Search Results (732)

We apply the free energy principle to free markets by treating the Market as a random dynamical system with an attracting set, i.e., some characteristic states. This licenses a normal form for stochastic dynamics that inherits from the Helmholtz–Hodge decomposition. Equipped with this functional form—and a suitable parameterization—one can create a generative model of fluctuations in the value of assets and accompanying indicator variables. This affords the opportunity for prospective (ex ante) prediction, scenario modelling and forecasting that could, in principle, be applied to any complex dynamical system exhibiting stochastic chaos. Here, we illustrate the application to portfolio management—in the context of financial services—and use the (posterior) predictive densities over future paths to evaluate the expected free energy that underwrites active inference. In this application, active inference reduces to risk-sensitive control, which can be used to model the optimal decision-making of an agent or investor. In this setting, an investor is characterized by their prior preferences for a high rate of return under drawdown constraints. Using numerical studies and historical financial data, we quantify the improvement in portfolio management, relative to baseline policies. Full article

Climate risk has become a defining challenge for businesses, influencing strategic decision-making, organisational resilience, and long-term performance. Despite the rapid growth of research in this area, the intellectual development and thematic evolution of climate-related corporate studies remain fragmented. This study provides a comprehensive synthesis of the literature through a bibliometric analysis and systematic review of 643 Scopus-indexed, peer-reviewed articles published between 1993 and 2025, with a systematic thematic synthesis of 23 empirical studies. Using Biblioshiny and VOSviewer, science-mapping techniques, including co-citation analysis and bibliographic coupling, were employed to examine publication trends, intellectual foundations, and major research themes. The findings indicate a shift from environmental measurement and compliance toward climate-risk management, carbon disclosure, and sustainable finance. Financial outcomes are heterogeneous, and context-dependent carbon exposure is generally associated with valuation penalties and downside risk, while the relevance of disclosure and climate strategies depends on credibility, substantive implementation, and organisational and institutional conditions. The integrated review shows that the financial implications of climate-related corporate actions are contingent upon climate-risk exposure, disclosure credibility, organisational capabilities, and institutional context. It further explains the coexistence of mixed empirical findings and identifies priorities for future research and policy. The findings offer valuable implications for researchers, corporate managers, investors, and policymakers seeking to strengthen sustainable business practices under an evolving climate risk landscape. Full article

This article analyzes how novice investors emotionally experience a stock market simulation conducted with virtual money. Financial simulations are sometimes criticized because they do not reproduce the real financial consequences of investment decisions. However, the fifteen semi-structured interviews conducted after the simulation show that the absence of monetary loss does not eliminate the emotional intensity of the experience. Participants report stress, fear, frustration, disgust, disappointment, satisfaction, and pride. This article argues that virtual money shifts emotional responses toward other forms of issues. Virtual losses are often interpreted as signs of poor decision-making, a lack of competence, or personal failure. Conversely, virtual gains may produce feelings of satisfaction or validation of one’s choices. Ranking also plays a central role: it transforms the exercise into a competitive situation and gives social value to individual performance. The academic bonus constitutes another form of stake because it situates the simulation within a framework aimed at improving academic performance. Based on a thematic analysis of the interviews, this article identifies five dimensions: virtual money as partial protection against financial loss; ranking as a symbolic substitute for financial stakes, the academic bonus as a real consequence of performance, virtual loss as a perceived loss of competence and frustration as a driver of recovery attempts, withdrawal, or disengagement. These findings show that stock market simulations are useful tools for observing how novices assign emotional meaning to gains, losses, ranking and their own competence. This article contributes to the literature on emotions and experimental simulations by demonstrating that emotional intensity does not depend exclusively on exposure to financial loss. Even when virtual, money becomes emotionally significant when it is associated with stakes related to performance, social comparison, academic success, and decision-making identity. Full article

The rapid growth of Türkiye’s savings finance sector under Law No. 7292 has created demand for objective, multidimensional performance evaluation tools. This study assesses the financial performance of six savings finance companies in Türkiye over 2022–2024 using a hybrid CRITIC-EDAS multi-criteria decision-making model. Criterion weights for eight financial indicators—spanning profitability, operational efficiency, growth, and financial structure—were derived objectively via CRITIC, while EDAS produced the rankings, which were applied to criterion-direction-normalized data because the dataset contains negative values. The operating expense-to-revenue ratio carried the greatest weight in 2022 and 2023; gross profit margin became dominant in 2024. Katılımevim led the rankings in 2022 (ASi = 1.000); Eminevim then took the lead in 2023 (ASi = 1.000) and held it in 2024 (ASi = 0.968), with Fuzulev second (ASi = 0.815). A normalization artifact affecting two revenue-denominator ratios for one company (İmece) in 2024 was corrected through winsorization. Validity was confirmed in two stages: Spearman correlations between EDAS and the TOPSIS, MABAC, and MARCOS rankings exceeded the 0.89 reliability threshold in all three years, and a nine-scenario sensitivity analysis supported the rankings’ robustness. These findings give regulators, investors, and managers a replicable framework for evaluating performance in this young, underexamined sector and point to operational efficiency and outlier management as priorities for oversight. Full article

The global energy sector is undergoing rapid and, in many respects, irreversible transformation driven by the convergence of digital disruption, sustainability mandates, and shifting investor expectations. Technologies such as artificial intelligence (AI), blockchain, and digital twin systems are fundamentally reshaping energy operations and strategic decision-making, while ESG governance quality and renewable energy adoption have emerged as two of the most consequential determinants of corporate financial competitiveness and equity valuation. Despite growing practitioner and regulatory interest in these dynamics, limited empirical evidence exists on how ESG governance, renewable adoption, and digital disruption jointly influence financial performance and environmental outcomes across multiple sectors simultaneously. This study addresses that gap using panel data from 26 large-cap US-listed firms across five sectors over 2015–2022 (N = 208 firm-year observations for Revenue/Market Cap/ROA models; N = 91 for the CO₂ model). A multi-method econometric framework is employed, comprising Fixed Effects and Random Effects panel regression with Hausman specification testing, Difference in Differences quasi-experimental analysis, and sequential OLS path analysis with HC3 robust standard errors. Three of four hypotheses are supported. ESG governance quality generates a significant market capitalisation premium of approximately 10–14% per unit Bloomberg ESG Score improvement, after controlling for firm size and R&D intensity; no significant revenue channel effect is found once firm size is properly accounted for. Renewable energy adoption shows a marginal association with market capitalisation at the 10% significance level (FE β = 0.019, p = 0.086; RE β = 0.016, p = 0.077), suggesting capital markets may price clean energy adoption as a forward-looking signal. ESG governance quality drives within-firm CO₂ emission reduction substantially more powerfully than renewable energy quantity alone, with the Fixed Effects estimator identifying a governance-led eco-efficiency mechanism. Firm profitability functions as a cross-model financial capacity moderator, enabling simultaneous ESG investment and environmental improvement. The findings carry direct implications for corporate managers, institutional investors, and policymakers aligned with SDG 7, SDG 9, and SDG 13. Full article

Environmental, social, and governance (ESG) considerations have become an increasingly important component of sustainable finance, investment decision-making, and banking regulation. As financial institutions face growing pressure to integrate sustainability objectives into their business models, understanding how sustainability performance relates to market valuation has become an important issue for investors, regulators, and bank management. Despite the growing ESG literature, evidence regarding the valuation relevance of individual ESG dimensions remains limited, particularly in the European banking sector. This study examines whether ESG dimensions are uniformly associated with the market valuation of European banks or whether financial markets differentiate among individual ESG pillars. Using a panel dataset of European banks covering 2021–2024 and Bloomberg ESG indicators, the study estimates panel econometric models to evaluate the associations between disaggregated ESG pillars and market-based valuation measures. The empirical findings reveal substantial heterogeneity across ESG dimensions. The social pillar is positively associated with market valuation, whereas the environmental pillar is negatively associated, while governance exhibits weak or statistically insignificant associations. The findings remain robust across several alternative model specifications. The results indicate that investors in highly regulated European banking markets differentiate between ESG dimensions, suggesting that financial markets differentiate among ESG dimensions and that analysing ESG at the pillar level provides a more nuanced understanding of market valuation than aggregate ESG measures. Full article

The housing sector is an important pillar of the economy. Understanding the trend and factors influencing it is essential for effective policy formulation and purchasing decisions. The objectives of this study are to assess the performance trajectory of the Australian housing market and to identify key macroeconomic factors influencing it over the past two decades. The presence of structural shifts in the housing market due to changing economic conditions highlights the importance of examining both short-run adjustments and long-run equilibrium relationships; however, limited recent studies have investigated these evolving dynamics, which this study addresses by employing the cointegration and Vector Error Correction Model (VECM). Empirical findings indicated the dynamics of previous period RPPI, interest rates, population growth, COVID-19 dummy, and adjustment components (ECT) are affecting housing market dynamics but with effects varying across different lag periods. On the other hand, the long-run estimates reported variables including population growth, the COVID-19 pandemic, household income, and unemployment rate have a significant impact on RPPI at the 5% significance level. The finding of the error correction model suggests a gradual adjustment of short-term deviation with ECTt-1 = −0.078 towards the long-term equilibrium quarterly. To further validate the robustness of the results, an Impulse Response Function (IRF) analysis was performed. The empirical findings are valuable as they provide insights that assist policymakers in formulating housing policies and support investors and purchasers in making informed decisions. Full article

Climate-related risks are now perceived as financially material, affecting banks, investors, firms, regulators, and central banks. This article provides a systematic literature review of the relationship between climate-related risks and financial decision-making. The review, which is based on Scopus and Web of Science and follows a PRISMA-based selection process, includes a final sample of 80 studies retrieved using database publication-year filters for 2015–2025, including three online-first records subsequently assigned to 2026 issues. A Quality Appraisal Matrix, descriptive analysis, keyword co-occurrence mapping with VOSviewer version 1.6.20, and cluster-based thematic synthesis are used. The results reveal four key streams in the literature: climate risk, Environmental, Social, and Governance (ESG), and financial modeling; climate change, sustainable finance, and systemic stability; transition risk, investment, and risk assessment; and banks, performance, and financial impact. The review underscores the role of climate risks in asset pricing, portfolio allocation, lending, credit-risk assessment, disclosure, stress testing, and financial stability supervision, and identifies key gaps with respect to emerging economies, data quality, and the practical incorporation of climate risk considerations into financial decisions. Full article

Property condition assessment is a critical step in residential real estate investment underwriting, motivating after-repair value (ARV) estimates and rehabilitation cost projections. Traditional approaches rely on in-person inspections or manual photo review by experienced investors—processes that are time-consuming, subjective, and do not scale. Prior computer vision work on building analysis has focused on structural defect detection using convolutional neural networks but has not addressed the holistic, room-level condition assessment needed for residential investment decision-making. This paper presents a per-room analysis framework that leverages multimodal large language models (MLLMs) to assess the condition of residential properties from photographs. The framework analyzes each photo independently at the room level—detecting the room type, condition category, condition score, material features, and visible issues. Condition output is intended to feed a separate downstream rehabilitation cost and ARV estimation model that is outside the scope of this paper; the present empirical evaluation is restricted to per-photo condition assessment and inter-rater agreement with human experts. I evaluate the framework on two complementary datasets: (i) a primary per-image condition evaluation on 57 photographs from 14 real off-market properties in the Memphis, TN MSA, spanning three condition tiers (Fixer, Outdated, Standard), with independent labels from two experienced real estate investors; (ii) a secondary room classification evaluation on the public REI Dataset (51 attempted, 39 successful, 12 HTTP-503 failures). The room classification accuracy was 76.5% intention-to-analyze on REI (100% per-protocol on the 39 successful calls; 23.5% API failure rate) and 82.5% on the concierge dataset. The inter-rater agreement on the concierge dataset, with 95% bootstrap CIs (5000 resamples) and Spearman’s $\rho$ as primary score statistic, was as follows: Cohen’s $\kappa =0.773$ ($95\%$ CI $[0.64,0.90]$) between Labeler A and the MLLM (weighted $\kappa =0.853$ $[0.76,0.94]$; $\rho =0.906$); and $\kappa =0.502$ $[0.35,0.66]$ between Labeler B and the MLLM ($\rho =0.858$); both bracket the human–human reliability of $\kappa =0.590$ $[0.42,0.74]$ ($\rho =0.807$). The MLLM’s $\kappa$ asymmetry across the two labelers is statistically significant ($\Delta \kappa =0.271$, $95\%$ bootstrap CI $[0.115,0.429]$, $p=0.0004$), which I attribute to plausible training distribution and labeling style differences. A blind re-labeling sensitivity analysis on a stratified 15-image subsample yields anchoring-corrected $\kappa$ estimates of approximately $0.65$ (Labeler A) and $0.35$ (Labeler B); the headline anchored values therefore sit at the upper bound of plausible blind-equivalent agreement. Failure modes concentrate at the Outdated tier and at the Outdated–Standard boundary, where humans themselves disagree most, indicating intrinsic taxonomy ambiguity rather than a model artifact. I make no claim to multi-market generalization and present multi-market extension as ongoing work. Full article

This study investigates how behavioral biases influence the investment decisions of retail investors in Kolkata, India, with particular emphasis on the moderating roles of financial literacy and financial awareness. Despite the rapid expansion of India’s financial markets and increased retail participation, investors often exhibit irrational behavior driven by psychological biases. This study seeks to answer the following research question: To what extent do behavioral biases affect the investment decisions of retail investors in Kolkata, India, and how effectively do financial literacy and financial awareness mitigate these effects? Using primary data collected from 444 retail investors in Kolkata, this study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the conceptual framework. The findings reveal that behavioral biases—namely overconfidence, anchoring, herd behavior, and loss aversion—significantly and negatively affect investment decisions. However, financial literacy and financial awareness not only positively influence decision-making but also significantly moderate the relationship between behavioral biases and investment outcomes. This study contributes to behavioral finance literature by distinguishing between financial literacy and financial awareness as separate constructs and demonstrating their dual role as both direct and moderating factors. The findings have important implications for policymakers, financial educators, and investment advisors in designing targeted interventions to improve investor decision-making. Full article

Public-sector sustainability and climate reporting increasingly address environmental exposure, governance, and financial effects, yet existing frameworks do not adequately disclose preparedness for low-probability, high-impact systemic risks whose probabilities, timing, thresholds, and transmission channels remain deeply uncertain. This article develops a Public-Sector Resilience Reporting Standard (PSRRS) as a pre-standard architecture for government preparedness disclosure. The design has three bounded objectives: diagnose cross-framework disclosure gaps, translate these gaps into a theoretically grounded capability-to-disclosure architecture, and demonstrate its analytical use through an illustrative Florida application and two hazard-neutral stress tests. The documentary corpus includes international sustainability and public-sector reporting standards, ISO and UNDRR resilience and continuity instruments, three Florida resilience documents, and peer-reviewed literature on resilience governance, decision-making under deep uncertainty, critical infrastructure interdependency, catastrophic uncertainty, climate-risk disclosure, public finance, climate-risk pricing, local-government credit risk, investor attention, and ransomware service disruption. A structured interpretive coding protocol classifies each framework as explicit, partial, or not explicit across nine disclosure dimensions; a codebook appendix identifies the assessment criteria, the a priori and inductively refined dimensions, and the validation boundaries. Florida is not treated as a basis for statistical or jurisdictional generalization. Instead, it illustrates how a comparatively developed resilience architecture may disclose statutory continuity, critical-asset data, project ranking, and output metrics while leaving systemic dependencies, adaptive triggers, long-horizon fiscal exposure, residual service risk, distributional effects, and assurance mechanisms insufficiently visible in the reviewed reporting corpus. AMOC and case-grounded cyber-fiscal stress tests show how the PSRRS shifts reporting from hazard inventories and funded projects toward auditable evidence of institutional capacity, adaptive readiness, and public-value protection. The article specifies mandatory, recommended, and optional clauses, evidence requirements, indicator examples, a disclosure index, a sample report structure, and a three-tier pilot conformity model. The contribution is conceptual and operational, but not yet a validated formal standard; cross-jurisdictional piloting, inter-rater coding, cost testing, assurance testing, and stakeholder consultation are identified as the next stage of standardization. Full article

Machine learning technologies are increasingly embedded in financial decision-making processes, yet their influence on investor behavior and market efficiency remains insufficiently understood. This study investigates how machine learning-assisted investment decisions affect investor behavioral biases and asset pricing efficiency and whether these effects exhibit nonlinear characteristics. Using investor-level trading records, survey data, and market data from the Chinese A-share market (N = 12,846 investors; 3876 questionnaires; 3 million+ transactions), we construct measures of machine learning adoption intensity, investor behavioral biases, and asset pricing efficiency. Employing fixed-effects models, instrumental-variable estimation (2SLS), and mediation analysis, we examine the behavioral and market consequences of machine learning adoption. The results reveal a significant U-shaped relationship between machine learning adoption intensity and investor behavioral biases (inflection point: AIDI* = 0.731), and an inverted U-shaped relationship between AI market penetration and asset pricing efficiency (threshold: AIPM* = 0.733). Investor behavioral bias mediates 26.34% of the total effect of AI adoption on pricing efficiency. Moderate adoption reduces behavioral biases by improving information processing and decision quality, whereas excessive reliance on algorithmic recommendations generates automation bias and weakens investors’ independent judgment. At the market level, machine learning adoption exhibits an inverted U-shaped relationship with asset pricing efficiency. While moderate adoption enhances information incorporation into prices and reduces pricing deviations, excessive market penetration may induce algorithmic homogeneity and diminish efficiency gains. Furthermore, investor behavioral bias serves as an important transmission mechanism linking machine learning adoption to asset pricing outcomes. Heterogeneity analyses indicate that institutional investors benefit more from machine learning tools than individual investors, and the effects are stronger during periods of high market uncertainty. These findings provide new evidence on the optimal adoption of machine learning in financial markets and offer practical implications for intelligent investment platforms, investor education, and financial regulation. Full article

Although employed women represent one of the fastest-growing segments of the investor population in emerging economies, their investment decision-making is still largely modelled through fragmented, single-determinant frameworks that treat women as a homogeneous group. This study develops and tests an integrated structural model of financial-goal-directed investment orientation among employed women, drawing together Behavioural Finance Theory, the Theory of Planned Behaviour, and the Life-Cycle Hypothesis. Primary data were collected through a structured questionnaire from 951 employed women across the four administrative zones of Bengaluru, India, using stratified random sampling. The measurement model was validated through exploratory and confirmatory factor analysis, and four competing structural specifications were estimated by maximum likelihood; the best-fitting model was selected on the basis of the corrected Akaike Information Criterion and approximate fit indices. The results indicate that risk tolerance is the strongest direct correlate of financial-goal-directed investment orientation, that confidence and self-efficacy operates as the pivotal psychological mediator linking macroeconomic perception to risk-taking, and that market sentiments are the strongest external correlate of investor confidence. Peer influence shows a theoretically important dual association, positively related to risk tolerance while negatively related to confidence. A serial mediation pathway running from market sentiments through confidence and risk tolerance to financial goals is supported. Because the design is cross-sectional, the associations are interpreted as structural relationships consistent with the proposed theoretical framework rather than as established causal effects. This study is exploratory and hypothesis-generating in character. The findings reframe financial-inclusion interventions for employed women around confidence-building rather than information provision, with implications for product design, advisory practice, and policy. Full article

As Saudi Arabia accelerates its Vision 2030 economic diversification, the domestic banking sector serves as the critical engine for capital deployment. However, evaluating these institutions is complicated by conflicting performance indicators, where high profitability is often offset by elevated market valuation multiples. This study applies an objective, established multi-criteria decision-making (MCDM) framework—combining Shannon’s Entropy for objective weighting with TOPSIS for ranking—to evaluate ten major banks listed on the Saudi Stock Exchange (Tadawul), tracked by the Tadawul All Share Index (TASI), over the 2021–2025 period. The contribution is contextual and empirical rather than methodological: the systematic application of established objective MCDM methods to the Saudi banking sector during the pivotal Vision 2030 window, with an investor-oriented criterion set. Comparative validation was executed using the CRITIC weighting algorithm and the VIKOR ranking method, complemented by a four-dimensional sensitivity analysis (Weight Perturbation, Leave-One-Criterion-Out, Alternative Normalization, and Equal-Weight scenarios). Spearman correlation coefficients (>0.86) confirm that the framework produces empirically stable rankings resistant to methodological perturbation, providing policymakers and investors with a data-driven decision-support tool for the Saudi banking sector under Vision 2030. Full article

Timely identification of company financial risks is crucial for investors and regulators. However, existing studies overlook the class imbalance caused by the scarcity of high-risk samples, and the interpretability of deep models is insufficient, making it difficult to meet the practical needs. To address these problems, we propose a classification model named FA-LSTFormer, which models a company’s financial risk as low-, medium- and high-warning tasks. FA-LSTFormer employs LSTM and Transformer to decouple the short-term continuity and long-term dependency inherent in financial data. To further attend to indicator-level nuances, we incorporate a Risk-Sensitive Hierarchical Indicator Attention (RSHA) module. Moreover, given the pronounced class imbalance where high-risk events are substantially underrepresented, we further propose a Class-Imbalance-Aware Focal Loss (CIFL) function to prioritize these minor yet critical samples and suppress false negatives. On the dataset of Chinese A-share manufacturing listed companies, experimental results show that our FA-LSTFormer achieves superior performance in accuracy, precision, recall, F1-score and AUC, achieving 92.76%, 93.13%, 91.84%, 92.48%, and 95.27%, respectively. Compared to the suboptimal LTR-Net, it improves these metrics by 1.64–3.60%. Compared to the LSTM–Transformer baseline, FA-LSTFormer improves on it by 4.30–9.13%. In the risk-oriented decision evaluation, FA-LSTFormer achieves a warning ROC of 0.954 for the high-risk class and lowers the error rate to 9.82%. It maintains an accuracy rate of 84.46% even after three years of early warning and exhibits strong robustness across different warning thresholds and company sizes. These results verify the advantages of FA-LSTFormer in both algorithmic performance and practical early-warning applications. Full article