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This study introduces a novel methodology that integrates the Black–Litterman model with Long Short-Term Memory Neural Networks (BL–LSTM). We use predictions from the LSTM as views in the Black–Litterman model. The resulting portfolio performs better than the traditional mean-variance (MV) and exchange-traded funds (ETFs) used as benchmarks. The proposal empowers investors to make more insightful decisions, drawing from a synthesis of historical data and advanced predictive techniques. This methodology is applied to a water market. Investing in the water market allows investors to actively support sustainable water solutions while potentially benefiting from the sector's growth, contributing to achieving SDG 6. In addition, our modeling allows for companies' environmental, social, and governance (ESG) scores to be considered in the portfolio construction process. In this case, investors' decisions take into account companies' socially responsible behavior in a broad sense, including aspects related to decent work, respect for indigenous communities and diversity, and the absence of corruption, among others. Therefore, this proposal provides investors with a tool for promoting sustainable investment practices. [ABSTRACT FROM AUTHOR]

The goal of this paper is to study the behavior of non-ferrous metal futures' volatilities in Chinese future market by applying a multifractal perspective. In particular, in order to obtain key indicators that describe the characterization of non-ferrous metal futures' volatility behavior, we uses noise-removed EMD-MF-DFA and original MF-DFA methods to conduct a comparative analysis on the return time series of four non-ferrous metal futures, which are Aluminum future, Copper future, Zinc future and Lead future traded on the Shanghai Futures Exchange. This numerical study shows that the indicator established in characterizing the volatility of four non-ferrous metal futures is robust. In addition, we have the following four conclusions: First, there are obvious multifractal phenomena of non-ferrous metal futures in Chinese market, and it shows that Aluminum future has the largest degree of multifractality, and Copper future has the smallest degree of multifractality, which indicates that Aluminum future has the highest volatility complexity, and Copper future has the smallest volatility complexity. Second, it is found that the volatility complexity of these four non-ferrous metal futures is caused by long-range correlation. Third, this study also supports the current judgment that "Copper future has the greatest investment opportunity". Finally, combined with analysis results, we also give suggestions to investors, producers, and regulators body for non-ferrous metal futures market in China. [ABSTRACT FROM AUTHOR]

During the planning and construction of electric vehicle charging stations (EVCSs), consideration of the long-term operating revenue loss for investors is often lacking. To address this issue, this study proposes an EVCS planning method that takes into account the potential loss of long-term operating revenues associated with charging facilities. First, the method combines the Bass model with electric vehicle (EV) user travel characteristics to generate a charging load dataset. Then, the cost of profit loss—which represents the EVCS utilization rate—is incorporated into the construction of the objective function. Additionally, a parallel computing method is introduced into the solution algorithm to generate the EVCS planning scheme. Finally, the cost-to-profit ratio of the EVCSs is used as a filtering condition to obtain the optimal EVCS planning scheme. The results show that the EVCS planning scheme considering the profit loss reduces the annual comprehensive cost by 24.25% and 16.93%, and increases the net profit by 22.14% and 24.49%, respectively, when compared to the traditional planning scheme under high and low oil prices. In particular, the charging station strategy proposed in this study has the best effect in the case of high oil prices. [ABSTRACT FROM AUTHOR]

Sub-national governments (SNGs) in developing countries have increasingly explored green municipal bonds as a financing tool, driven by promises of lower borrowing costs, enhanced reputation, and support for sustainable economic development. This study aims to critically examine these claims by analysing the actual costs and complexities associated with green municipal bonds (GMBs). The research involves a comparative analysis of traditional municipal bonds and GMBs, focusing on the financial and operational challenges faced by SNGs. Detailed case studies from Mexico City and Cape Town are used to illustrate the practical implications of issuing GMBs. The findings reveal that, despite similar or slightly lower interest rates, GMBs often entail higher issuance costs due to the need for certification, monitoring, and reporting of green projects. These additional costs, coupled with the necessity of hiring external consultants, make GMBs more expensive and difficult to replicate after initial transactions. Furthermore, the study highlights that the demand from investors has not met initial expectations. While green-aligned investors show a higher appetite for GMBs, they constitute only a small portion of the finance market. Traditional investors, primarily focused on financial returns, are less inclined to invest in GMBs solely based on their green label. Consequently, many SNGs are increasingly turning to traditional "vanilla" municipal bonds as a more viable option for financing green initiatives. This shift underscores the practical challenges of GMB issuance in developing countries and calls for a strategic approach to balance the use of GMBs with other financing options to promote sustainable economic development. [ABSTRACT FROM AUTHOR]

Candlestick charts provide a visual representation of price trends and market sentiment, enabling investors to identify key trends, support, and resistance levels, thus improving the success rate of stock trading. The research presented in this paper aims to overcome the limitations of traditional candlestick pattern analysis, which is constrained by fixed pattern definitions, quantity limitations, and subjectivity in pattern recognition, thus improving its effectiveness in dynamic market environments. To address this, a two-layer clustering method based on a candlestick sequence simlarity matching model is proposed for identifying valid candlestick patterns and constructing a pattern library. First, the candlestick sequence similarity matching model is used to address the pattern matching issue; then, a two-layer clustering method based on the K-means algorithm is designed to identify valid candlestick patterns. Finally, a valid candlestick pattern library is built, and the predictive ability and profitability of some patterns in the library are evaluated. In this study, ten stocks from different industries and of various sizes listed on the Shanghai Stock Exchange were selected, using nearly 1000 days of their data as the test set. The predictive ability of some patterns in the library was evaluated using out-of-sample data from the same period. This selection method ensures the diversity of the dataset. The experimental results show that the proposed method can effectively distinguish between bullish and bearish patterns, breaking through the limitations of traditional candlestick pattern classification methods that rely on predefined patterns. By clearly distinguishing these two patterns, it provides clear buy and sell signals for investors, significantly improving the reliability and profitability of trading strategies. [ABSTRACT FROM AUTHOR]

Accurate stock prediction plays an important role in financial markets and can aid investors in making well-informed decisions and optimizing their investment strategies. Relationships exist among stocks in the market, leading to high correlation in their prices. Recently, several methods have been proposed to mine such relationships in order to enhance forecasting results. However, previous works have focused on exploring the correlations among stocks while neglecting the causal characteristics, thereby restricting the predictive performance. Furthermore, due to the diversity of relationships, existing methods are unable to handle both dynamic and static relationships simultaneously. To address the limitations of prior research, we introduce a novel stock trend forecasting framework capable of mining the causal relationships that affect changes in companies' stock prices and simultaneously extracts both dynamic and static features to enhance the forecasting performance. Extensive experimental results in the Chinese stock market demonstrate that the proposed framework achieves obvious improvement against multiple state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Classic options can no longer meet the diversified needs of investors; thus, it is of great significance to construct and price new options for enriching the financial market. This paper proposes a new option pricing model that integrates the logarithmic investment strategy with the classic Black–Scholes theory. Specifically, this paper focus on put options, introducing a threshold-based strategy whereby investors sell stocks when prices fall to a certain value. This approach mitigates losses from adverse price movements, enhancing risk management capabilities. After deriving an analytical solution, we utilized mathematical software to visualize the factors influencing new option prices in three-dimensional space. The findings suggest that the pricing of these new options is influenced not only by standard factors such as the underlying asset price, volatility, risk-free rate of interest, and time to expiration, but also by investment strategy parameters such as the investment strategy index, investment sensitivity, and holding ratios. Most importantly, the pricing of new put options is generally lower than that of classic options, with numerical simulations demonstrating that under optimal parameters the new options can achieve similar hedging effectiveness at approximately three-quarters the cost of standard options. These findings highlight the potential of logarithmic investment strategies as effective tools for risk management in volatile markets. To validate our theoretical model, numerical simulations using data from Shanghai 50 ETF options were used to confirm its accuracy, aligning well with theoretical predictions. The new option model proposed in this paper contributes to enhancing the efficiency of resource allocation in capital markets at a macro level, while at a micro level, it helps investors to apply investment strategies more flexibly and reduce decision-making errors. [ABSTRACT FROM AUTHOR]

Using data that cover the annual period from 1258 to 2023, we studied the link between real gold returns and climate risks. We documented a positive contemporaneous link and a negative predictive link. Our findings further show that the predictive link historically gave rise to significant out-of-sample forecasting gains. The positive contemporaneous link is consistent with the view that investors viewed gold as a safe haven in times of elevated climate risks. The negative predictive link, in turn, is consistent with an overshooting scenario in which the real gold price overshot in response to climate risks, only to return subsequently to a lower value. Our findings should provide important implications for investors and policymakers, given that our analysis covered the longest possible data sample involving the gold market, and hence, was independent of any sample selection bias. [ABSTRACT FROM AUTHOR]

The cryptocurrency market is characterized by extreme volatility, with events such as the Terra-LUNA crash of 2022 raising significant questions about the resilience of algorithmic stablecoins. This paper investigates the collapse of LUNA Classic during the USTC depeg, focusing on the role of trading volumes and collateral assets like Bitcoin in amplifying the price crash. Using a Vector Logistic Smooth Transition AutoRegressive (VLSTAR) model, we analyze daily data from October 2020 to November 2022 to uncover how exogenous volumes influenced LUNA's price trajectory during the crisis. Our findings reveal that high trading volumes, particularly during regime two (the post-depeg period), significantly exacerbated the price decline, validating the impact of large-scale liquidations on LUNA's price path. Additionally, Bitcoin volumes played a critical role in destabilizing the system, confirming that the liquidity of underlying collateral assets is pivotal in maintaining price stability. These insights contribute to understanding the systemic vulnerabilities in algorithmic stablecoins and offer implications for future stablecoin design and risk management strategies. They are relevant for investors, policymakers, and researchers seeking to be aware of market volatility and prevent future crises in stablecoin ecosystems. [ABSTRACT FROM AUTHOR]

As a versatile metal, nickel will experience increased demand in the coming years, with a specific focus on its importance in the battery industry and its role in achieving net-zero emissions. Recognizing the need to ensure sustainable resource management, this study analyses the flow dynamics of nickel's supply and demand by employing a modelling approach. This is done with a focus on understanding how recycling can contribute to meeting the growing nickel demand. By considering the interaction between mining production, product applications, and recycling rates, this study contributes to a better understanding of the long-term prospects for meeting the nickel demand. It can assist policymakers, industry stakeholders, and investors in making informed decisions regarding resource management and developing sustainable practices in the nickel industry. The results revealed that mining would still play an important role in the supply of nickel for at least the next 40 years. Nickel mining and recycling practices are sufficient to meet future nickel demand if sufficient recycling practices are (rapidly) implemented. Modelling results show that nickel recycling will account for 90% of the total demand (primary nickel mining accounts for the remaining 10%) between the years 2062 and 2096. [ABSTRACT FROM AUTHOR]

This study investigates how an increase in the quality of business ventures, measured as their success probability, affects trust and return on investment (ROI) in situations where the investor–entrepreneur interaction is affected by moral hazard and asymmetric information. We model a repeated trust problem between investors and entrepreneurs, featuring moral hazard and adverse selection. Hidden Markov techniques and computer simulations are used to derive the main results. We find that trust and ROI may decline as quality improves. Although lenders tend to reduce the requirements for granting initial credit, they nevertheless become less tolerant of current borrowers who fail to pay back. Additionally, we demonstrate a novel substitution effect, where lenders prefer new borrowers over existing borrowers that experienced early failures. The main conclusions of our study are that while impressing early on is effective in gaining first access to credit, it may nevertheless hurt the cause of getting credit in subsequent periods, following an early failure. In business environments plagued with ex post moral hazard, entrepreneurs might do better by gaining trust first and impressing later. Furthermore, our results imply that in a thriving economy, not only are bad loans made, but good loans are lost as well. [ABSTRACT FROM AUTHOR]

Corporate social responsibility (CSR) is increasingly becoming a major concern for investors and consumers, prompting companies to devote more resources to community engagement to manage conflict and improve business performance. In this study, we conducted an empirical analysis with a sample of 385 listed companies in China's new energy vehicle (NEV) industry to analyze the relationship between CSR performance and corporate value (CV). With the ordinary least squares (OLS) regression analysis, our study's results show a positive relationship between the CSR performance of these companies and corporate value. In addition, our findings indicate a lagged effect in the relationship between CSR and CV. The mechanism analysis suggests that corporate CSR performance helps to improve corporate reputation, reduce financing constraints, and thus increase corporate value. Moreover, high analyst attention and information transparency can enhance the positive effects of corporate CSR. This study contributes to the existing literature and empirical evidence by exploring the correlation between CSR performance and firm value in the context of emerging countries and the NEV industry. [ABSTRACT FROM AUTHOR]

This study investigates the financial behavior of Thai investors in equity crowdfunding (ECF), focusing on the factors that influence their investment intentions. Drawing upon the Information System Success Model (ISSM), the Theory of Diffusion of Innovations, and the Technology Acceptance Model 3 (TAM3), the research examines the platform quality (PQ), platform characteristics (PC), and social influence (SI) as independent variables, with the perceived usefulness (PU) and perceived ease of use (PEOU) acting as mediators. Data were gathered from 275 Thai investors and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that the PU significantly influences investment decisions both directly and indirectly through the PEOU, which also directly affects investment intention. Furthermore, SI, PC, and PQ have indirect effects on investment decisions via the PU and PEOU, with SI being the most influential factor. This study provides valuable insights into optimizing ECF platform design, fostering investor trust, and enhancing regulatory frameworks to facilitate financial inclusion and innovation in the Thai crowdfunding landscape. [ABSTRACT FROM AUTHOR]

The performance of foreign investors relative to domestic investors has been a subject of mixed evidence. While foreign investors are often perceived to underperform due to an information disadvantage, they are also known for their aggressive trading and superior performance in initiated orders. We provide further clarity on this issue. Specifically, by analyzing over five million transactions on the Jakarta Stock Exchange, our findings reveal that foreign investors consistently outperform domestic investors in terms of both annualized returns and profit amounts. Further investigation attributes this outperformance to the higher sophistication of foreign investors, who demonstrate superior stock-picking abilities and effective growth investing strategies. [ABSTRACT FROM AUTHOR]

Understanding the density of possible prices in one-minute intervals provides traders, investors, and financial institutions with the data necessary for making informed decisions, managing risk, optimizing trading strategies, and enhancing the overall efficiency of the cryptocurrency market. While high accuracy is critical for researchers and investors, market nonlinearity and hidden dependencies pose challenges. In this study, the filtered historical simulation is used to generate pathways for the next hour on the one-minute step for Bitcoin and Ethereum quotes. The innovations in the simulation are standardized historical returns resampled with the method of block bootstrapping, which helps to capture any hidden dependencies in the residuals of a conditional parameterization in the mean and variance. Ordinary bootstrapping requires the feed innovations to be free of any dependencies. To deal with complex data structures and dependencies found in ultra-high-frequency data, this study employs block bootstrap to resample contiguous segments, thereby preserving the sequential dependencies and sectoral clustering within the market. These techniques enhance decision-making and risk measures in investment strategies despite the complexities inherent in financial data. This offers a new dimension in measuring the market risk of cryptocurrency prices and can help market participants price these assets, as well as improve the timing of their entry and exit trades. [ABSTRACT FROM AUTHOR]

This research delves into the influence of adopting international financial reporting standards (IFRSs) on the financial reporting quality (FRQ) of Indian multinational corporations (MNCs). It also investigates the moderating impact of the internal control system (ICS) on the relationship between IFRSs and FRQ. The data collection involves a survey using a previously validated and adjusted scale from earlier studies. A sample of 512 participants is selected through purposive sampling methods. The analysis employs partial least square structural equation modelling (PLS-SEM) to validate the data and test the hypotheses. The results indicate a significantly positive influence of perceived benefits, perceived ease of implementation, and government policy on IFRS adoption within Indian MNCs. However, the impact of legal requirements on IFRS adoption in Indian MNCs is insignificantly positive. Furthermore, adopting IFRSs substantially positively affects FRQ within Indian MNCs. Similarly, FRQ significantly positively affects the relevance, accuracy, understandability, comparability, and timeliness of MNCs' financial reports in India. The moderating role of the ICS in the connections between IFRS adoption and FRQ is positive yet insignificant within Indian MNCs. The insights derived from this study are valuable for investors, shareholders, government authorities, financiers, board members, and top executives of organisations. [ABSTRACT FROM AUTHOR]

This paper seeks to examine the extent to which environmental, social, and governance (ESG) disclosure affects capital structure and cost of capital for non-financial Fortune 500 firms. With a sample period from 2007 to 2022 and a system (Generalized Method of Moments) GMM estimation method, we investigate the linkage between ESG disclosure scores and both leverage and the weighted average cost of capital (WACC). Thus, we find that firms with stronger ESG performance have higher ESG disclosure and lower leverage ratios and WACC, highlighting that firms with good ESG outcomes have better equity financing facilities and are perceived to be less risky. We also find the moderation effect where the effects of ESG disclosure depend on the level of ESG disclosure. The empirical results thus show that the environmental and social factors have significant influences on leverage and WACC than the governance factors. Furthermore, we show that firm size affects these relationships in that larger firms are more affected by the variables. These findings extend the literature on ESG, and provide relevant information for corporate financial managers, investors, and policymakers about the financial effects of ESG disclosure. This paper therefore provides evidence of the relevance of ESG factors in decisions on capital structure and cost of capital especially for large firms. [ABSTRACT FROM AUTHOR]

We examine the relationship between managerial social capital (MSC) and firms' dividend policies. For an unbalanced panel of publicly listed UK FTSE 350 firms from 2006 to 2017, we find that MSC has a negative impact on a firm's dividend policy. Firms pay lower dividends when a higher proportion of well-connected directors join corporate boards. Our main result is consistent with the notion that a high degree of social capital leads to better monitoring and control; therefore, social capital works under the substitution effect between governance quality and dividend payouts. In further analyses, we explore potential differences in this relationship between financial and non-financial firms and show that the association between MSC and dividend policy is weaker in financial firms than in non-financial firms. Taken together, our findings infer that investors should consider the social capital status of firms when they make investment decisions. Our results proved to be robust when subjected to a battery of tests, including alternative model specifications and definitions of MSC. [ABSTRACT FROM AUTHOR]

This study sought to ascertain a machine learning algorithm capable of predicting crises in the African stock market with the highest accuracy. Seven different machine-learning algorithms were employed on historical stock prices of the eight stock markets, three main sentiment indicators, and the exchange rate of the respective countries' currencies against the US dollar, each spanning from 1 May 2007 to 1 April 2023. It was revealed that extreme gradient boosting (XGBoost) emerged as the most effective way of predicting crises. Historical stock prices and exchange rates were found to be the most important features, exerting strong influences on stock market crises. Regarding the sentiment front, investors' perceptions of possible volatility on the S&P 500 (Chicago Board Options Exchange (CBOE) VIX) and the Daily News Sentiment Index were identified as influential predictors. The study advances an understanding of market sentiment and emphasizes the importance of employing advanced computational techniques for risk management and market stability. [ABSTRACT FROM AUTHOR]

Long-term investors are often hesitant to invest in assets or strategies prone to significant drawdowns, primarily due to the challenge of predicting these drawdowns. This study presents a multivariate Markov-switching model for small- and large-cap returns in the U.S. equity markets, demonstrating that three distinct regimes are necessary to capture the negative trends in expected returns during financial crises. Our findings indicate that this framework enhances the prediction of conditional drawdowns compared to standard alternative models of financial returns. Furthermore, out-of-sample analysis shows that investment strategies based on these predictions outperform those relying on models with one or two regimes. [ABSTRACT FROM AUTHOR]

Drug discovery and development require significant costs and time, making investment acquisition crucial. However, there are few biopharmaceutical startups with high valuations in Japan. Unlike other countries, entrepreneurship in Japan is relatively inactive, and startups have a minimal presence in the drug-discovery field. Instead, in Japan's innovation system, research and development (R&D) has been led by large incumbent companies, which are believed to have a wealth of promising assets and talent. This study tested the hypothesis that biopharmaceutical startups leveraging these assets and talent might be more attractive to investors by regression analysis using a dataset of Japanese unlisted biopharmaceutical startups. The results demonstrated that Japanese biopharmaceutical startups showed significantly higher valuations and total funding amounts if they were corporate spin-offs (CSOs). Additionally, they achieved significantly higher valuations and total funding amounts if their R&D lead persons had corporate backgrounds. These findings suggest that in Japan's innovation system, which is centered on large companies, CSOs and startups leveraging R&D talent with corporate experience may be more appealing to investors. [ABSTRACT FROM AUTHOR]

Property stocks are an attractive alternative investment for investors who want passive income. Investors' decisions focus not only on maximizing returns but also on reducing risk. This study examines the extent to which macroeconomic factors affect stock performance by comparing the effectiveness of the Fama–French five-factor model (5FF) and Fama–French seven-factor model (7FF) in estimating returns. This study also verifies Fisher's theory in the context of property and real estate stocks. The research data used are property and real estate stocks in the Indonesian capital market. The data are processed using the OLS estimation method, and Akaike's Information Criterion (AIC) is used to choose the optimal model. The results show that property and real estate stocks in Indonesia with negative profitability at all quantiles can hedge inflation and interest rates. However, the interest rates are not the only factor affecting the market risk. The 7FF model is better at explaining the variability of stock portfolio returns. This research makes an essential contribution to the financial literature in Indonesia, particularly in the context of portfolio management in the property and real estate sector. [ABSTRACT FROM AUTHOR]

The study examines the time-varying correlation and return spillover mechanism among developed (U.S.) and emerging (BRIC) stock markets during major crises from 2000 to 2023, namely the global financial crisis, COVID-19, and the Russia–Ukraine war. To do so, we used dynamic conditional correlation (DCC-GARCH) and time-varying parameter vector autoregression (TVP-VAR) models. This study finds that the nature of market crises plays a significant role in the interrelationship and return spillover mechanisms among the U.S. and BRIC stock markets. The interconnectedness of the stock markets was strengthened by crises such as the GFC and the COVID-19 pandemic. On the other hand, the Russia–Ukraine war temporarily disrupted the interrelationships between the markets. The study yields valuable insight to local and international investors in portfolio diversification and risk management strategies during market turbulence. [ABSTRACT FROM AUTHOR]

The aim of this study is to explore the influence of environmental, social, and governance (ESG) factors on business failure in Brazil by employing advanced machine learning techniques. We collected data from 235 companies and conducted principal component analysis (PCA) on 40 variables already used in the bankruptcy failure literature, resulting in the formation of seven variables that predict business failure. The results indicate that ESG factors significantly predict business failure in Brazil. This study has implications for investors, policymakers, and business leaders, offering a more precise tool for risk assessment and strategic decision-making. [ABSTRACT FROM AUTHOR]

In the energy-saving retrofit of existing buildings, investors are particularly concerned about the energy-saving performance of exterior windows and the payback period of additional costs. This study evaluates representative cities in four different climate zones in China to simulate the energy consumption of large office buildings after replacing different glass windows and conducting energy-saving and economic feasibility assessments. The research method includes the following steps: First, a baseline model of large office buildings in four cities was established using AutoBPS and OpenStudio. Then, the baseline and retrofit models of replacing glass windows were simulated using the EnergyPlus V9.3.0 to obtain multiple hourly energy consumption results. The commercial electricity and gas prices in the four cities were adjusted to calculate the total cost within 20 years after replacing different types of windows. Using the discounted payback period (DPP), net present value (NPV), and profitability index (PI) as evaluation indicators, a feasibility analysis was conducted in the four regions to evaluate the economic feasibility of replacing building windows. The simulation results show that considering economic feasibility and meeting energy-saving standards, it is more economical to choose windows with moderate U-value and SHGC value in the four regions than to choose windows with the smallest U-value and SHGC value, and that both energy savings and economic benefits are closely related to building age, with older buildings (especially those in Changsha and Shenzhen) showing greater benefits. Furthermore, the optimal window types in the four cities determined in this study can recover the investment cost within the window life, with Harbin (SC), Beijing (C), Changsha (HC), and Shenzhen (HW) with the payback period of 6.60, 15.66, 10.16, and 11.42 years, respectively. The research model established in this study provides a useful evaluation path for selecting windows for the energy-saving retrofit of large office buildings in cities in different climate zones and provides data support for the decision making of energy-saving retrofit investors. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed hybrid crow search algorithm (CSA) and long short-term memory (LSTM) system can be utilized for stock price forecasting by simultaneously leveraging multiple datasets, such as technical indicators, leading indicators, and various time series data related to stock prices. This system enhances prediction accuracy. This system can help financial institutions, investment firms, and individual traders make more informed trading decisions and optimize portfolio strategies by accurately forecasting future stock prices. This study presents a hybrid crow search algorithm–long short-term memory (CSLSTM) system for forecasting stock prices. This system allows investors to effectively avoid risks and enhance profits by predicting the closing price the following day. This method utilizes a stacking ensemble of long short-term memory (LSTM) networks, with the crow search algorithm (CSA) optimizing the weights assigned to the predictions from multiple LSTM models. To improve the overall accuracy, this system leverages three distinct datasets: technical analysis indicators; price fluctuation limits; and variation mode decomposition (VMD) subsignal sequences. The predictions for the three reference-data types are more comprehensive than single-model or single-data-type approaches. The prediction accuracies of the recurrent neural network, gate recurrent unit, and the LSTM network for five stocks were compared. The proposed CSLSTM system outperforms the other standalone models. Furthermore, we conducted backtesting to demonstrate that the prediction information from our model could generate profit in the stock market, enabling users to benefit from complex stock-market dynamics. The stock prices in this study are expressed in New Taiwan Dollars (TWD), the official currency of Taiwan. [ABSTRACT FROM AUTHOR]

Featured Application: This work provides a comprehensive analysis of various prediction architectures tailored for tabular data, specifically applied to predicting art auction results. By leveraging machine learning models such as LinearModel, K-Nearest Neighbors, DecisionTree, RandomForest, XGBoost, CatBoost, LightGBM, MLP, VIME, ModelTree, DeepGBM, DeepFM and SAINT, the study offers insights into the relative strengths and weaknesses of each approach when forecasting auction outcomes based on historical data, artwork characteristics, and market trends. These findings can help auction houses, art investors, and market analysts understand which methods may offer practical, although currently limited, predictive value, and highlight areas for further improvement in prediction accuracy and strategy optimization. Predicting art auction results presents a unique challenge due to the complexity and variability of factors influencing artwork prices. This study explores a range of machine learning architectures designed to forecast auction outcomes using tabular data, including historical auction records, artwork characteristics, artist profiles, and market indicators. We evaluate traditional models such as LinearModel, K-Nearest Neighbors, DecisionTree, RandomForest, XGBoost, CatBoost, LightGBM, MLP, VIME, ModelTree, DeepGBM, DeepFM, and SAINT. By comparing the performance of these models on a dataset comprising extensive auction results, we provide insights into their relative effectiveness across different scenarios. Additionally, we address the interpretability of models, which is crucial for understanding the influence of various features on predictions. The results suggest that while some models perform better than others, no single approach offers consistently high accuracy across all cases. This study provides guidance for auction houses, art investors, and market analysts in refining predictive approaches, identifying key challenges, and understanding where further improvements are needed for more accurate data-driven decisions in the art market. [ABSTRACT FROM AUTHOR]

Existing risk measurement methods often fail to fully consider the impact of climatic conditions on stock market risk, making it difficult to capture dynamic patterns and long-term dependencies. To address these issues, we propose the TS-GRU method: this approach utilizes a temporal convolutional network (TCN) to extract underlying features from historical data, capturing key characteristics of time series data. Subsequently, a gated recurrent unit (GRU) model is employed to capture dynamic patterns and long-term dependencies within the stock market. Finally, the TS-GRU model is optimized using the Sparrow algorithm based on collective behavior, iteratively evaluating and refining model parameters to obtain improved solutions. Experimental results demonstrate the effectiveness of the TS-GRU method in providing accurate risk assessment and forecasting. This comprehensive approach takes into account carbon finance, climate change, and environmental factors, offering valuable insights to investors to help them to understand and manage investment risks in the ever-changing stock market. [ABSTRACT FROM AUTHOR]

This study investigates the integration of the Indonesian stock market with eight major trading partner countries, namely, China, Japan, the United States, Malaysia, India, Singapore, the Philippines, and South Korea. The analysis of the stock-market integration investigation includes the following two main things: short-term and long-term dynamic relationships within the Vector Autoregressive (VAR) model framework based on the unit root test, multivariate Johansen cointegration, and paired Granger causality test. The VAR model was analyzed using weekly closing index data of the Indonesian stock exchange and eight major trading partners from January 2013 to June 2024. The results of the study show that the integration of the Indonesian stock market with those of its main trading partners in the long term is relatively low. This finding implies that investors from the eight major trading partner countries can diversify their portfolios in international investments via the Indonesian stock market and vice versa. In the short term, these results prove that Indonesia's stock markets and those of its major trading partners are integrated, excluding China. The Chinese stock market has become segmented and more attractive for Indonesian investors who want to benefit from diversification and vice versa. Furthermore, the Indonesian stock market has two-way causal relationships with the US, Japanese, Indian, and Singaporean stock markets. In addition, the Indonesian stock market has unidirectional reciprocal-lagged relationships with Malaysia and the Philippines. An essential contribution of this study is helping policymakers and, especially, international investors understand the dynamic relationships of the Indonesian stock market with its major trading partners. Furthermore, this study contributes to the development of empirical literature on the comovement of the Indonesian stock market and those of its major trading partners, as well as the stock markets of developing and developed countries. [ABSTRACT FROM AUTHOR]

The study aims to critically assess the safe-haven properties of Bitcoin and a diverse set of commodities in mitigating stock market risks during periods of extreme financial turbulence. Specifically, this research seeks to evaluate the effectiveness of these assets as hedging tools or diversifiers in the portfolios of both OPEC and non-OPEC countries, focusing on their behavior during the COVID-19 pandemic. We employ a wavelet coherence approach to analyze the dynamic relationships between the variables. Portfolio optimization is conducted using CVaR to assess the effectiveness of these assets as safe havens, hedges, or diversification tools in mitigating financial risks during periods of heightened market volatility. The diversification benefits of commodities and Bitcoin in OPEC and non-OPEC stock portfolios decrease over time as their co-movement with stock markets increases. During the COVID-19 period, BTC did not act as a safe haven. However, gold served as a hedge for non-OPEC countries. Using CVaR, we found that BTC provides stronger diversification benefits than commodities, followed by gold. We examine the safe-haven role of Bitcoin and various commodities, specifically within the context of both OPEC and non-OPEC countries. Our study offers a more comprehensive analysis of how BTC and commodities function as portfolio assets during financial stress, providing valuable insights for investors and policymakers. [ABSTRACT FROM AUTHOR]

Every economy's prosperity is determined by the quantity of tax income it receives. Over the years, studies have demonstrated that inflows from foreign investments and openness to international trade are important contributors to a country's tax income. Based on this assumption, this study seeks to examine the impact of foreign direct investment (FDI) and open trade on tax income in a number of sub-Saharan African nations. The World Bank Development Indicators data on tax revenue, FDI, exports, imports, and exchange rates from 1990 to 2022 are used in the study. We also use the pooled mean group/panel autoregressive distributed lag approach to examine the data gathered for this inquiry. The results reveal that, in the long term, FDI has a significant negative impact on tax income; nevertheless, in the short run, Ghana's tax revenue collection suffers while other nations profit from FDI. The results reveal that Nigeria's exporting is detrimental to tax revenue collection, but South Africa's export of goods and services is beneficial. However, imports and currency rates benefit Nigeria, Ghana, and South Africa in the near term. Thus, the research suggests improving tax rules and administration to prevent the movement of resources by foreign investors out of the host countries in order to avoid the imposition of huge tax burdens on their firms. Countries with low exports, such as Nigeria, are urged to enhance local manufacturing to meet international export standards in order to alleviate the continual negative balance of payments, which is primarily fixed by the adequate export of products and services. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the main mineral exchange-traded funds (ETFs) in terms of trading volumes on the New York Stock Exchange by measuring the volatility transmission among them and the connectivity of this market. Daily closing ETF data from 2019 to 2023 for platinum, silver, copper, lead, nickel, gold, and a diversified set of precious metals are considered to estimate a spillover index and apply complex network metrics that identify and cluster the intensity of these relationships. The results indicate that the ETFs that transmit and receive the most volatility in the modeled complex network, in ascending order, are precious metals: gold, silver, and platinum. They are described by the cluster analysis of the modularity optimization process as the group most used for hedging purposes in critical periods. These findings are helpful for the scientific literature about derivatives by bringing empirical evidence from metals markets, supply chain agents, and investors. [ABSTRACT FROM AUTHOR]

We used three different databases that cover different years from 2006 through 2023 that provide different insights into the associations of firm features with their E, S, and G scores, as well as the composite ESG score. We find that large market capitalization firms are associated with higher ESG and environmental scores as well as with ESG-related strengths. These associations likely stem from the increased scrutiny and expectations from investors, regulators, and the public for large-cap firms. However, we find that changes in ESG scores have no significant associations with changes in basic firm characteristics—market cap, growth options, or profitability. That is, changes in ESG scores over time do not seem to significantly enhance or alter these basic features of a firm. [ABSTRACT FROM AUTHOR]

We analyze the predictive effect of monthly global, regional, and country-level financial uncertainties on daily gold market volatility using univariate and multivariate GARCH-MIDAS models, with the latter characterized by variable selection. Based on data over the period of July 1992 to May 2020, we highlight the role of the global financial uncertainty factor in accurately forecasting gold price volatility relative to the benchmark GARCH-MIDAS-realized volatility model, with a dominant role of European financial uncertainties, and 36 out of the 42 regional financial market uncertainties. The forecasting performance of the global financial uncertainty factor is as good as an index of global economic conditions, with results based on a combination of these two models depicting evidence of complementary information. Moreover, the GARCH-MIDAS model with global financial uncertainty cannot be outperformed by the multivariate version of the GARCH-MIDAS framework, estimated using the adaptive LASSO, involving the top five developed and developing countries each, chosen based on their ability to explain the movements of overall global financial uncertainty. Our results imply that as financial uncertainties can improve the accuracy of the forecasts of gold returns volatility, it would help investors to design optimal portfolios to counteract financial risks. Also, as gold returns volatility reflects financial uncertainty, accurate forecasts of it would provide information about the future path of economic activity, and assist policy authorities in preventing possible economic slowdowns. [ABSTRACT FROM AUTHOR]

The Chinese stock market, marked by rapid growth and significant volatility, presents unique challenges for investors and analysts. A-share stocks, traded on the Shanghai and Shenzhen exchanges, are crucial to China's financial system and offer opportunities for both domestic and international investors. Accurate stock recommendation tools are vital for informed decision making, especially given the ongoing regulatory changes and economic reforms in China. Current stock recommendation methods often fall short, as they typically fail to capture the complex inter-company relationships and rely heavily on financial reports, neglecting the potential of unlabeled data and historical price trends. In response, we propose a novel approach that combines graph-based structures with historical price data to develop self-learned stock embeddings for A-share recommendations. Our method leverages self-supervised learning, bypassing the need for human-generated labels and autonomously uncovering latent relationships and patterns within the data. This dual-input strategy enhances the understanding of market dynamics, leading to more accurate stock predictions. Our contributions include a novel framework for label-free stock recommendations with modeling stock connections and pricing information, and empirical evidence demonstrating the robustness and adaptability of our approach in the volatile Chinese stock market. [ABSTRACT FROM AUTHOR]

Hyperbolic discounting is a psychological phenomenon in which individuals prioritize smaller immediate rewards over larger future rewards. Time-inconsistent behavior is deemed irrational as it negatively impacts savings and investment, investment in financial knowledge, and long-term financial and personal well-being. This study hypothesizes that improving financial knowledge, promoting positive financial behavior, and fostering a future-oriented financial attitude can mitigate hyperbolic discounting bias and that these three components of financial literacy enable investors to make long-term economic decisions maximizing utility. We analyzed the responses of 114,170 active investors in Japan to examine the interactions between financial knowledge, behavior, and attitude. Our findings reveal a strong negative relationship between these dimensions and hyperbolic discounting, underscoring their crucial role in shaping individuals' intertemporal preferences. For researchers, our results highlight the need to integrate multidimensional aspects of financial literacy into investigations of intertemporal discounting behaviors. Policymakers should implement holistic financial education programs that improve knowledge, transform behavior, and shape attitudes. Financial institutions and advisors should prioritize programs that mitigate hyperbolic discounting tendencies among clients. This study represents a significant advancement in the research on financial literacy, offering a comprehensive framework for future studies and practical applications aimed at improving financial decision-making outcomes. [ABSTRACT FROM AUTHOR]

Currently, the need for a clean transition has made the upscaling of sustainable energy investment projects imperative. This paper addresses the increasing importance of sustainable energy investment projects in the context of climate change and the urgent need for a global energy transition. Given the complexity of decision-making in this field, a multi-criteria decision-making (MCDM) approach is employed to assess the main criteria considered by project developers and financial institutions. Using the Analytic Hierarchy Process (AHP) method, eight criteria are identified and evaluated. Results highlight differing priorities between project developers and investors, emphasizing the need for adaptable approaches to accelerate sustainable energy investments. The study underscores the importance of understanding diverse stakeholder preferences and priorities in formulating effective strategies and managing associated risks to effectively promote sustainable energy projects. Future research should focus on real-life case studies and policy assessments to further enhance the understanding of sustainable energy investment dynamics. [ABSTRACT FROM AUTHOR]

This study presents a comparative analysis of two advanced attention-based deep learning models—Attention-LSTM and Attention-GRU—for predicting Bitcoin price movements. The significance of this research lies in integrating moving average technical indicators with deep learning models to enhance sensitivity to market momentum, and in normalizing these indicators to accurately reflect market trends and reversals. Utilizing historical OHLCV data along with four key technical indicators (SMA, EMA, TEMA, and MACD), the models classify trends into uptrend, downtrend, and neutral categories. Experimental results demonstrate that the inclusion of technical indicators, particularly MACD, significantly improves prediction accuracy. Furthermore, the Attention-GRU model offers computational efficiency suitable for real-time applications, while the Attention-LSTM model excels in capturing long-term dependencies. These findings contribute valuable insights for financial forecasting, providing practical tools for cryptocurrency traders and investors. [ABSTRACT FROM AUTHOR]

This study documents the comparative financial performance of the Islamic Banking Services Industry (IBSI) in the Gulf Cooperation Council (GCC) region. After drawing the performance evaluation framework (based on the CAMEL framework), the research conducted data analysis of the Islamic Banking Services Industry (IBSI) in the GCC region for 31 quarters (2013Q4–2021Q4). The analysis examines capital adequacy, asset quality, management performance, earnings, and liquidity management. Objectively classified data trends are reported through graphs. Additionally, the research documents internal determinants of financial performance. Findings suggest that the GCC-IBSI has shown overall progress in achieving primary objectives (commercial performance), including healthy capital adequacy, cost control, equity returns, and liquidity management. Capital adequacy, cost control, and liquidity management significantly contribute to financial performance. Managerial implications include cost control, reduction in non-performing loans, and prudent liquidity management. There exist opportunities in the GCC-IBSI for investors, given the mismatch in demand and supply of Islamic financial services. This study contributes to the literature by documenting findings on the achievements of the primary objective of IBSI in multiple GCC-IBSI markets comparatively. [ABSTRACT FROM AUTHOR]

This study investigated the interactive effect of key audit matters (KAMs) and stock ownership on German audit committee (AC) members' preferences regarding accounting issues in an experimental setting. Specifically, we explored whether the increased accountability of AC members to investors, resulting from KAM disclosures, is influenced by whether an AC member owns shares in the audited company. Our findings suggest that the disclosure of KAMs supports a conservative management accounting preference. However, KAMs can be used by stock-owning AC members as disclaimers that inform the investing public and might serve as legitimation tools for ACs with a preference for less conservative accounting. Hence, our study provides evidence from a two-tier governance country that audit committee members' accounting preferences based on disclosed KAMs depend on their financial independence. [ABSTRACT FROM AUTHOR]

This study examines how investors incorporate unrealized gains or losses reported in Other Comprehensive Income (OCI) into their investment judgments. Since unrealized gains or losses can be presented in either OCI or net income—gains from trading securities are included in net income, while those from available-for-sale securities are reported in OCI (ASC 320 and ASC 851)—it raises the question of whether OCI items are perceived as equally significant as net income items. To explore this, we conducted a 2 × 2 experiment with 240 individual investors, manipulating the presentation of unrealized gains or losses in either net income or OCI. Our findings reveal that unrealized gains are valued significantly lower when presented in OCI compared to net income, indicating that investors see OCI-reported gains as less relevant. However, for unrealized losses, the incorporation degree remained consistent across both presentations, reflecting a general aversion to unrealized losses regardless of how they are reported. [ABSTRACT FROM AUTHOR]

This study uses a hybrid model of the exponential generalised auto-regressive conditional heteroscedasticity (eGARCH)-extreme value theory (EVT)-Gumbel copula model to investigate the dependence structure between Bitcoin and the South African Rand, and quantify the portfolio risk of an equally weighted portfolio. The Gumbel copula, an extreme value copula, is preferred due to its versatile ability to capture various tail dependence structures. To model marginals, firstly, the eGARCH(1, 1) model is fitted to the growth rate data. Secondly, a mixture model featuring the generalised Pareto distribution (GPD) and the Gaussian kernel is fitted to the standardised residuals from an eGARCH(1, 1) model. The GPD is fitted to the tails while the Gaussian kernel is used in the central parts of the data set. The Gumbel copula parameter is estimated to be α = 1.007 , implying that the two currencies are independent. At 90%, 95%, and 99% levels of confidence, the portfolio's diversification effects (DE) quantities using value at risk (VaR) and expected shortfall (ES) show that there is evidence of a reduction in losses (diversification benefits) in the portfolio compared to the risk of the simple sum of single assets. These results can be used by fund managers, risk practitioners, and investors to decide on diversification strategies that reduce their risk exposure. [ABSTRACT FROM AUTHOR]

Attention to environmental, social, and governance (ESG) investing has grown in recent years. Even after the SARS-CoV-2 (COVID-19) global pandemic, there has been a rise in financial instruments that are structured according to certain prescribed "sustainable finance" objectives. From a risk management perspective, and as we continue to see a rise in inflows into such instruments, it is important to appreciate that ESG markets will have a growing influence on our financial system and its development. In light of this, and using a sample of some of the most common and popular US-based ESG index funds, this study explores the extent to which herding behaviors are present in such markets. From a regulatory point of view, such behaviors are important to identify, given that they can lead to excess price volatility, bubbles, and other such market-destabilizing phenomena. In addition, this study builds a framework for exploring whether Twitter-based economic uncertainty, which is arguably a forward-looking indicator of investors' expectations, can exacerbate herding behaviors in ESG markets. Overall, this study shows the following: (i) herding behaviors are present in ESG markets; (ii) rises in Twitter economic uncertainty can potentially exacerbate such herding; (iii) although ESG funds, like traditional asset classes, generally show a negative risk–return tradeoff, this can be driven by changes in Twitter economic uncertainty. [ABSTRACT FROM AUTHOR]

Recent research in algorithmic trading has primarily focused on ultra-high-frequency strategies and index estimation. In response to the need for a low-frequency, real-world trading model, we developed an enhanced algorithm that builds on existing models with high hit ratios and low maximum drawdowns. We utilized established price indicators, including the stochastic oscillator and Williams %R, while introducing a volume factor to improve the model's robustness and performance. The refined algorithm achieved superior returns while maintaining its high hit ratio and low maximum drawdown. Specifically, we leveraged 2X and 3X signals, incorporating volume data, the 52-week average, standard deviation, and other variables. The dataset comprised SPY ETF price and volume data spanning from 2010 to 2023, over 13 years. Our enhanced algorithmic model outperformed both the benchmark and previous iterations, achieving a hit rate of over 90%, a maximum drawdown of less than 1%, an average of 1.5 trades per year, a total return of 519.3%, and an annualized return (AnnR) of 15.1%. This analysis demonstrates that the model's simplicity, ease of use, and interpretability provide valuable tools for investors, although it is important to note that past performance does not guarantee future returns. [ABSTRACT FROM AUTHOR]

Understanding the psychological factors that influence investment decisions is crucial for predicting stock investment retention. This study investigates the mediating role of the Big Five personality traits in stock investment retention, utilizing a modified version of the theory of planned behavior. By examining the influence of investors' perceived risk and attitudes toward stock investment, data collected via an online survey with The Association of Thai Securities Companies (ASCO) were analyzed using Structural Equation Modeling (SEM). The findings reveal that extraversion, openness, and conscientiousness significantly impact attitudes toward stock investing, which in turn affects investment retention. However, personality traits do not directly influence risk perception. This research provides unique empirical evidence of the independence between the Big Five personality traits and risk perception among Thai stock investors, underscoring the importance of personality in shaping investment behavior through its effect on attitudes. [ABSTRACT FROM AUTHOR]

This study aims to investigate the interaction between market volatility, economic growth, and cognitive biases over the period from April 2006 to March 2024. Market volatility and economic growth are critical indicators that influence economic stability and investment behavior. Financial market volatility, defined by abrupt and erratic changes in asset values, can have a big impact on the expansion and stability of the economy. According to conventional economic theory, there should be an inverse relationship between market volatility and economic growth since high volatility can discourage investment and erode trust. Market participants' cognitive biases are a major aspect that complicates this connection. Due to our innate susceptibility to cognitive biases, including herd mentality, overconfidence, and loss aversion, humans can make poor decisions and increase market volatility. These prejudices frequently cause investors to behave erratically and irrationally, departing from reasonable expectations and causing inefficiencies in the market. Cognitive biases have the capacity to sustain feedback loops, which heighten market turbulence and may hinder economic expansion. Similarly, cognitive biases have the potential to cause investors to misread economic indicators or ignore important details, which would increase volatility. This study uses the generalized autoregressive conditional heteroskedasticity (GARCH) model on GDP growth data from the US, the UK, and India, alongside S&P 500, FTSE 100, and NIFTY 50 data sourced from Bloomberg, to examine evidence of these biases. The results show evidence of the predictive nature of market fluctuations on economic performance across the markets and highlight the substantial effects of cognitive biases on market volatility, disregarding economic fundamentals and growth, emphasizing the necessity of considering psychological factors in financial market analyses and developing strategies to mitigate their adverse effects. [ABSTRACT FROM AUTHOR]

With the advent of greening the global economy and the introduction of green financial assets, this study examines the connectedness and spillover effect of green assets using a QVAR approach focusing on the average connectedness and connectedness under extreme market conditions. The time of the study captures the crucial global incidents of COVID-19 and Russia–Ukraine war to investigate the effect of major incidents on the connectedness of green assets. The results of the QVAR analysis reveal that green assets are moderately connected under normal market conditions; however, their connection is strengthened under extreme market conditions. IOTA and SP Green Bonds are the net receivers of shocks from other assets, and SP Green Bonds are connected to green energy indices and green cryptocurrencies during turbulent markets. Since green cryptocurrencies are closely connected, a lower portion of them should be added to portfolios, whereas SP Green Bonds qualify as a good diversifying agent in a portfolio. The study has significant implications for market participants, investors, and policymakers. [ABSTRACT FROM AUTHOR]

In this study, we examine the critical question of whether global equity and bond assets (both green and non-green) offer effective hedging and safe haven properties against stock market risks in South Asia, with a focus on Bangladesh, India, Pakistan, and Sri Lanka. The increasing integration of global financial markets and the volatility experienced during recent economic crises raise important questions regarding the resilience of South Asian markets and the potential protective role of global assets. Drawing on methods like VaR and CVaR tail risk estimators, the DCC-GJR-GARCH time-varying connectedness approach, and cost-effectiveness tools for hedging, we analyze data spanning from 2014 to 2022 to assess these relationships comprehensively. Our findings demonstrate that stock markets in Bangladesh experience lower levels of downside risk in each quantile; however, safe haven properties from the global financial markets are effective for Bangladeshi, Indian, and Pakistani stock markets during the crisis period. Meanwhile, the Sri Lankan stock market neither receives hedging usefulness nor safe haven benefits from the same marketplaces. Additionally, global green assets, specifically green bond assets, are more reliable sources to ensure the safest investment for South Asian investors. Finally, the portfolio implications suggest that while traditional global equity assets offer ideal portfolio weights for South Asian investors, global equity and bond assets (both green and non-green) are the cheapest hedgers for equity investors, particularly in the Bangladeshi, Pakistani, and Sri Lankan stock markets. Moreover, these results hold significant implications for investors seeking to optimize portfolios and manage risk, as well as for policymakers aiming to strengthen regional market resilience. By clarifying the protective capacities of global assets, particularly green ones, our study contributes to a nuanced understanding of portfolio diversification and financial stability strategies within emerging markets in South Asia. [ABSTRACT FROM AUTHOR]

In this paper, we propose utilizing machine learning methods to determine the expected aggregated stock market risk premium based on online investor sentiment and employing the multifold forward-validation method to select the relevant hyperparameters. Our empirical studies provide strong evidence that some machine learning methods, such as extreme gradient boosting or random forest, show significant predictive ability in terms of their out-of-sample performances with high-dimensional investor sentiment proxies. They also outperform the traditional linear models, which shows a possible unobserved nonlinear relationship between online investor sentiment and risk premium. Moreover, this predictability based on online investor sentiment has a better economic value, so it improves portfolio performance for investors who need to decide the optimal asset allocation in terms of the certainty equivalent return gain and the Sharpe ratio. [ABSTRACT FROM AUTHOR]

The Swiss Federal Council has formulated a policy requiring each canton to establish housing conducive to independent living during old age. Maintaining the independence of older adults at home rather than transitioning to more expensive institutional care has gained traction. This study investigates the heterogeneity of strategies arising from the autonomy granted to each canton. Using mandatory statistics from the Swiss Federal Statistical Office for 2020 on 1550 institutions for the elderly and 2546 at-home care providers, we rank the 26 cantons by permanent and temporary care and support, distinguishing between at-home and institutional care. We use univariate regressions to explain differences and perform efficiency analyses of the respective systems. We find that the French- and Italian-speaking cantons prioritise at-home care, while the German-speaking regions rely more on institutional care. The analyses reveal an inefficient stationary approach in German-speaking cantons, which often places elderly individuals with minimal care needs in institutions. Conversely, with an ambulatory approach favouring independence, cantons like Vaud and Valais prioritize counselling and at-home services, deferring entry into institutions and allowing for short convalescence stays. Our findings are relevant for governmental infrastructure planning and the property owners, investors, and property managers involved in senior housing decisions. [ABSTRACT FROM AUTHOR]