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Factor investment is booming in global asset management, especially environmental, social, and governance (ESG), dividend yield, and volatility factors. In this chapter, we use data from the US securities market from 2003 to 2019 to predict dividends and volatility factors through machine learning and historical data–based methods. After that, we utilize particle swarm optimization to construct the Markowitz portfolio with limits on the number of assets and weight restrictions. The empirical results show that that the prediction ability using XGBoost is superior to the historical factor investment method. Moreover, the investment performance of our portfolio with ESG, high-yield, and low-volatility factors outperforms baseline methods, especially the S&P 500 ETF.

Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains.

Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022.

The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research.

The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.

The existing literature offers various perspectives on integrating cryptocurrencies into investment portfolios; yet, there is a gap in understanding the behaviours, attitudes and cross-investment links of individual investors. This study, grounded in the modern portfolio theory and the random walk theory, aims to add empirical insights that are specific to the UK context. It explores four hypotheses related to the influence of socio-demographics, digital adoption, cross-investment behaviours and financial attitudes on cryptocurrency owners.

This study uses a logistic regression model with secondary data from the Financial Lives Survey 2020 to assess the factors impacting cryptocurrency ownership. A total of 29 variables are used, categorized into four groups aligned with the hypotheses. Additionally, hierarchical clustering analysis was conducted to further explore the cross-investment links.

The study reveals a significant lack of diversification among UK cryptocurrency investors, a pronounced inclination towards high-risk investments such as peer-to-peer lending and crowdfunding, and parallels with gambling behaviours, including financial dissatisfaction and a propensity for risk-taking. It highlights the influence of demographic traits, risk tolerance, technological literacy and emotional attitudes on cryptocurrency investment decisions.

This study provides valuable insights into cryptocurrency regulation and retail investor protection, underscoring the necessity for tailored financial education and a holistic regulatory approach for investment products with comparable risk levels, with the aim of minimizing regulatory arbitrage. It significantly enhances our understanding of the unique dynamics of cryptocurrency investments within the evolving financial landscape.

Understandings of house prices and their interrelationships have undoubtedly drawn a great amount of attention from various market participants. This study aims to investigate the monthly newly-built residential house price indices of seventy Chinese cities during a 10-year period spanning January 2011–December 2020 for understandings of issues related to their interdependence and synchronizations.

Analysis here is facilitated through network analysis together with topological and hierarchical characterizations of price comovements.

This study determines eight sectoral groups of cities whose house price indices are directly connected and the price synchronization within each group is higher than that at the national level, although each shows rather idiosyncratic patterns. Degrees of house price comovements are generally lower starting from 2018 at the national level and for the eight sectoral groups. Similarly, this study finds that the synchronization intensity associated with the house price index of each city generally switches to a lower level starting from early 2019.

Results here should be of use to policy design and analysis aiming at housing market evaluations and monitoring.

This paper aims to examine the relationship between environmental, social and governance (ESG) performance and systemic risk sensitivity of Chinese listed companies. From the consumer loyalty and investor structure perspectives, the relationship between ESG performance and systemic risk sensitivity is analyzed.

Since Morgan Stanley Capital International (MSCI) ESG officially began to analyze and track China A-shares from 2018, 275 listed companies in the SynTao Green ESG testing list for 2015–2021 are selected as the initial model. To measure the systematic risk sensitivity, this study uses the beta coefficient, from capital asset pricing model (CPAM), employing statistics and data (STATA) software.

The study reveals that high ESG rating companies have high corresponding consumer loyalty and healthy trading structure of institutional investors, thereby the systemic risk sensitivity is lower. This paper reveals that companies with high ESG rating are significantly less sensitive to systemic risk than those with low ESG rating. At the same time, ESG has a weaker impact on the systemic risk of high-cap companies than low-cap companies.

The study helps the companies understand the influence of market value on the relationship between ESG performance and systemic risk sensitivity. Moreover, this paper explains explicitly why ESG performance insulates a firm’s stock from market downturns with the lens of consumer loyalty theory and investor structure theory.

The paper provides new insights on the company’s ESG performance that significantly affects the company’s systemic risk sensitivity.

This study aims to endeavour to decode artificial intelligence (AI)-based tokens' complex dynamics and predictability using a comprehensive multivariate framework that integrates technical and macroeconomic indicators.

In this study we used advance machine learning techniques, such as gradient boosting regression (GBR), random forest (RF) and notably long short-term memory (LSTM) networks, this research provides a nuanced understanding of the factors driving the performance of AI tokens. The study’s comparative analysis highlights the superior predictive capabilities of LSTM models, as evidenced by their performance across various AI digital tokens such as AGIX-singularity-NET, Cortex and numeraire NMR.

This study finding shows that through an intricate exploration of feature importance and the impact of speculative behaviour, the research elucidates the long-term patterns and resilience of AI-based tokens against economic shifts. The SHapley Additive exPlanations (SHAP) analysis results show that technical and some macroeconomic factors play a dominant role in price production. It also examines the potential of these models for strategic investment and hedging, underscoring their relevance in an increasingly digital economy.

According to our knowledge, the absence of AI research frameworks for forecasting and modelling current aria-leading AI tokens is apparent. Due to a lack of study on understanding the relationship between the AI token market and other factors, forecasting is outstandingly demanding. This study provides a robust predictive framework to accurately identify the changing trends of AI tokens within a multivariate context and fill the gaps in existing research. We can investigate detailed predictive analytics with the help of modern AI algorithms and correct model interpretation to elaborate on the behaviour patterns of developing decentralised digital AI-based token prices.

The paper aims to provide a decision-support model to ensure a proper use of the limited resources, financial and not, for the enhancement of the cultural heritage and comprehensive development of small towns from sustainable perspective.

The assessment model is set up using a multi-criteria method that combines elements of linear planning with a performance indicators system that may represent the complexity of the territory’s cultural identity as a result of existing cultural-historical assets.

The model reliability is tested in a case study in a Municipality in southern Italy. The case study’s findings highlight the advantages for the public/private operators, who can consciously choose which preservation and restoration projects to fund while taking into account the effects those decisions will have on the economic, social and environmental context of reference.

Due to the suggested operational approach and the selection of variables for accounting economic, social and environmental impacts by the renewal project, the research findings may not be generalizable. Therefore, it is recommended that researchers look into the suggested theories in more detail.

The study offers implications for designing a user-friendly tool to help decision-making processes from a private–public viewpoint in a reasonable allocation of financial resources among investments for cultural property asset enhancement.

The suggested operational approach provides a reliable information apparatus to depict the decision-making process under small-town development in accordance with sustainability dimensions.

This study aims to empirically explore and compare the dynamic dependency between health-care sector and Islamic industries before, during and after the COVID-19 pandemic.

Time-varying student-t copula is used for before, during and after COVID-19 periods. The data used are the daily frequency price series of the selected markets from February 2017 to October 2023.

Empirical results found strong evidence of significant impact of the COVID-19 pandemic on the dependence structure of the studied indexes: Co-movements between various sectors are certain. The authors assist also in the birth of new dependence structure with the health-care industry in response to the COVID-19 crisis. This reflects the contagion occurrence from the health-care sector to other sectors.

By specifically examining the Islamic industry, this study sheds light on the resilience, challenges and opportunities within this sector, contributing novel perspectives to the broader discourse on pandemic-related impacts on economies and industries. Also, this paper conducts a comprehensive temporal analysis, examining the dynamics before, during and after the COVID-19 lockdown. Such approach enables an understanding of how the relationship between the health-care sector and the Islamic industry evolves over time, accounting for both short-term disruptions and long-term effects. By considering the pre-pandemic context, the paper adopts a longitudinal perspective, enabling a deeper understanding of how historical trends, structural factors and institutional frameworks shape the interplay between the health-care sector and the Islamic industry.

This study aims to provide a precise understanding of how corporate sustainability information is used in socially responsible investing (SRI). The study is motivated by the lack of a recognised body of knowledge on this issue. This study, therefore, collates and reviews relevant studies (67 studies) to provide guidance to investors interested in SRI and identify a research agenda for academics desiring to contribute to this area.

This study conducts a systemic literature review employing recognised key words and searching the Web of Science. HistCite is utilised to ensure important cited studies are not missed from the collection. The review was conducted from two perspectives: (1) sources of sustainability information and (2) how the information is used in SRI.

The review identifies five major sources of sustainability information, including corporate reports, ESG ratings, industry affiliation, news and private communication with firms. These sources of information play different roles in the cross section of SRI strategies (i.e. negative and positive screening, active ownership and integration). This study provides guidance on how to use this information in SRI and provides recommendations for future research on how analysts interact with the information, how different informational characteristics impact implementation, ways to improve data quality, improvements to analysis methods and where data use needs to be extended into new strategies.

This review contributes to the SRI literature by inventorying studies of an important, yet omitted aspect, namely, sustainability information. This work also enriches the literature on corporate sustainability information by investigating how this information can be used for a specific purpose, namely, SRI. Given the increasing interest in SRI, this review will provide much-needed guidance for a range of practitioners, including investors and regulators.

With the use of the grey incidence analysis (GIA), indicators such as the absolute degree of grey incidence (ε_ij), relative degree of grey incidence (r_ij) or synthetic degree of grey incidence (ρ_ij) are calculated. However, it seems that some assumptions made to calculate them are arguable, which may also have a material impact on the reliability of test results. In this paper, the authors analyse one of the indicators of the GIA, namely the relative degree of grey incidence. The aim of the article was to verify the hypothesis: in determining the relative degree of grey incidence, the method of standardisation of elements in a series significantly affects the test results.

To achieve the purpose of the article, the authors used the numerical simulation method and the logical analysis method (in order to draw conclusions from our tests).

It turned out that the applied method of standardising elements in series when calculating the relative degree of grey incidence significantly affects the test results. Moreover, the manner of standardisation used in the original method (which involves dividing all elements by the first element) is not the best. Much more reliable results are obtained by a standardisation that involves dividing all elements by their arithmetic mean.

Limitations of the conducted evaluation involve in particular the limited scope of inference. This is since the obtained results referred to only one of the indicators classified into the GIA.

In this article, the authors have evaluated the model of GIA in which the relative degree of grey incidence is determined. As a result of the research, the authors have proposed a recommendation regarding a change in the method of standardising variables, which will contribute to obtaining more reliable results in relational tests using the grey system theory.