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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.

In this chapter, the author explains the three components of Trilogy of Taoist Leadership – responsible business, responsible management and responsible leadership. The concepts of business, management and leadership are defined from multiple angles, including an etymological perspective. The historical origins and evolution of these three areas are explored to provide a comprehensive understanding. The chapter concludes with a discussion of the interconnections among the trilogy's components and illustrates how the trilogy can contribute to the development of sustainable organizations.

With production systems become more digitized, data-driven maintenance decisions can improve the performance of production systems. While manufacturers are introducing predictive maintenance and maintenance reporting to increase maintenance operation efficiency, operational data may also be used to improve maintenance management. Research on the value of data-driven decision support to foster increased internal integration of maintenance with related functions is less explored. This paper explores the potential for further development of solutions for cross-functional responsibilities that maintenance shares with production and logistics through data-driven approaches.

Fifteen maintenance experts were interviewed in semi-structured interviews. The interview questions were derived based on topics identified through a structured literature analysis of 126 papers.

The main findings show that data-driven decision-making can support maintenance, asset, production and material planning to coordinate and collaborate on cross-functional responsibilities. While solutions for maintenance planning and scheduling have been explored for various operational conditions, collaborative solutions for maintenance, production and logistics offer the potential for further development. Enablers for data-driven collaboration are the internal synchronization and central definition of goals, harmonization of information systems and information visualization for decision-making.

This paper outlines future research directions for data-driven decision-making in maintenance management as well as the practical requirements for implementation.

This study aims to examine the role of fund family size on the money flow of Saudi Arabian open-end equity mutual funds. The author also investigates whether the relationship between fund flow and past return varies based on the fund's family size.

The study analyses 256 equity funds that operated in Saudi Arabia from 2006 until 2017. Pooled and fixed-effect regression models are used to test the relationship between mutual fund flow and family size.

The results indicate that fund flow is higher for large size family funds. The results also show that the relationship between mutual fund flow and past performance is more pronounced for large size families, which supports the concept that investors pay extra attention to funds' return and size.

The author provides evidence of the significant effect of family size of mutual funds on future money flow, which helps fund managers to understand investors' motivations for allocating their cash.

This paper contributes to the literature by examining the impact of family size level on the interaction between fund flow and past performance. This study is believed to be the first to investigate the family size factor in Saudi Arabia using a comprehensive data set.

The purpose of this paper is to investigate the relationship between dividend policy and the life cycle of firms in India. In addition, this study intends to examine the variation in dividend behaviour over the life cycle of a firm. The study anticipates that a firm's dividend behaviour varies over its life cycle.

To scrutinize the validity of the proposition, the authors classify 1968 non-financial industrial firms listed at Bombay Stock Exchange (BSE) into growth, mature and stagnant firms over the period 2000–20. Additionally, to check the robustness of the results, they use an array of techniques such as analysis of variance, pooled ordinary least squares, fixed effects models and random effects models.

The empirical findings suggest that dividend behaviour varies over a firm's life cycle. Specifically, stagnant firms are paying significantly higher dividends than growth firms. Mature firms are paying significantly higher dividends than growth firms. The results are consistent after controlling the effects of firm's size, profitability, leverage, operating risk, systematic risk and growth opportunities.

The findings are useful for corporate decision makers in establishing an appropriate dividend policy conditional on firms' life cycle stage and for shareholders in making investment decisions.

The relation between dividend policy and firm life cycle has not been examined before in the context of Indian stock market. Thus, this research bridges this gap in the literature.

This study aims to investigate the effects of peers on corporate payout policies in one of the largest emerging markets – India. It also examines the motives for mimicking payout decisions.

The sample is composed of 3,024 non-financial and non-government firms listed on the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE) for the period 1995 to 2020. To encounter the endogeneity problem, the instrumental variable technique based on peer firms' idiosyncratic risk is used to estimate the effects of peers on firms' payout policy. To define peer reference groups, the authors use the basic industry classification of the firms.

The results indicate a significant positive impact of peers on firms' dividend policies in India. A firm with all dividend-paying peers is more likely to declare dividends than the one with no dividend-paying peers. Further, peer effects are found to be more pronounced amongst larger and older firms, thus supporting the rivalry theory of mimicking.

To the best of the authors' knowledge, the present study is the first of its kind that attempts to understand peer effects on payout decisions in an emerging market India, that offers a unique institutional setting. Moreover, the authors extend the existing literature by investigating the peer effects on a firm's payout policies considering various firm-level characteristics, such as growth opportunity, cash holding, financial constraint and profitability, which previous studies have not taken into consideration. These results provide additional insights into the heterogeneity and motives behind peer effects.

This study investigates the origins and elaboration of the managerial “unitary” frame of reference associated with Alan Fox, focusing on unionised firms: the industrial relations context, intellectual roots, elaboration, adaptation by other writers, and international applicability.

Tracing the above requirements through contemporaneous sources.

Fox’s designation of the unitary frame needs to be understood in its 1960s’ context, particularly the promotion of “productivity bargaining”, and its furthering through management training and education. Fox’s specific contribution is identified. Subsequent UK writers have underplayed the importance of the legal dimension of managerial authority, especially relevant in the US context, while other extra-economic factors bolster the managerial unitary frame in authoritarian societies such as China.

The use of Fox's neglected 1960s’ writings; tracking how Fox developed the unitary frame concept and how it was funnelled into the narrow parameters of non-unionism by subsequent writers; identifying its applicability beyond the UK (with the USA as a historical example and China as a contemporary one).

Competitiveness is an individual difference variable that incorporates factors generally associated with the desire to excel in comparison to others and the enjoyment of competition. There is still much debate on whether it is helpful or harmful, which may stem from the scattered ways in which it is studied. Thereby, this study aims to properly synthesize the literature concerning the prevailing correlates, underlying theory and frequent applications of competitiveness and to set forth an outline of domains in need of further research and exploration.

The authors do so by using two methods of analysis on a representative sample of 546 peer-reviewed publications.

The authors find that competitiveness research has and will continue to grow expeditiously, but its complexity and cloudiness have not yet been attenuated.

The study uncovers opportunities for pertinent future research on competitiveness to grow more productively and collaboratively by highlighting salient works and identifying the fragmentations that have led the literature into a state of disarray.

This study explored the influence of Dervin’s sensemaking methodology (SMM).

Citation context analysis was used to identify the most influential SMM concepts in 948 articles citing 34 SMM-related studies by Dervin that were published between 1983 and 2017. Moreover, the bibliometric method and content analysis were incorporated to examine the disciplines and research topics influenced by the SMM-related studies and the role of cited content in SMM-related studies.

The influence of SMM is concentrated in information behavior research in the field of library and information science (LIS). The 1992 book chapter From the mind’s eye of the user was most frequently cited, followed by the first SMM study from 1983; 14 of the 18 content categories were relevant to SMM. “Sensemaking,” at the core of SMM, was the most influential cited concept, primarily cited from the 1983 SMM-related study. Although the SMM was developed as a research method, it has not been primarily applied to design research methods in other studies.

This study explored the interdisciplinary influence of Dervin’s SMM from several aspects and demonstrated the complex information dynamics between SMM-related works and citing articles.