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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.

Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.

This review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.

The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.

Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.

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.

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 study aims to investigate the relationship between risks and the expected return of financial investment because the relationship between them is negative; if the investors agree to the higher level of risk, they have the greater the expected return; therefore, investors always require a degree of proportionality between the risks and returns.

This study applied the standard deviation, variance, coefficient of variation methods and matrix function to measure risks. Besides, the dataset is a return on equity ROE, which is collected in three companies at time series from 2005 to 2020.

When the variance or the standard deviation is higher, the return on the securities is higher, but the securities are a higher risk and vice versa. The results showed risk levels of stocks that are 2.509%, 0.367%, 3.666% and the corresponding return mean of 38.68%, 23.99% and 14.02%.

The results support the portfolio management policy appropriately. This study identifies issues for managers, investors and readers to consider: have a comprehensive solution among microcosmic policies, finance policy, investment policy and other policies to control and balance the relationship between risks and returns; have appropriate policies to regulate funds to stimulate investment in the long term.

Research and development (R&D) projects are often pursued through a project portfolio (PP). R&D PPs involve many stakeholders, and without proactive management, their interactions may lead to conflict risks. These conflict risks change dynamically with different stages of the PP life cycle, increasing the challenge of PP risk management. Existing conflict risk research mainly focuses on source identification but lacks risk assessment work. To better manage the stakeholder conflict risks (SCRs) of R&D PPs, this study employs the dynamic Bayesian network (DBN) to construct its dynamic assessment model.

This study constructs a DBN model to assess the SCRs in R&D PP. First, an indicator system of SCRs is constructed from the life cycle perspective. Then, the risk relationships within each R&D PPs life cycle stage are identified via interpretative structural modeling (ISM). The prior and conditional probabilities of risks are obtained by expert judgment and Monte Carlo simulation (MCS). Finally, crucial SCRs at each stage are identified utilizing propagation analysis, and the corresponding risk responses are proposed.

The results of the study identify the crucial risks at each stage. Also, for the crucial risks, this study suggests appropriate risk response strategies to help managers better perform risk response activities.

This study dynamically assesses the stakeholder conflict risks in R&D PPs from a life-cycle perspective, extending the stakeholder risk management research. Meanwhile, the crucial risks are identified at each stage accordingly, providing managerial insights for R&D PPs.

The purpose of this study is to conduct a systematic content review and bibliometric analysis of the current research trends, core concepts and knowledge mapping on the topic Islamic Banking and Finance (IBF) during Covid-19. Apart from highlighting the contributions of prolific authors, prominent institutions and countries, a comprehensive review of a significant number of documents using co-citation and co-word analysis is carried out for the science mapping.

A data set of 125 papers was collected published in Scopus database during the period December, 2019 and January 5th, 2023. Yearly publications, most-cited papers and authors, active sources, affiliations and countries are highlighted with descriptive analysis. Knowledge structure of the topic was mapped with investigating the social, intellectual and conceptual structures of IBF research. Content analysis is carried out to uncover the underlying research clusters that shape the scientific knowledge structure of studies.

A diverse group of authors and institutions contribute to the growing body of knowledge on the topic. IBF is adopting new paradigms and frameworks to integrate FinTech, crowd funding and Islamic social finance to provide sustainable solutions in both crisis and normal periods. The research on IBF is classified in to three themes: “financial markets in Covid-19,” “modeling risk and market regimes” and “FinTech and Islamic social finance.”

This study collects data only from Scopus database. Future studies must include research articles from other databases such as, Web of Sciences.

This study highlights research gaps in the existing literature and provides directions for future research.

This study aims to explore credit scoring regulations, governance, variables and methods used by peer-to-peer (P2P) lending platforms in key players of the Association of Southeast Asian Nations (ASEAN) region’s P2P, Indonesia, Malaysia and Singapore.

This study explores the P2P Lending characteristics of the three countries using qualitative literature review, interview, focus group discussion and desk research.

This study concludes that the credit scoring variables used by the countries’ companies are almost the same. Key drivers of the differences are countries’ regulations, management/business core value and credit scoring data processing methods.

Ultimately, this research provides a comprehensive view for investors, businesses and researchers on the topic of ASEAN credit scoring governance and will help them navigate the complexities and improve their awareness on the importance of credit scoring governance in P2P lending companies.

This research provides an in-depth perspective on how P2P lending companies, credit scoring governance and regulations in the biggest three countries in Southeast Asia.

This study analyzes sustainable practices adopted by Italian firms to enhance the circularity of packaging and related results in terms of environmental improvements.

The authors developed an empirical analysis using publicly available data from the National Consortium of Packaging (CONAI) in Italy, which consists of 603 circular packaging projects. The authors ran both descriptive and prescriptive analyses to determine individual sustainable practices and portfolios adopted to enhance packaging circularity and to verify related reductions in terms of CO2 emissions as well as energy usage and water consumption.

The findings reveal that firms are more accustomed to focusing on single sustainable practices than on portfolios of practices to achieve packaging circularity. Raw material saving and logistics optimization are the most frequent sustainable practices adopted by firms to improve circularity of packaging. The reuse of packaging allows firms to simultaneously reduce CO2 emissions, energy usage and water consumption. Preferences in terms of portfolio of sustainable practices are strictly linked to the types of materials used for packaging and environmental targets.

The authors investigate environmental practices that firms adopt to support packaging circularity, and the authors detect portfolios of sustainable practices that positively impact environmental performance indicators. This research extends a significant glimpse into the portfolio of sustainable practices for packaging in the circular economy implemented by firms, filling academic gaps and indicating business opportunities and avenues for economic development.

One important study in the portfolio investment is the study of the optimal asset allocations. Markowitz is the pioneer of modern portfolio theory that analyses the performance of portfolio based on the mean (reward) and variance (risk). Motivated by the Markowitz's mean variance model, the purpose of this paper is to propose a new portfolio optimization model that takes into consideration both processes of purification and screening, which are key to constructing a Shariah-compliant portfolio. In practice, this paper introduces a stochastic purification variable and a probabilistic screening constraint into a portfolio model.

First, the authors study the stochastic nature of purification variable and apply it to both investment and dividend purification. Second, recognizing that the importance of on-going screening could adversely affect the portfolio strategy, the authors impose probabilistic constraints to control the risk of compliance change. They evaluate the proposed model by formulating the screening constraints at both asset and portfolio levels, together with three different financial screening divisors that are broadly used by the international Shariah boards. The authors also conduct an extensive empirical study using a sample of Shariah-compliant public companies listed on the Indonesia Stock Exchange.

Based on the empirical example presented in this paper, the authors found that the purification variable in the proposed model is closer to the practice in the Sharia capital market in terms of the nature of the non-constant data, and this variable reduces the total income of portfolio which has not been captured in the previous literature. The authors also have successfully derived the portfolio screening constraint to mitigate the risk of the asset change to be non-compliant in the future.

Based on the authors’ knowledge, this is the first paper that proposed the stochastic purification and the dynamic of screening processes into the Shariah portfolio model. This paper also examines the impact of non-short-selling, purification and screening policies to the performance of Shariah portfolio.