This study optimizes revenue management strategies for Cathay Pacific Airways amid post pandemic market volatility and intensified Asia-Pacific competition. Addressing critical issues including demand forecast lag, imprecise price elasticity measurement, channel pricing inconsistencies, and insufficient transit passenger segmentation, this research proposes quantitative finance-driven solutions. Key strategies include rebuilding dynamic demand price elasticity models, implementing real-time cost fluctuation pricing mechanisms, and adopting differentiated unbundled pricing for premium cabins. Implementation safeguards encompass digital system upgrades, organizational restructuring, and compliance framework to enhance Revenue per Available Seat Kilometer (RASK). By integrating financial engineering with airline revenue management, this paper provides a replicable optimization framework that helps airlines maximize revenue, respond rapidly to cost changes, and innovate dynamic pricing during structural market shifts. Ultimately, these recommendations offer a forward-looking blueprint for sustaining profitability and long-term resilience in an increasingly global aviation landscape. Meanwhile, help airlines to chase profit maximization and decrease the pressure form fuel price or other cost fluctuation

This study investigates whether supervised learning models can predict short-term directional movements in the Standard & Poor's 500 (S&P 500) and whether these predictions can be converted into a viable trading strategy using the Kelly Criterion. Using 26 years of daily Open, High, Low, Close, Volume (OHLCV) data (2000–2026), 18 features — including lagged returns, technical indicators, volatility and volume measures, and calendar effects — were engineered, and three classifiers — Logistic Regression, Random Forest, and eXtreme Gradient Boosting (XGBoost) — were compared. Model outputs were transformed into optimal position sizes via the Kelly Criterion and evaluated on cumulative return, Sharpe ratio, and maximum drawdown, benchmarked against a full-investment variant and Buy-and-Hold. Results show that all models achieved approximately 40% test accuracy — above the 33% random baseline — with Logistic Regression performing best on accuracy (40.69%) and Area Under the Curve (AUC) (0.589). Kelly-based strategies cut maximum drawdown by more than half relative to full investment, to between 10% and 14%, though Buy-and-Hold still achieved a higher Sharpe ratio. These findings suggest that while supervised learning offers a modest predictive signal, the Kelly Criterion is effective primarily as a risk-management tool rather than a return-enhancing strategy.

Against the backdrop of growing interconnectedness of global tech asset pricing, this paper investigates cross-market return spillovers from U.S. tech stocks to China's Hong Kong technology sector. Using 733 daily observations spanning January 2023 to December 2025, the study aligns each trading day in China's Hong Kong market with the prior completed U.S. session before China's Hong Kong opens, and adopts OLS and XGBoost to examine linear linkage, out-of-sample performance and feature contributions. After controlling for Hang Seng Index returns, USD/HKD returns, VIX and Sino-U.S. 10-year bond yields, the Nasdaq-100 return coefficient remains significantly positive at the 5% level, revealing the U.S. tech market delivers modest yet robust incremental information for Hang Seng TECH Index returns. Out-of-sample results show OLS marginally outperforms XGBoost, meaning nonlinear tree models fail to boost fitting power within this sample and variable set. SHAP analysis verifies Hang Seng Index returns dominate model explanatory power, while Nasdaq-100 returns carry limited feature importance. By matching non-overlapping trading hours and interpretable machine learning, this paper distinguishes average marginal effects from variable fitting contributions, offering new empirical evidence for tech stock co-movement and risk evaluation of China's Hong Kong tech market.

Since the launch of ChatGPT in late 2022, generative AI has become a major focus for global capital markets. However, the market reactions to AI-related announcements of listed companies are very heterogeneous, and the underlying mechanisms are still underexplored. In this study, this paper analyze AI announcements by firms in a fixed list of S&P 100 constituents over the period 2023–2024. Researchers test, using event study methods and cross-sectional regressions, whether implementation intensity and technological capabilities are associated with short-term market reactions. The mean cumulative abnormal return (CAR) for the [-5, +5] window is 3.0%, which is statistically different from zero at the 5% level and is based on 26 announcements with complete return data. This paper find that implementation-oriented announcements are associated with CARs that are about 4.6 percentage points higher than intention-oriented announcements, although the effect is only marginally significant at the 10% level and becomes insignificant under alternative specifications. The implementation hypothesis is thus supported in a suggestive but not robust way. The interaction of implementation stage with R&D intensity is not significant. Hence, the technological-capability hypothesis is not supported. Overall, the observed implementation evidence seems to be associated with more positive market reactions within the short window, but the small sample and the sensitivity of the results to the specification urge caution in interpretation.

Traditional asset pricing theory assumes that investors are aware of the "single true model", and thus fails to explain the widespread phenomenon of model uncertainty in the financial market. To investigate how asset prices are determined in a multi-model environment, this paper constructs a system that integrates Bayesian belief updating with the transmission of beliefs to asset prices. Using the Capital Asset Pricing Model (CAPM) and the Fama-French three-factor model (FF3) as competing benchmark models, and in conjunction with the Gordon Growth Model, this paper derives the evolution of asset prices after new information shocks. Therefore, based on the above results, investors use Bayes' theorem to adjust the weight given to different asset pricing models according to new information. The above non-linear changes in subjective beliefs alter the overall discount rate of the asset and thus have dynamic effects on its risk premium and absolute valuation. Based on the above derivation, it can be seen that a shift in investors' model beliefs is an internal source of excess volatility in asset prices, and this paper offers a new theoretical perspective on price formation under multiple models.

Customer summaries record how much a household has bought, not how its orders unfolded. We reconstruct ten-order histories for 1,477,553 target orders from 83,755 Instacart users and derive two sequence measures from each history: the coefficient of variation of interpurchase gaps, and the mean Jaccard overlap of consecutive fresh-item baskets. The outcome is the share of fresh items in a target basket that the household had bought before. Fractional-logit models split each measure into a customer mean and a window-level deviation. Timing irregularity is negative in both comparisons. Basket overlap is not: a one-standard-deviation increase in a customer's average overlap corresponds to 1.77 percentage points more repurchase, whereas the same increase relative to that customer's own average corresponds to 0.84 points less. The sign reversal survives eight-, ten- and twelve-order windows, strictly non-overlapping windows, a fixed complete-user sample, two opposite treatments of censored gaps, a narrower fresh-product definition, and an outcome whose reference period is aligned to the same ten orders. It also survives a customer fixed-effects linear model and a control for the preceding order's repurchase intensity. A customer's usual sequence and a recent departure from it are therefore distinct signals, and storing them as one similarity score discards the second.

With the rapid expansion of the digital economy, enterprises face substantial changes in their operational environment and daily management. Conventional enterprise business management can no longer adapt to the current highly competitive market. This paper looks at how Chinese businesses change their management in the digital age. It describes new features of modern business management, points out three key weaknesses within older management systems, and searches for practical improvement methods. Adopting the literature research method, this study draws on Chinese journal articles, industry survey data, and public cases of enterprise digital transformation. It shows that old-fashioned management ideas, imperfect data systems and a shortage of digitally literate talent are the main barriers to business reform. Companies can address these practical problems by renewing management ideas, building shared data platforms, perfecting talent training and establishing robust data security policies.

As China's concert market expands rapidly, the traditional ticket-sales-dependent model has proven structurally fragile. This study reconceptualizes the concert industry through the business ecosystem framework, arguing that sustainable development requires the coordinated value circulation among multiple interdependent actors rather than single-point revenue extraction. Mapping the concert ecosystem across upstream, midstream, and downstream layers, the study identifies three mechanisms through which ecosystem health drives sustainability: diversified monetization, long-term fan community retention, and intentional city-tourism symbiosis. Building on these mechanisms, a pre-event–in-event–post-event marketing framework is proposed, integrating dedicated hashtags, UGC-triggering experience design, and dual-track post-event content strategy. By shifting from a transaction-centric logic to an ecosystem-synergy logic, concert promoters can transform live concerts from isolated commercial events into a value system that circulates continuously and benefits all stakeholders. The study concludes that concert marketing is fundamentally the management of a self-reinforcing value loop, while cautioning that over-commercialization, experiential homogenization, and ticket scalping pose systemic risks to ecosystem integrity.

Against deepening global financial integration, traditional linear risk tools cannot capture time-lagged cascading risk spillovers, while existing epidemic-network studies separately analyze multilayer topology, temporal transmission and node centrality without unified frameworks. This paper develops a temporal multilayer Susceptible-Exposed-Infected-Recovered (SEIR) coupled contagion model integrating time-resolved paths and statistically validated Katz-Bonacich centrality. It sorts three branches of contagion literature and identifies three key defects in prior static, fragmented models. Two progressive dynamic models are constructed, incorporating latent risk states and liquidity-endogenous recovery coefficients. Three core diffusion drivers-network phase-transition traits, time-lagged linkages and asset community heterogeneity—are clarified. These mechanisms jointly explain how local financial shocks can evolve into broader cross-market and cross-sector contagion through interconnected network structures. A multi-tiered governance framework covering micro capital regulation, spectral macro supervision and targeted systemically important financial institution (SIFI) oversight is put forward. The study acknowledges linear vector autoregression (VAR) and exogenous liquidity limitations, and proposes cross-border, fintech-inclusive extensions for follow-up research.

If portfolio optimization uses only point estimates of returns or risk, estimation error often becomes extreme weights and too much trading. Interval forecasts give ranges for parameters or returns. Distributional forecasts give conditional distributions, quantiles, and tail features. Together they give fuller inputs for choosing weights. This review studies how those inputs enter a weight rule. Interval uncertainty sets, conformal coverage, distributionally robust optimization, and conditional value-at-risk (CVaR) can be written into the objective or the constraints and then mapped into tradable weights. Interval width κ acts as a penalty on effective expected return or as a bound on the feasible set, so a wider interval shrinks active positions. Distribution shape changes risk measurement: a thicker left tail raises CVaR and lowers the weight on that asset. The two channels can be placed in one program from a point forecast μ ^ , an interval half width κ , and a conditional law F to a weight vector w under trading cost constraints. Putting interval width and distribution shape into the allocation rule can reduce overconfident concentration and improve risk budgeting and rebalancing. The argument is theoretical. It does not rest on a new backtest.