Army Institute of Management, Kolkata

This study aims at bridging critical gaps in the existing cryptocurrency research by exploring combinations of technological, macroeconomic and behavioural factors, namely, economic agents' expectations and the size of influence that each of them has on the Bitcoin price movements. In contrast to the existing studies that focused on individual determinants and estimated aggregate effects thereof, in this study, fuzzy-set qualitative comparative analysis (fsQCA) is applied to determine configurations of drivers to determine the Bitcoin price and used necessary condition analysis (NCA) to quantify the magnitude of the effects using the monthly data between 2011 and 2022. Findings show that economic agents' expectations such as OECD's Business Confidence Index, Consumer Confidence Index and Composite Leading Indicator emerge as influential variables of Bitcoin, surpassing traditional drivers like Gold and Financial Stress Index. Among these, Business Confidence Index and Composite Leading Indicator exhibit a very large effect on Bitcoin prices, and from the technology variable group, Average Block Size exhibits a very large effect on Bitcoin prices. fsQCA indicates that nine distinct configurations contribute to high Bitcoin prices and eight configurations lead to low Bitcoin prices, thus depicting equifinality in Bitcoin price determination. These insights can provide policymakers and investors with a better understanding of the Bitcoin price dynamic by finding out necessary variables and equifinal pathways towards either high or low prices, thus promoting better risk management activities, as well as regulatory approaches to this highly dynamic asset class.

Ensuring reliability in adversarial settings necessitates treating privacy as a foundational component of data-driven systems. While differential privacy and cryptographic protocols offer strong guarantees, existing schemes rely on a fixed privacy budget, leading to a rigid utility-privacy trade-off that fails under heterogeneous user trust. Moreover, noise-only differential privacy preserves geometric structure, which inference attacks exploit, causing privacy leakage. We propose TADP-RME (Trust-Adaptive Differential Privacy with Reverse Manifold Embedding), a framework that enhances reliability under varying levels of user trust. It introduces an inverse trust score in the range [0,1] to adaptively modulate the privacy budget, enabling smooth transitions between utility and privacy. Additionally, Reverse Manifold Embedding applies a nonlinear transformation to disrupt local geometric relationships while preserving formal differential privacy guarantees through post-processing. Theoretical and empirical results demonstrate improved privacy-utility trade-offs, reducing attack success rates by up to 3.1 percent without significant utility degradation. The framework consistently outperforms existing methods against inference attacks, providing a unified approach for reliable learning in adversarial environments.

Purpose This study developed a cluster-based framework for measuring MSME ecosystem performance that addresses the limitations of monolithic rankings by accounting for structural heterogeneity across Indian states. Design/methodology/approach A two-stage analytical framework is employed using panel data from 28 Indian states (2016–2021). First, K-means clustering grouped states into homogeneous ecosystem configurations based on 20 indicators across five facilitators: Regulatory Framework, Entrepreneurial Capabilities, Credit Support, Market Conditions and Cultural Factors. Second, TOPSIS with Shannon Entropy objective weighting generated performance rankings within each cluster. Temporal alignment analysis examined correspondence between ranking patterns and documented policy interventions. Findings The analysis identified two stable ecosystem configurations – less-industrialized and industrialized – validating regional heterogeneity hypotheses. Intra-cluster rankings demonstrated meaningful performance differentiation and temporal correspondence with state-level MSME policies. Bihar’s rise in the less-industrialized cluster aligned with agro-processing incentives; sustained leadership by Tamil Nadu, Andhra Pradesh and Karnataka in the industrialized cluster corresponded with fiscal and innovation policies. Research limitations/implications Secondary data limitations and contradictory cases prevent definitive causal attribution. Integrating firm-level longitudinal data through primary survey will strengthen construct validity and enable the development of a formal, operationalizable MSME Performance Index for entrepreneurial ecosystem heterogeneity and policy effectiveness. Practical implications The framework will enable state governments to benchmark performance against structurally similar peers so less-industrialized states should compare against Cluster 1 comparators rather than industrialized Cluster 2 states and to prioritize cluster-specific interventions focus should be on credit access for less-industrialized ecosystems and innovation infrastructure for industrialized ones. Shannon Entropy weights identify high-impact facilitators within each cluster, enabling targeted rather than uniform policy design. Cluster transition signals (such as West Bengal’s movements between configurations) provide early warning mechanisms for ecosystem instability, supporting proactive rather than reactive governance. Originality/value This study advances MSME ecosystem measurements by demonstrating that cluster-based performance assessment captures policy-responsive dynamics that uniform benchmarks obscure. This framework establishes a methodological foundation for context-sensitive policy evaluation in federal economies with substantial regional disparities, offering a replicable approach for developing countries pursuing MSME-led inclusive growth.

Healthcare sector indices consolidate the economic health of pharmaceutical, biotechnology, and healthcare service firms. The short-term movements in these indices are closely intertwined with capital allocation decisions affecting research and development investment, drug availability, and long-term health outcomes. This research investigates whether historical open-high-low-close (OHLC) index data contain sufficient information for predicting the directional movement of the opening index on the subsequent trading day. The problem is formulated as a supervised classification task involving a one-step-ahead rolling window. A diverse feature set is constructed, comprising original prices, volatility-based technical indicators, and a novel class of nowcasting features derived from mutual OHLC ratios. The framework is evaluated on data from healthcare indices in the U.S. and Indian markets over a five-year period spanning multiple economic phases, including the COVID-19 pandemic. The results demonstrate robust predictive performance, with accuracy exceeding 0.8 and Matthews correlation coefficients above 0.6. Notably, the proposed nowcasting features have emerged as a key determinant of the market movement. We have employed the Shapley-based explainability paradigm to further elucidate the contribution of the features: outcomes reveal the dominant role of the nowcasting features, followed by a more moderate contribution of original prices. This research offers a societal utility: the proposed features and model for short-term forecasting of healthcare indices can reduce information asymmetry and support a more stable and equitable health economy.

Motivation:Multiomics data offer a rich data mine for modeling complex as well as day-to-day diseases, but their practical deployment is constrained by the limited sample availability. To this end, generating synthetic samples is a viable remedy. Extant schemes operating along this line, however, are mostly limited to augmenting the minority class in imbalanced datasets and often produce synthetic samples that lack sufficient diversity and fail to faithfully capture the underlying data distribution. As a result, the full potential of synthetic augmentation in multi-omics learning remains underexplored. The aim is to address the data scarcity problem in multi-omics domain. We propose a synthetic oversampling framework, which is dedicated to addressing overall data scarcity in multi-omics datasets and the lack of diversity in synthetic samples. Contrary to conventional methods that restrict augmentation to minority classes and rely on interpolation of two neighbors, our method generates diverse yet distribution-aligned synthetic samples by interpolating three neighbors and extends this augmentation paradigm to the majority class. The framework first balances the dataset by generating synthetic minority samples, and subsequently augments the balanced dataset by oversampling both majority and minority classes. Results:Empirical evaluation on multi-omics data obtained from three heterogeneous health scenarios-inflammatory bowel disease, multi-organ dysfunction syndrome, and colorectal cancer-substantiates the utility of the proposed scheme in improving the predictive performance. The models trained on T-SMmOTE-augmented data achieve higher Matthews correlation coefficient values, along with improved F 1 scores for both majority and minority classes. Notably, oversampling of the majority class improves the cognition of the minority class as well. We also explore the consistency of the class distributions between the original and augmented class-specific datasets. These findings confirm the capability of our scheme to learn from small, high-dimensional multi-omics datasets and highlight its potential for non-invasive disease detection. Availability and implementation:https://github.com/payelu/TSMm.