arXane › Quantitative Finance › Quantitative Finance
Wednesday, 4 November 2026: 1 new paper, 1 cross-list and 3 replacements in General Finance (q-fin.GN), in arXiv's order, with full abstracts. The same papers as arXiv's own listing, set as a newspaper; every title links to arXiv.
While financial technology (FinTech) is seen as a contributor to bank performance, the empirical evidence of digitization's ability to translate into improved profit and stability outcomes and efficiency gains, within emerging systems dominated by banks is not conclusive. The direct impact of a composite FinTech Adoption Index (FTI) on the profitability (ROE) and stability (ln(Z-score)) of Bangladesh's commercial banks is explored in this study along with whether operational cost efficiency (CIR) acts as a mediator. The study is conducted using a balanced panel of 30 commercial banks between 2015 and 2024, supplemented by an entropy-weighted index of FinTech as a robustness check, using a two-step System GMM to address dynamic persistence and endogeneity issues. The adoption of FinTech presents no significant relationship with profitability, stability or cost efficiency, while cost efficiency does not significantly transmit the effect of FinTech to either outcome. The alternative measure suggests that adoption is not only significantly negatively related to profitability, but is also associated with a significantly improved (not worse) cost-to-income ratio, which indicates the sensitivity to measurement rather than any one consistent effect on performance. These findings qualify the expected performance benefits of FinTech and have implications for investment sequencing and regulation of digital transformation in emerging banking markets.
Using quarterly FRED and CSO Ireland data over 1995Q1-2025Q4, we examine whether U.S.\ IT investment and Irish data-centre electricity are recomposing the Kitchin cycle around energy- and compute-backed assets. Three findings stand out. First, a Quandt-Andrews sup-Wald test locates a significant break in the U.S.\ IT-GDP correlation at 2014Q3 (sup-W $= 9.69$; $r$ falls from $+0.45$ to $-0.12$); the Fisher $r$-to-$z$ test survives Bonferroni adjustment ($z = +3.11$, $p_{\text{Bonf}} = 0.006$), whereas the analogous test on the imposed Cloud-vs-AI boundary does not ($p_{\text{Bonf}} = 0.164$), so ``Strategic Decoupling'' pre-dates the AI Era by approximately six years. Second, in Ireland the synchronous AI-Era correlation between metered data-centre electricity and Real GDP is large ($r = +0.41$, $N = 16$) but \emph{collapses to $r \approx 0$ when Real GDP is replaced by Real GNP}, suggesting the headline is largely an artefact of the shared multinational tax-structure footprint between hyperscaler operations and Irish gross output. Third, the Irish cross-correlation profile is asymmetric in the opposite direction to a leading-indicator reading: the largest positive coefficients cluster on the $k < 0$ side (GDP precedes data-centre load), so compute capacity is better read as coincident or moderately lagging. Given the small AI-Era samples and the absence of a quarterly multinational-adjusted aggregate such as GNI*, all coefficients are exploratory; subject to these caveats, the U.S.\ Kitchin cycle is best dated to 2014Q3, and the Irish Energy-Data Nexus is dominated by multinational firm activity.