Modeling for the Growth of Unorganized Retailing in the Presence of Organized and E-Retailing in Indian Pharmaceutical Industry ArXiv ID: 2507.17023 “View on arXiv” Authors: Koushik Mondal, Balagopal G Menon, Sunil Sahadev Abstract The present study considers the rural pharmaceutical retail sector in India, where the arrival of organized retailers and e-retailers is testing the survival strategies of unorganized retailers. Grounded in a field investigation of the Indian pharmaceutical retail sector, this study integrates primary data collection, consumer conjoint analysis and design of experiments to develop an empirically grounded agent-based simulation of multi-channel competition among unorganized, organized and e-pharmaceutical retailers. The results of the conjoint analysis reveal that store attributes of price discount, quality of products offered, variety of assortment, and degree of personalized service, and customer attributes of distance, degree of mobility, and degree of emergency are key determinants of optimal store choice strategies. The primary insight obtained from the agent-based modeling is that the attribute levels of each individual retailer have some effect on other retailers performance. The field-calibrated simulation also evidenced counterintuitive behavior that an increase in unorganized price discounts initially leads to an increase in average footprint at unorganized retailers, but eventually leads to these retailers moving out of the market. Hence, the unorganized retailers should not increase the price discount offered beyond a tipping point or it will be detrimental to them. Another counterintuitive behavior found was that high emergency customers give less importance to variety of assortment than low emergency customers. This study aids in understanding the levers for policy design towards improving the competition dynamics among retail channels in the pharmaceutical retail sector in India. ...

Prediction of linear fractional stable motions using codifference, with application to non-Gaussian rough volatility ArXiv ID: 2507.15437 “View on arXiv” Authors: Matthieu Garcin, Karl Sawaya, Thomas Valade Abstract The linear fractional stable motion (LFSM) extends the fractional Brownian motion (fBm) by considering $α$-stable increments. We propose a method to forecast future increments of the LFSM from past discrete-time observations, using the conditional expectation when $α>1$ or a semimetric projection otherwise. It relies on the codifference, which describes the serial dependence of the process, instead of the covariance. Indeed, covariance is commonly used for predicting an fBm but it is infinite when $α<2$. Some theoretical properties of the method and of its accuracy are studied and both a simulation study and an application to real data confirm the relevance of the approach. The LFSM-based method outperforms the fBm, when forecasting high-frequency FX rates. It also shows a promising performance in the forecast of time series of volatilities, decomposing properly, in the fractal dynamic of rough volatilities, the contribution of the kurtosis of the increments and the contribution of their serial dependence. Moreover, the analysis of hit ratios suggests that, beside independence, persistence, and antipersistence, a fourth regime of serial dependence exists for fractional processes, characterized by a selective memory controlled by a few large increments. ...

A Comparative Analysis of Statistical and Machine Learning Models for Outlier Detection in Bitcoin Limit Order Books ArXiv ID: 2507.14960 “View on arXiv” Authors: Ivan Letteri Abstract The detection of outliers within cryptocurrency limit order books (LOBs) is of paramount importance for comprehending market dynamics, particularly in highly volatile and nascent regulatory environments. This study conducts a comprehensive comparative analysis of robust statistical methods and advanced machine learning techniques for real-time anomaly identification in cryptocurrency LOBs. Within a unified testing environment, named AITA Order Book Signal (AITA-OBS), we evaluate the efficacy of thirteen diverse models to identify which approaches are most suitable for detecting potentially manipulative trading behaviours. An empirical evaluation, conducted via backtesting on a dataset of 26,204 records from a major exchange, demonstrates that the top-performing model, Empirical Covariance (EC), achieves a 6.70% gain, significantly outperforming a standard Buy-and-Hold benchmark. These findings underscore the effectiveness of outlier-driven strategies and provide insights into the trade-offs between model complexity, trade frequency, and performance. This study contributes to the growing corpus of research on cryptocurrency market microstructure by furnishing a rigorous benchmark of anomaly detection models and highlighting their potential for augmenting algorithmic trading and risk management. ...

Isotonic Quantile Regression Averaging for uncertainty quantification of electricity price forecasts ArXiv ID: 2507.15079 “View on arXiv” Authors: Arkadiusz Lipiecki, Bartosz Uniejewski Abstract Quantifying the uncertainty of forecasting models is essential to assess and mitigate the risks associated with data-driven decisions, especially in volatile domains such as electricity markets. Machine learning methods can provide highly accurate electricity price forecasts, critical for informing the decisions of market participants. However, these models often lack uncertainty estimates, which limits the ability of decision makers to avoid unnecessary risks. In this paper, we propose a novel method for generating probabilistic forecasts from ensembles of point forecasts, called Isotonic Quantile Regression Averaging (iQRA). Building on the established framework of Quantile Regression Averaging (QRA), we introduce stochastic order constraints to improve forecast accuracy, reliability, and computational costs. In an extensive forecasting study of the German day-ahead electricity market, we show that iQRA consistently outperforms state-of-the-art postprocessing methods in terms of both reliability and sharpness. It produces well-calibrated prediction intervals across multiple confidence levels, providing superior reliability to all benchmark methods, particularly coverage-based conformal prediction. In addition, isotonic regularization decreases the complexity of the quantile regression problem and offers a hyperparameter-free approach to variable selection. ...

Longitudinal review of portfolios with minimum variance approach before during and after the pandemic ArXiv ID: 2507.15111 “View on arXiv” Authors: Genjis A. Ossa, Luis H. Restrepo Abstract This study investigates the impact of the pandemic on the most traded stocks in the Colombian stock market for the date of January 17, 2024. Based on the daily data of the most traded companies in Colombia for said date and covering a period general from 2015 to 2023, in a summarized way our analysis reveals that in the period 2015-2019, the return reached 5.70%, with a relatively low risk of 18.45%. However, in the following period 2016 -2020, although the yield decreased to 5.40%, the risk experienced a significant increase, reaching 24.64%. The beta also showed variations, being lowest in 2015-2019 with 0.61 and increasing to 1.02 in 2016-2020. The capital market line (LMC) in the constructed portfolios has a downward trend, indicating that the portfolio offers an expected rate of return lower than the risk-free rate. This finding is supported by the Sharpe index, which shows negative values throughout the periods studied. ...

Novel Risk Measures for Portfolio Optimization Using Equal-Correlation Portfolio Strategy ArXiv ID: 2508.03704 “View on arXiv” Authors: Biswarup Chakraborty Abstract Portfolio optimization has long been dominated by covariance-based strategies, such as the Markowitz Mean-Variance framework. However, these approaches often fail to ensure a balanced risk structure across assets, leading to concentration in a few securities. In this paper, we introduce novel risk measures grounded in the equal-correlation portfolio strategy, aiming to construct portfolios where each asset maintains an equal correlation with the overall portfolio return. We formulate a mathematical optimization framework that explicitly controls portfolio-wide correlation while preserving desirable risk-return trade-offs. The proposed models are empirically validated using historical stock market data. Our findings show that portfolios constructed via this approach demonstrate superior risk diversification and more stable returns under diverse market conditions. This methodology offers a compelling alternative to conventional diversification techniques and holds practical relevance for institutional investors, asset managers, and quantitative trading strategies. ...

Through the Looking Glass: Bitcoin Treasury Companies ArXiv ID: 2507.14910 “View on arXiv” Authors: B K Meister Abstract Bitcoin treasury companies have taken stock markets by storm amassing billions of dollars worth of tokens in hundreds of entities. The paper discusses, how leverage - whether created through corporate debt or investors using stock as loan collateral - fuels this trend. The extension of the binary-choice Kelly criterion to incorporate uncertainty in the form of the Kullback-Leibler divergence or more generally Bregman divergence is also briefly discussed. ...

Transaction Profiling and Address Role Inference in Tokenized U.S. Treasuries ArXiv ID: 2507.14808 “View on arXiv” Authors: Junliang Luo, Katrin Tinn, Samuel Ferreira Duran, Di Wu, Xue Liu Abstract Tokenized U.S. Treasuries have emerged as a prominent subclass of real-world assets (RWAs), offering cryptographically enforced, yield-bearing instruments collateralized by sovereign debt and deployed across multiple blockchain networks. While the market has expanded rapidly, empirical analyses of transaction-level behaviour remain limited. This paper conducts a quantitative, function-level dissection of U.S. Treasury-backed RWA tokens including BUIDL, BENJI, and USDY, across multi-chain: mostly Ethereum and Layer-2s. We analyze decoded contract calls to isolate core functional primitives such as issuance, redemption, transfer, and bridge activity, revealing segmentation in behaviour between institutional actors and retail users. To model address-level economic roles, we introduce a curvature-aware representation learning framework using Poincaré embeddings and liquidity-based graph features. Our method outperforms baseline models on our RWA Treasury dataset in role inference and generalizes to downstream tasks such as anomaly detection and wallet classification in broader blockchain transaction networks. These findings provide a structured understanding of functional heterogeneity and participant roles in tokenized Treasury in a transaction-level perspective, contributing new empirical evidence to the study of on-chain financialization. ...

Eigenvalue Distribution of Empirical Correlation Matrices for Multiscale Complex Systems and Application to Financial Data ArXiv ID: 2507.14325 “View on arXiv” Authors: Luan M. T. de Moraes, Antônio M. S. Macêdo, Giovani L. Vasconcelos, Raydonal Ospina Abstract We introduce a method for describing eigenvalue distributions of correlation matrices from multidimensional time series. Using our newly developed matrix H theory, we improve the description of eigenvalue spectra for empirical correlation matrices in multivariate financial data by considering an informational cascade modeled as a hierarchical structure akin to the Kolmogorov statistical theory of turbulence. Our approach extends the Marchenko-Pastur distribution to account for distinct characteristic scales, capturing a larger fraction of data variance, and challenging the traditional view of noise-dressed financial markets. We conjecture that the effectiveness of our method stems from the increased complexity in financial markets, reflected by new characteristic scales and the growth of computational trading. These findings not only support the turbulent market hypothesis as a source of noise but also provide a practical framework for noise reduction in empirical correlation matrices, enhancing the inference of true market correlations between assets. ...

Governance, productivity and economic development ArXiv ID: 2507.13099 “View on arXiv” Authors: Cuong Le Van, Ngoc-Sang Pham, Thi Kim Cuong Pham, Binh Tran-Nam Abstract This paper explores the interplay between transfer policies, R&D, corruption, and economic development using a general equilibrium model with heterogeneous agents and a government. The government collects taxes, redistributes fiscal revenues, and undertakes public investment (in R&D, infrastructure, etc.). Corruption is modeled as a fraction of tax revenues that is siphoned off and removed from the economy. We first establish the existence of a political-economic equilibrium. Then, using an analytically tractable framework with two private agents, we examine the effects of corruption and evaluate the impact of various policies, including redistribution and innovation-led strategies. ...