arXiv subjects

Ioana Boier

Publications and source records attributed to Ioana Boier.

4 recordsLinked to original sources

arXiv2026

Same Book, Different Fills: Partial Identification of FIFO Execution from Aggregate Order Books

Price-level limit order book (L2) data reveal aggregate liquidity but not the ordered queue required by price--time priority. Passive-execution backtests can therefore depend on an unobserved cancellation-allocation rule even when observed prices, quantities, and trades are held fixed. We frame recovery of market-by-order histories from aggregate snapshots as a conditional partial identification problem: multiple histories can reproduce the same aggregate path. Holding that path, reconciled market removals, latent order partitions, and additions fixed, our path-preserving compiler varies only cancellation allocation among front, quantity-weighted-random, and back rules. Within this compiler class, we establish front--back fill ordering for a virtual tagged order during one touch-price spell. We study seven months of synchronized 2025 Tokyo Stock Exchange data for two instruments with different trading activity: RIC 1301.T and RIC 7911.T. Ten-level L2 snapshots provide book states, while L1 trades permit inference of market removals and resting side through reconciliation. Each instrument contributes 1,080 matched five-minute episodes over the same 18 held-out trading days. The aggressive benchmark is invariant across FIFO realizations, but passive execution is sensitive to the cancellation rule. For 1301.T, front rather than back cancellation raises preterminal completion by 8.01 percentage points and reduces implementation shortfall by 1.010 bps. For 7911.T, the corresponding differences are 7.39 percentage points and 0.384 bps. Thus, observationally equivalent aggregate-book paths can imply economically different passive-execution outcomes. Execution policies evaluated from aggregate data should be accompanied by FIFO sensitivity analysis rather than reported as single-point estimates based on an unobservable queue assumption.

q-fin.CP↗

arXiv2026

Scaling Laws for Task-Specific LLM Distillation

Large Language Models (LLMs) achieve strong performance across a growing range of domains, yet their scale poses deployment challenges in applications where latency and cost constraints are critical. This paper derives empirical scaling laws for domain-specific LLM compression, quantifying how in-domain and general knowledge performance scale with dataset size, compression ratio, supervision format, and iterative pruning schedule. Using quantitative finance as our application domain, we compare logit-based and LoRA-based distillation under iterative structural pruning, introducing a blended chain-of-thought supervision loss that stabilizes KL-divergence distillation over reasoning traces. In-domain task quality degrades predictably under compression while general-knowledge benchmarks collapse well before the same point; supervision format is the key driver of this tradeoff, with chain-of-thought supervision actively recovering general knowledge that pruning erases. We release the headline dataset FinHeadlineMix, scaling law results, and practical recommendations to provide a reusable framework for domain-specific compression decisions.

cs.AI↗

arXiv2025

Scaling Conditional Autoencoders for Portfolio Optimization via Uncertainty-Aware Factor Selection

Conditional Autoencoders (CAEs) offer a flexible, interpretable approach for estimating latent asset-pricing factors from firm characteristics. However, existing studies usually limit the latent factor dimension to around K=5 due to concerns that larger K can degrade performance. To overcome this challenge, we propose a scalable framework that couples a high-dimensional CAE with an uncertainty-aware factor selection procedure. We employ three models for quantile prediction: zero-shot Chronos, a pretrained time-series foundation model (ZS-Chronos), gradient-boosted quantile regression trees using XGBoost and RAPIDS (Q-Boost), and an I.I.D bootstrap-based sample mean model (IID-BS). For each model, we rank factors by forecast uncertainty and retain the top-k most predictable factors for portfolio construction, where k denotes the selected subset of factors. This pruning strategy delivers substantial gains in risk-adjusted performance across all forecasting models. Furthermore, due to each model's uncorrelated predictions, a performance-weighted ensemble consistently outperforms individual models with higher Sharpe, Sortino, and Omega ratios.

q-fin.PM↗

arXiv2022

Multiresolution Signal Processing of Financial Market Objects

Multiresolution analysis has applications across many disciplines in the study of complex systems and their dynamics. Financial markets are among the most complex entities in our environment, yet mainstream quantitative models operate at predetermined scale, rely on linear correlation measures, and struggle to recognize non-linear or causal structures. In this paper, we combine neural networks known to capture non-linear associations with a multiscale decomposition to facilitate a better understanding of financial market data substructures. Quantization keeps our decompositions calibrated to market at every scale. We illustrate our approach in the context of seven use cases.

q-fin.CP↗