arXiv subjects

Francesca Medda

Publications and source records attributed to Francesca Medda.

14 recordsLinked to original sources

arXiv2026

Authority-Inference Separation in Agentic Finance: First-Line Control, Blockchain Enforcement, and Replayable Assurance

AI agents can select tools, counterparties, and transaction parameters, yet inference should not itself confer authority to execute a financial action. This study develops and evaluates Authority-Inference Separation (AIS), an intent-centered architecture for bounded agentic finance. AIS treats a financial action intent as the control object: a machine-generated proposal can receive temporary executable authority only after an independent deterministic control plane validates registered agent identity, accountable ownership, mandate and risk-appetite lineage, policy version, state, approvals, and exact economic semantics. Blockchain can then enforce the operational representation of granted authority and record portable settlement evidence, while institutional legitimacy, service delivery, accounting classification, and human accountability remain off-chain obligations. Evaluation combines four-domain instantiation, official BIS and MAS cases, a 48-fixture executable prototype, and a public-ledger observability test. Across 36 synthetic authorization attacks, a direct-agent baseline accepted 36 attack effects, a prompt-policy baseline accepted 20, and AIS accepted none; all three accepted 8/8 admissible fixtures. AIS also rejected 4/4 token replays and 8/8 recipient or rail substitutions, withheld completion in 4/4 service-delivery failures, and populated all 13 defined evidence fields. A test of 1,700 recent Base transactions associated with public x402 facilitator addresses shows that public ledgers can evidence settlement and selected authorization parameters but cannot establish institutional mandate, legal accountability, service delivery, or accounting treatment. AIS and blockchain are therefore complementary: AIS decides whether a specific intent may act, while blockchain can make granted authority bounded, executable, and independently observable.

q-fin.GN↗

arXiv2026

The Price of Permission: Classification Uncertainty in Constrained Capital Markets

Shariah-compliant equity screening provides a transparent setting in which institutional rules determine who may own a stock. A binary label identifies current eligibility but not whether the feasible investor base is fragmented across standards or close to changing. We define this instability as classification uncertainty and formalize its investor-base consequence through permitted investor mass. In a 1999-2024 CRSP-Compustat panel of 13,188 securities classified under seven researcher-emulated Shariah rulebooks, screening-rule disagreement and proximity to active boundaries rank next-month screen-implied transitions. U.S. Fama-MacBeth diagnostics do not support an unconditional equal-weighted permission premium, and a September 2023 DJIM/S&P methodology change produces no robust matched repricing. The central event evidence uses 25 official Securities Commission Malaysia lists. The 410 inclusions already trading before the preceding review have positive but imprecise matched returns. Applying the pre-event turnover floor yields 295 inclusions with 1.76 percentage points over $[0,10]$ trading days ($p_{\mathrm{date}}=0.008$; $p_{\mathrm{wild}}=0.017$) and 2.25 points over $[0,20]$ ($p_{\mathrm{date}}=0.018$; $p_{\mathrm{wild}}=0.035$). Leave-one-date-out, first-inclusion-only, and mid-review placebo checks are supportive, although a joint 20-day pre-event test rejects. Ownership and demand-pressure diagnostics do not identify a unique marginal buyer or clean causal demand shock. The evidence supports treating classification risk as a portfolio-monitoring state. Official Shariah permission is associated with price effects in a recognized local market among sufficiently tradable securities; formal eligibility alone is insufficient.

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arXiv2026

Settlement Infrastructure, Inside Money Elasticity, and the Network Economics of Distributed Ledger Technology

We construct the Settlement Modernisation Index, a panel dataset of 809 reform events across 24 advanced economies between 1993 and 2024, decomposed into three economic channels and three adoption phases. We document an S-curve in inside money elasticity with two interior turning points at SMI = 0.27 and 0.93, separating a liberation phase, a post-global-financial-crisis compliance valley, and a mature-infrastructure recovery phase. We show that settlement modernisation generates network-conditional balance sheet efficiencies through a T2S event-study with year-by-year EMIR decomposition (saturation beta = +0.557, p < 0.01) and an out-of-sample synthetic control null on Switzerland's post-2021 SDX deployment. Applied along the BIS three-layer connectivity taxonomy, the framework forecasts +13.4 percent efficiency recovery from the ECB's Pontes initiative over 2027-2032. Conditional UK and US accession to the Appia composability layer (2028) raises the ceiling to +37.5 percent. Balance-sheet efficiencies from atomic settlement are a property of the bilateral pair, not the node.

q-fin.GN↗

arXiv2026

Quantum Computing for Financial Transformation: A Review of Optimisation, Pricing, Risk, Machine Learning, and Post-Quantum Security

Quantum computing is becoming strategically relevant to finance because several core financial bottlenecks are already defined by combinatorial search, expectation estimation, rare-event analysis, representation learning, and long-horizon cryptographic resilience. This review examines that landscape across five connected domains: constrained portfolio optimisation, derivative pricing, tail-risk and scenario estimation, quantum machine learning, and post-quantum security. Rather than treating these topics as isolated demonstrations, the article studies them as linked layers of a financial-computation stack. Across all five domains, the review applies a common evaluative logic: identify the financial bottleneck, specify the relevant quantum primitive, compare it with an explicit classical benchmark, and assess the result under realistic implementation and governance constraints. The main conclusion is measured but consequential. The strongest near-term case for quantum finance lies in carefully designed hybrid workflows rather than blanket claims of universal advantage. Quantum optimisation is most credible when constrained search dominates; amplitude-estimation methods matter most when repeated expectation evaluation is the binding cost; quantum machine learning remains task dependent; and post-quantum cryptography is already strategically necessary because financial infrastructures must migrate before fault-tolerant attacks arrive. By combining system-level synthesis with locally reproducible small-scale case studies on simulated qubit registers, the article is intended both as a review of the field and as a handbook-style entry point for future work.

q-fin.CP↗

arXiv2026

Latent Variable Phillips Curve

This paper re-examines the empirical Phillips curve (PC) model and its usefulness in the context of medium-term inflation forecasting. A latent variable Phillips curve hypothesis is formulated and tested using 3,968 randomly generated factor combinations. Evidence from US core PCE inflation between Q1 1983 and Q1 2025 suggests that latent variable PC models reliably outperform traditional PC models six to eight quarters ahead and stand a greater chance of outperforming a univariate benchmark. Incorporating an MA(1) residual process improves the accuracy of empirical PC models across the board, although the gains relative to univariate models remain small. The findings presented in this paper have two important implications: First, they corroborate a new conceptual view on the Phillips curve theory; second, they offer a novel path towards improving the competitiveness of Phillips curve forecasts in future empirical work.

q-fin.ST↗

arXiv2025

From Binary Screens to Continuous Compliance: A Shariah Screening Measure for Portfolio Design

Islamic equity screening relies on multiple binary rulebooks that often classify the same firm differently. This paper develops a Continuous Shariah Compliance Index (CSCI) on $[0,1]$ that embeds the published business-activity and financial-ratio thresholds of six leading standards in a single transparent measure. Using CRSP/Compustat U.S. equities from 1999-2024 with lagged accounting inputs and monthly portfolio formation, we document four results. First, existing binary standards map to distinct regions of a common compliance scale, so firms that receive the same pass/fail label can still differ materially in compliance strength. Second, CSCI-threshold portfolios provide a transparent way to vary compliance intensity while retaining economically meaningful diversification, although baseline risk-adjusted performance declines modestly as thresholds tighten. Third, the September 2023 DJIM/S&P methodology change admits firms with materially lower CSCI scores than firms that remained compliant under both the old and new rules. Fourth, in cross-sectional return tests, CSCI is not reliably associated with higher expected returns once standard characteristics are controlled for. The main contribution of CSCI is therefore measurement and portfolio design rather than the discovery of a new priced factor.

q-fin.PM↗

arXiv2025

Quantum Network of Assets (QNA): A Density-Operator Framework for Market Dependence and Structural Risk Diagnostics

Classical correlation and rolling PCA summarize market dependence through covariance spectra, but they do not provide a unified operator representation for entropy, purity-based mixing, and standardized structural deviations built from rolling multi-feature trajectories. We propose the Quantum Network of Assets (QNA), a quantum-inspired but non-physical density-operator framework in which normalized asset-level state vectors induce a time-varying market operator and an associated overlap network. The framework yields two structural diagnostics: the Entanglement Risk Index (ERI) and the Quantum Early-Warning Signal (QEWS). Using a stable NASDAQ-100 panel over 2020-2025, spanning the pandemic aftermath, the 2022 tightening cycle, and the 2025 tariff repricing episode, we show that QNA entropy remains strongly related to covariance spectral entropy and effective rank at the regime level, but that the method becomes empirically distinct once the operator is constructed from multi-feature rolling trajectories rather than returns alone. In the returns-only limit, QNA lies close to classical spectral summaries; with volatility and liquidity channels included, it captures broader dependence reconfiguration and produces the clearest incremental signal during the April 2025 tariff escalation, when QEWS shifts sharply while rolling-z classical spectral benchmarks move only modestly. QNA therefore does not replace covariance-spectrum methods; instead, it provides a unified operator representation in which entropy, purity-based mixing, and event-aligned structural deviations are analyzed jointly across rolling multi-feature market states.

q-fin.RM↗

arXiv2024

Longitudinal market structure detection using a dynamic modularity-spectral algorithm

In this paper, we introduce the Dynamic Modularity-Spectral Algorithm (DynMSA), a novel approach to identify clusters of stocks with high intra-cluster correlations and low inter-cluster correlations by combining Random Matrix Theory with modularity optimisation and spectral clustering. The primary objective is to uncover hidden market structures and find diversifiers based on return correlations, thereby achieving a more effective risk-reducing portfolio allocation. We applied DynMSA to constituents of the S&P 500 and compared the results to sector- and market-based benchmarks. Besides the conception of this algorithm, our contributions further include implementing a sector-based calibration for modularity optimisation and a correlation-based distance function for spectral clustering. Testing revealed that DynMSA outperforms baseline models in intra- and inter-cluster correlation differences, particularly over medium-term correlation look-backs. It also identifies stable clusters and detects regime changes due to exogenous shocks, such as the COVID-19 pandemic. Portfolios constructed using our clusters showed higher Sortino and Sharpe ratios, lower downside volatility, reduced maximum drawdown and higher annualised returns compared to an equally weighted market benchmark.

q-fin.PM↗

arXiv2024

Dimensionality reduction techniques to support insider trading detection

Identification of market abuse is an extremely complicated activity that requires the analysis of large and complex datasets. We propose an unsupervised machine learning method for contextual anomaly detection, which allows to support market surveillance aimed at identifying potential insider trading activities. This method lies in the reconstruction-based paradigm and employs principal component analysis and autoencoders as dimensionality reduction techniques. The only input of this method is the trading position of each investor active on the asset for which we have a price sensitive event (PSE). After determining reconstruction errors related to the trading profiles, several conditions are imposed in order to identify investors whose behavior could be suspicious of insider trading related to the PSE. As a case study, we apply our method to investor resolved data of Italian stocks around takeover bids.

q-fin.ST↗

arXiv2022

A machine learning approach to support decision in insider trading detection

Identifying market abuse activity from data on investors' trading activity is very challenging both for the data volume and for the low signal to noise ratio. Here we propose two complementary unsupervised machine learning methods to support market surveillance aimed at identifying potential insider trading activities. The first one uses clustering to identify, in the vicinity of a price sensitive event such as a takeover bid, discontinuities in the trading activity of an investor with respect to his/her own past trading history and on the present trading activity of his/her peers. The second unsupervised approach aims at identifying (small) groups of investors that act coherently around price sensitive events, pointing to potential insider rings, i.e. a group of synchronised traders displaying strong directional trading in rewarding position in a period before the price sensitive event. As a case study, we apply our methods to investor resolved data of Italian stocks around takeover bids.

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arXiv2022

How Covid mobility restrictions modified the population of investors in Italian stock markets

This paper investigates how Covid mobility restrictions impacted the population of investors of the Italian stock market. The analysis tracks the trading activity of individual investors in Italian stocks in the period January 2019-September 2021, investigating how their composition and the trading activity changed around the Covid-19 lockdown period (March 9 - May 19, 2020) and more generally in the period of the pandemic. The results pinpoint that the lockdown restriction was accompanied by a surge in interest toward stock market, as testified by the trading volume by households. Given the generically falling prices during the lockdown, the households, which are typically contrarian, were net buyers, even if less than expected from their trading activity in 2019. This can be explained by the arrival, during the lockdown, of a group of about 185k new investors (i.e. which had never traded since January 2019) which were on average ten year younger and with a larger fraction of males than the pre-lockdown investors. By looking at the gross P&L, there is clear evidence that these new investors were more skilled in trading. There are thus indications that the lockdown, and more generally the Covid pandemic, created a sort of regime change in the population of financial investors.

q-fin.TR↗

arXiv2022

Information Extraction through AI techniques: The KIDs use case at CONSOB

In this paper we report on the initial activities carried out within a collaboration between Consob and Sapienza University. We focus on Information Extraction from documents describing financial instruments. We discuss how we automate this task, via both rule-based and machine learning-based methods and provide our first results.

cs.CL↗

arXiv2021

A Baseline for Shapley Values in MLPs: from Missingness to Neutrality

Deep neural networks have gained momentum based on their accuracy, but their interpretability is often criticised. As a result, they are labelled as black boxes. In response, several methods have been proposed in the literature to explain their predictions. Among the explanatory methods, Shapley values is a feature attribution method favoured for its robust theoretical foundation. However, the analysis of feature attributions using Shapley values requires choosing a baseline that represents the concept of missingness. An arbitrary choice of baseline could negatively impact the explanatory power of the method and possibly lead to incorrect interpretations. In this paper, we present a method for choosing a baseline according to a neutrality value: as a parameter selected by decision-makers, the point at which their choices are determined by the model predictions being either above or below it. Hence, the proposed baseline is set based on a parameter that depends on the actual use of the model. This procedure stands in contrast to how other baselines are set, i.e. without accounting for how the model is used. We empirically validate our choice of baseline in the context of binary classification tasks, using two datasets: a synthetic dataset and a dataset derived from the financial domain.

cs.LG↗

arXiv2017

The effect of heterogeneity on financial contagion due to overlapping portfolios

We consider a model of financial contagion in a bipartite network of assets and banks recently introduced in the literature, and we study the effect of power law distributions of degree and balance-sheet size on the stability of the system. Relative to the benchmark case of banks with homogeneous degrees and balance-sheet sizes, we find that if banks have a power-law degree distribution the system becomes less robust with respect to the initial failure of a random bank, and that targeted shocks to the most specialised banks (i.e. banks with low degrees) or biggest banks increases the probability of observing a cascade of defaults. In contrast, we find that a power-law degree distribution for assets increases stability with respect to random shocks, but not with respect to targeted shocks. We also study how allocations of capital buffers between banks affects the system's stability, and we find that assigning capital to banks in relation to their level of diversification reduces the probability of observing cascades of defaults relative to size based allocations. Finally, we propose a non-capital based policy that improves the resilience of the system by introducing disassortative mixing between banks and assets.

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