This paper aims to discuss the dynamics of gold pricing through two key approaches: the Error Correction Model (ECM) and the Gold Supply and Demand Balance Valuation Model. These models provide insights into how short-term fluctuations and long-term trends in gold prices are shaped. The ECM is particularly helpful for analyzing non- stationary data like gold prices, while the valuation model focuses on the interaction between supply and demand across different economic conditions. By combining these methods, we can better understand the key drivers of gold prices and offer practical tools for investors, policymakers, and businesses. This study aims to bridge the gap between theoretical models and real-world market behavior, providing a comprehensive framework for understanding and forecasting gold prices in both stable and volatile markets.

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The study proposes a machine learning framework that optimizes the approval processes of Neo Banks loan applications and overcomes the limitations of traditional credit scoring models. Using feature engineering, ensemble methods, and hyperparameter optimization on customer demographics, financials, and transaction details to determine loan acceptance. The results show that the improved models have the accuracy of 97.87% and 88.59% recall and are far better compared to traditional methods (logistic regression: 63.76% recall). Significant predictors are education level (importance: 0.35), income (0.30), and family size, with the probability of approval among high-income, high-education customers being 45% higher. The framework reduces the false negative rate, allowing the Neo Banks to focus on the cream of the crop applicants but avoiding risks. Examples of practical strategies are customized marketing and dynamic pricing. This work talks about limitations such as data imbalance, and future research suggests integrating real-time behavioral data and fairness-aware modeling. It fills a gap between tech innovation and operational requirements, providing a scalable method for updating credit risk evaluation in digital banking.

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ESG (Environmental, Social, and Governance) investment has been building up as a values based special purpose strategy to a theoretically sound and empirically valid constituent of the contemporary portfolios. The current research summary provides a conceptual description of the observed empirical steps through which ESG characteristic can influence the prices of assets, the communications of risk and capital decision. In line with nonpecuniary utility, information asymmetry, and stakeholder theory models, we examine empirical evidence concerning the presence or absence of pricing of ESG attributes as risk factors, safeguarding of downside risk, or, merely,, simply industry and firm-specific factors. We research in terms of techniques methodological instruments of econometric identification panel regressions, quasi-natural experiments, and factor-model extensions and novel methods of forecasting ESG-adjusted returns with machine-learning and identifying non-linear relationships. The literature has shown that the ESG integration is always associated with reduced volatility and tail-risk exposure but the return premia is sporadic and is contingent on the design of the measure. The review also displays the increased importance of the ESG factors in the decision in allocating venture capital particularly venture capital in capital-intensive green technology. The typical issues include the data fragmentation, the difference in scores, the absence of inference of causality and the complicatedness of the dynamics modeling of the formation of ESG factors. Our research areas of interest will include standardized ESG data construction, and introduction of ESG into multi-factor models of asset-pricing, and construction of ESG valuation of more diversity of assets other than household and alternative assets.

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Based on the data of A-share listed companies in China from 2010 to 2024, this study uses A panel model to analyze and finds that higher ESG disclosure quality is generally conducive to improving corporate performance. From a multi-dimensional perspective, the promoting effect of governance disclosure is the strongest, mainly due to the reduction of agency costs. Social disclosure also has a positive impact, but to a lesser extent, and the path lies in improving stakeholder relationships. Environmental disclosure does not bring significant financial returns in the short term, and its value may need to be manifested over the long term. This indicates that the influence mechanisms and timeliness of each dimension of ESG are different, and enterprises and policy-making need to adopt differentiated strategies.

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