Massive and parallel changes have occurred in New York City since the late 1970s and in London and Tokyo since the early 1980s. What transformed these urban centers, with their diverse histories, into "global cities" that share comparable economic and social structures? Saskia Sassen argues that their remarkable similarity arises from their position as command posts in international finance and advanced services for business.
Oana Panazan, Catalin Gheorghe, Aamir Aijaz Syed
et al.
This study examines the dynamic interactions between precious metals, cryptocurrencies, stablecoins, safe-haven currencies, and two key macroeconomic indicators, the 5-year breakeven inflation expectation (T5YIE) and the 10-year minus 3-month Treasury yield spread (T10Y3M), over January 2016–July 2025. To capture nonlinear and multi-scale dependencies, the study applies Quantile-on-Quantile Regression (QQR) in combination with wavelet coherence (WCO) and wavelet transform coherence (WTC). The results indicate that major cryptocurrencies such as Bitcoin and Ethereum do not display robust or systematic links with inflation expectations or recession risk, limiting their role as macro-financial hedges. By contrast, the Japanese yen and Swiss franc show pronounced tail sensitivities, reaffirming their safe-haven status, while gold and its tokenized counterparts (DGX, PAXG) exhibit persistent long-run coherence with inflation expectations. Stablecoins demonstrate unstable short-term linkages shaped by liquidity shocks and market frictions. The research provides new evidence on the heterogeneous roles of digital and traditional assets in shaping macroeconomic expectations. The findings carry implications for investors, who should continue to rely on gold and safe-haven currencies for crisis hedging, and for regulators concerned with the systemic stability of emerging digital instruments.
This study investigates the determinants of profitability among Indian commercial banks from 2005 to 2024, with a specific focus on the novel role of dynamic growth efficiency (GE), a concept capturing a bank's ability to transform input growth into output growth, alongside conventional static efficiency measures such as level efficiency (LE) and cost-to-income ratio (CIR). As the first to operationalize GE in the Indian context, the study employs data envelopment analysis (DEA) on a panel dataset of 50 commercial banks (12 public, 17 private, and 21 foreign). Results from a system GMM estimator reveal GE to be a consistently significant driver of profitability, outperforming both LE and CIR across various market power indicators and model specifications. A key methodological advance supporting this analysis is the inclusion of technology expenditures (which account for 29% of operating and 13% of total expenses in 2024) as a fundamental input, correcting a major misspecification in prior literature. We demonstrate that omitting this crucial input artificially inflates market power and deflates efficiency estimates. The positive impact of GE is more pronounced for public-sector and new private banks, underscoring divergent strategic drivers across ownership structures and highlighting the paramount importance of fostering dynamic capabilities for sustaining profitability in a rapidly evolving banking landscape.
Domain-specific enhancement of Large Language Models (LLMs) within the financial context has long been a focal point of industrial application. While previous models such as BloombergGPT and Baichuan-Finance primarily focused on knowledge enhancement, the deepening complexity of financial services has driven a growing demand for models that possess not only domain knowledge but also robust financial reasoning and agentic capabilities. In this paper, we present QianfanHuijin, a financial domain LLM, and propose a generalizable multi-stage training paradigm for industrial model enhancement. Our approach begins with Continual Pre-training (CPT) on financial corpora to consolidate the knowledge base. This is followed by a fine-grained Post-training pipeline designed with increasing specificity: starting with Financial SFT, progressing to Finance Reasoning RL and Finance Agentic RL, and culminating in General RL aligned with real-world business scenarios. Empirical results demonstrate that QianfanHuijin achieves superior performance across various authoritative financial benchmarks. Furthermore, ablation studies confirm that the targeted Reasoning RL and Agentic RL stages yield significant gains in their respective capabilities. These findings validate our motivation and suggest that this fine-grained, progressive post-training methodology is poised to become a mainstream paradigm for various industrial-enhanced LLMs.
A growing trend in financial technology (fintech) is the use of mobile phone data and machine learning (ML) to provide credit scores- and subsequently, opportunities to access loans- to groups left out of traditional banking. This paper draws on interview data with leaders, investors, and data scientists at fintech companies developing ML-based alternative lending apps in low- and middle-income countries to explore financial inclusion and gender implications. More specifically, it examines how the underlying logics, design choices, and management decisions of ML-based alternative lending tools by fintechs embed or challenge gender biases, and consequently influence gender equity in access to finance. Findings reveal developers follow 'gender blind' approaches, grounded in beliefs that ML is objective and data reflects the truth. This leads to a lack of grappling with the ways data, features for creditworthiness, and access to apps are gendered. Overall, tools increase access to finance, but not gender equitably: Interviewees report less women access loans and receive lower amounts than men, despite being better repayers. Fintechs identify demand- and supply-side reasons for gender differences, but frame them as outside their responsibility. However, that women are observed as better repayers reveals a market inefficiency and potential discriminatory effect, further linked to profit optimization objectives. This research introduces the concept of encoded gender norms, whereby without explicit attention to the gendered nature of data and algorithmic design, AI tools reproduce existing inequalities. In doing so, they reinforce gender norms as self-fulfilling prophecies. The idea that AI is inherently objective and, when left alone, 'fair', is seductive and misleading. In reality, algorithms reflect the perspectives, priorities, and values of the people and institutions that design them.
Soufiane El Amine El Alami, Abderazzak Mouiha, Abdelatif Hafid
et al.
This systematic review examines how machine learning (ML) and deep learning (DL) have transformed forecasting, decision-making, and financial modelling, promoting innovation and efficiency in financial systems. Following PRISMA 2020 guidelines, we analyze 22 peer-reviewed and open-access articles (2024 to 2026) indexed in Scopus, applying ML and DL models across credit risk prediction, cryptocurrency, asset pricing, and macroeconomic policy modeling. The most used models include Random Forest, XG-Boost, Support Vector Machine, Long Short-Term Memory (LSTM), Bidirectional LSTM, Convolutional Neural Network (CNN), and hybrid or ensemble approaches combining statistical and AI methods. ML and DL techniques outperform traditional models by capturing nonlinear dependencies and enhancing predictive accuracy, while explainable AI methods (e.g., SHAP and feature importance analysis) improve transparency and interpretability. Emerging trends include cross-domain applications and the integration of responsible AI in finance. Despite notable progress, challenges remain in interpretability, generalizability, and data quality. Overall, this review provides a comprehensive overview of AI-driven computational finance and outlines future research directions.