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This paper investigates the impact of online interaction between investors and enterprises on stock liquidity, using data from A-share listed companies in China from 2010 to 2021. Firstly, our findings reveal that more frequent interaction leads to better stock liquidity, and this result remains consistent across various robustness tests. Secondly, we observe that the expected tenure of senior executives and the ratio of institutional investor ownership exert a significant moderating effect on this relationship. Thirdly, this effect varies across enterprises at different development stages and with different ownership structures, being more pronounced in growing and privately-owned companies. Furthermore, this paper finds an inverted U-shaped relationship between reply length and stock liquidity, indicating that excessively long replies may introduce noise and negatively affect liquidity. This study provides new insights into how online interactions can improve market efficiency and offers practical implications for corporate governance and investor relations. [ABSTRACT FROM AUTHOR]

Today, with a growing emphasis on sustainable economic development, corporate environmental, social and governance (ESG) performance is attracting increasing attention and favor from investors. This triggers a question: can good ESG performance of listed companies mitigate the "up and down" of the stock market by drawing investor attention? This paper utilizes the data from China's A-share listed companies from 2011 to 2020, with investor attention as a mediating variable, to explore how the ESG performance of listed companies influences abnormal stock price volatility. The findings suggest that stronger ESG performance of listed companies significantly reduces abnormal stock price volatility, in which investor attention plays a partial mediating role. This paper confirms the robustness of the findings through multiple robustness and endogeneity tests. Heterogeneity analysis reveals that listed companies with good ESG performance during the growth period are more likely to significantly mitigate abnormal stock price volatility. Similarly, firms that maintain commendable ESG performance in bear markets significantly reduce abnormal stock price volatility. These findings enrich the theoretical research on the impact of ESG performance on abnormal stock price volatility, provide empirical evidence for listed companies to emphasize ESG investment and encourage investors to consider ESG ratings. Additionally, the study provides a new perspective for government agencies to utilize corporate ESG performance to maintain the sound development of the capital market. [ABSTRACT FROM AUTHOR]

Tax incentives are a powerful driver for the development of infrastructure real estate investment trusts (REITs). Complementary antiavoidance regulations are essential to ensure the sustainable and healthy development of the REIT market. This paper breaks through the traditional policy approach that emphasizes incentives but neglects supervision. Based on factors such as information asymmetry and regulatory capture, a tripartite game model of REIT tax incentives and antiavoidance regulation is established. Utilizing global REIT policy and market data, the paper sets parameter values and conducts numerical simulations to explore the dynamic strategic interaction mechanisms among the government, REIT stakeholders, and financial regulators. The study finds that (1) government's tax incentive policies and regulatory strategies show a phase transition characteristic with the changes of REIT market returns; and (2) based on the project's profit status, financial regulators and investors are prone to develop a collusive avoidance evolutionary strategy. Therefore, antiavoidance regulations and mechanisms to prevent regulatory capture should be implemented in tandem to ensure the efficiency of tax incentives. The conclusion of the study provides a theoretical basis for the formulation of REIT tax incentive policy and the promotion of the sustainable development of the REIT market. [ABSTRACT FROM AUTHOR]

Enterprise financial risk refers to the uncertainty of financial status caused by various internal and external factors during investment, financing, operations, and other activities, which may result in losses for the enterprise. Agricultural group enterprises engage in diversified businesses and have a large number of transactions. Characteristics such as natural disasters, financing difficulties, and strong seasonality of income and expenditure make agricultural enterprises face greater financial risks. Based on the perspective of evidence fusion, this paper takes the JS Agricultural Reclamation Group as the research object, puts forward a financial risk early warning model based on evidence theory, and innovatively puts forward the basic probability assignment of evidence obtained by whitening weight function, and uses evidence theory to fuse uncertain risk information. This paper makes an empirical study of 420 subsidiaries of the group and selects 10 subsidiaries as case study objects. The results show that the model proposed in this study can improve the prediction accuracy of the group's intelligent financial decision system and the existing literature and reduce the subjective factors and information loss in the risk assessment process. In addition, this study provides the probability interval of early warning results, which can expose small probability risks and provide a more reliable basis for regulators, enterprise managers, and investors to make efficient and correct decisions. [ABSTRACT FROM AUTHOR]

Media information plays an essential role in the stock market. Recent financial research has verified that media information could shock stock price by influencing investors' expectation. Now, a new type of interactive media, called Digital Interactive Media (DIM), is popular in Chinese stock market and becomes the main channel for investors to understand listed companies. Unlike general news media or investor forums, DIM enables direct interaction between listed companies and investors. In the modern society where digital economy is booming, media information would largely affect investors' decisions. Therefore, it is urgent to use natural language processing (NLP) technology to deconstruct the massive questions and answers (Q&A) interactive information in DIM and extract valuable factors that affect stock prices and stock performances to explore the influence mechanism of digital interactive information on stock performances. This paper firstly uses web crawling technology to obtain approximately 110000 Q&A text information from the digital interactive platform ('Panoramic Network') from 2015 to 2021. Then we use big data text analysis technology and emotional quantification technology to extract valuable influencing factors from the massive text. A Multiple Linear Regression (MLR) model was created to explore specific influence mechanism of digital interactive information on stock price performance. The empirical results show that the emotions implicit in investors' questions do not significantly impact stock performance. However, the emotions and attitudes of the answers by listed companies can significantly affect corresponding stock prices, which indirectly confirms the Proximate Cause Effect of behavioral finance. This effect is particularly evident in the stock prices on the current trading day and the next trading day. In the Robustness Test, this paper replaces dependent variable and adds relevant control variables, and the conclusion remains valid. In the Endogeneity Test, this paper selects sample data before the launch of Panorama Network in 2014 as a comparison, and uses a Difference-in-Difference (DID) model to prove the significant impact of the launch of Panorama Network on Chinese stock market. In the Heterogeneity Test, the paper classifies the market value, region, and industry of listed companies and regressed the sub samples, once again confirming the reliability of the empirical conclusions. The results of Robustness Test, Endogeneity Test, and Heterogeneity Test conducted in this paper all support empirical conclusions. [ABSTRACT FROM AUTHOR]

With the increasing importance of the stock market, it is of great practical significance to accurately describe the systemic risk of the stock market and conduct more accurate early warning research on it. However, the existing research on the systemic risk of the stock market lacks multi-dimensional factors, and there is still room for improvement in the forecasting model. Therefore, to further measure the systemic risk profile of the Chinese stock market, establish a risk early warning system suitable for the Chinese stock market, and improve the risk management awareness of investors and regulators. This paper proposes a combination model of EEMD-LSTM, which can describe the complex nonlinear interaction. Firstly, 35 stock market systemic risk indicators are selected from the perspectives of macroeconomic operation, market cross-contagion and the stock market itself to build a comprehensive indicator system that conforms to the reality of China. Furthermore, based on TEI@I complex system methodology, an EEMD-LSTM model is proposed. The EEMD method is adopted to decompose the composite index sequence into intrinsic mode function components (IMF) of different scales and one trend term. Then the LSTM algorithm is used to predicted and model the decomposed sub-sequences. Finally, the forecast result of the composite index is obtained through integration. The empirical results show that the stock market systemic risk index constructed in this paper can effectively identify important risk events within the sample period. In addition, compared with the benchmark model, the EEMD-LSTM model constructed in this paper shows a stronger early warning ability for systemic financial risks in the stock market. [ABSTRACT FROM AUTHOR]

This paper explores the application of Random Matrix Theory (RMT) as a methodological enhancement for portfolio selection within financial markets. Traditional approaches to portfolio optimization often rely on historical estimates of correlation matrices, which are particularly susceptible to instabilities. To address this challenge, we combine a data preprocessing technique based on the Hilbert transformation of returns with RMT to refine the accuracy and robustness of correlation matrix estimation. By comparing empirical correlations with those generated through RMT, we reveal non-random properties and uncover underlying relationships within financial data. We then utilize this methodology to construct the correlation network dependence structure used in portfolio optimization. The empirical analysis presented in this paper validates the effectiveness of RMT in enhancing portfolio diversification and risk management strategies. This research contributes by offering investors and portfolio managers with methodological insights to construct portfolios that are more stable, robust, and diversified. At the same time, it advances our comprehension of the intricate statistical principles underlying multivariate financial data. [ABSTRACT FROM AUTHOR]

This paper compares the hedging and safe haven properties of clean (renewable) energy and unclean (non-renewable) energy stocks. Using around 20 years of monthly clean and unclean energy indices and applying the GARCH and quantile regression models with dummy variables, we find that likewise the unclean energy stocks renewable energy stocks provide a weak hedge and a safe haven against the global economic policy uncertainty and its extreme values even during the pandemic and low and high return environments. These findings suggest that clean energy stocks provide immunity to investors against unfavourable changes to economic policy uncertainty and market volatility. [ABSTRACT FROM AUTHOR]

In the context of global economic digitalization, financial information is highly susceptible to internet financial public opinion due to the overwhelming and misleading nature of information on internet platforms. This paper delves into the core entities in the diffusion process of internet financial public opinions, including financial institutions, governments, media, and investors, and models the behavioral characteristics of these entities in the diffusion process. On this basis, we comprehensively use the multi-agent model and the SIR model to construct a dynamic evolution model of internet financial public opinion. We conduct a simulation analysis of the impact effects and interaction mechanisms of multi-agent behaviors in the financial market on the evolution of internet financial public opinion. The research results are as follows. Firstly, the financial institutions' digitalization levels, government guidance, and the media authority positively promote the diffusion of internet financial public opinion. Secondly, the improvement of investors' financial literacy can inhibit the diffusion of internet financial public opinion. Thirdly, under the interaction of multi-agent behaviors in the financial market, the effects of financial institutions' digitalization level and investors' financial literacy are more significant, while the effects of government guidance and media authority tend to converge. [ABSTRACT FROM AUTHOR]

This paper delves into the critical role of tax credits, specifically Sections 45Q and 45V, in the financing and economic feasibility of low-carbon-intensity hydrogen projects, with a focus on natural-gas-based hydrogen production plants integrated with carbon capture and storage (CCS). This study covers the current clean energy landscape, underscoring the importance of low-carbon hydrogen as a key component in the transition to a sustainable energy future, and then explicates the mechanics of the 45Q and 45V tax credits, illustrating their direct impact on enhancing the economic attractiveness of such projects through a detailed net present value (NPV) model analysis. Our analysis reveals that the application of 45Q and 45V tax credits significantly reduces the levelized cost of hydrogen production, with scenarios indicating a reduction in cost ranging from USD 0.41/kg to USD 0.81/kg of hydrogen. Specifically, the 45Q tax credit demonstrates a slightly more advantageous impact on reducing costs compared to the 45V tax credit, underpinning the critical role of these fiscal measures in enhancing project returns and feasibility. Furthermore, this paper addresses the inherent limitations of utilizing tax credits, primarily the challenge posed by the mismatch between the scale of tax credits and the tax liability of the project developers. The concept and role of tax equity investments are discussed in response to this challenge. These findings contribute to the broader dialogue on the financing of sustainable energy projects, providing valuable insights for policymakers, investors, and developers in the hydrogen energy sector. By quantifying the economic benefits of tax credits and elucidating the role of tax equity investments, our research supports informed decision-making and strategic planning in the pursuit of a sustainable energy future. [ABSTRACT FROM AUTHOR]

This paper discusses finding solutions to the modified Fractional Black–Scholes equation. As is well known, the options theory is beneficial in the stock market. Using call-and-pull options, investors can theoretically decide when to sell, hold, or buy shares for maximum profits. However, the process of forming the Black–Scholes model uses a normal distribution, where, in reality, the call option formula obtained is less realistic in the stock market. Therefore, it is necessary to modify the model to make the option values obtained more realistic. In this paper, the method used to determine the solution to the modified Fractional Black–Scholes equation is a combination of the finite difference method and the fractional differential transformation method. The results show that the combined method of finite difference and fractional differential transformation is a very good approximation for the solution of the Fractional Black–Scholes equation. [ABSTRACT FROM AUTHOR]

At present, with the increasing digital competition among enterprises, it is of great importance for enterprises to promote digital transformation and achieve sustainable development. Taking China's A-share listed companies from 2007 to 2021 as samples, this paper performs empirical tests in order to explore the impact of enterprise digital transformation on the comparability of accounting information and its mechanism. The results show that digital transformation has significantly enhanced accounting information comparability, which is still significant after a series of robustness tests. The mechanism test shows that the improvement of the comparability of accounting information by the digital transformation is mainly achieved by alleviating earnings management and agency problem. Further research shows that when the degree of market competition or transparency is high, the role of enterprise digital transformation in enhancing the comparability is more significant. This paper enriches the literature on the economic consequences of digital transformation and the factors affecting the comparability of accounting information. At the same time, the conclusions of this paper confirm the governance effect of enterprise digital transformation, provide evidential support for investors to improve decision-making efficiency, and urge enterprises to accelerate digital transformation. [ABSTRACT FROM AUTHOR]

Since financial assets on stock exchanges were created, investors have sought to predict their future values. Currently, cryptocurrencies are also seen as assets. Machine learning is increasingly adopted to assist and automate investments. The main objective of this paper is to make daily predictions about the movement direction of financial time series through classification models, financial time series preprocessing methods, and feature selection with genetic algorithms. The target time series are Bitcoin, Ibovespa, and Vale. The methodology of this paper includes the following steps: collecting time series of financial assets; data preprocessing; feature selection with genetic algorithms; and the training and testing of machine learning models. The results were obtained by evaluating the models with the area under the ROC curve metric. For the best prediction models for Bitcoin, Ibovespa, and Vale, values of 0.61, 0.62, and 0.58 were obtained, respectively. In conclusion, the feature selection allowed the improvement of performance in most models, and the input series in the form of percentage variation obtained a good performance, although it was composed of fewer attributes in relation to the other sets tested. [ABSTRACT FROM AUTHOR]

Portfolio optimization problems are easy to address if single linear objective functions are considered, with the assumption of normality of asset returns distributions, subject to different risks, returns, and investment constraints. Higher complexities arise if combinations of multi-objective formulations, non-linear assets, non-normal asset return distributions, and uncertainty in parameter estimates are studied. In this paper, we solve two interesting variants of multi-objective investment analysis problems considering both non-normal asset return distributions and uncertainty in parameter estimates. Data used for the optimization models are pre-processed using ARCH/GARCH combined with extreme value asset returns distribution (EVD). The efficacy of our proposed multi-objective reliability-based portfolio optimization (MORBPO) problems is validated using Indian financial market data (Details of plan of codes, pseudo-codes and other set of detailed runs results (not discussed in this paper) are given in the open access link, https://github.com/RNSengupta/Bi-Objective%5fRBDO%5fPaper). We present the optimal values of investment weights, portfolio returns, portfolio risks (variance, CVaR, EVaR), reliability indices (β ) as well as Pareto optimal frontiers and analyze the outputs in the context of their practical implications. The run results highlight the fact that investors' uncertainty levels (i.e., β ) play a crucial role in deciding the investment outcomes and thus facilitates him/her in choosing the optimal risk-return combinations of the portfolios. [ABSTRACT FROM AUTHOR]

Crowdfunding is a growing source of finance for entrepreneurs. In this paper, we investigate the existence of a gender effect in the time needed to obtain a business loan through crowdfunding. Using data from three Dutch crowdfunding platforms, survival analysis of the time to completion for 934 business loan campaigns shows that female entrepreneurs have a 20% shorter campaign completion time compared to male entrepreneurs, whereas couples do not differ from males. This effect persists across the different platforms. Subsequent analysis shows that female entrepreneurs do not have the disadvantage they face in traditional lending channels when requesting funds through crowdfunding, and that herding behavior by investors benefits female entrepreneurs most. [ABSTRACT FROM AUTHOR]

This paper proposes a novel censored autoregressive conditional Fréchet (CAcF) model with a flexible evolution scheme for the time-varying parameters, which allows deciphering tail risk dynamics constrained by price limits from the viewpoints of different risk preferences. The proposed model can well accommodate many important empirical characteristics of financial data, such as heavy-tailedness, volatility clustering, extreme event clustering, and price limits. We then investigate tail risk dynamics via the CAcF model in the price-limited stock markets, taking entropic value at risk (EVaR) as a risk measurement. Our findings suggest that tail risk will be seriously underestimated in price-limited stock markets when the censored property of limit prices is ignored. Additionally, the evidence from the Chinese Taiwan stock market shows that widening price limits would lead to a decrease in the incidence of extreme events (hitting limit-down) but a significant increase in tail risk. Moreover, we find that investors with different risk preferences may make opposing decisions about an extreme event. In summary, the empirical results reveal the effectiveness of our model in interpreting and predicting time-varying tail behaviors in price-limited stock markets, providing a new tool for financial risk management. [ABSTRACT FROM AUTHOR]

In the present paper, we investigate the complex dynamics arising from a behavioral exchange rate discontinuous model with heterogeneous agents. Unlike previous works explaining the emergence of chaos in the exchange rate models as the resulting of nonlinearity, our model is able to produce endogenous exchange rate dynamics due to the presence of discontinuity induced by a sentiment index, which affects the way investors take their trading decisions. In particular, it affects the level of optimism/pessimism of fundamentalists regarding their perception on the value of fundamental. Moreover, it also affects the strength to which one kind of chartists places her buying/selling orders. We show that our model, represented by a two-dimensional discontinuous map, has the ability to produce interesting endogenous exchange rate dynamics. In addition, when each component of the map is buffeted by a stochastic component, the model closely replicates the stylized facts of the EUR/USD and EUR/JPY exchange rate markets. [ABSTRACT FROM AUTHOR]

This review paper thoroughly examines the role of technical analysis in energy and financial markets with a primary focus on its application, effectiveness, and comparative analysis with fundamental analysis. The discussion encompasses fundamental principles, investment strategies, and emerging trends in technical analysis, underscoring their critical relevance for traders, investors, and analysts operating within these markets. Through the analysis of historical price data, technical analysis serves as a crucial tool for recognizing market trends, determining trade timing, and managing risk effectively. Given the complex nature of energy and financial markets, where many factors influence prices, the significance of technical analysis is particularly pronounced. This review aims to provide practical insights and serve as a roadmap for future research in the realm of technical analysis within energy and financial markets. This review contributes to the ongoing discourse and advancement of knowledge in this crucial field by synthesizing existing perspectives and proposing avenues for further exploration. [ABSTRACT FROM AUTHOR]

This research conducted a thorough investigation of Bitcoin volatility patterns using three interrelated methodologies: R/S investigation, simple moving average (SMA), and the relative strength index (RSI). The paper jointly employes the above techniques on volatility range-based estimators to effectively capture the unpredictable volatility patterns of Bitcoin. R/S analysis, SMA, and RSI calculations assess time series data obtained from our volatility estimators. Although Bitcoin is known for its high volatility and price instability, our analysis using R/S analysis and moving averages suggests the existence of underlying patterns. The estimated Hurst exponents for our volatility estimators indicate a level of persistence in these patterns, with some estimators displaying more persistence than others. This persistence underscores the potential of momentum-based trading strategies, reinforcing the expectation of additional price rises after declines and vice versa. However, significant volatility often interrupts this upward movement. The SMA analysis also demonstrates Bitcoin's susceptibility to external market forces. These observations indicate that traders and investors should modify their risk management approaches in accordance with market circumstances, perhaps integrating a combination of momentum-based and mean-reversion tactics to reduce the risks linked to Bitcoin's volatility. Furthermore, the existence of robust patterns, as demonstrated by our investigation, presents promising opportunities for investing in Bitcoin. [ABSTRACT FROM AUTHOR]

Can technology protect investors from extreme losses? This paper investigates the short- and long-run hedging and safe haven properties of Bitcoin for the US dollar over the period 2010–2023, incorporating the COVID-19-related market turmoil. Our findings reveal that (i) Bitcoin acts as a strong hedge for all US dollar currency pairs examined, (ii) Bitcoin functions as a weak safe haven for the US dollar at short investment horizons, as indicated by a limited relationship during acute negative price movements, (iii) Bitcoin, instead of acting as a safe haven may, instead, increase aggregate risk at long horizons during periods of extreme losses. The analysis, performed using a series of horizon-dependent econometric tests, provides evidence of some US dollar risk-reduction benefits from Bitcoin but limited potential for enduring relief from long-run extreme negative US dollar rate movements. [ABSTRACT FROM AUTHOR]

The aim of this paper is to examine some American-style financial instruments that lead to two-sided optimal hitting problems. We pay particular attention to derivatives that are similar to strangle options but have a quadratic payoff function. We consider these derivatives in light of much more general payoff structures under certain conditions which guarantee that the optimal strategy is an exit from a strip. Closed-form formulas for the optimal boundaries and the fair price are derived when the contract has no maturity constraints. We obtain the form of the optimal boundaries under the finite maturity horizon and approximate them by maximizing the financial utility of the derivative holder. The Crank–Nicolson finite difference method is applied to the pricing problem. The importance of these novel financial instruments is supported by several features that are very useful for financial practice. They combine the characteristics of the power options and the ordinary American straddles. Quadratic strangles are suitable for investors who need to hedge strongly, far from the strike positions. In contrast, the near-the-money positions offer a relatively lower payoff than the ordinary straddles. Note that the usual options pay exactly the overprice; no more, no less. In addition, the quadratic strangles allow investors to hedge the positions below and above the strike together. This is very useful in periods of high volatility when large market movements are expected but their direction is unknown. [ABSTRACT FROM AUTHOR]

To profit from price oscillations, investors frequently use threshold-type strategies where changes in the portfolio position are triggered by some indicators reaching prescribed levels. In this paper we investigate threshold-type strategies in the context of ergodic control. We make the first steps towards their optimization by proving ergodic properties of related functionals. Assuming Markovian price increments satisfying a minorization condition and (one-sided) boundedness we show, in particular, that for given thresholds, the distribution of the gains converges in the long run. We also extend recent results on the stability of overshoots of random walks from the i.i.d. increment case to Markovian increments, under suitable conditions. [ABSTRACT FROM AUTHOR]

This paper addresses errors in mean return estimates in continuous-time asset allocation models. A standard approach postulates that stochastic factors explain expected asset returns. The problem is then to estimate these factors from observed asset prices via filtering. Recent advances have also combined asset prices with expert opinions to improve the estimates. However, these methods have limitations: stocks prices favor momentum strategies, and expert opinions require careful debiasing. To resolve these issues, we propose a jump-diffusion risk-sensitive benchmarked asset management model in which investors estimate the factors from both traditional and alternative data. We show that this model admits a unique C 1 , 2 solution, and we derive the optimal investment policy in quasi-closed form. We find that investors construct their portfolios from a passive core and an active satellite. The passive core adds considerations for jump risk to a simple benchmark replication. The active satellite blends security selection, and factor tilts with event-driven strategies unique to jump-diffusion problems. Thus, our model explains the most popular investment strategies. Furthermore, the improved expert forecast model and the introduction of alternative data provide factor tilters with new tools to sharpen their asset allocation. [ABSTRACT FROM AUTHOR]

Even the best project of a wind power plant (WPP) can fail if there are not favourable legal regulations for its completion. Most of the research has dealt with identification of various obstacles to implement WPP (political, social, legal, environmental). Analyses of legal barriers (LBs) have been usually made at a high degree of generality. This paper offers a thorough overview of LBs for localization of WPPs in Poland. This is the country where restrictive regulations have blocked the possibility of implementing such projects in many areas. Unfriendly law may persuade investors to choose worse wind turbines foundation conditions. In our research we focus on a problem little dealt in scientific studies, i.e. on the localization of WPP in difficult geotechnical conditions. The article presents the analytical engineering method, which includes the mutual influence between foundation piles in carrying on the construction load on a subsoil. The paper presents the geotechnical parameters responsible for calculation outcomes, the theoretical basis of the curve analysis method of settlement of a single pile and of the calculation of piles settlement working in a group and fastened with a stiff head. It also shows the effect of pile arrangement in a foundation and a load distribution of in-dividual piles, as well as a settlement and leaning of foundation of wind power turbine towers. The method enables a more precise, safer and optimal design of a wind turbine foundation. [ABSTRACT FROM AUTHOR]

Uttarakhand is witnessing a heightened demand for the revision of its land laws, a pivotal issue since the state's formation. This paper delves into the call for 'bhoo kanoon' by Uttarakhand's 'natives', against the backdrop of investments by 'outsiders' in the form of hotels, resorts, and homes. It chronicles the history of land law adoption in the state and explores the impact of these laws on the local populace and environment. Utilizing a qualitative research approach, including existing literature and interviews with stakeholders, the study reveals that the push for law revision stems from the influx of urban investors, resulting in land scarcity and inflated property prices for locals. The residents firmly believe that the current laws favor investors over locals, restricting their access to resources. The paper advocates for a balanced approach to urban investment and native rights, proposing a collaborative model that allows locals to define development's nature and rate. [ABSTRACT FROM AUTHOR]

Environmental, social, and governance (ESG) criteria, currently used as a set of standards by socially conscious investors to evaluate a company's operations before investing, are becoming an important global trend today. In particular, environmental and ecological crises are increasingly being seen as issues that will determine the sustainability of human civilization. Scholars of religion have been paying more attention to the issue as well. In fact, religion and environmentalism have emerged as sub-disciplines in, among others, religious ethics, religious studies, the sociology of religion, and theology. In view of this development, this paper aims to reexamine Minjung theology, literally meaning "the people's theology", which arose as a form of liberation theology in South Korea in the 1970s, from an ecological perspective, particularly focusing on the former's view on the relationship and interrelationship between the individual and the environment. The paper pays special attention to the work of Ahn Byung-Mu, a founding scholar of Minjung theology, shedding light on the connection between his concept of gong, literally meaning "publicness", and ecology, the characteristics of his ecological thoughts and their relevance to his view of god, and his views on bapsanggongdongche, literally meaning "the table community". [ABSTRACT FROM AUTHOR]

• To solve the forecasting problems of time-lead and nonlinearity in a system, a novel grey multivariable model is proposed. • To reduce the morbidity of the model, the solution of the proposed model is deduced through a derivation method. • The grey prediction evolution algorithm is employed to find the optimal value of parameters. • The validity of the new model was verified by three examples and applied to house price forecasting. Time lags or leads and non-linearity of complex systems are critical to address in accurate scientific prediction. However, few studies have focused on time-lead and nonlinear models with limited information. Therefore, this paper aims to develop a novel grey multivariate model with time-lead operator τ and nonlinear factor r from the perspective of this problem. Next, the solution of the proposed model is deduced through a derivation method to reduce the morbidity of the model. Then, the parameter identification of the model is given on the basis of the least-squares method. The grey prediction evolution algorithm is employed to find the optimal value of parameters τ and r to meet the minimum errors. Finally, house prices of Wuhan, Beijing and Shanghai in China are performed to verify the validity and reliability of the new model. The results demonstrate the model we proposed has satisfactory prediction precision, demonstrating that time-lead and nonlinear factor in the model is reasonable. At the end of the paper, the house prices of these three cities from 2022 to 2028 are also predicted. The average annual growth rate in the three regions is 2.49%, 3.57% and 2.97%, respectively. These results are useful to policy analysis regarding house price correlations. They provide insights for market investors and policy makers. [ABSTRACT FROM AUTHOR]

Social entrepreneurs and socially motivated investors share a belief in the power of social enterprise: ventures that pursue a -double bottom line- of profit and social good. Unfortunately, they also share a deep mutual suspicion. Recognizing that social ventures-just like tradi-tional for-profit and nonprofit enterprises-need capital to flourish, this Article offers a financing tool to transform that skepticism into commit-ment. Unlike the array of new entities that have emerged in recent years-including L3Cs, benefit corporations, and flexible purpose corpo-rations-the hybrid financial instrument this Article describes provides a robust and transparent solution to the puzzle that lies at the heart of every social enterprise: how to blend a profit motive with a social mission. Recognizing their shared dilemma as an example of what economists call a stag hunt, FLY Paper strikes that elusive balance by allowing investors and entrepreneurs to signal credibly a reciprocal commitment to the pur-suit of a double bottom line. [ABSTRACT FROM AUTHOR]

Poverty Reduction Strategy Papers (PRSPs) are a central instrument in international development cooperation and of key importance in guiding donors' policies towards southern recipients. However, many aspects of the PRSP have been criticised by a variety of development stakeholders. A checklist of 85 questions has been developed to analyse the inclusion of aspects of sustainable development in PRSPs and to point to sustainability challenges and opportunities. It thus facilitates targeted advice and quality improvements. The checklist was applied to a selection of 12 PRSPs of developing countries. The results reveal an insufficient integration of the ecosystem services concept, of biodiversity and of climate issues. Property rights, gender issues, water and energy are as a rule well elaborated. PRSPs show significant variation in the integration of environmental sustainability issues. Sustainability assessments can contribute to significant improvements between first and second generation PRSPs provided the assessments are supported by a strong institutional framework and a dedicated political commitment towards sustainability. [ABSTRACT FROM AUTHOR]

The study explores the relevance of eco-industrial clusters in promoting Ukraine's post-war reconstruction and sustainable development. To this aim, the paper carries out a comprehensive literature review of academic articles, reports, and case studies on eco-industrial clusters and sustainable development, collect data on Ukraine's post-war reconstruction efforts and the current state of its industrial sector, and analyze relevant legislation and policies. The results show that the success of eco-industrial clusters can be attributed to several critical factors, including robust collaboration between companies, a supportive regulatory framework, and a long-term vision for sustainable development. In the context of Ukraine's post-war reconstruction, an eco-industrial cluster offers an opportunity to build back better by creating new jobs, promoting sustainable economic growth, and enhancing environmental protection. This suggests that an effective ecoindustrial cluster in Ukraine should align with the country's long-term vision for sustainable development, identify industries and companies that can benefit from collaboration and resource sharing, and prioritize stakeholder engagement and public awareness. The involvement of research institutions, a supportive regulatory framework, and financial resources from international donors and investors are also critical for the success of an eco-industrial cluster in Ukraine. By adopting a collaborative, innovative, and sustainable approach to economic development, Ukraine can build a more resilient, inclusive, and prosperous future for its people. [ABSTRACT FROM AUTHOR]

As renewable energy (RE) costs decrease, private non-subsidized revenue sources, such as power purchase agreements (PPA), will increase in off-grid areas. This paper's objective is to improve policymakers' and utilities' understanding of PPA's role in mitigating private investors' risks in off-grid areas to accelerate the energy transition. The paper built a PPA dataset for the largest off-grid area in the Philippines and developed novel and efficient techniques to evaluate the risk mitigation ability of a PPA. While oil-based technologies are expensive, they are low-capital-intensive, and the fuel, the bulk of the cost, is passed through to consumers and primarily funded by subsidies. In contrast, the most affordable energy source, RE, requires higher upfront capital investments, financed primarily with equity. Investors chose low-capital-intensive technologies (oil), rehabilitated power plants, and utilized old equipment, all investment decisions to mitigate residual PPA risks, i.e., distribution utilities' low creditworthiness and capital recovery uncertainty. Presenting the investment as a PPA residual mitigation tool is one of the paper's contributions to the literature. The distribution utility needs to reduce investors' uncertainty by covering reasonable investors' costs. Policymakers need to level the playing field between fossil fuels and RE by reducing subsidies and strengthening distribution utility creditworthiness. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to contribute to the existing literature by investigating the determinants of the profitability of Islamic and conventional banks in the Middle East region and revealing the most important factors for these two types of banks. Few papers have studied the performance of Islamic banks and compared their performance with conventional banks. The results from these limited research papers are also various, mainly because the sample sizes are small, or they have analysed data only from one country. Our research used a fixed effect panel data analysis on a sample of 270 banks (111 Islamic and 159 conventional banks) from 12 Middle East countries. We used an unbalanced annual panel of data covering the period 2012–2020. The results show that bank size, equity to assets, annual GDP growth, and annual average oil price have a significant positive effect on Islamic banks' profitability, while non-performing loans to total gross loans and cost of running operations to operating income have a significant negative effect on both bank types. The results also show that non-performing loans to total gross loans and annual GDP growth contribute more to conventional banks profitability, while oil price contributes only to Islamic banks performance. Inflation and net loans to total assets have no effect on bank profitability for either Islamic or conventional banks. Furthermore, we also found that the Islamic banking industry had a more competitive structure. Our findings have important implications for managers, policy makers, investors and other stakeholders. They can help them to make decisions regarding investments, plans, budgeting, evaluation and the management of business operations. [ABSTRACT FROM AUTHOR]

Portfolio optimization involves finding the ideal combination of securities and shares to reduce risk and increase profit in an investment. To assess the impact of risk in portfolio optimization, we utilize a significant volatility risk measure series. Behavioral finance biases play a critical role in portfolio optimization and the efficient allocation of stocks. Regret, within the realm of behavioral finance, is the feeling of remorse that causes hesitation in making significant decisions and avoiding actions that could lead to poor investment choices. This behavior often leads investors to hold onto losing investments for extended periods, refusing to acknowledge mistakes and accept losses. Ironically, by evading regret, investors may miss out on potential opportunities. in this paper, our purpose is to compare investment scenarios in the decision-making process and calculate the amount of regret obtained in each scenario. To accomplish this, we consider volatility risk metrics and utilize stochastic optimization to identify the most suitable scenario that not only maximizes yield in the investment portfolio and minimizes risk, but also minimizes resulting regret. To convert each multi-objective model into a single objective, we employ the augmented epsilon constraint (AEC) method to establish the Pareto efficiency frontier. As a means of validating the solution of this method, we analyze data spanning 20, 50, and 100 weeks from 150 selected stocks in the New York market based on fundamental analysis. The results show that the selection of the mad risk measure in the time horizon of 100 weeks with a regret rate of 0.104 is the most appropriate research scenario. this article recommended that investors diversify their portfolios by investing in a variety of assets. This can help reduce risk and increase overall returns and improve financial literacy among investors. [ABSTRACT FROM AUTHOR]

This study aims to determine, from the perspective of investors, the factors that predict Islamic unit trust (IUT) investment intentions. Additionally, this paper examines the moderating effect of fintech self-efficacy (FSE) on the relationship between attitude and investment intention. A total of 392 data were collected from IUT investors in Malaysia and analyzed using partial least squares structural equation modeling. The findings reveal that subjective norms have the highest impact on investment intention, followed by attitude and FSE, while religiosity is not significantly associated with investment intention in Islamic unit trust funds. Attitude significantly mediates religiosity-intention and Islamic financial literacy-intention relationships. FSE significantly moderates the attitude-intention relationship. The results shed light on the key factors that increase investing behavior and have direct managerial implications with regard to marketing strategies and target markets. These findings suggest that IUT service providers should take the lead in attracting customers through effective and targeted marketing initiatives, particularly by enhancing customers' FSE and capabilities. This study provides empirical evidence on the interrelationships between Islamic financial literacy, religiosity, and FSE in examining investors' behavior using the Theory of Planned Behavior framework. The study explores the moderating role of FSE on the relationship between attitude and investment intention. [ABSTRACT FROM AUTHOR]

World widely, efforts of governments and industry focus on the global sustainability. Facing global warming and rapidly growing electricity demand, it is crucial to develop technologies that will allow humanity use planet resources in the most efficient way, which is possible. Energy storage systems are vast and common concepts nowadays, concerning also small residential energy systems with renewable energy sources like photovoltaic installations. This paper describes different solutions for this issue. Characteristics of mentioned methods include basic features and values, advantages and disadvantages, estimated investing (CAPEX) and operating (OPEX) costs for the investors and issues related with environment like efficiency and emissions. As researched in the document, current technologies base on well-known solutions implemented in residential installations but also there is also possibility to develop new methods and combine few of them to use any possible energy surplus, later when it is needed the most. Description of various energy storage system includes both technical and commercial aspects. As most of storage applications differ from each other, choosing proper energy storage system implies economic and environmental benefits. The review has provided sufficient information to conclude that there is no one-sizefits-all solution to store electricity. Suitable solution should be selected based on size of the installation, geographical conditions as well as economic possibilities. For examined energy storage systems there is still necessity of further research and development but overview of those presented in the article, makes it possible to deduce comparison and conclusion. [ABSTRACT FROM AUTHOR]

The literature has confirmed that when managers increase profits through earnings management, the readability of annual reports may be reduced Lo (2017), Ye (2018). Whether this conclusion is suitable for Chinese corporate social responsibility (CSR) reports, however, is still unclear. Based on the panel data of 5083 Chinese non-financial listed companies from 2010 to 2019, this paper adopts multiple linear regression to investigate the impact of earnings management on the readability of Chinese CSR reports. The results show that: (1) There is a significant negative correlation between earnings management and the readability of Chinese CSR reports, with the readability of Chinese annual reports as a mediating variable. (2) The negative effect is more significant when companies are not punished for violations, when the internal control index is low, when companies lack ISO14001 certification and when companies do not have independent third-party authentication for Chinese CSR reports. (3) When earnings management just exceeds zero, the readability of Chinese CSR reports decreases. (4) The economic consequences of reducing the readability of Chinese CSR reports are that financing costs are increased and environmental performance is decreased. To improve the quality of information disclosure of listed companies, the recommendations are as follows: First, the government should issue CSR reporting standards to reduce the manipulation of Chinese CSR reports. Second, Chinese CSR reports disclosed by listed companies must be audited by independent third parties to enhance the credibility of the information. Third, the company needs to strengthen its external and internal supervision to reduce the manipulation space for the readability of Chinese CSR reports. This study extends the negative relationship between earnings management and the readability from annual reports to Chinese CSR reports. To prevent investors from detecting earnings management, the readability of Chinese CSR reports may be reduced. At the same time, the study has definitely added value to the existing literature in the domain of CSR. [ABSTRACT FROM AUTHOR]

Our paper investigates how qualified foreign institutional investors (QFII) impact stock liquidity in the Chinese A-share market using data for 2005 to 2019. Contrary to previous findings, we find that QFII enhance stock liquidity. Specifically, QFII's participation is negatively associated with the individual stock illiquidity and positively related to stock trading volume. Moreover, using a step-by-step procedure, we provide evidence that QFII raises stock liquidity by ameliorating information asymmetry. QFII will attract more market attention and improve firm disclosure quality. We address possible endogeneity with fixed effects and instrumental variables, and our findings are robust to self-selection bias, stock market shocks, and alternative explanatory variables. [ABSTRACT FROM AUTHOR]

This research explores the moderating effect of institutional ownership on the relationship between audit committee attributes and audit report lag. The paper sampled data from 102 Saudi non-financial listed firms from 2012 to 2021. The data was analyzed using a fixed effect regression, while the generalized method of moments approach (GMM) was employed for robustness check. The research finding strongly suggests that the audit committee size may increase audit report lag. In contrast, it was found that audit committee independence and financial expertise may reduce audit report lag. In addition, the interaction effect reveals that the impact of audit committee financial expertise and the committee meetings on reducing audit report lag may be higher as institutional shareholding rises. Thus, the outcome validates the agency theory perspective that the active monitoring of institutional investors may shape firms' internal governance systems. More importantly, the finding implies that firms should prefer a smaller audit size with a higher ratio of independent directors and financial experts to improve financial reporting quality. They should also embrace institutional shareholding to reduce audit report lag. Thus, lowering audit report lag may mitigate information disparity between firms and prospective investors, reducing agency conflicts and boosting firm value. [ABSTRACT FROM AUTHOR]

What is the impact of large swings in economic policy on the risk-return trade-off faced by i nvestors? What is the impact of changes in policy regimes on investment strategies? In this paper we study the impact on returns of switches between periods of market-friendly economic policies and periods of populist policies. To quantify the impact of policy instability, we use data from Argentina--a country that has experienced frequent and very large regime changes--and find that the risk-return for individual assets and minimum variance portfolios are quite different across regimes. We then develop a dynamic model to understand optimal portfolios when investors are cognizant that regimes can change. We find that when portfolios are unrestricted, it is optimal for investors to take a large amount of risk. On the other hand, when portfolios are restricted to include only long positions, a real asset (real estate) dominates financial assets. [ABSTRACT FROM AUTHOR]

In the context of a low‐carbon economy, considering the output uncertainties of the renewable energy sources and the fluctuation of electrical price, the cost–benefit analysis is proposed for multi‐investor virtual power plant (VPP) under different risk preferences, and multi‐investor VPP capacity sizing problem is researched. First, the structural organization and flexible regulation characteristics of multi‐investor VPPs are analyzed. Second, integrated model and cost–benefit analysis indexes of VPP are built. Third, an optimal capacity sizing model of multi‐investor VPP is established, aiming at income maximization. Game theory is used to configure the distributed generators in the VPP under various cooperative modes and noncooperative modes of multiple investors. Fourth, according to the installed proportion of each distributed generator and the risk preference coefficient of each investor, the income allocation correction model based on the improved Shapley value method is proposed to stabilize the optimal VPP alliance. Finally, the demonstration project in the animation industry park of Sino‐Singapore Tianjin Eco‐City is employed as representative scenarios, which is used to validate the effectiveness of the proposed model. The results prove that the method proposed in this paper can guide knowledge for the investors with different risk preferences when planning the optimal capacity of multiple distributed generators in the VPP. [ABSTRACT FROM AUTHOR]

Quantitative investment theory has emerged as a prominent and widely researched domain within the financial markets, where investors predominantly focus on discerning the intricate influences of market dynamics. In this paper, we proposed a short-term prediction-based trading strategy, which can equiponderate between return and risk, considerations while accounting for investor risk preferences. This strategy employs GM(1,1) to capture nuanced features of price dynamics in short-term intervals and update the GM(1,1) model with the latest data. Subsequently, a multi-objective planning equation is formulated to optimize asset allocations by determining the optimal holding that strikes between specific returns and risk mitigation. In the end, this work conducts a case study and sensitivity analysis using five years of gold and bitcoin price data spanning from 2016 to 2021. This empirical examination serves to affirm the efficacy and resilience of the proposed trading strategy. The case study reveals that proficient short-term price forecasting serves as a potent means to proactively mitigate risk, facilitating, judicious and objective trading practices. Moreover, it underscores the strategy's tangible utility as a guide for real-world investment decisions. [ABSTRACT FROM AUTHOR]

After the establishment of any facility, it is quite necessary to determine the profitability of the unit. The present research provides a detailed analysis of the case unit along with the necessary costs and returns on investment. This research paper also includes the cost and benefits analysis of a newly installed digital printing department at Sapphire Diamond Company. Since the relationship between fabric printing and most of the costs is non-linear, this is the reason the system dynamics modelling technique was used in the present research; it was used to model the requirement of material, electricity, and human resources. The model was developed by using the stocks, flows, variables, and parameters in the Anylogic PLE version and the simulated results were validated from the real-time scenario. The data regarding the labour cost, electricity cost, and material cost were collected from the case department. Furthermore, the scenario analysis was conducted and simulated results were downloaded in Microsoft Excel. Several line charts were plotted in Microsoft Excel and Origin software. It was indicated from the scenario analysis that the cost per meter decreased by increasing the production rate. It was also indicated that if the digital printing machine was operated at its maximum capacity, the ROI would be maximum. Incurred overheads were also the subject of concern in the present scenario. After the overall analysis, it was concluded that the digital printing facility was still in profit even if its production rate per day was 150+50. Since the present research encompasses the detailed cost and benefit analysis of a newly installed digital printing facility, therefore, it can be beneficial for investors as analytical support to decide on installing digital textile printing machines. [ABSTRACT FROM AUTHOR]

Stock market forecasting is a significant task, and investing in the stock marketplace is a significant part of monetary research due to its high risk. Therefore, accurate forecasting of stock market analysis is still a challenge. Due to stable and volatile data, stock market forecasting remains a major challenge for investors. Recent machine learning (ML) models have been able to reduce the risk of stock market forecasting. However, diversity remains a key challenge in developing better erudition models and extracts more intellectually priceless qualities to auxiliary advanced predictability. In this paper, we propose an efficient strategy optimization using a hybrid ML model for stock market prediction (HM-SMP). The first contribution of the proposed HM-SMP model is to introduce chaos-enhanced firefly bowerbird optimization (CEFBO) algorithm for optimal feature selection among multiple features which reduce the data dimensionality. Second, we develop a hybrid multi-objective capuchin with a recurrent neural network (HC-RNN) for the prediction of the stock market which enhances the prediction accuracy. We use supervised RNN to predict the closing price. Finally, to estimate the presence of the proposed HM-SMP model through the benchmark, stock market datasets and the performance can be compared with the existing state-of-the-art models in terms of accuracy, precision, recall, and F-measure. [ABSTRACT FROM AUTHOR]

The multiple global environments have triggered changes in the international environment, leading to a sharp decline of foreign direct investment (FDI) compared to pre-pandemic level. To evaluate the investment risk of FDI and make optimal investment decision becomes the most important issue for investors. This paper focuses on the evaluation of investment risk for FDI. First, an index system for risk evaluation of FDI is constructed. Then, we introduce the probabilistic linguistic entropy and cross entropy measures, based on which, a programming model is developed to identify the objective attribute weights. A composite weight derivation method, which takes both the objective attribute weights and the subjective attribute weights into account, is further introduced. In view of attributes' uncertainty and fuzziness and the conflicting characteristics of some attributes, the VIKOR (the Serbian name: VlseKriterijumska Optimizacija I Kompromisno Resenje, means multi-criteria optimization and compromise solution) method is used to evaluate the risk of FDI under the probabilistic linguistic environment. Furthermore, a case study is presented to illustrate the proposed method. The comparative analysis and some further discussions verify the validity of the proposed method for the FDI risk evaluation. [ABSTRACT FROM AUTHOR]

This paper analyzes the implications of investors' short-term oriented asset holding and portfolio decisions (or short-termism), and its consequences on green investments. We adopt a dynamic portfolio model, which contrary to conventional static mean-variance models, allows us to study optimal portfolios for different decision horizons. Our baseline model contains two assets, one asset with fluctuating returns and another asset with a constant risk-free return. The asset with fluctuating returns can arise from fossil-fuel based sectors or from clean energy related sectors. We consider different drivers of short-termism: the discount rate, the nature of discounting (exponential vs. hyperbolic), and the decision horizon of investors itself. We study first the implications of these determinants of short-termism on the portfolio wealth dynamics of the baseline model. We find that portfolio wealth declines faster with a higher discount rate, with hyperbolic discounting, and with shorter decision horizon. We extend our model to include a portfolio of two assets with fluctuating returns. For both model variants, we explore the cases where innovation efforts are spent on fossil fuel or clean energy sources. Detailing dynamic portfolio decisions in such a way may allow us for better pathways to empirical tests and may provide guidance to some online financial decision making. [ABSTRACT FROM AUTHOR]

In this paper, we extend the research on the effect of corporate social responsibility (CSR) on firm risk by analyzing the CSR–idiosyncratic risk nexus and how CSR can be integrated as insurance in a global risk management strategy. First, the causality between CSR and risk was tested. Second, copulas were estimated to strengthen the existing results on the structure of the dependence between the different dimensions of CSR activities and idiosyncratic risk levels. The empirical analysis was conducted on a sample of 254 European-listed firms over the 2018–2020 period. The main results showed a directional causality effect between CSR and idiosyncratic risk, and the dependences were modeled between CSR and realized idiosyncratic risk. This allows a better understanding of the risk implications of CSR for investors, corporate managers, and policy makers. [ABSTRACT FROM AUTHOR]

This paper explores the double-edged sword effect of investor attention on market liquidity. Based on the analysis on 597 cryptocurrencies from 2014 to 2020, our findings show that static investor attention improves cryptocurrency market liquidity over the next three months by attracting more investors into the market and stimulating buy and sell transactions. By contrast, abnormal attention persistently and negatively affects the liquidity and leads to excessive net buying pressure in the market and a crowded buyers' market, resulting in a sharp deterioration of liquidity. Moreover, these effects intensify during low global economic policy uncertainty periods and for cryptocurrencies with small market capitalization and low idiosyncratic volatility. Overall, our results have important implications for investors, portfolio managers, and policymakers. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to analyse investment returns using a stochastic model and inform investors about the best stock market to invest in. To this effect, a Markov chain random walk model was successfully developed and implemented on 450 monthly market returns data spanning from January 1976 to December 2020 for Canada, India, Mexico, South Africa, and Switzerland obtained from the Federal Reserves of the Bank of St. Louis. The limiting state probabilities and six-month moving crush probabilities were estimated for each country, and these were used to assess the performance of the markets. The Mexican market was observed to have the least probabilities for all the negative states, while the Indian market recorded the largest limiting probabilities. In the case of positive states, the Mexican market recorded the highest limiting probabilities, while the Indian market recorded the lowest limiting probabilities. The results showed that the Mexican market performed better than the others over the study period, whilst India performed poorly. These findings provide crucial information for market regulators and investors in setting regulations and decision-making in investment. [ABSTRACT FROM AUTHOR]

Often, investors are limited by cognitive and emotional biases in their decision making which leads to poor portfolio investment choices. Robo-advisors can assist in overcoming these biases. This paper seeks to develop a financial robo-advisor prototype based on hybrid programming. It uses ensemble artificial neural networks to predict portfolio returns and variances with input nodes of Ornstein-Uhlenbeck processes (OU) and geometric Brownian motion (GBM) processes' estimates. The results are subsequently channeled into a probability quadratic optimization algorithm which considers target return probability and value-at-risk constraints as proxies for investor's risk tolerance so as to provide the optimal portfolio allocation strategy that minimizes portfolio risk given a prescribed investment horizon and target return. The results show that the ensemble artificial neural network method implementation accurately predicted the level of 2 of 5 assets and the trends of the remaining 3 assets. However, it yielded low standard deviations and low returns compared to the OU and GBM estimates for short horizons. The quadratic optimization algorithm supported investment in shorter time horizons since portfolio risk was lowest. Diversified allocation was achieved in the shorter time horizons while longer horizon allocations were biased towards assets with lower standard deviations. The lowest risk portfolios were the ones with a lower certainty probability for the target return and vice versa. This paper clearly demonstrates that ensemble methods are accurate in prediction, and that a hybrid programming paradigm effectively leverages the strengths, speed and functionality of different programming languages -- an elixir for multifaceted dissociable programming problems. [ABSTRACT FROM AUTHOR]

This paper uses the case of Spain to investigate whether and how disruptive technology impacts banking stock returns under a high volatility regime and a low volatility regime. For this purpose, a two-factor model with heteroscedastic Markov switching regimes has been applied. The results indicate that disruptive technologies have an impact on Spanish banking stock returns and that the effects are volatility regime dependent, having a relevant positive impact in high volatility regimes and a less relevant negative impact in low volatility regimes. These findings suggest that investors are informed about and acknowledge the advantages of disruptive technologies and will use their adoption as a business strategy to offset adverse market circumstances. During stable market conditions, on the other hand, Spanish banking seems to have less expectations about disruptive technology as a business strategy. To summarise, this paper provides insights into the role of the pricing of banking-related assets and has other relevant implications for investors that include disruptive technology or banking exposed investments in their portfolios. [ABSTRACT FROM AUTHOR]