(1) Social Impact Bonds (SIBs) foster the relationships between public and private sectors while adding value to new forms of investment that are closely linked to Socially Responsible Investments (SRIs). In this context, Sustainable Developments Goals (SDGs) aim to strengthen global partnerships in order to achieve the 2030 Agenda. Sustainable banking should consider its role in both new responsible investment products and the 2030 Agenda. This study aims to: (i) estimate the ROI of SIBS, (ii) define a financial formulation and a measurement system, and (iii) explain the relationship between SIBs and SDGs. (2) This research analyzes SIBs from an SDG approach, and proposes a valuation model based on a financial options valuation methodology that clarifies the financial value of the world’s first SIB (Peterborough Prison, UK). (3) Findings suggest that investors expect to have a negative return of 16.48%, and that this expected loss may be compensated for by the short- and long-term positive impact of an intervention in society. (4) It is shown that SIBs provide an opportunity to reach SDG 17 and improve sustainable investment portfolios, while providing an opportunity to strengthen a company’s Corporate Social Responsibility policy and its corporate reputation.

Financial innovation by means of Fintech firms is one of the more disruptive business model innovations from the latest years. Specifically, in the financial advisor sector, worldwide assets under management of artificial intelligence (AI)-based investment firms, or robo-advisors, currently amount to US$975.5 B. Since 2008, robo-advisors have evolved from passive advising to active data-driven investment management, requiring AI models capable of predicting financial asset prices on time to switch positions. In this research, an artificial neural network modelling framework is specifically designed to be used as an active data-driven robo-advisor due to its ability to forecast with today’s copper prices five days ahead of changes in prices using input data that can be fed automatically in the model. The model, tested using data of the two periods with a higher volatility of the returns of the recent history of copper prices (May 2006 to September 2008 and September 2008 to September 2010) showed that the method is capable of predicting in-sample and out-of-sample prices and consequently changes in prices with high levels of accuracy. Additionally, with a 24-day window of out-of-sample data, a trading simulation exercise was performed, consisting of staying long if the model predicts a rise in price or switching to a short position if the model predicts a decrease in price, and comparing the results with the passive strategies, buy and hold or sell and hold. The results obtained seem promising in terms of both statistical and trading metrics. Our contribution is twofold: 1) we propose a set of input variables based on financial theory that can be collected and fed automatically by the algorithm. 2) We generate predictions five days in advance that can be used to reposition the portfolio in active investment strategies.

Natural gas markets exhibit violent, non-linear volatility regimes. The 2022 energy crisis introduced persistent supply shocks and cross-Atlantic spillover effects. Traditional valuation models assume single-regime environments; consequently, they systematically misprice storage assets during extreme stress. We propose a valuation framework tailored to these specific market volatilities. By integrating a hidden Markov model (HMM) and Diebold–Yilmaz (DY) spillover indices into a stochastic dynamic programming (SDP) engine, the framework isolates the persistence of stressed market conditions. The model captures structural arbitrage opportunities. We demonstrate a 197 percent net present value (NPV) premium over conventional benchmarks using synthetic data. The optimal policy expands inventory holding periods during high spillover intensity. The algorithm executes decisions with sub-millisecond latency. This approach provides a computationally viable tool for high-frequency risk management.

The Spanish tourism sector is a driving force of the Spanish economy. In 2024, it was the country with the second largest number of foreign tourists in the world (93.8 million), surpassed only by France. Tourism in Spain is, therefore, a productive sector that generates employment and accounted for 13.1% of GDP in 2024. Like other productive sectors, tourism consumes water resources. The analysis carried out in this respect shows that water consumption per person in tourist dwellings is similar to residential consumption, while consumption per person in hotel establishments is approximately four times residential consumption. On the other hand, it is clear that the concentrated tourism model, which is the one followed by Benidorm, generates a lower unitary water consumption than the extended model. Another highly relevant issue is that the Spanish tourist destinations that receive the most visitors are located in areas of very high water stress and, moreover, are concentrated in the summer months, which are the months in which this water stress is most acute. This means that water consumption in the tourism sector must be adjusted in situations where consumption restrictions are necessary.

The Discounted Cash Flow (DCF) methodology is widely regarded as one of the most theoretically well-established approaches to firm valuation. The most critical issue within this methodology is the reliable and well-grounded estimation of those cash flows, particularly the systematic incorporation of qualitative enterprise risks into future cash-flow projections. The objective of this study is to develop a systematic and standardized methodology for estimating risk-adjusted cash flows based on a set of parameters derived from the Internal Control System (ICS) of the firm under valuation, operationalized through a rating system focused on the qualitative dimensions of the company. The proposed framework systematically translates qualitative risk assessments into explicit adjustments of the economic drivers underlying projected cash flows, thereby establishing a structured link between Internal Control Systems (ICS), Enterprise Risk Management (ERM), and Discounted Cash Flow (DCF) valuation. The methodology is applied to a baseline financial scenario representing the central financial projection of the firm, which is subsequently adjusted for enterprise risks derived from the Internal Control System. Naturally, quantitative factors must also be considered; however, this issue has already been extensively examined in the literature and is firmly established in corporate finance textbooks. The contribution of this paper lies in providing the academic and professional communities with a systematic framework designed to improve the transparency, consistency, and traceability of firm valuation while supporting a more structured incorporation of qualitative risk factors into projected cash flows. The proposed framework is methodological in nature and is intended to provide a conceptual foundation for future empirical calibration and validation using real-world corporate data.