Invesco Ltd. is an American independent investment management company that is headquartered in Atlanta, Georgia, with additional branch offices in 20 countries. Its common stock is a constituent of the S&P 500 and trades on the New York stock exchange. Invesco operates under the Invesco, Trimark, Invesco Perpetual, WL Ross & Co and Powershares brand names..
We examine whether and when ESG capital is associated with operating value creation and how its effects depend on other capitals emphasized in the Integrated Reporting lens. Using a large cross-country panel with firm-year ESG data, we model nonlinearity and cross-capital interactions and evaluate both contemporaneous and multi-year operating outcomes. Results show a clear nonlinear ESG - performance association: the largest gains arise when firms move from very low to mid-range ESG, with benefits that continue but flatten at higher levels. Crucially, ESG appears to act as a complement to other resources. Under stronger financial capital, ESG's association with operating performance is larger; under richer intellectual capital, ESG is more strongly linked to the translation of knowledge into results; and with better manufacturing capital, ESG aligns with process and asset-utilization improvements. Component analysis indicates diminishing returns for the environmental dimension, small stand-alone effects for social, and governance effects that become economically meaningful when paired with other capitals. Effects persist over one-, three-, and five-year horizons and are robust to alternative performance measures and sample splits. Managerially, the evidence supports calibrating ESG investment and integrating it with financial discipline, capability development, and operational upgrades.
Accurate prediction of infectious disease outbreaks are vital for effective public health planning and intervention. This paper proposes a novel spatio-temporal prediction framework that integrates Graph Convolutional Networks (GCNs) with Long Short-Term Memory (LSTM) networks to model the complex spatial and temporal dynamics of disease spread. By leveraging multi-source data—including epidemiological time-series, climatic variables, demographic distributions, and human mobility patterns—the model learns region-wise interdependencies and sequential trends in disease progression. The spatial component, implemented via GCN, captures inter-regional transmission patterns, while the temporal component, powered by LSTM, models evolving outbreak trends within each region. Experimental evaluation on real-world datasets demonstrates that the proposed GCN+LSTM model significantly outperforms traditional models such as ARIMA, Random Forest, and standalone LSTM in terms of MAE, RMSE, and F1-score. It also meets high classification performance at low false negative rate and therefore very suitable for real-time outbreak surveillance and early warning system.
Three real-world applications of derivatives in managing equity portfolios focusing on enhancing income using stock index options are illustrated in this article. Where these strategies are applied, providing practical examples and detailed analyses of their outcomes, is explained. The common pitfalls of option income strategies, particularly the impact of market volatility on yields, and how adjusting strike prices systematically can help achieve more stable income are described. This strategic insight is crucial for portfolio managers looking to enhance their income while managing risk effectively in their equity portfolios.
This article provides applications of derivatives to asset allocation and multi-asset management. The four applications include using futures for top-down asset allocation, deploying portable alpha strategies using derivatives to achieve desired convexity in payoff profiles, developing effective hedging strategies, and using derivatives for active speculative views by proprietary traders.
Market conditions change over the course of the business cycle. When are investors compensated to take risk? And what type of risk? This article proposes a practical regime-based framework for tactical asset allocation (TAA), combining leading economic indicators and global risk appetite to identify four macro regimes: recovery, expansion, slowdown, and contraction. The authors document distinct performance characteristics across regimes for traditional asset classes and their underlying risk factors, focusing on the term premium, credit premium, and equity premium. They provide simple and practical examples of TAA strategies for long-only multi-asset and fixed-income portfolios with the potential to generate attractive excess returns. Results are statistically significant and economically relevant after transaction costs, with information ratios between 0.70 and 0.80.
