Abstract Mutual funds are growing in size and gradually becoming one of the most widely used investment tools. Thus, exploring and evaluating the operational efficiency of mutual fund is essential. However, research on the effectiveness evaluation of Chinese mutual funds that comprehensively incorporates both internal processes and factors such as nonpositive data and undesirable outputs remains limited. For this reason, a two-stage slacks-based measure (SBM) model with nonpositive data and undesirable output is proposed. This paper constructs a two-stage network SBM model with nonpositive-data to complement the existing literature and explore the overall efficiency and processes’ efficiencies of Chinese mutual funds. Meanwhile, this paper incorporates Max retracement rate into the calculation by combining the undesirable SBM model. Then, the global Malmquist index is constructed and decomposed to derive the path of efficiency improvement. Finally, the proposed approach is utilized to measure the efficiencies of 32 Chinese mutual funds. Several managerial implications are proposed based on the empirical results.
Abstract Climate-related financial risks are characterized by path dependence, non-linear feedbacks, regime shifts and the possibility of endogenous instability. Yet most models used in climate scenario analysis and financial stress testing utilize an equilibrium paradigm that imposes precisely the stability properties that such risks call into question. We revisit the Sonnenschein–Mantel–Debreu (SMD) theorems to clarify the mathematical foundations of this problem. We then examine the use of integrated assessment models (IAMs) in the analysis of climate-related financial risk, with particular attention to the scenario frameworks promoted by the Network for Greening the Financial System (NGFS), and argue that equilibrium-based models are poorly suited to describe mechanisms involving leverage, credit contraction, asset repricing, physical constraints and self-reinforcing financial feedbacks. Finally, we review the alternative modelling paradigms of system dynamics, agent-based modelling and stock-flow consistent macroeconomics through the MEDEAS, DSK and GEMMES frameworks, respectively. These models illustrate how explicitly modelling adjustment processes, biophysical constraints, heterogeneity, balance sheets and private debt can generate instability, regime shifts and climate-financial outcomes that are ruled out or strongly constrained in conventional equilibrium IAMs. The central methodological implication is that, in climate finance, stability should be treated as an outcome to be investigated rather than an assumption imposed at the outset.
Abstract Artificial Intelligence (AI) has advanced significantly in the 21st century, evolving into a crucial tool for decision-making. A prominent trend is its integration with Multi-Criteria Decision-Making/Aiding (MCDM/A) methods to support complex decisions across diverse engineering domains. This paper presents a systematic literature review analyzing 111 papers from scientific databases on integrating AI with MCDM/A methods. Unlike prior reviews that primarily catalogue methods or hybrid techniques, this study introduces a socio-technical analytical framework comprising three layers—technical configurations, functional mechanisms, and human–AI collaboration patterns—to explain how and why AI reshapes multicriteria decision processes. The findings reveal recurrent architectural patterns, identify dominant functional roles of AI across decision-process phases, and uncover an emerging shift from automation-oriented systems toward augmentation-based decision support. Rather than providing a purely descriptive mapping of the literature, this review undertakes an investigative task guided by a socio-technical framework. By examining how structural configurations of AI–MCDM/A integration reshape the stages of the decision process and redistribute roles between humans and AI, the study moves beyond cataloguing techniques to uncover underlying integration logics, structural tensions, and developmental trajectories. In doing so, it offers both a conceptual consolidation for scholars and a structured foundation for the design of next-generation, human-centered intelligent decision support systems (IDSS).
Abstract This paper analyzes the electricity consumption of Bitcoin mining as a component of blockchain-based financial infrastructure and develops a hybrid forecasting framework that combines a Neural Network Autoregressive model with Exogenous Inputs (NARX) and Mixed Data Sampling (MIDAS). The specification embeds nonlinear state dependence within a feedforward neural network structured as a NARX and exploits mixed-frequency information from daily and monthly indicators to forecast weekly electricity consumption. A key methodological contribution lies in reframing exogenous variable selection as a ranking-based optimization problem grounded in individual explanatory power. To support this, a large language model (LLM)-assisted screening procedure is used to construct a theory-consistent pool of candidate predictors drawn from the finance, energy and cryptocurrency literature. From this pool, an optimization-based strategy identifies a parsimonious subset of variables that minimizes forecast error within the NARX–MIDAS framework. Empirical results demonstrate that the optimized model significantly outperforms benchmark specifications, achieving reductions of 15–20% in root mean squared error and 10–12% in mean absolute error. Beyond predictive performance, the proposed framework yields interpretable insights into how macroeconomic conditions, policy-related uncertainty and financial market dynamics influence Bitcoin mining activity. These findings have direct implications for risk management, energy planning and regulatory oversight in blockchain-based financial systems, highlighting the value of integrating LLM-assisted knowledge extraction with rigorous optimization-driven forecasting methodologies.
Abstract Financial planning is rarely a one-shot decision: today’s saving, spending and investment choices shape tomorrow’s wealth, liabilities and goal attainment. This survey traces the mathematical evolution of multi-period financial planning over the past several decades, focusing on selected key methods ranging from classical stochastic optimization to learning-enabled decision systems. We begin with Markowitz’s single-period mean–variance optimization and trace the field’s evolution toward two major multi-period optimization paradigms: scenario-based approaches, such as multi-stage stochastic programming (MSP), and state-space approaches, including model-based dynamic programming (DP) and model-free reinforcement learning (RL). While traditional MSP and DP provide the mathematical backbone for sequential financial decision-making, their practical application has long been constrained by the curse of dimensionality, model misspecification and restrictive assumptions required for tractability. Recent advances in model-free RL create new opportunities for adaptive and scalable sequential decision-making without relying on rigid transition models or handcrafted scenario trees. Building on this foundation, we review recent RL applications in multi-period financial planning for both individuals and institutions and classify the literature into three roles: hybrid RL, scalable RL and end-to-end RL. Finally, we discuss the evolving landscape of AI/machine learning-driven automated investing, highlighting its promise for algorithmic financial planning as well as key challenges regarding data, interpretability, trust and regulation.
