The sparse portfolio optimization (SPO) problem is increasingly crucial in portfolio management, focusing on selecting a few stocks with the potential for strong market performance. However, sparse portfolio strategies often face significant short-term drawdowns during periods of market volatility. To this end, a news-driven portfolio strategy offers valuable insights to capture sudden market changes. Nevertheless, it encounters two main challenges:how to reasonably map the relationships between news and stocksandhow to effectively utilize the irregular timing of news releases. To tackle the SPO problem in fluctuating markets while addressing these challenges, we propose a novel news-driven sparse portfolio strategy, named SPIN. Specifically, SPIN not only leverages industry-specific group structures existing among stocks for a more reasonable news-stock mapping and models news sequential patterns based on our devised novel news-driven forecaster to handle the irregularity of news releases. We rigorously prove that SPIN achieves a sub-linear regret. Extensive experiments on three real-world datasets demonstrate SPIN's superiority over state-of-the-art portfolio strategies in terms of cumulative wealth and short-term drawdowns.

Positive Unlabeled (PU) learning, which has a wide range of applications, is becoming increasingly prevalent. However, it suffers from problems such as data imbalance, selection bias, and prior agnostic in real scenarios. Existing studies focus on addressing part of these problems, which fail to provide a unified perspective to understand these problems. In this paper, we first rethink these problems by analyzing a typical PU scenario and come up with an insightful point of view that all these problems are inherently connected to one problem, i.e., positive distribution pollution, which refers to the inaccuracy in estimating positive data distribution under very little labeled data. Then, inspired by this insight, we devise a variational model named CoVPU, which addresses all three problems in a unified perspective by targeting the positive distribution pollution problem. CoVPU not only accurately separates the positive data from the unlabeled data based on discrete normalizing flows, but also effectively approximates the positive distribution based on our derived unbiased rebalanced risk estimator and supervises the approximation based on a novel prior-free variational loss. Rigorous theoretical analysis proves the convergence of CoVPU to an optimal Bayesian classifier. Extensive experiments demonstrate the superiority of CoVPU over the state-of-the-art PU learning methods under these problems.

News-driven quantitative trading (NQT) has been popularly studied in recent years. Most existing NQT methods are performed in a two-step paradigm, i.e., first analyzing markets by a financial prediction task and then making trading decisions, which is doomed to failure due to the nearly futile financial prediction task. To bypass the financial prediction task, in this paper, we focus on reinforcement learning (RL) based NQT paradigm, which leverages news to make profitable trading decisions directly. In this paper, we propose a novel NQT framework SpotlightTrader based on decision trajectory optimization, which can effectively stitch together a continuous and flexible sequence of trading decisions to maximize profits. In addition, we enhance this framework by constructing a spotlight-driven state trajectory that obeys a stochastic process with irregular abrupt jumps caused by spotlight news. Furthermore, in order to adapt to non-stationary financial markets, we propose an effective training pipeline for this framework, which blends offline pretraining with online finetuning to balance exploration and exploitation effectively during online tradings. Extensive experiments on three real-world datasets demonstrate our proposed model’s superiority over the state-of-the-art NQT methods.

Financing needs exploration (FNE), which explores financially constrained small- and medium-sized enterprises (SMEs), has become increasingly important in industry for financial institutions to facilitate SMEs’ development. In this paper, we first perform an insightful exploratory analysis to exploit the transfer phenomenon of financing needs among SMEs, which motivates us to fully exploit the multi-relation enterprise social network for boosting the effectiveness of FNE. The main challenge lies in modeling two kinds of heterogeneity, i.e., transfer heterogeneity and SMEs’ behavior heterogeneity, under different relation types simultaneously. To address these challenges, we propose a graph neural network named Multi-relation tRanslatIonal GrapH aTtention network (M-RIGHT), which not only models the transfer heterogeneity of financing needs along different relation types based on a novel entity—relation composition operator but also enables heterogeneous SMEs’ representations based on a translation mechanism on relational hyperplanes to distinguish SMEs’ heterogeneous behaviors under different relation types. Extensive experiments on two large-scale real-world datasets demonstrate M-RIGHT’s superiority over the state-of-the-art methods in the FNE task.

In this paper, we study a new kind of portfolio problem, named trading point aware portfolio optimization (TPPO), which aims to obtain excess intraday profit by deciding the portfolio weights and their trading points simultaneously based on microscopic information. However, a strategy for the TPPO problem faces two challenging problems, i.e., modeling the ever-changing and irregular microscopic stock price time series and deciding the scattering candidate trading points. To address these problems, we propose a novel TPPO strategy named STrader based on normalizing flows. STrader is not only promising in reversibly transforming the geometric Brownian motion process to the unobservable and complicated stochastic process of the microscopic stock price time series for modeling such series, but also has the ability to earn excess intraday profit by capturing the appropriate trading points of the portfolio. Extensive experiments conducted on three public datasets demonstrate STrader's superiority over the state-of-the-art portfolio strategies.

CVaR-sensitive online portfolio selection (CS-OLPS) becomes increasingly important for investors because of its effectiveness to minimize conditional value at risk (CVaR) and control extreme losses. However, the non-stationary nature of financial markets makes it very difficult to address the CS-OLPS problem effectively. To address the CS-OLPS problem in non-stationary markets, we propose an effective news-driven method, named CAND, which adaptively exploits news to determine the adjustment tendency and adjustment scale for tracking the dynamic optimal portfolio with minimal CVaR in each trading round. In addition, we devise a filtering mechanism to reduce the errors caused by the noisy news for further improving CAND's effectiveness. We rigorously prove a sub-linear regret of CAND. Extensive experiments on three real-world datasets demonstrate CAND’s superiority over the state-of-the-art portfolio methods in terms of returns and risks.

Online portfolio selection (OLPS) is a fundamental and challenging problem in financial engineering, which faces two practical constraints during the real trading, i.e., cardinality constraint and non-zero transaction costs. In order to achieve greater feasibility in financial markets, in this paper, we propose a novel online portfolio selection method named LExp4.TCGP with theoretical guarantee of sublinear regret to address the OLPS problem with the two constraints. In addition, we incorporate side information into our method based on contextual bandit, which further improves the effectiveness of our method. Extensive experiments conducted on four representative real-world datasets demonstrate that our method significantly outperforms the state-of-the-art methods when cardinality constraint and non-zero transaction costs co-exist.