A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
The paper uses machine learning to detect malicious executable files.
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 …
Fine-grained event tagging system for SEC 8-K filings improves precision to 96%.
SIVF k-means algorithm speeds up sparse data clustering.
In this paper, we consider the problem of malware detection and classification based on image analysis. We convert executable files to images and apply image recognition using deep learning (DL) models. To train these models, we employ transfer learning based on existing DL models that have been pre-trained on massive …
With the rapid proliferation and increased sophistication of malicious software (malware), detection methods no longer rely only on manually generated signatures but have also incorporated more general approaches like machine learning detection. Although powerful for conviction of malicious artifacts, these methods do …
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…
Model predicts trade volume changes from financial filings.
Study reveals opacity in insider sales, leading to inefficiencies in capital allocation.
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…
Solves the challenge of retrieving item-specific financial information from Form 10-Q filings.
AI analyzes corporate ESG filings to identify key dimensions and investor reactions.
The study improves sentiment analysis of 10-K filings, revealing aggregation effects on accuracy and correlation with market outcomes.
New architecture separates object state and behavior for better game dynamics.
Traditionally, music was treated as an analogue signal and was generated manually. In recent years, music is conspicuous to technology which can generate a suite of music automatically without any human intervention. To accomplish this task, we need to overcome some technical challenges which are discussed descriptivel…
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 …
We address the problem of adversarial examples in machine learning where an adversary tries to misguide a classifier by making functionality-preserving modifications to original samples. We assume a black-box scenario where the adversary has access to only the feature set, and the final hard-decision output of the clas…
We design a classifier for transactional datasets with application in malware detection. We build the classifier based on the minimum description length (MDL) principle. This involves selecting a model that best compresses the training dataset for each class considering the MDL criterion. To select a model for a datase…
A new method for energy-efficient file delivery in small cell networks.
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…
Malware is constantly adapting in order to avoid detection. Model based malware detectors, such as SVM and neural networks, are vulnerable to so-called adversarial examples which are modest changes to detectable malware that allows the resulting malware to evade detection. Continuous-valued methods that are robust to a…
IVF k-means algorithm improves performance on large sparse data sets.
Storage has become a constrained resource on smartphones. Gaming is a popular activity on mobile devices and the explosive growth in the number of games coupled with their growing size contributes to the storage crunch. Even where storage is plentiful, it takes a long time to download and install a heavy app before it …
Study uses LLM to extract and compare segment disclosures from financial filings.
CIfly simplifies causal inference tasks with linear-time reachability primitives.
Nowadays, CAPTCHAs are computer generated tests that human can pass but current computer systems can not. They have common usage in various web services in order to be able to detect a human from computer programs autonomously. In this way, owners can protect their web services from bots. In addition to visual CAPTCHAs…
Detects bots in code commits and characterizes their activity.
This work proposes a meta-learning approach for better adaptation of source code models.
FinReflectKG - EvalBench benchmarks financial KG extraction from SEC 10-K filings.
IPO Finance Agent extends Finance Agent v2 for SpaceX S-1 filings, improving accuracy and cost-efficiency.
Large language models learn company embeddings from SEC filings.
This paper provides a holistic study of how stock prices vary in their response to financial disclosures across different topics. Thereby, we specifically shed light into the extensive amount of filings for which no a priori categorization of their content exists. For this purpose, we utilize an approach from data mini…
Improved surname geocoding and name supplements enhance race imputation accuracy.
AutoStan improves Bayesian models via predictive feedback.
Narrative disclosures in 10-K filings improve bankruptcy prediction beyond accounting ratios.
A brief introduction to exterior differential systems for graduate students familiar with manifolds and differential forms. For complete files, see https://github.com/Ben-McKay/introduction-to-exterior-differential-systems
New AI stock indices classify firms' AI engagement using 10-K filings.
Anomaly detection is a crucial step for preventing malicious activities in the network and keeping resources available all the time for legitimate users. It is noticed from various studies that classical anomaly detectors work well with small and sampled data, but the chances of failures increase with real-time (non-sa…
IPO Finance Agent evaluates LLMs on SpaceX IPO due diligence, surpassing Finance Agent v2.
Recent research has repeatedly shown that machine learning techniques can be applied to either whole files or file fragments to classify them for analysis. We build upon these techniques to show that for samples of un-labeled compiled computer object code, one can apply the same type of analysis to classify important a…
LEMs extend transformer-based architectures for complex execution problems.
FinReflectKG builds a comprehensive financial knowledge graph from SEC filings, improving extraction quality.
Proposes a method to allocate time budgets in mixed criticality systems.
Optimal crypto order execution using cross-exchange signals.
We demonstrate an application of risk-sensitive reinforcement learning to optimizing execution in limit order book markets. We represent taking order execution decisions based on limit order book knowledge by a Markov Decision Process; and train a trading agent in a market simulator, which emulates multi-agent interact…
Proposes a max-utility arm selection strategy for reducing cumulative regret in sequential query recommendations.
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…
The paper analyzes trade execution strategies for large traders in a stochastic market environment.