysis of holdings variations and imbalances between consecutive reporting periods.
result A profitable trading strategy can be implemented by betting against the sign of imbalances with a 1-2 month time horizon.
FinReflectKG - EvalBench benchmarks financial KG extraction from SEC 10-K filings.
problem Lack of universal benchmark and evaluation framework for financial KG construction.
method Agentic and holistic evaluation principles, deterministic commit-then-justify judging protocol, binary and ordinal evaluations.
result Reflection-based extraction outperforms single-pass extraction in comprehensiveness, precision, and relevance.
Study uses LLMs to generate investor briefs from company reports and SEC filings.
problem Improving data analysis for individual investors.
method Preprocessed data, used gpt-4o model in RAG regime, evaluated by investors.
result LLMs can generate useful investor briefs from company reports and SEC filings.
Paper reviews Viro's definition of Khovanov homology for tangles.
problem Defining Khovanov homology for unoriented framed tangles.
method Explains Viro's definition and the relation between R-matrix and Kauffman bracket. result Clarifies the relation between R-matrix and Kauffman bracket in Khovanov homology. FinReflectKG builds a comprehensive financial knowledge graph from SEC filings, improving extraction quality.
problem Lack of large-scale, open-source financial knowledge graph datasets.
method Intelligent document parsing, table-aware chunking, schema-guided iterative extraction, reflection-driven feedback loop.
result Reflection-agent-based mode achieves best balance of efficiency, accuracy, and reliability.
IPO Finance Agent extends Finance Agent v2 for SpaceX S-1 filings, improving accuracy and cost-efficiency.
problem Evaluating IPO due diligence tasks with long-form documents.
method Extended task domain, improved agentic harness with contextual retrieval, automated rubric generation.
result Best-performing model reaches 79.8% accuracy, cost-efficient model at 77.2% with 0.05 USD per query.
IPO Finance Agent evaluates LLMs on SpaceX IPO due diligence, surpassing Finance Agent v2.
problem Evaluating language models on financial tasks like IPO due diligence.
method Introducing IPO Finance Agent and an evaluator-optimizer pipeline.
result The best-performing model reaches 79.4% accuracy at 0.30 USD per query.
Benchmark evaluates AI-generated financial QA hallucinations, highlighting system vulnerabilities.
problem Ensuring factual accuracy of AI-generated financial QA outputs.
method Developed a benchmark dataset and evaluated six detection methods under clean and noisy conditions.
result LLM-based judges and embedding methods perform best, but degrade under noisy conditions.
Study finds CNNs perform better with financial ratio data than fundamental data.
problem Improving CNN performance with financial data.
method Developed and analyzed three image encoding methods for financial data.
result Image encoding methods improve CNN performance for financial ratio data but not significantly for fundamental data.
Narrative disclosures in 10-K filings improve bankruptcy prediction beyond accounting ratios.
problem Traditional bankruptcy prediction models rely on accounting ratios, which may not capture early warning signals.
method Developed a PB Stress Score based on distress-specific language in 10-K narratives, evaluated against accounting and dictionary benchmarks.
result Adding the PB Stress Score increases AUC from 0.8323 to 0.9019 and improves top-decile bankruptcy capture from 44.12% to 64.71%.
We establish a new algebraic characterization of sectional curvature bounds sec≥k and sec≤k using only curvature terms in the Weitzenböck formulae for symmetric p-tensors. By introducing a symmetric analogue of the Kulkarni-Nomizu product, we provide a simple formula for such curvature terms. We also gi…
Deep RL model uses multimodal data for better stock portfolio optimization.
problem Optimizing trading strategies for SP100 stocks using complex data sources.
method Multimodal deep reinforcement learning with state tensors, CNNs, and RNNs.
result Agent outperforms standard benchmarks in portfolio performance.
Research explores how local communities and corporations interact in finance.
problem Impact of local government subsidies and corporate bankruptcy on bond yields.
method Difference-in-differences analysis, econometric models, deep-learning model.
result Corporate subsidies and bankruptcy filings affect bond yields significantly.
The paper uses machine learning to detect malicious executable files.
problem Detecting malicious executable files using static analysis.
method Pre-processing, cleaning, encoding, feature selection, and ensemble training of classifiers.
result An ensemble of classifiers effectively detects malicious executable files.
Study shows SEC crypto classification led to significant market reactions.
problem Impact of SEC classification of crypto assets as securities.
method Event study methodology focusing on explicitly named crypto assets.
result Significant adverse market reactions, with returns plummeting 12% over one week.
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 shows fiduciary duty reduces municipal bond yields by 9% after SEC rule.
problem Effect of fiduciary duty on municipal bond yields and fees.
method Difference-in-differences analysis using hand-collected data.
result Bond yields decrease by 9% after SEC rule, but smaller issuers see increased borrowing costs.
We classify four-dimensional shrinking Ricci solitons satisfying Sec≥241R, where Sec and R denote the sectional and the scalar curvature, respectively. They are isometric to either R4 (and quotients), S4, RP4 or CP2 with their standard metrics…
Gradient boosting detects insider purchases predicting abnormal returns in microcap stocks.
problem Predicting abnormal returns in U.S. microcap stocks using insider purchase signals.
method Gradient boosting classifier trained on insider identity, transaction history, and market conditions.
result Gradient boosting classifier achieves AUC of 0.70 on out-of-sample data, with precision and recall of 0.38 and 0.69 respectively.
We aim to model unknown file processing. As the content of log files often evolves over time, we established a dynamic statistical model which learns and adapts processing and parsing rules. First, we limit the amount of unstructured text by focusing only on those frequent patterns which lead to the desired output tabl…
New rigidity results for critical metrics with curvature pinching.
problem Understanding critical metrics with curvature pinching conditions.
method Proving rigidity for metrics defined on closed smooth manifolds that are critical for a quadratic functional.
result Bach-flat metrics with constant scalar curvature satisfying Sec > 1/48 R are Einstein and isometric to specific spaces.
We study Weil-Petersson (WP) geodesics with narrow end invariant and develop techniques to control length-functions and twist parameters along them and prescribe their itinerary in the moduli space of Riemann surfaces. This class of geodesics is rich enough to provide for examples of closed WP geodesics in the thin par…
Measuring the similarity of two files is an important task in malware analysis, with fuzzy hash functions being a popular approach. Traditional fuzzy hash functions are data agnostic: they do not learn from a particular dataset how to determine similarity; their behavior is fixed across all datasets. In this paper, we …
FinCARE combines financial data and AI reasoning to improve causal analysis of financial performance.
problem Correlation-based analysis fails to capture true causal relationships in financial performance.
method Hybrid framework integrating causal discovery algorithms with financial domain knowledge from SEC filings and LLM reasoning.
result KG+LLM-enhanced methods improve causal discovery across PC, GES, and NOTEARS by 36-366%.
Model predicts trade volume changes from financial filings.
problem Improving financial market understanding through machine learning.
method Hierarchical Reformer model trained on SEDAR filings.
result Model can predict trade volume changes without explicit training.
We investigate length decreasing maps f:M→N between Riemannian manifolds M, N of dimensions m≥2 and n, respectively. Assuming that M is compact and N is complete such that $$\sec_M>-σ\quad\text{and}\quad{\Ric}_M\ge(m-1)σ\ge(m-1)\sec_N\ge-μ,$$ where σ, μ are positive constants, we show that the m…
In record linkage (RL), or exact file matching, the goal is to identify the links between entities with information on two or more files. RL is an important activity in areas including counting the population, enhancing survey frames and data, and conducting epidemiological and follow-up studies. RL is challenging when…
We consider minimal maps f:M→N between Riemannian manifolds (M,gM) and (N,gN), where M is compact and where the sectional curvatures satisfy secN≤σ≤secM for some σ>0. Under certain assumptions on the differential of the map and the second fundamental form of the graph Γ(f)…
A Lorentzian manifold is defined here as a smooth pseudo-Riemannian manifold with a metric tensor of signature ((2n +1, 1)). A Robinson manifold is a Lorentzian manifold (M) of dimension (\geqslant 4) with a subbundle (N) of the complexification of (TM) such that the fibers of (N\to M) are maximal totally null (isotrop…
Meta clustering categorizes learners for collaborative learning.
problem Filtering out unqualified collaborators in collaborative learning.
method Select-Exchange-Cluster (SEC) method to classify learners by their supervised functions.
result SEC can cluster learners into accurate collaboration sets and enhance single-learner performance.
Two new metrics assess LLM faithfulness and entropy, improving model reliability.
problem Evaluating the accuracy of LLMs in generating coherent responses.
method Proposes SF and SEP metrics based on information theory and thermodynamics.
result High SF and SEP scores indicate more faithful LLM responses.
Solves the challenge of retrieving item-specific financial information from Form 10-Q filings.
problem Retrieving item-specific information from Form 10-Q filings with varying formats and machine-readable hierarchy.
method Complements a rule-based algorithm with a Convolutional Neural Network (CNN) image classifier to itemize 10-Q files.
result Demonstrates a generalized pipeline for rapid data retrieval from a large volume of textual data.
AI analyzes corporate ESG filings to identify key dimensions and investor reactions.
problem Lack of reliable ESG ratings systems in corporate filings.
method AI techniques to separate and measure ESG dimensions and investor responses.
result AI can improve ESG ratings systems by identifying key dimensions and investor reactions.
We show that in each dimension n≥10 there exist infinite sequences of homotopy equivalent but mutually non-homeomorphic closed simply connected Riemannian n-manifolds with 0≤sec≤1, positive Ricci curvature and uniformly bounded diameter. We also construct open manifolds of fixed diffeomorphism type whic…
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.
New architecture separates object state and behavior for better game dynamics.
problem Lack of systematicity in black-box models for dynamic environments.
method Factorizes declarative and procedural knowledge into object files and schemata.
result Improves generalization on environments with multiple object tokens.
Malware detection is a popular application of Machine Learning for Information Security (ML-Sec), in which an ML classifier is trained to predict whether a given file is malware or benignware. Parameters of this classifier are typically optimized such that outputs from the model over a set of input samples most closely…
FinBloom enhances LLMs for real-time financial queries.
problem Limited access to real-time financial data by LLMs.
method Developed a custom 7B parameter LLM, Financial Context Dataset, and a Financial Agent.
result Significantly improved LLMs' capability to handle dynamic financial tasks.
InvestLM is a financial domain LLM tuned on LLaMA-65B for investment advice.
problem Improving financial text understanding and advice generation for investment.
method Curated financial instruction dataset, LLaMA-65B, less-is-more-for-alignment approach.
result InvestLM provides comparable responses to state-of-the-art commercial models.
The study of fibrations of the target manifolds of string/M/F-theories has provided many insights to the dualities among these theories or even as a tool to build up dualities since the work of Strominger, Yau, and Zaslow on the Calabi-Yau case. For M-theory compactified on a Joyce manifold M7, the fact that M7 i…
Model shows AI adoption amplifies financial market risk through prediction, herding, and cognitive dependency.
problem Systemic risk in financial markets due to AI adoption.
method Developed a unified model within an extended rational expectations framework, incorporating endogenous adoption, performative prediction, algorithmic herding, and cognitive dependency.
result Systemic risk multiplier grows superlinearly with AI penetration, implying tail-loss amplification of 18-54%.
The development of models for learning music similarity and feature extraction from audio media files is an increasingly important task for the entertainment industry. This work proposes a novel music classification model based on metric learning and feature extraction from MP3 audio files. The metric learning process …
AI-driven investment strategies self-defeat at scale due to signal crowding and erosion.
problem Excess returns from AI-driven investment strategies diminish at scale due to signal crowding and erosion.
method Theoretical model and empirical validation using SEC Form 13F filings and hedge fund return dynamics.
result The alpha half-life of signals decreases significantly with AI adoption, leading to diminishing returns.
A new method for energy-efficient file delivery in small cell networks.
problem Efficient resource management in femto-caching with time-variant statistical properties.
method Formulates a resource allocation problem as a stochastic knapsack problem and a multi-armed bandit problem, developing solutions for each.
result The proposed method maximizes the accumulated utility over the horizon, especially suitable for networks with time-variant statistical properties.
Detecting PE malware files is now commonly approached using statistical and machine learning models. While these models commonly use features extracted from the structure of PE files, we propose that icons from these files can also help better predict malware. We propose an innovative machine learning approach to extra…
GenAI adoption paradoxically lowers ROE for U.S. banks, with spillovers but systemic risk concerns.
problem Productivity paradox and implementation costs in U.S. banking sector with AI adoption.
method Dynamic Spatial Durbin Models (DSDM) and Synthetic Difference-in-Differences (SDID) for causal inference.
result AI adoption leads to a 428-basis-point decline in ROE for banks, with spillovers but systemic risk implications.