Search Results (261)

This paper explores the effectiveness of an innovative FinTech risk-scoring model to predict the risk-appropriate return for short-term credit sales. The risk score serves to mitigate the information asymmetry between the seller of receivables (“Seller”) and the purchaser (“Funder”), at the same time providing an opportunity for the Funder to earn returns as well as to diversify its portfolio on a risk-appropriate basis. Selling receivables/credit to potential Funders at a risk-appropriate discount also helps Sellers to maintain their short-term financial liquidity and provide the necessary cash flow for operations and other immediate financial needs. We use 18,304 short-term credit-sale transactions between 23 April 2020 and 30 September 2022 from the private FinTech startup Crowdz and its Sustainability, Underwriting, Risk & Financial (SURF) risk-scoring system to analyze the risk/return relationship. The data includes risk scores for both Sellers of receivables (e.g., invoices) along with the Obligors (firms purchasing goods and services from the Seller) on those receivables and provides, as outputs, the mutual gains by the Sellers and the financial institutions or other investors funding the receivables (i.e., the Funders). Our analysis shows that the SURF Score is instrumental in mitigating the information asymmetry between the Sellers and the Funders and provides risk-appropriate periodic returns to the Funders across industries. A comparative analysis shows that the use of SURF technology generates higher risk-appropriate annualized internal rates of return (IRR) as compared to nonuse of the SURF Score risk-scoring system in these transactions. While Sellers and Funders enter into a win-win relationship (in the absence of a default), Sellers of credit instruments are not often scored based on the potential diversification by industry classification. Crowdz’s SURF technology does so and provides Funders with diversification opportunities through numerous invoices of differing amounts and SURF Scores in a wide range of industries. The analysis also shows that Sellers generally have lower financing stability as compared to the Obligors (payers on receivables), a fact captured in the SURF Scores. Full article

This study explores modernization strategies for existing district heating (DH) networks to enhance their efficiency and sustainability, focusing on achieving net-zero emissions in urban heating systems. Building upon a literature review and expert interviews, we developed a strategic decision-support framework that outlines distinct strategies for retrofitting district heating grids and includes a portfolio analysis. This framework serves as a tool to guide DH operators and stakeholders in selecting well-founded modernization pathways by considering technical, economic, and social dimensions. The review identifies several promising measures, such as reducing operational temperatures at substations, implementing optimized substations, integrating renewable and waste heat sources, implementing thermal energy storage (TES), deploying smart metering and monitoring infrastructure, and expanding networks while addressing public concerns. Additionally, the review highlights the importance of stakeholder engagement and policy support in successfully implementing these strategies. The developed strategic decision-support framework helps practitioners select a tailored modernization strategy aligned with the local context. Furthermore, the findings show the necessity of adopting a comprehensive approach that combines technical upgrades with robust stakeholder involvement and supportive policy measures to facilitate the transition to sustainable urban heating solutions. For example, the development of decision-support tools enables stakeholders to systematically evaluate and select grid modernization strategies, directly helping to reduce transmission losses and lower greenhouse gas (GHG) emissions contributing to climate goals and enhancing energy security. Indeed, as shown in the reviewed literature, retrofitting high-temperature district heating networks with low-temperature distribution and integrating renewables can lead to near-complete decarbonization of the supplied heat. Additionally, integrating advanced digital technologies, such as smart grid systems, can enhance grid efficiency and enable a greater share of variable renewable energy thus supporting national decarbonization targets. Further investigation could point to the most determining context factors for best choices to improve the sustainability and efficiency of existing DH systems. Full article

The recent economic turmoil and the increasing volatility of bitcoins have necessitated the need for exploring safe-haven assets for bitcoins. In this quest, the present study aims to investigate the safe haven for bitcoins by examining the dynamic relationship between bitcoins, gold, foreign exchange, and stablecoins. This is achieved by calculating hedge ratios and portfolio weight ratios for various asset classes, by employing adaptive-based techniques such as generalized orthogonal generalized autoregressive conditional heteroscedasticity, corrected dynamic conditional correlation, corrected asymmetric dynamic conditional correlation, and asymmetric dynamic conditional correlation under various market and time-varying conditions. The empirical estimate reveals that all the selected asset classes are effective risk diversifiers for bitcoins. However, among all the asset classes, as per the hedge and portfolio weight ratio, Japanese yen, stablecoin for Japanese yen and Great Britain Pound, and Crypto Holding Frank Token (lowest-cost hedging strategies) are the most effective risk diversifiers when compared with bitcoins. Moreover, while considering external economic shocks, the empirical estimate posits that stablecoins are more stable risk diversifiers compared to the asset class they represent. Furthermore, in terms of the bivariate portfolio analysis formed with bitcoin, this study concludes that the weight of bitcoin is more stable when combined with gold, tether gold, Euro, Great Britain Pound, Swiss franc, and Japanese Yen. Thus, these assets are attractive for long-term investment strategies. This study provides investors and policymakers with significant insight into understanding safe-haven assets for bitcoin’s volatility and constructing a flexible portfolio that is dependent on the investment timeline and the prevailing market conditions. Full article

Globalization and the spread of technological innovations have made world markets and economies increasingly unified and conditioned by international trade, not only for sales markets but above all for the supply of raw materials necessary for the functioning of the production complex of each country. Alongside oil and gold, the main commodities traded include industrial metals, such as aluminum and copper, mineral products such as gas, electrical and electronic components, agricultural products, and precious metals. The conflict between Russia and Ukraine tested the unification of markets, given that these are countries with notable raw materials and are strongly dedicated to exports. This suggests that commodity prices were able to influence the stock markets, especially in the countries most closely linked to the two belligerents in terms of import-export. Given the importance of industrial metals in this period of energy transition, the aim of our study is to analyze whether Industrial Metals volatility affects G7 stock markets. To this end, the BEKK-GARCH model is used. The sample period spans from 3 January 2018 to 17 September 2024. The results show that lagged shocks and volatility significantly and positively influence the current conditional volatility of commodity and stock returns during all periods. In fact, past shocks inversely influence the current volatility of stock indices in periods when external events disrupt financial markets. The results show a non-linear and positive impact of commodity volatility on the implied volatility of the stock markets. The findings suggest that the war significantly affected stock prices and exacerbated volatility, so investors should diversify their portfolios to maximize returns and reduce risk differently in times of crisis, and a lack of diversification of raw materials is a risky factor for investors. Full article

There has been growing interest among investors to include cryptocurrencies in their portfolios because of their diversification potential. However, the diversification role of cryptocurrencies when added to South African bank equities is yet to be determined. This study rigorously evaluates asset co-movement and diversification benefits of integrating cryptocurrencies into South African bank equity portfolios. Using advanced financial engineering techniques, including multi-asset particle swarm optimizer (MA-PSO), random optimizer, and a static equal-weighted portfolio (EWP) model, this study analyzed the dynamic portfolio performance and diversification of cryptocurrencies in the 2017–2024 period. The portfolio performance of the three methods is also compared with the results from the traditional one-period mean–variance optimization (MVO) method. The findings underscore the superiority of dynamic models over static EWP in assessing the impact of cryptocurrency inclusion in bank equity portfolios. While pre-COVID-19 studies identified cryptocurrencies as effective hedges against market downturns, this protective role appears attenuated in the post-COVID-19 era. The dynamic MA-PSO model emerges as the optimal approach, delivering better-diversified portfolios. Consequently, South African portfolio managers must carefully evaluate investor risk tolerance before incorporating cryptocurrencies, with regulators imposing stringent guidelines to mitigate potential losses. Full article

Non-Intrusive Load Monitoring (NILM), a technique that extracts appliance-level energy consumption information through analysis of aggregated electrical measurements, has become essential for smart grids and energy management applications. Given the increasing diversification of electrical appliances, real-time NILM systems require continuous integration of knowledge from new client-side appliance data to maintain monitoring effectiveness. However, current methods face challenges with inter-client knowledge conflicts and catastrophic forgetting in distributed multi-client continual learning scenarios. This study addresses these challenges by proposing a confidence-based collaborative distributed continual learning framework for NILM. A lightweight layer-wise dual-supervised autoencoder (LWDSAE) model is initially designed for smart meter deployment, supporting both load identification and confidence-based collaboration tasks. Clients with learning capabilities generate new models through one-time fine-tuning, facilitating collaboration among client models and enhancing individual client load identification performance via a confidence judgment method based on signal reconstruction deviations. Furthermore, an anomaly sample detection-driven model portfolios update method is developed to assist each client in maintaining optimal local performance under model quantity constraints. Comprehensive evaluations on two public datasets and real-world applications demonstrate that the framework achieves sustained performance improvements in distributed continual learning scenarios, consistently outperforming state-of-the-art methods. Full article

This research investigates the spillover effects between assets of the Fourth Industrial Revolution (4IR), focusing on the role of climate policy uncertainty in shaping these interactions. Using a time-varying parameter vector autoregressive (TVP-VAR) approach and a joint connectedness method, the analysis incorporates five global indices representing key 4IR domains: the internet, cybersecurity, artificial intelligence and robotics, fintech, and blockchain. The findings reveal significant interdependencies among 4IR assets and evaluate the effect of risk factors, including climate policy uncertainty, as a critical driver of the determinants of returns. The results indicate the growing impact of climate-related risks on the structure of connectedness between 4IR assets, highlighting their implications for portfolio diversification and risk management. These insights are vital for investors and policymakers navigating the intersection of technological innovation and environmental challenges in a rapidly changing global economy. Full article

This research examines the challenges of issuing green bonds due to a lack of established benchmarks. We compare regional differences between the U.S. and the E.U., hypothesizing that issuers of green bonds stand to benefit from comparing them to conventional (black) bonds. As most investors prioritize net positive returns as opposed to intangible sustainability metrics, the existence of a “green premium”, defined as the opportunity to price green bonds differently, remains to be proven. To this end, we employ a time-varying parameter vector autoregression (TVP-VAR), first deriving dynamic variance–covariance matrices and then conducting variance decomposition analysis to gauge connectedness and spillover effects of various bond benchmarks. Implementing multivariate portfolio construction strategies, we investigate the hedging capabilities of green and black bonds. Our findings show that both green and black bonds contribute to portfolio diversification as a risk management strategy. The paper highlights the role played by green bonds in promoting financial stability. Full article

This study examines whether portfolio optimization can be effectively based on annual changes in the harmonized index of consumer prices (HICP) data. Specifically, we assess whether asset allocation based on consumer expenditure can generate superior returns compared to static or equal-weighted asset allocation. To explore this, we use consumer expenditure data from HICP statistics categorized by COICOP. Our findings indicate that this strategy outperforms a buy-and-hold benchmark by 13.32% in terms of the Sharpe Ratio and exceeds an annual equal-weighted rebalancing strategy by 3.11%. Additionally, both the Calmar and Sterling Ratios demonstrate improved performance, further reinforcing the robustness of this approach. Furthermore, a hypothetical scenario where sector weights from the end of the given year—though not yet available during the year—are used suggests even greater improvements in performance. A high-sample bootstrap simulation confirms that the observed performance differences are not random but reflect the independent effectiveness of asset allocation based on consumer expenditure trends. This result strengthens the validity of our backtesting findings, indicating that the examined strategy could generate excess returns compared to passive portfolio managment and fixed-weight rebalancing approaches. The result of the study is therefore the development of an effective portfolio rebalancing strategy. Full article

South American coffee producers face growing challenges due to external trade dependencies and climate-induced disruptions. This study investigates the role of export market diversification as a sustainability strategy for four major regional exporters of roasted non-decaffeinated coffee: Brazil, Colombia, Peru, and Ecuador. A quantitative and comparative methodology was applied over a ten-year period using the Herfindahl–Hirschman Index (HHI) to evaluate export market concentration and the Revealed Comparative Advantage (RCA) Index—including its normalized variant—to assess international competitiveness by destination. The results reveal substantial disparities: Brazil and Colombia exhibit moderate to high diversification and relative competitiveness in select markets, while Peru and Ecuador remain dependent on a few strategic buyers, with limited or declining comparative advantages. The findings emphasize that sustained export performance in the coffee sector requires not only a broader destination portfolio but also improved positioning through trade agreements, infrastructure development, and climate-resilient innovation. This study concludes with a strategic proposal based on three pillars—commercial, logistical, and technological—to support structural transformation and enhance the long-term sustainability of the coffee trade in South America. Full article

Against the backdrop of the current rural economic transformation and the intensification of the ageing process, land transfer, as an important land policy tool, has gradually become a key factor influencing the consumption behaviour of farmers, especially older farmers. Based on the four-period panel data of the China Family Panel Studies (CFPS), this study uses a two-way fixed-effects model to examine the impact of land transfer (land transfer-out, land transfer-in, and two-way land transfer) on the consumption behaviour of older farmers. This study finds that land transfer-out significantly increases the total consumption of older farmers and promotes subsistence, healthy, and hedonic consumption. In contrast, land transfer-in does not show a significant effect on hedonic consumption. The mechanism test reveals that household income plays a key mediating role in the process of land transfer, affecting the consumption behaviour of older farmers. Two-way land transfer promotes the consumption level and the upgrading of the consumption structure of older farmers through income portfolio optimisation and risk diversification. Full article

Portfolio optimization is a fundamental problem in financial theory, aiming to balance risk and return in asset allocation. Traditional models, such as Mean–Variance optimization, are effective, but often fail to account for diversification adequately. This study introduces the Mean–Variance–Entropy (MVE) model, which integrates Tsallis entropy into the classic Mean–Variance framework to enhance portfolio diversification and risk management. Entropy, specifically second-order entropy, penalizes excessive concentration in the portfolio, encouraging a more balanced and diversified allocation of assets. The model is applied to a portfolio of five major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Solana (SOL), Cardano (ADA), and Binance Coin (BNB). The performance of the MVE model is compared with that of the traditional Mean–Variance model, and results demonstrate that the entropy-enhanced model provides better diversification, although with a slightly lower Sharpe ratio. The findings suggest that while the entropy-adjusted model results in a slightly lower Sharpe ratio, it offers better diversification and a more resilient portfolio, especially in volatile markets. This study demonstrates the potential of incorporating entropy into portfolio optimization as a means to mitigate concentration risk and improve portfolio performance. The approach is particularly beneficial for markets such as cryptocurrency, where volatility and asset correlations fluctuate rapidly. This paper contributes to the growing body of literature on portfolio optimization by offering a more diversified, robust, and risk-adjusted approach to asset allocation Full article

As photovoltaics’ (PVs) capacity will probably rapidly expand to tens of terawatts globally, the diversification of the PV technology portfolio becomes essential. Perovskite technology proffers promise for expanding solar energy market segments like building-integrated PVs and flexible PVs for the residential and industrial sectors. In this perspective, we calculate that under reasonably attainable values for the module cost, conversion efficiency, and degradation rate, a levelized cost of electricity (LCOE) of 10 EURct/kWh can be reached for perovskite PV in 2035. Furthermore, if, in 2035, the conversion efficiency can be increased to 25% and the degradation rate falls to below 1%, with a module cost of 50 EUR/m², the LCOE for perovskite PV could become around 8 EURct/kWh. For lower module costs, the LCOE would drop further, by which cost competitiveness with c-Si PV is in sight. We point out that even if the LCOE of perovskite solar modules may remain relatively high, they could still occupy an important role, particularly in the residential sector, thanks to their flexibility and lightweight properties, enabling a large suite of new applications. Overall, to push perovskite PVs towards successful commercialization, policy support will be indispensable. Full article

This study examines the dynamic interconnectedness between digital and traditional assets, with an emphasis on fiat currencies (such as JPY/USD and CHF/USD), cryptocurrencies (such as Bitcoin), and digital assets backed by gold (such as Tether Gold and Digix Gold Token) under various economic conditions. The study uses sophisticated techniques, including dynamic connectedness, quantile connectedness, and time-frequency connectedness analyses, to test non-linear and asymmetric interactions between various asset classes. The findings reveal that while cryptocurrencies, especially Bitcoin, frequently serve as net recipients of shocks during times of economic instability, gold and gold-backed assets are the primary shock transmitters. These findings highlight the increasing importance that digital assets play amid economic and geopolitical crises as well as their growing incorporation into the larger financial ecosystem. The study contributes to the literature on asset interconnection and provides implications for systemic risk management and financial stability; specifically, it offers insightful information for hedging and portfolio diversification techniques. Full article

This paper presents a robust multi-period portfolio optimization framework that integrates interval analysis, entropy-based diversification, and downside risk control. In contrast to classical models relying on precise probabilistic assumptions, our approach captures uncertainty through interval-valued parameters for asset returns, risk, and liquidity—particularly suitable for volatile markets such as cryptocurrencies. The model seeks to maximize terminal portfolio wealth over a finite investment horizon while ensuring compliance with return, risk, liquidity, and diversification constraints at each rebalancing stage. Risk is modeled using semi-absolute deviation, which better reflects investor sensitivity to downside outcomes than variance-based measures, and diversification is promoted through Shannon entropy to prevent excessive concentration. A nonlinear multi-objective formulation ensures computational tractability while preserving decision realism. To illustrate the practical applicability of the proposed framework, a simulated case study is conducted on four major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB). The model evaluates three strategic profiles based on investor risk attitude: pessimistic (lower return bounds and upper risk bounds), optimistic (upper return bounds and lower risk bounds), and mixed (average values). The resulting final terminal wealth intervals are [1085.32, 1163.77] for the pessimistic strategy, [1123.89, 1245.16] for the mixed strategy, and [1167.42, 1323.55] for the optimistic strategy. These results demonstrate the model’s adaptability to different investor preferences and its empirical relevance in managing uncertainty under real-world volatility conditions. Full article