We propose a hybrid quantum-classical reinforcement learning framework for sector rotation in the Taiwan stock market. Our system employs Proximal Policy Optimization (PPO) as the backbone algorithm and integrates both classical architectures (LSTM, Transformer) and quantum-enhanced models (QNN, QRWKV, QASA) as policy and value networks. An automated feature engineering pipeline extracts financial indicators from capital share data to ensure consistent model input across all configurations. Empirical backtesting reveals a key finding: although quantum-enhanced models consistently achieve higher training rewards, they underperform classical models in real-world investment metrics such as cumulative return and Sharpe ratio. This discrepancy highlights a core challenge in applying reinforcement learning to financial domains -- namely, the mismatch between proxy reward signals and true investment objectives. Our analysis suggests that current reward designs may incentivize overfitting to short-term volatility rather than optimizing risk-adjusted returns. This issue is compounded by the inherent expressiveness and optimization instability of quantum circuits under Noisy Intermediate-Scale Quantum (NISQ) constraints. We discuss the implications of this reward-performance gap and propose directions for future improvement, including reward shaping, model regularization, and validation-based early stopping. Our work offers a reproducible benchmark and critical insights into the practical challenges of deploying quantum reinforcement learning in real-world finance.

In complex financial markets, recommender systems can play a crucial role in empowering individuals to make informed decisions. Existing studies predominantly focus on price prediction, but even the most sophisticated models cannot accurately predict stock prices. Also, many studies show that most individual investors do not follow established investment theories because they have their own preferences. Hence, the tricky point in stock recommendation is that recommendations should give good investment performance but also should not ignore individual preferences. To develop effective stock recommender systems, it is essential to consider three key aspects: 1) individual preferences, 2) portfolio diversification, and 3) temporal aspect of both stock features and individual preferences. In response, we develop the portfolio temporal graph network recommender PfoTGNRec, which can handle time-varying collaborative signals and incorporates diversification-enhancing contrastive learning. As a result, our model demonstrated superior performance compared to various baselines, including cutting-edge dynamic embedding models and existing stock recommendation models, in a sense that our model exhibited good investment performance while maintaining competitive in capturing individual preferences. The source code and data are available at https://anonymous.4open.science/r/IJCAI2024-12F4.

Arria NLG, a leading provider of natural language generation (NLG) technologies, has appointed Managing Director and Innovation Strategist, Mark Goodey, to cement Arria Investment Analyst as the Banking, Financial Services, and Insurance (BFSI) industry leader. Arria Investment Analyst uses natural language technologies to bring 100 percent accuracy to investment analysis and to create data-driven investment commentary. "I am excited to lead this initiative," said Goodey. "Arria's Investment Analyst uses natural language technology to analyze investment portfolio performance. It's a technology uniquely placed to support asset managers, asset owners, and the financial services industry, so what used to take hours or days can now be accomplished in seconds."

The growing challenges confronting asset management were confirmed by the industry's global performance in 2016. For the first time since the 2008 financial crisis, the revenue pool of traditional managers fell worldwide, along with their profits. Margins contracted as fee pressures continued to increase. Assets under management (AuM) returned to growth, largely thanks to rising asset values on financial markets. Net new flows, the industry's wellspring of growth, remains tepid and little changed from recent years.

CircleUp exists to help promising entrepreneurs raise capital and sophisticated investors invest in breakthrough brands with confidence and efficiency on both sides. Since we opened our doors in 2012 we've been challenging private equity norms by revolutionizing manual company sourcing and vetting with a technology-driven private marketplace. The ability to standardize and extract deep insights from large data sets has long been core to CircleUp's approach. Early last year, we introduced The Classifier, which analyzes each company that applies to CircleUp based on an average of 90,000 data points. Helio proactively collects billions of data points on over 1.2 million consumer and retail companies in the U.S. to analyze the relative strength of each company across key metrics.