Stock return prediction is a fundamental but difficult issue in financial research because of the noise and dynamics of market data. Although recent research has improved the ability of existing machine learning model prediction, these methods are difficult to interpret and lack practical application value due to their "black box" nature. This paper constructs an explainable machine learning framework to achieve high-accuracy predictions of short-term stock excess returns and strive for greater interpretability in economics; Compared with other complex models. Taking the daily trading data of Apple Inc. (AAPL) as a single-stock example, this paper constructs a set of economically meaningful features based on historical returns, volatility and trading volume. A linear regression model is used as the central prediction tool, and interpretability can be built into this specific form of a linear model. Out-of-sample forecasts use the rolling window method, and prediction performance is compared with a naive historical mean benchmark. Based on experiments, the current basis of explanation using an explainable linear model has been shown to be relatively stable in baseline prediction. According to the coefficient analysis, in addition to conforming to some basic Finance theory patterns. A return pattern that exists for one day appears. A Volatility-Return relationship also holds currently. This paper shows that interpretive models, in combination with carefully selected features, achieve satisfactory prediction performance; at the same time, it is also found through the analysis of economic implications that such an approach helps explore the essence of changes in stock returns.

Based on machine learning methods, this paper systematically studies the cross-sectional excess returns of A-share stocks. Taking 5,648 stocks in China's A-share market from 2020 to 2024 as a sample, a multi-dimensional factor system covering fundamentals and macro variables was constructed, and the out-of-sample performance of multiple models was compared under the rolling forecast framework of Expanding Window. The empirical results show that the nonlinear machine learning model continues to obtain positive information coefficients in most quarters, and its prediction signals can be converted into stable long-short portfolio returns, while the prediction ability and investment performance of traditional linear models decay significantly with time. In terms of risk control, LightGBM shows a relatively better trade-off between return level and drawdown magnitude. The research in this paper confirms that the machine learning method that combines a nonlinear structure and cross-sectional heterogeneity can effectively improve the return prediction ability and investment performance of the A-share market and provides practical empirical evidence for quantitative stock selection.

The emergence of cross-border e-commerce as a crucial component of the global digital economy has been achieved albeit its advancement being impeded by the high-browsiness, but low-conversion challenge associated with cross-border shopping as it involves logistics delay, information asymmetry and policy uncertainties. The purpose of the study is to find the optimal way of cross-border e-commerce platforms to increase the user conversion rate. It initially examines the existing development of the cross-border e-commerce industry in the context of the scale of the market, the competitiveness, and the nature of the industry and examines user purchasing behavior in terms of demand, behavior, and channels. Based on these premises, the research develops a comprehensive analysis model of user portraits that can be segmented into fundamental, behavioral and value traits and staged conversion paths. It then suggests a three-fold approach to optimization strategy system comprising accurate marketing, product and service improvement and user lifecycle management and defines critical issues in the conversion process at the level of product, platform, and external factors with specific countermeasures. Such research offers theoretical directions and practical examples to a cross-border e-commerce platform to improve the efficiency of operations and user experience, as well as add to the body of knowledge about cross-border e-commerce user behavior and platform optimization.

Recommendation algorithms have become a key driving force for the commercialization of platforms in the contemporary digital economy, profoundly influencing user experience and revenue generation methods. E-commerce platforms reshape consumption patterns by relying on massive user data and commodity resources. This article uses a comparative analysis method to examine the different commercialization paths of recommendation algorithms on content platform Douyin and e-commerce platform Taobao. Research shows that the Douyin algorithm takes models such as graph neural networks (GNN) as its core. Its primary optimization goal is to increase user dwell time and interaction rate to accumulate traffic value. Subsequently, it achieves commercialization through native advertising and the "content seeding combined with live-streaming sales" model. Taobao's algorithm is based on collaborative filtering and transformer architecture, directly and precisely optimizing the transaction conversion rate (GMV) by "goods finding people". Its commercialization mainly relies on advertising monetization and transaction commissions. This article delves deeply into the causes of the differences, attributing them to the fundamental differences in the essence of the platform ecosystem, the logic of user behavior, the iterative paths of algorithms, and the priorities of business goals. This research can deeply analyze the coupling relationship between technical design and business strategy, providing decision-making references for platform operators and algorithm designers.

The recent high rates of digitalization of the financial sector have enhanced the requirement of effective management of credit risks, especially regarding the possible detection of possible defaults in time to avert systemic risks. This work introduces a unified approach to credit default prediction with the American Express data, overcoming the two problems of high necessity of the data processing and the ability to interpret the models. Namely, the proposed study applies a Temporal Aggregation Strategy to downsize 13-month historical customer data into a data-rich set of statistical constructs, e.g. mean, variance, recent trends, without compromise of vital behavioral indicators. These artificial characteristics are then passed to a Light Gradient Boosting Machine (LightGBM) classifier. The test scores reveal that it is an outstanding predictor with an Area Under the Curve (AUC) of 0.958, and an Amex Metric of 0.796 and has been able to reduce false negativity to a minimum. In addition, Shapley Additive Explanations (SHAP) are used in this research to decode the decision-making process of the model to meet tough regulatory compliance in banking. It is found that the recent repayment abilities and volatility of delinquency are the main contributors to the default risk. In the end, this study will present a practical, highly correct and fully transparent White-Box solution of industrial-scale credit scoring.

This paper proposes a spatiotemporal heterogeneous graph attention network-distributed robust inventory optimization joint algorithm (ST-HGAT-DRIO). The algorithm first constructs a multi-level spatiotemporal heterogeneous graph of the retail supply chain to fully characterize the heterogeneous relationships and spatiotemporal evolution characteristics among supply chain nodes. Second, it designs a perturbation-aware dual-branch heterogeneous graph attention module, integrating temporal dependence and spatial correlation features to achieve demand probability distribution prediction under complex scenarios. Finally, through end-to-end joint training, the prediction results are embedded into a distributed robust optimization module to output the optimal inventory allocation strategy for enhancing resilience. Experiments based on M5 public data set and self-built retail supply chain data show that compared with the optimal baseline model, the proposed algorithm can reduce MAPE by 18.37% and RMSE by 15.62%. Under normal scenario, supply chain α-service level improved 4.23%, stockout rate decreased 27.59%, total stock cost decreased 12.15%. Under severe disturbance scenarios, the recovery time of supply chain can be reduced by 32.41%, which significantly improves the capability of anti-disturbance and operational resilience of retail supply chain.

In the process of urbanization, the effective classified collection and transportation of domestic waste is crucial for the realization of circular economy, among which multi-compartment vehicles capable of handling multiple types of waste simultaneously show great application potential. However, two tough problems are often encountered in practical scheduling: fluctuations in vehicle speed caused by the complex urban road conditions, and the difficulty in predicting the waste generation amount in advance. To address these uncertain factors, this research focuses on the route optimization problem of classified collection and transportation. We attempt to introduce a new type of equipment—vehicles with flexibly adjustable compartment ratios, and on this basis, construct a scheduling model based on chance-constrained programming, aiming to improve the overall efficiency of collection and transportation by dynamically adjusting compartment configurations. Simulation experiments show that in areas with large fluctuations in waste generation, this flexible compartment design can help sanitation enterprises effectively reduce operational costs; meanwhile, if the differences in vehicle speed at different time periods are fully considered in route planning, vehicles can better avoid traffic congestion, thereby reducing carbon emissions during transportation. It is hoped that these findings can provide some practical references for the complex and changeable sanitation scheduling work in reality.

This paper compares Nike's Zoom Kobe 4–7 series and Apple's iPhone 4–17 to examine how successful product lines evolve from technological breakthroughs to long-term symbolic and system-level value. Based on this comparison, the paper proposes the Product Value Evolution Framework, a four-stage model consisting of technological innovation, user experience optimization, product system evolution, and brand narrative. The study argues that both product lines follow a similar developmental path. Early breakthrough generations, such as the Zoom Kobe 4 and the iPhone 4–5s, established new standards in performance and consumer expectations. As the market expanded, both brands refined usability and product experience at scale, before introducing system-level upgrades that reshaped key touchpoints, as seen in the Zoom Kobe 6 and the iPhone X–13. In later stages, both firms increasingly relied on tiered product lines, ecosystem value, and narrative framing to sustain long-term demand, as illustrated by the Kobe 7 System and the iPhone 14–17 lineup. Economic theories including product differentiation, monopolistic competition, price discrimination, diminishing marginal utility, and behavioral economics are used to explain shifts in demand, pricing, and consumer attachment across these stages. Beyond these two cases, the framework offers a broader analytical tool for understanding product evolution, industry trends, and new market development across sectors.

This paper concentrates on strategies for optimizing the supply chain of cold chain products within the context of live-streaming e-commerce. Salmon is selected as a representative case for detailed examination. In comparison to conventional sales models, live-streaming e-commerce is plagued by a consistently high rate of returns that has also become a staple feature of this new sales channel. Salmon requires a very high level of temperature control in all the stages of logistics, so that the same process of returning to the initial process that is applied in regular goods causes the degradation of the quality of salmon to take place at a very high rate. Subsequently, it renders the product reseller inappropriate. In a bid to manage this situation, the proposed paper follows a case study approach in order to develop a systematic inquiry. The framework covers two important dimensions, including the forward supply chain and the reverse supply chain. On the basis of this two-dimensional analysis, the given paper proposes specific improvements to each of the corresponding angles. Also, the article promotes the creation of a real-time information-sharing hub to enable two-way communication in supply chain management.

In the stage of deep integration of digital economy and real economy, the supply chain operation of enterprises is facing some challenges such as information asymmetry, accelerated risk transmission and insufficient capabilities of sustainable development. As one of the most important links in the system of deals when handling returns of a product, recycling and remanufacturing processes, reverse logistics has become a bottleneck that restricts the improvement of the overall supply chain efficiency due to many problems such as high management difficulty and disorganized data systems. Taking digital intelligence transformation as the research background, this paper adopts literature research method, case analysis method and comparative analysis method to comprehensively analyze the existing problems of supply chain reverse logistics from four dimensions: on the surface, theory, technology, process and organization, construct an integrated optimization framework of technology-process-organization collaboration mechanism and explore the optimization path of reverse logistics in the era of digital intelligence. The research reveals that the current digital transformation of reverse logistics in terms of intelligence has come across four dilemmas; theoretical lag, the technological mismatch, process rigidity and organizational marginalization. It is necessary to take systematic measures, from the aspect of reconstructing the theoretical system, optimizing the allocation of technical resources, establishing the intelligent processes and the promotion of organizational value transformation. The research conclusions offer theoretical reference and practical guidance for enterprises to enhance the operational efficiency of reverse logistics as well as build a green supply chain system.