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This paper aims to propose an improved version of portfolio optimization model through the prediction of the future behavior of stock returns using a combined wavelet-based long short-term memory (LSTM).

First, data are gathered and divided into two parts, namely, “past data” and “real data.” In the second stage, the wavelet transform is proposed to decompose the stock closing price time series into a set of coefficients. The derived coefficients are taken as an input to the LSTM model to predict the stock closing price time series and the “future data” is created. In the third stage, the mean-variance portfolio optimization problem (MVPOP) has iteratively been run using the “past,” “future” and “real” data sets. The epsilon-constraint method is adapted to generate the Pareto front for all three runes of MVPOP.

The real daily stock closing price time series of six stocks from the FTSE 100 between January 1, 2000, and December 30, 2020, is used to check the applicability and efficacy of the proposed approach. The comparisons of “future,” “past” and “real” Pareto fronts showed that the “future” Pareto front is closer to the “real” Pareto front. This demonstrates the efficacy and applicability of proposed approach.

Most of the classic Markowitz-based portfolio optimization models used past information to estimate the associated parameters of the stocks. This study revealed that the prediction of the future behavior of stock returns using a combined wavelet-based LSTM improved the performance of the portfolio.

In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field.

To provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992–2022. The bibliographic data was processed and analysed using VOSviewer and R software.

The results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, “Expert Systems with Applications” as the leading journal, “computer science” as the dominant subject area, and “stock price forecasting” as the predominantly explored research theme in the field. Furthermore, “portfolio optimization”, “sentiment analysis”, “algorithmic trading”, and “crisis prediction” are found as recently emerged research areas.

To the best of the authors’ knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.

This paper aims to minimize the mean-risk cost of sustainable and resilient supplier selection, order allocation and production scheduling (SS,OA&PS) problem under uncertainty of disruptions. The authors use conditional value at risk (CVaR) as a risk measure in optimizing the combined objective function of the total expected value and CVaR cost. A sustainable supply chain can create significant competitive advantages for companies through social justice, human rights and environmental progress. To control disruptions, the authors applied (proactive and reactive) resilient strategies. In this study, the authors combine resilience and social responsibility issues that lead to synergy in supply chain activities.

The present paper proposes a risk-averse two-stage mixed-integer stochastic programming model for sustainable and resilient SS,OA&PS problem under supply disruptions. In this decision-making process, determining the primary supplier portfolio according to the minimum sustainable-resilient score establishes the first-stage decisions. The recourse or second-stage decisions are: determining the amount of order allocation and scheduling of parts by each supplier, determining the reactive risk management strategies, determining the amount of order allocation and scheduling by each of reaction strategies and determining the number of products and scheduling of products on the planning time horizon. Uncertain parameters of this study are the start time of disruption, remaining capacity rate of suppliers and lead times associated with each reactive strategy.

In this paper, several numerical examples along with different sensitivity analyses (on risk parameters, minimum sustainable-resilience score of suppliers and shortage costs) were presented to evaluate the applicability of the proposed model. The results showed that the two-stage risk-averse stochastic mixed-integer programming model for designing the SS,OA&PS problem by considering economic and social aspects and resilience strategies is an effective and flexible tool and leads to optimal decisions with the least cost. In addition, the managerial insights obtained from this study are extracted and stated in Section 4.6.

This work proposes a risk-averse stochastic programming approach for a new multi-product sustainable and resilient SS,OA&PS problem. The planning horizon includes three periods before the disruption, during the disruption period and the recovery period. Other contributions of this work are: selecting the main supply portfolio based on the minimum score of sustainable-resilient criteria of suppliers, allocating and scheduling suppliers orders before and after disruptions, considering the balance constraint in receiving parts and using proactive and reactive risk management strategies simultaneously. Also, the scheduling of reactive strategies in different investment modes is applied to this problem.

Using data spanning 145 years for Sweden, the authors investigate the benefits of holding multi-family properties for investors who aim to hedge wage growth.

The authors assess the risk-adjusted excess return that results from adding multi-family properties to a mixed-asset portfolio that aims to track wage growth. The authors also analyse the macroeconomic determinants of asset returns. Finally, the authors test whether a causal relationship exists between the growth rate of real wages and that of real net operating income.

The benefits from holding multi-family properties are the greatest for low-risk allocation approaches. For more risky strategies, the role of real estate is more muted, and it varies greatly over time. Holding real estate was most beneficial during the first two decades of the 21st century. Multi-family properties are found to be the only asset class to be positively related to wage growth. The authors show that the net operating income acts as the transmission channel between wages and property returns.

The paper assesses whether the growing interest of pension funds for multi-family properties is warranted in the context of a portfolio that aims to track wage growth.

Using long term data makes it possible to use a rolling windows approach and hence to consider multiple outcomes for an allocation strategy over a typical investment horizon. This permits to assess the dispersion of performance across several periods rather than just one as is commonly done in the literature. The results show that the conclusions that would be drawn from looking at the past two or three decades of data differ substantially from those for earlier time periods.

By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.

This paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.

(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.

The originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

External stakeholder risks (ESRs) caused by unfavorable behaviors hinder the success of project portfolios (PPs). However, due to complex project dependency and numerous risk causality in PPs, assessing ESRs is difficult. This research aims to solve this problem by developing an ESR-PP two-layer fuzzy Bayesian network (FBN) model.

A two-layer FBN model for evaluating ESRs with risk causality and project dependency is proposed. The directed acyclic graph (DAG) of an ESR-PP network is first constructed, and the conditional probability tables (CPTs) of the two-layer network are further presented. Next, based on the fuzzy Bayesian network, key variables and the impact of ESRs are assessed and analyzed by using GeNIe2.3. Finally, a numerical example is used to demonstrate and verify the application of the proposed model.

The proposed model is a useable and effective approach for ESR assessment while considering risk causality and project dependency in PPs. The impact of ESRs on PP can be calculated to determine whether to control risk, and the most critical and heavily contributing risks and project(s) in the developed model are identified based on this.

This study extends prior research on PP risk in terms of stakeholders. ESRs that have received limited attention in the past are explored from an interaction perspective in the PP domain. A new two-layer FBN model considering risk causality and project dependency is proposed, which can synthesize different dependencies between projects.

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.

Introduction: Cost competitiveness, customer focus, and sustainability compliance are essential for new-age firms to survive and succeed in the VUCA market environment. This study examines how automobile corporations have improved cost competitiveness, productivity, and product quality.

Purpose: This study examines the importance of cost competitiveness, customer focus, and sustainability compliance for the long-term survival of organisations in VUCA markets, looking at the practical efforts made by automobile corporations to enhance cost competitiveness, productivity, and quality.

Methodology: The study utilises a comprehensive analysis of the strategies and initiatives implemented by the selected automobile companies. It involves a review of relevant literature, case studies, financial data analysis, and interviews with key industry experts, providing a holistic understanding of the actions taken by these organisations to achieve their goals.

Findings: The study reveals that cost competitiveness, customer focus, and sustainability compliance are critical factors for the long-term survival and success of organisations in the automotive industry. The analysed automobile companies have undertaken practical efforts to improve cost competitiveness, enhance productivity, and ensure high-quality products, enabling them to navigate the challenges and maintain a competitive edge.

Significance: The findings of this study contribute to a deeper understanding of the importance of cost competitiveness, customer focus, and sustainability compliance in the automotive industry. It highlights the need for organisations to constantly monitor both qualitative and quantitative profit to avoid complacency and ensure long-term efficiency. The study’s insights are relevant to businesses operating in other sectors, as they face similar challenges in the VUCA market environment.

The optimal payment in the whole operation and maintenance period of water environment treatment PPP projects has become the main approach to realize sustainable development of projects. This study is aimed at constructing an effective payment model for the whole life period of projects to achieve win-win among all stakeholders, so as to provide a theoretical reference and managerial implications for the public sector in the whole operation and maintenance period.

In the whole operation and maintenance period of water environment treatment PPP projects, this article investigates how the public sector optimizes the payment in the whole operation and maintenance period of projects. Firstly, the projects' whole operation and maintenance period is divided into several stages according to the performance appraisal period. And then, the multi-stage dynamic programming model is constructed to design the payment construct model for the public sector in each performance appraisal stage. The payment from the public sector is the decision variable, and the deduction from the private sector is a random variable.

The optimal payment model showed that the relatively less objective weight of public sector leaded to its relatively more total payment and vice versa. Therefore, the sustainable development of the projects can only be ensured when the objective weights both of them should be balanced. Additionally, the deduction from the performance appraisal of private sector plays an important role in the model construction. The larger deduction the private sector undertakes, the smaller profits private sector has. Since the deduction at each stage is a random variable, the deduction varies with the different probability distributions obeyed by the practical deduction in each stage.

The findings from this study have provided theoretical and application references, and some managerial implications are also given. First, the improvement of the pricing system of public sector should be accelerated. Second, the reasonable profit of the private sector must be guaranteed. While pursuing the maximization of social benefits, the public sector should make full use of the price sharing mechanism in the market and supervise the real income situation of the private sector. Third is increasing the public to participate in pricing. Additionally, it is a limitation that the deduction is assumed to conform to a uniform distribution in this study. Other probability distributions on deduction can be essentially further sought, so as to be more line with the actual situation of the projects.

The optimal payment in whole operation and maintenance period of the projects has become an important issue, which is a key to project success. This study constructs a multi-stage dynamic programming model to optimize payment in the whole period of projects. Additionally, this study adds its value through deeply developing the new theories of optimal payment to more suitable for the practical problems, so that to optimize the design of payment mechanism. Meanwhile, a valuable reference for public and private sectors is provided to ensure the sustainable development of the projects.

Unplanned urban mobility causes negative effects on the population and the environment. This study aims to understand how higher education institutions (HEIs) are managing the transportation issues related to their activities and how they are implementing actions towards more sustainable practices in this regard.

A systematic review and bibliometric analysis were performed using the Proknow-C method, and the bibliographic portfolio was evaluated to answer guiding questions about distribution of articles over the years and around the world, the most relevant and frequent topics, the travel behavior of university life and the main methodologies used. The meta-analysis was modeled using a programming language in R to execute Bibliometrix package.

Sharing systems, active transport, public transport, urban planning, car parking management and travel behavior are the most relevant topics related to sustainable mobility in HEIs. Different strategies to reduce car use are adopted, and the geographic location of the university and the availability of housing and shopping services in close proximity directly influence travel patterns. As a result, seven guidelines and strategic actions associated were proposed to promote the engagement of institutions in the development of sustainable transport and guide future studies about new solutions to promote sustainable university commutes.

This paper presents a new perspective by performing a critical literature review based on the experiences reported by several isolated studies on the subject. Initiatives of sustainable transport guidelines can be used by academics, urban planners, higher education administrators and other stakeholders to make universities more environmentally friendly, inclusive and accessible.