On-device research index

arXiv research

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.

168,657 papers · 148 categories

Trend · papers per month

count % of corpus

5 results for “NASDAQ100”

Intraday Limit Order Price Change Transition Dynamics Across Market Capitalizations Through Markov Analysisq-fin.ST

Study analyzes price change patterns across different market capitalizations using Markov chains.

problem Understanding price dynamics in limit order markets across various market capitalizations.

method Discrete-time Markov chain analysis of intraday price changes in NASDAQ100 tick data.

result Systematic patterns in price inertia and stability across market capitalizations are identified.

Markov chains limit order markets price dynamics NASDAQ100 market capitalization

2026-01-08abs ↗pdf ↗

Intraday order transition dynamics in high, medium, and low market cap stocks: A Markov chain approachq-fin.ST

Study analyzes order transitions in high, medium, and low market cap stocks using Markov chains.

problem Understanding order transitions in stocks of different market caps.

method First-order discrete-time Markov chain model applied to NASDAQ100 stocks.

result Limit orders exhibit higher inertia during opening hours but decrease in subsequent hours, while market orders increase.

Markov chain order dynamics NASDAQ100 high market cap medium market cap

2025-02-11abs ↗pdf ↗

Comparing Deep Learning Models for the Task of Volatility Prediction Using Multivariate Dataq-fin.ST

Study compares deep learning models for volatility prediction using multivariate data.

problem Predicting volatility using multivariate data.

method Evaluated multiple deep learning models including MLP, RNN, TCN, and Temporal Fusion Transformer.

result Temporal Fusion Transformer and TCN variants outperform classical models and shallow networks.

volatility prediction deep learning multivariate data temporal fusion transformer temporal convolutional networks

2023-06-20abs ↗pdf ↗

The presence of log-periodic structures before and after stock market crashes is considered to be an imprint of an intrinsic discrete scale invariance (DSI) in this complex system. The fractal framework of the theory leaves open the possibility of observing self-similar log-periodic structures at different time scales.…

2005-01-21abs ↗pdf ↗

Forecasting Financial Market Structure from Network Features using Machine Learningq-fin.CP

Model forecasts market structure from financial networks using machine learning.

problem Predicting market correlation structure from financial networks.

method Dynamic Asset Graph (DAG), Dynamic Minimal Spanning Tree (DMST), Dynamic Threshold Networks (DTN).

result Model improves market structure forecasting by up to 40% over benchmarks.

Financial networks Machine learning Market structure Dynamic graphs Correlation forecasting

2021-10-22abs ↗pdf ↗