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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

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7 results for “LMF”

Exact solution to a generalised Lillo-Mike-Farmer model with heterogeneous order-splitting strategiesq-fin.TR

This study generalizes an econophysics model to account for trader heterogeneity, finding robust power-law exponents but sensitive prefactors.

problem The original Lillo-Mike-Farmer model assumed homogeneity in traders' order-splitting strategies, which this study generalizes.

method The study proposes a generalised Lillo-Mike-Farmer model and solves it exactly without heuristic assumptions.

result The power-law exponent in the order-sign ACF is robust for arbitrary heterogeneous intensity distributions, but the prefactor is sensitive to heterogeneity.

econophysics order-splitting heterogeneity power-law prefactor

2023-06-23abs ↗pdf ↗

Metaorder modelling and identification from public dataq-fin.TR

Researchers validate LMF order-splitting theory using public JSE data.

problem Lack of reproducibility and cross-market validation of LMF theory due to proprietary data.

method Synthetic metaorder reconstruction using publicly available JSE data.

result LMF theory validated using JSE data for 100 largest stocks.

metaorder order-splitting LMF theory public data JSE

2026-02-23abs ↗pdf ↗

Quantitative statistical analysis of order-splitting behaviour of individual trading accounts in the Japanese stock market over nine yearsq-fin.TR

Quantitative analysis of order-splitting behavior in Japanese stock market.

problem Understanding and quantifying the order-splitting behavior of traders in the Japanese stock market.

method Analysis of a large dataset of trading accounts over nine years, clustering traders into order-splitting and random traders, and applying statistical methods to analyze metaorder length and sign correlation.

result The metaorder length distribution follows power laws with exponent α, and the sign correlation exponent γ is approximately α-1, supporting the LMF model.

order-splitting trading accounts Japanese stock market power laws sign correlation

2023-08-02abs ↗pdf ↗

Can we infer microscopic financial information from the long memory in market-order flow?: a quantitative test of the Lillo-Mike-Farmer modelq-fin.TR

Study validates Lillo-Mike-Farmer model predicting financial market long-range correlations.

problem Quantifying long-range correlations in financial markets.

method Analyzed nine years of market data to classify traders as order-splitting or random, measured metaorder-length distributions, and compared to LMF model predictions.

result Agreement between LMF model predictions and actual data, validating the model.

financial markets long-range correlation order flow order-splitting metaorder-length distributions

2023-01-31abs ↗pdf ↗

Revisiting Trade-sign Long-memory and Square-root Law price impactq-fin.TR

Revisiting Trade-sign Long-memory and Square-root Law price impact

problem Revisiting the Lillo-Mike-Farmer (LMF) theory and the square-root law (SQRL) of meta-order impact

method Using a coupled discrete reaction-diffusion formulation

result Long-memory of trade signs and square-root law of meta-order impact

LMF SQRL meta-order order book microstructure

2026-06-15abs ↗pdf ↗

EMAHA-DB1: A New Upper Limb sEMG Dataset for Classification of Activities of Daily Livingeess.SP

New sEMG dataset for ADL activities recognized with high accuracy.

problem Developing a robust dataset for sEMG-based ADL activity recognition.

method Acquired sEMG data from 25 subjects performing 22 ADL activities. Used 4 classifiers with various feature sets.

result SVM classifier achieved 83.21% accuracy on 5 FAABOS categories.

sEMG ADL classification sensors machine learning

2023-01-09abs ↗pdf ↗

Compressing Language Models using Doped Kronecker Productscs.LG

Kronecker Products (KP) have been used to compress IoT RNN Applications by 15-38x compression factors, achieving better results than traditional compression methods. However when KP is applied to large Natural Language Processing tasks, it leads to significant accuracy loss (approx 26%). This paper proposes a way to re…

2020-01-24abs ↗pdf ↗