arXiv:2608. 05255v1 Announce Type: cross Abstract: Retail investors lack access to the kind of personalized, tax-aware portfolio management that institutional clients take for granted -- existing robo-advisors use static, rule-based allocation, and institutional-grade systems require account minimums and technology stacks unavailable to individual investors.
By Ramin Pishehvar
arXiv:2606. 00143v1 Announce Type: cross Abstract: Financial markets are inherently non-stationary, exhibiting frequent regime shifts and structural changes that render traditional Portfolio Management (PM) approaches ineffective.
By Chaofan Pan, Lingfei Ren, Linbo Xiong, Yonghao Li, Wei Wei, Xin Yang
arXiv:2606. 08450v1 Announce Type: new Abstract: Financial portfolio trading is naturally formulated as a reinforcement learning problem, where an agent sequentially rebalances assets under changing market conditions to balance return, risk, and transaction costs.
By Yanyan Wu, Boyi Zhang, Yanlin Liu, Xinyu Fang, Jining Luan, Meiqi Zhang, Jiacheng Liu, Hao Zeng, Dexu Yu, Chang Liu, Hanwen Du, Yongxin Ni, Youhua Li
FinSkillBench is an evaluation suite that tests whether language model agents can use financial domain skills to solve investment management tasks across portfolio construction, risk management, and fundamental analysis. The benchmark contains 12 subtasks with 2,603 episodes, each providing point‑in‑time inputs, hidden ground truth, and a verifier. Experiments show that curated skill packages improve performance significantly, while self‑generated skills offer little benefit, indicating that reliable procedural skills are crucial for effective AI agents in this domain.
By Jermyn Zhen Yong Bek, Zhuang Qiang Bok, Zhongtian Sun
arXiv:2608. 15841v1 Announce Type: new Abstract: Reinforcement learning has gained increasing attention as a data-driven approach for stock trading.
By Arishi Orra, Himanshu Choudhary, Manoj Thakur
arXiv:2607. 16028v1 Announce Type: new Abstract: This paper presents our system for Task 3 of the CLEF 2026 FinMMEval Lab, which requires daily long, flat, or short trading decisions for Bitcoin (BTC) and Tesla (TSLA) using news and historical market data.
By Andrei Neagu, Eeham Khan, Leila Kosseim
The paper proposes a Multi-Objective Reinforcement Learning framework for portfolio optimization that incorporates ratings from three ESG agencies, addressing the divergence in ESG rating methodologies. It couples this with a Preference Elicitation system using Gaussian Processes, allowing users to infer latent utility functions via pairwise comparisons of portfolios based on Sharpe ratios and ESG scores. Experiments with LLM-generated portfolio managers show that regional background influences preference weights, with European personas prioritizing ESG alignment and Texas personas favoring risk‑adjusted returns.
By Giovanni Dispoto, Marcello Restelli, Carmine Ventre
arXiv:2606. 10412v1 Announce Type: new Abstract: The rapid evolution of financial technology demands sophisticated artificial intelligence systems capable of handling diverse challenges across multiple domains simultaneously.
By Fanrong Liu, Zhang Yuwei, Mingni Luo
arXiv:2501. 00826v3 Announce Type: replace-cross Abstract: Cryptocurrency portfolio management requires the fusion of heterogeneous multi-modal signals, including structured price and on-chain time series, unstructured news text, and technical indicators, under high-volatility and real-time constraints.
By Yichen Luo, Yebo Feng, Jiahua Xu, Paolo Tasca, Yang Liu
The study compares a custom capital gains calculation engine with a retrieval‑augmented generation (RAG) vector store of market advisory reports in a multi‑agent financial advisory system. A 2x2 factorial experiment showed that the tax‑optimization engine significantly reduced tax savings, while the RAG component had no significant effect. The RAG‑only condition yielded the highest tax savings, suggesting that pretrained language model knowledge may suffice for tax‑loss harvesting without specialized tooling.
By Aryan Brar, Justin Du, Avery Lor, Kylie Seto, Eric Taylor
arXiv:2608. 11785v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential for financial analysis and trading, but direct trading remains challenging because the predictive capabilities required can vary across assets, decision fields, and market conditions.
By Chang Zhou, Xingtong Yu, Minbin Huang, Zhennan Wu, Yuan Fang, Hong Cheng, Xinming Zhang
META (Memory Enhanced Trading Agent) is a new agent-based trading framework that augments large language models with episodic memory. It combines specialized indicator agents—such as Trend, MACD, Stochastic, RSI, SMA, AVWAP, and Heikin‑Ashi—with a Decision Agent that fuses their reports, while a Memory module retrieves and updates past trading episodes encoded as market state embeddings with outcomes and reflections. By recalling relevant experiences and adaptively reweighting signals under similar market regimes, META improves directional accuracy and robustness in short‑horizon evaluations, offering regime‑aware, interpretable, and low‑latency decision‑making for financial trading.
By Nuoyue Xu, Jiang Liu, Wenxuan Huang, Xiang Zhang, Juntai Cao, Jiaqi Wei