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The purpose of this study is to explore the ways in which the cost-of-living crisis affected emotional support access and availability among multiply-marginalised UK-based youth.

This study reports findings from early stages of a multiphase youth participatory action research (YPAR) project. In all, 12 young residents of Tower Hamlets London (ages 16–22 years) employed as peer researchers conducted 14 focus groups with 44 residents of Tower Hamlets over a six-month period. Data were analysed using principles of reflective thematic analysis.

Analyses produced salient themes that identified barriers to obtaining emotional support from parents and carers, described the utility of diverse support networks and elucidated the impact of the cost-of-living crisis on emotional support and youth well-being.

This study has several limitations pertaining primarily to study design, sample size and sample composition that limit generalizability of findings. The findings indicate that the cost-of-living crisis markedly constrained the participants’ access to and availability of formal and informal support from others.

The findings from this research will influence the design and delivery of policy and services to better meet the needs and experiences of UK-based young people and their families.

This project has the potential to increase understanding of how families can provide effective emotional support to young people and so improve the lives of Londoners now and in the future.

To the best of the authors’ study, this study is the first to use a YPAR approach to exploring the impact of the cost-of-living crisis on UK-based youth.

In the present study, we investigate whether enhanced momentum strategies outperform price momentum strategies and if they show greater resilience and stability under adverse market conditions. We also examine if such strategies are explained by prominent asset pricing models or are a result of behavioral mispricing.

Data consist of the equity shares of all companies listed on National Stock Exchange over the study period. To check the efficacy of enhanced momentum over price momentum, six momentum strategies have been designed and their raw as well as risk-adjusted returns using multi-factor models have been observed. Behavioral mispricing has been examined by constructing an investor attention index. Finally, few robustness tests have been performed to confirm the results.

We find that an enhanced momentum strategy which combines relative and absolute strength momentum outperforms conventional price momentum strategy in India. We also demonstrate that rational pricing models are not able to explain momentum profits for any of the strategies. Finally, we observe that investor overreaction is the possible explanation of momentum profits in India. Thus, our results confirm the role of behavioral mispricing in explaining momentum returns.

Our research is the first major attempt to study enhanced momentum strategies in the Indian context. We experiment with several new enhanced momentum strategies which have not been explored in prior literature. The findings have strong implications for global portfolio managers who wish to design profitable trading strategies.

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.

This paper aims to explore the effects of parents’ support factors on Chinese university students’ digital entrepreneurship motivation on live streaming platforms. Based on the Social support theory, this study divides influencing factors into emotional, instrumental, informational and appraisal aspects. Meanwhile, considering the impact of China’s regional differences, the paper refers to the Regional difference theory and performs a multi-group analysis to assess the differences based on Chinese university students’ regional backgrounds.

By testing 556 samples based on the partial least squares path modelling and variance-based structural equation modelling, all support factors parents provide can stimulate Chinese university students’ digital entrepreneurship motivation.

Based on the multi-group comparison, parents’ informational support exerts a more substantial influence on the digital entrepreneurship motivation for university students from central and east regions rather than those from the western region, and parents’ instrumental support exerts a lower influence on digital entrepreneurship motivation for east university students than for west university students.

This paper applies the Social support theory as a theoretical framework to divide the impact factors, and it uses the Regional difference theory as a guide for the multi-group analysis of correlations, which is significant for online entrepreneurial motivation research and a better understanding of student groups. In addition to testing the hypotheses, the study also measures the importance–performance map analysis to explore additional findings of influencing factors and discuss managerial implications.

We evaluate the effect of political connections on the stock valuation of emerging market firms following the announcement of cross-border mergers and acquisitions (M&As). We further analyze the moderating roles of home and host market environments.

Our analysis of 361 Chinese cross-border M&A transactions during 2014–2018 employs an event-study methodology to assess the cumulative abnormal return (CAR) for acquirers. To test our hypotheses, we utilize a multiple regression model.

Politically connected firms experience a decrease in firm value following the announcement of cross-border M&As. However, this negative effect is weakened when the firm’s home region is more market-oriented, reflected by economic activity driven primarily by market mechanisms rather than government intervention. In contrast, the negative effect is strengthened when the host country exhibits higher governance quality, characterized by sound legal structures, labor regulations and developed capital markets.

Extending beyond previous studies on cross-border M&A performance, we analyze firm value based on signaling theory. Our findings reveal that market investors view cross-border M&As undertaken by politically connected firms from emerging economies with caution, resulting in a decline in acquirer value. Moreover, investors react more positively to cross-border M&As by politically connected acquirers in truly market-based regions. Conversely, investors expect that politically connected acquirers would encounter additional hurdles when executing cross-border M&As if the host country has high-quality governance.

Building on expectancy violations theory, this study aims to investigate the role of negative performance feedback in firm’s mergers and acquisitions (M&A) intensity, a typical risky strategic option which might entail negative reactions from shareholders, and also examine the moderating effects of top management teams (TMTs) regulatory focus on this relationship.

The authors use a longitudinal panel sample of 2,042 Chinese A-share listed manufacturing firms and data for the years between 2007 and 2019 collected from multiple data sources. Furthermore, the authors also conducted supplementary analyses and various robustness checks of the key variables.

The findings show that both the intensity and duration of negative performance feedback negatively impact firms’ M&A intensity. Besides, the effect of negative performance feedback on M&A intensity will be magnified when the focal firm of TMTs with high prevention focus.

During the period of performance depression, TMTs are supposed to focus on stability, keep an eye on potential risks and be prudent in making decisions like walking on eggshells to avoid making further losses.

This study develops a core mechanism – managers of underperformance firms prioritize meeting shareholder expectations as their foremost task to ensure minimal negative repercussions – and also highlights the role of fit between TMT prevention focus and negative performance feedback on M&A intensity.

The suppliers of experimental resources required in megaprojects are driven by short-term interests, presuming that participation in the digital platform would only increase their inputs and fail to rapidly expand their revenue, resulting in their insufficient motivation to participate. This paper aims to design effective incentives for these suppliers exhibiting the aforementioned behaviour to drive them to participate and actively share their resources on the platform.

This paper develops incentives for applying the digital platform for experimental resource sharing by using a reverse induction approach to model and solve an incomplete information game. It compares the traditional experiment management mode and the new mode of applying the digital platform, taking the degree of sharing experimental resources on the platform as the variable and constructing three incentive models. By analysing these different degrees of sharing and the different experimental and informatisation capabilities of the suppliers, it could obtain the optimal incentive scheme for changes in sharing behaviour.

The results show that the designed incentives could increase the participation of suppliers in the platform and the number of their shared resources and make the benefits of both the supplier and the demand side reach the optimal state of a win-win situation. However, a higher degree of sharing by suppliers does not yield better results. In addition, the incentive coefficients for this degree should be set based on the suppliers’ different experimental and informatisation capabilities and the ratio of input cost-sharing, so as to avoid blind inputs from both supply and demand.

This study fills the research gap regarding incentives of the digital platform of experimental resource-sharing for megaprojects; it contributes to the body of knowledge by providing a quantitative perspective of understanding the experimental resource-sharing behaviour that motivates the usage of the digital platform. Furthermore, it reveals the incentive mechanism for application in different scenarios, and quantitative analysis is conducted to provide practical insights into promoting the new experiment management mode in megaprojects for more effective incentivisation.

The present research aims to study the behavioural intention to use the digital currencies issued by the central bank through the lens of technology acceptance and switching behaviour perspective. The study also proposes to analyse the role of financial constructs to explain the adoption intention.

The current study develops a model by integrating the unified theory of acceptance and use of technology (UTAUT) and the push–pull–mooring (PPM) theory of switching behaviour. It amends the same by including financial literacy, financial inclusion and trust. A sample data of 419 respondents has been collected through a structured questionnaire and the PLS-SEM approach has been used for data analysis.

The findings suggest that UTAUT and PPM models can significantly predict individuals' readiness to adopt the central bank digital currency (CBDC). More precisely, performance expectancy, social influence, government support, relative advantage and task-technology fit jointly determine the adoption behaviour. Besides, the financial constructs also affect the intention to use CBDC.

The study is largely based on a quantitative approach with cross-sectional data from an Indian sample. Thus, the findings may benefit from a longitudinal approach with mixed-method data analysis. However, the study elaborates on several implications for policymakers and research scholars.

The present study uniquely integrates the technology adoption perspective with switching behaviour applied to the migration studies. Given the nascent stage of CBDC implementation in many countries, the current study uses a triangulation approach to enhance the understanding of its adoption behaviour.

This paper aims to examine how the number of short videos posted and the number of influencers employed, two important strategies in short video marketing, affect consumer behavior and how price discounts moderate the effects of influencer endorsement on consumer browsing and purchasing behavior.

Drawing on the literature on influencer endorsement, this study used an ordinary least square model to empirically examine the two effects of endorsement strategies in increasing product traffic and sales for consumers at a short video app, Douyin (TikTok).

The results show that the number of short video ads produces the classic inverted U-shape for traffic and sales, and both effects were strengthened under a high discount condition. Whereas the number of influencers has a positive effect on traffic but produces an inverted U-shape for sales, both effects were undermined under a high discount condition.

This study is the first to explore the two distinct effects (repetition effect and diffusion effect) of influencer endorsement on browsing and purchasing behavior and theorize about the moderate effects of discounts on these effects.

Recently, due to the practicability in several domains, generative adversarial network (GAN) has successfully been adopted in the field of natural language generation (NLG). The purpose of this paper focuses on improving the quality of text and generating sequences similar to human writing for several real applications.

A novel model, GAN², is developed based on a GAN with dual adversarial architecture. We train the generator by an internal discriminator with a beam search technique to improve the quality of generated sequences. Then, we enhance the generator with an external discriminator to optimize and strengthen the learning process of sequence generation.

The proposed GAN² model could be utilized in widespread applications, such as chatbots, machine translation and image description. By the proposed dual adversarial structure, we significantly improve the quality of the generated text. The average and top-1 metrics, such as NLL, BLEU and ROUGE, are used to measure the generated sentences from the GAN² model over all baselines. Several experiments are conducted to demonstrate the performance and superiority of the proposed model compared with the state-of-the-art methods on numerous evaluation metrics.

Generally, reward sparsity and mode collapse are two main challenging issues when adopt GAN to real NLG applications. In this study, GAN² exploits a dual adversarial architecture which facilitates the learning process in the early training stage for solving the problem of reward sparsity. The occurrence of mode collapse also could be reduced in the later training stage with the introduced comparative discriminator by avoiding high rewards for training in a specific mode. Furthermore, the proposed model is applied to several synthetic and real datasets to show the practicability and exhibit great generalization with all discussed metrics.