Micro-structural models of contagion and systemic risk emphasize that shock propagation is inherently multi-channel, spanning counterparty exposures, short-term funding and roll-over risk, securities cross-holdings, and common-asset (fire-sale) spillovers. Empirical implementations, however, often rely on stylized or simulated networks, or focus on a single exposure dimension, reflecting the practical difficulty of reconciling heterogeneous granular collections into a coherent representation with consistent identifiers and consolidation rules. We close part of this gap by constructing an empirically grounded multilayer network for euro area significant banking groups that integrates several supervisory and statistical datasets into layer-consistent exposure matrices defined on a common node set. Each layer corresponds to a distinct transmission channel, long- and short-term credit, securities cross-holdings, short-term secured funding, and overlapping external portfolios, and nodes are enriched with balance-sheet information to support model calibration. We document pronounced cross-layer heterogeneity in connectivity and centrality, and show that an aggregated (flattened) representation can mask economically relevant structure and misidentify the institutions that are systemically important in specific markets. We then illustrate how the resulting network disciplines standard systemic-risk analytics by implementing a centrality-based propagation measure and a micro-structural agent-based framework on real exposures. The approach provides a data-grounded basis for layer-aware systemic-risk assessment and stress testing across multiple dimensions of the banking network.

Realized ecosystem services (ES) are the actual use of ES by societies, which is more directly linked to human well-being than potential ES. However, there is a lack of a general analysis framework to understand how much ES was realized. In this study, we first proposed a Supply-Demand-Flow-Use (SDFU) framework that integrates the supply, demand, flow, and use of ES and differentiates these concepts into different aspects (e.g., potential vs. actual ES demand, export and import flows of supply, etc.). Then, we applied the framework to three examples of ES that can be found in typical urban green parks (i.e., wild berry supply, pollination, and recreation). We showed how the framework could assess the actual use of ES and identify the supply-limited, demand-limited, and supply-demand-balanced types of realized ES. We also discussed the scaling features, temporal dynamics, and spatial characteristics of realized ES, as well as some critical questions for future studies. Although facing challenges, we believe that the applications of the SDFU framework can provide a systematic way to accurately assess the actual use of ES and better inform management and policy-making for sustainable use of nature's benefits. Therefore, we hope that our study will stimulate more research on realized ES and contribute to a deeper understanding of their roles in enhancing human well-being.

To completely understand the effects of urban ecosystems, the effects of ecosystem disservices should be considered along with the ecosystem services and require more research attention. In this study, we tried to better understand its formation through the use of cascade flowchart and classification systems and compare their effects with ecosystem services. It is vitally important to differentiate final and intermediate ecosystem disservices for understanding the negative effects of the ecosystem on human well-being. The proposed functional classification of EDS (i.e. provisioning, regulating and cultural EDS) should also help better bridging EDS and ES studies. In addition, we used Beijing as a case study area to value the EDS caused by urban ecosystems and compare the findings with ES values. The results suggested that although EDS caused great financial loss the potential economic gain from ecosystem services still significantly outweigh the loss. Our study only sheds light on valuating the net effects of urban ecosystems. In the future, we believe that EDS valuation should be at least equally considered in ecosystem valuation studies to create more comprehensive and sustainable development policies, land use proposals and management plans.

This study introduces a novel approach to walk-forward optimization by parameterizing the lengths of training and testing windows. We demonstrate that the performance of a trading strategy using the Exponential Moving Average (EMA) evaluated within a walk-forward procedure based on the Robust Sharpe Ratio is highly dependent on the chosen window size. We investigated the strategy on intraday Bitcoin data at six frequencies (1 minute to 60 minutes) using 81 combinations of walk-forward window lengths (1 day to 28 days) over a 19-month training period. The two best-performing parameter sets from the training data were applied to a 21-month out-of-sample testing period to ensure data independence. The strategy was only executed once during the testing period. To further validate the framework, strategy parameters estimated on Bitcoin were applied to Binance Coin and Ethereum. Our results suggest the robustness of our custom approach. In the training period for Bitcoin, all combinations of walk-forward windows outperformed a Buy-and-Hold strategy. During the testing period, the strategy performed similarly to Buy-and-Hold but with lower drawdown and a higher Information Ratio. Similar results were observed for Binance Coin and Ethereum. The real strength was demonstrated when a portfolio combining Buy-and-Hold with our strategies outperformed all individual strategies and Buy-and-Hold alone, achieving the highest overall performance and a 50 percent reduction in drawdown. A conservative fee of 0.1 percent per transaction was included in all calculations. A cost sensitivity analysis was performed as a sanity check, revealing that the strategy's break-even point was around 0.4 percent per transaction. This research highlights the importance of optimizing walk-forward window lengths and emphasizing the value of single-time out-of-sample testing for reliable strategy evaluation.

Fluctuations in stock prices are influenced by a complex interplay of factors that go beyond mere historical data. These factors, themselves influenced by external forces, encompass inter-stock dynamics, broader economic factors, various government policy decisions, outbreaks of wars, etc. Furthermore, all of these factors are dynamic and exhibit changes over time. In this paper, for the first time, we tackle the forecasting problem under external influence by proposing learning mechanisms that not only learn from historical trends but also incorporate external knowledge from temporal knowledge graphs. Since there are no such datasets or temporal knowledge graphs available, we study this problem with stock market data, and we construct comprehensive temporal knowledge graph datasets. In our proposed approach, we model relations on external temporal knowledge graphs as events of a Hawkes process on graphs. With extensive experiments, we show that learned dynamic representations effectively rank stocks based on returns across multiple holding periods, outperforming related baselines on relevant metrics.

We study how shareholder values shape firms' costly prosocial actions and who bears their costs. We develop a model in which some shareholders are publicly associated with a firm (e.g., founders or other prominent individual blockholders). When the firm takes a visible action under intense media scrutiny, these shareholders can plausibly claim credit and gain reputation, while diversified institutional investors cannot. The key empirical challenge is that influence is rarely observed: many consequential decisions are not subject to shareholder proposals or votes. We therefore use predetermined annual general meeting (AGM) timing combined with large, sudden crises -- COVID-19 and the invasion of Ukraine -- to generate quasi-experimental variation in attention and attribution, and to study highly visible, high-cost actions that were not legally required at onset. Firms with prominent individual blockholders are more likely to donate or exit when their AGM falls at crisis onset, while firms with large diversified institutional owners are less likely to do so. Consistent with our mechanism, online searches rise for prominent individuals after firm actions but not for institutions. Using an intent-to-treat triple-difference design on the 1,000 largest U.S.-listed firms, we find that exposed firms reduce investment, productivity, and profitability by 1--3\% for up to two years, highlighting the shared costs of pursuing the values of a visible minority.