The integration of generative AI (GAI) into the financial sector has brought about significant advancements, offering new solutions for various financial tasks. This review paper provides a comprehensive examination of recent trends and developments at the intersection of GAI and finance. By utilizing an advanced topic modeling method, BERTopic, we systematically categorize and analyze existing research to uncover predominant themes and emerging areas of interest. Our findings reveal the transformative impact of finance-specific large language models (LLMs), the innovative use of generative adversarial networks (GANs) in synthetic financial data generation, and the pressing necessity of a new regulatory framework to govern the use of GAI in the finance sector. This paper aims to provide researchers and practitioners with a structured overview of the current landscape of GAI in finance, offering insights into both the opportunities and challenges presented by these advanced technologies.

In this paper, we shall solve a time-fractional nonlinear Schrödinger equation by using the quintic non-polynomial spline and theL1 formula. The unconditional stability, unique solvability and convergence of our numerical scheme are proved by the Fourier method. It is shown that our method is sixth order accurate in the spatial dimension and $(2-\gamma )$(2−γ) th order accurate in the temporal dimension, whereγis the fractional order. The efficiency of the proposed numerical scheme is further illustrated by numerical experiments, meanwhile the simulation results indicate better performance over previous work in the literature.

In modern financial markets, decision-makers increasingly rely on quantitative methods to navigate complex trade-offs among multiple, often conflicting objectives. This paper addresses constrained multi-objective optimization (MOO) with an application to portfolio optimization for minimizing risk and maximizing return. To this end, and to address existing gaps, we propose a novel reinforcement learning (RL)-guided non-dominated sorting genetic algorithm II (NSGA-II) enhanced with gray relational coefficients (GRC), termed RL-NSGA-II-GRC, which combines an RL agent controller and GRC-based selection to improve the convergence and diversity of the Pareto-optimal fronts. The agent adapts key evolutionary parameters online using population-level metrics of hypervolume, feasibility, and diversity, while the GRC-enhanced tournament operator ranks parents via a unified score simultaneously considering dominance rank, crowding distance, and geometric proximity to ideal reference. We evaluate the framework on the Kursawe and CONSTR benchmark problems and on a NASDAQ portfolio optimization application. On the benchmarks, RL-NSGA-II-GRC achieves convergence metric improvements of about 5.8% and 4.4% over the original NSGA-II, while preserving a well-distributed set of non-dominated solutions. In the portfolio application, the method produces a smooth and densely populated efficient frontier that supports the identification of the maximum Sharpe ratio portfolio (with annualized Sharpe ratio = 1.92), as well as utility-optimal portfolios for different risk-aversion levels. The main contributions of this work are three-fold: (1) we propose an RL-NSGA-II-GRC method that integrates an RL agent into the evolutionary framework to adaptively control key parameters using generational feedback; (2) we design a GRC-enhanced binary tournament selection operator that provides a comprehensive performance indicator to efficiently guide the search toward the Pareto-optimal front; (3) we demonstrate, on benchmark MOO problems and a NASDAQ portfolio case study, that the proposed method delivers improved convergence and well-populated efficient frontiers that support actionable investment insights.

This study examines whether retail social media sentiment and community attention explain daily net capital flows into U.S. spot Bitcoin exchange-traded funds (ETFs), and whether issuer brand visibility conditions that relationship. We construct a balanced panel of $N=10$ ETFs over $T=514$ trading days (January 2024 to January 2026) and combine it with 162,819 cleaned Reddit posts to derive three AI-driven discourse variables: engagement-weighted sentiment, community attention, and a novel issuer-specific BrandScore. Entity fixed-effects regressions show that neither aggregate sentiment nor BrandScore level alone significantly predicts fund-level flows; however, the Sentiment $\times$ BrandScore interaction is significant ($\widehat{\beta }=2.930$, $p=0.038$), indicating that sentiment becomes economically meaningful only when attached to a visible issuer. This interaction survives two-way (entity + date) fixed effects ($p=0.012$) and winsorization ($p=0.004$). Panel quantile regressions reveal distributional heterogeneity in the brand-sentiment channel. Rolling 90-day window estimation confirms the mechanism is episodic, with the interaction achieving significance in 62.8% of subsample windows. These results provide suggestive evidence for a brand-filtered sentiment transmission mechanism in digital asset markets.

The integration of generative AI (GAI) into the financial sector has brought about significant advancements, offering new solutions for various financial tasks. This review paper provides a comprehensive examination of recent trends and developments at the intersection of GAI and finance. By utilizing an advanced topic modeling method, BERTopic, we systematically categorize and analyze existing research to uncover predominant themes and emerging areas of interest. Our findings reveal the transformative impact of finance-specific large language models (LLMs), the innovative use of generative adversarial networks (GANs) in synthetic financial data generation, and the pressing necessity of a new regulatory framework to govern the use of GAI in the finance sector. This paper aims to provide researchers and practitioners with a structured overview of the current landscape of GAI in finance, offering insights into both the opportunities and challenges presented by these advanced technologies.

This study investigates the correlation between the audio features of top-charting music and Non-Fungible Tokens (NFT) market dynamics, presenting a novel perspective within the realm of behavioral finance. Drawing on the regulatory focus theory and existing research on music's affective influence, the authors argue that popular music, as a reflection of society's collective regulatory focus, can significantly impact trading behaviours in NFTs, an asset class known for its susceptibility to emotional drivers and speculative activity. By employing a Long Short-Term Memory (LSTM) machine learning model and permutation importance technique, the analysis demonstrates that specific musical attributes—such as danceability, loudness, and mode—exhibit predictive power over daily NFT trading volumes. The study not only provides evidence of music's capacity to signal shifts in trading behaviors, offering innovative insights into the drivers of digital asset markets, but introduces a new interdisciplinary approach focusing on the collective regulatory focus reflected in the music.