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.
Interactive DR framework for comparing datasets.
Study compares LLMs vs classical models for financial sentiment analysis.
Paper presents a faster method for computing cost of equity and performing comparable company analysis.
In this dissertation, the main goal is visualisation of financial time series. We expect that visualisation of financial time series will be a useful auxiliary for technical analysis. Firstly, we review the technical analysis methods and test our trading rules, which are built by the essential concepts of technical ana…
Study compares machine learning models and BERT on SQuAD dataset.
Classifiers are among the most widely used supervised machine learning algorithms. Many classification models exist, and choosing the right one for a given task is difficult. During model selection and debugging, data scientists need to assess classifiers' performances, evaluate their learning behavior over time, and c…
The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording, and analyzing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods ha…
Study compares BERT with other sentiment analysis models.
This paper presents a financial analysis over Twitter sentiment analytics extracted from listed retail brands. We investigate whether there is statistically-significant information between the Twitter sentiment and volume, and stock returns and volatility. Traditional newswires are also considered as a proxy for the ma…
This study compares decentralized banks and finds some lack decentralization.
The paper compares clustering techniques for personalized food kits.
For multiple multivariate data sets, we derive conditions under which Generalized Canonical Correlation Analysis (GCCA) improves classification performance of the projected datasets, compared to standard Canonical Correlation Analysis (CCA) using only two data sets. We illustrate our theoretical results with simulation…
In this paper we utilize a survival analysis methodology incorporating Bayesian additive regression trees to account for nonlinear and additive covariate effects. We compare the performance of Bayesian additive regression trees, Cox proportional hazards and random survival forests models for censored survival data, usi…
We compared the regular Singular Value Decomposition (SVD), truncated SVD, Krylov method and Randomized PCA, in terms of time and space complexity. It is well-known that Krylov method and Randomized PCA only performs well when k << n, i.e. the number of eigenpair needed is far less than that of matrix size. We compared…
Paper compares stock price prediction models using Heston and Geometric Brownian Motion.
Comparative study of neural networks for short-term FOREX forecasting.
MarketSenseAI uses LLMs to improve stock analysis and outperforms benchmarks.
Paper compares dimension reduction methods using topological analysis on EEG data.
Improved analysis of UCBVI algorithm with better empirical performance.
New method compresses large sample data for faster discriminant analysis.
We generalize the momentum indicator idea taking into account the volume of transactions as a multiplicative factor. We compare returns obtained following strategies based on the classical or the generalized technical analysis, taking into account a sort of risk investor criterion.
Survival analysis models predict economic convergence across Americas.
A new algorithm for parallel transport on shape spaces is presented and compared to existing methods.
Deep learning improves MRI analysis of MSK disorders.
Toehold purchase, defined here as purchase of one share in a firm by an investor preparing a tender offer to acquire majority of shares in it, reduces by one the number of shares this investor needs for majority. In the paper we construct mathematical models for the toehold and no-toehold strategies and compare the exp…
Kernel testing compares cell states in single-cell data.
Study compares LSTM models with sentiment analysis for stock price prediction.
This study compares three volatility metrics for Bitcoin, highlighting high expected volatility.
The paper analyzes the performance of delay-based reservoir computing using eigenvalue analysis.
Improved IFA with Generative Adversarial Networks for high-dimensional latent variables.
Factor Engine simplifies financial factor computation and analysis in Python.
In this paper, we use replica analysis to investigate the influence of correlation among the return rates of assets on the solution of the portfolio optimization problem. We consider the behavior of the optimal solution for the case where the return rate is described with a single-factor model and compare the findings …
Paper uses IGA for efficient pricing of financial derivatives, comparing it to FDM and FEM.
We present the multiplicative recurrent neural network as a general model for compositional meaning in language, and evaluate it on the task of fine-grained sentiment analysis. We establish a connection to the previously investigated matrix-space models for compositionality, and show they are special cases of the multi…
The authors study the method of scaling in the context of the study of automorphism groups of complex domains in multiple dimensions. Various types of scaling techniques are compared and contrasted. Applications are given in a number of areas of complex geometric analysis. Relations with other parts of mathematics are …
HACSurv models dependencies between competing risks and censoring for improved survival analysis.
This paper models yearly exchange rates between USD/KZT, EUR/KZT and SGD/KZT, and compares the actual data with developed forecasts using time series analysis over the period from 2006 to 2014. The official yearly data of National Bank of the Republic of Kazakhstan is used for present study. The main goal of this paper…
In the work, a comparative correlation and fractal analysis of time series of Bitcoin crypto currency rate and community activities in social networks associated with Bitcoin was conducted. A significant correlation between the Bitcoin rate and the community activities was detected. Time series fractal analysis indicat…
A new classification rule for FDA improves classification performance by accounting for unequal covariance matrices.
Multi-subject fMRI data analysis is an interesting and challenging problem in human brain decoding studies. The inherent anatomical and functional variability across subjects make it necessary to do both anatomical and functional alignment before classification analysis. Besides, when it comes to big data, time complex…
UMAP compared to other methods for dimensionality reduction.
This study compares three portfolio optimization methods on Indian stocks.
Learning a similarity metric has gained much attention recently, where the goal is to learn a function that maps input patterns to a target space while preserving the semantic distance in the input space. While most related work focused on images, we focus instead on learning a similarity metric for neuroimages, such a…
Study uses LLM to extract and compare segment disclosures from financial filings.
NCFA uses deep learning and causal discovery to analyze complex data.
This paper compares ML algorithms for PD prediction, finding XGBoost to be the most effective.
This paper compares LSTM, GRU, and Transformer models for stock price prediction.
In this report we describe a tool for comparing the performance of graphical causal structure learning algorithms implemented in the TETRAD freeware suite of causal analysis methods. Currently the tool is available as package in the TETRAD source code (written in Java). Simulations can be done varying the number of run…