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The debate between modernity and post-modernity has taken centre stage of philosophical discourse since before the outbreak of the COVID-19 pandemic. Even after the advent of the pandemic, it has yet to be resolved with each camp claiming to be the victor over the other, and therefore sealed a permanent dichotomy between both schools of thought. With COVID-19 menacing the world, it is timely to revisit the debate and to come to a conclusion that once and for all will dissolve the fissure between these two dominant theoretical paradigms. It is also hoped that the conclusion made can pave the way for a new theoretical paradigm that is more comprehensive and efficient in facing the post-COVID world. By referring to Southeast Asian communities namely the Malaysian, Indonesian, and Singaporean publics, the chapter puts forward the argument that the existing dichotomy between these two theoretical frameworks could no longer sustain its status-quo. This is because the abruption of confusions amongst the public over the issues surrounding the COVID-19 vaccines – and hence on modern science and modernity itself – requires us to stop from rigidly devoting ourselves towards either one of these theoretical paradigms. Based on the case studies, the chapter then suggests for revisions to be made by benefitting from the best of both theoretical paradigms and by omitting those concepts that are no longer effective to be adopted in facing the post-pandemic world.

In 2016, the oversight body for policing in England and Wales reported a national shortage of 5,000 qualified detectives and other investigators. Commissioned by the National Police Chiefs Council, this research critically assessed initiatives taken by the Police Service of England and Wales since that time to remedy that shortfall. The purpose of this study is to answer the question, “To what extent can fast-track investigator recruitment and training schemes enhance the PSEW’s investigative capacity and capability?”

Between 2019 and 2020, the authors examined four cases in England and Wales. Three were novel fast-track programmes for new joiners. The fourth was an investigator resilience programme. This study was qualitative and interpretive in nature. The authors carried out systematic reviews of the literature on investigative policy and detective work. The authors reviewed internal evaluations completed by Forces A, B and C. The authors interviewed respondents (n = 82) and supplemented the interview data with survey data (n = 45; N = 127); the authors analysed the data thematically and reviewed the findings in the context of systems theory and social identity theory.

The major themes identified by the analysis presented in this study were marketing and recruitment, attrition and progression, acceptance of the trainees, training and trainees’ welfare and well-being. The programmes were not as successful as they might have been because of systems failures. Principally, ineffective coordination of the programmes with other elements of the forces on which their success also relied (such as training, human resources and detective, departments). A critical limiting factor was the lack of experienced, skilled detectives able to train, support and mentor the trainees.

This paper provides empirical evidence of the efficacy of a whole systems approach to organisational change. This study evidences the crisis in police investigative practice that limits the service that the police can provide to victims and communities and ultimately threatens police legitimacy. This study provides insights from police practitioners into detective work in the modern era and highlights areas where improvement is necessary. The research on which this study is based was commissioned by the National Police Chiefs Council. Thus, there is institutional buy in to its findings.

With its roots firmly in empiricism, this paper presents the first scholarly evaluation of the police's attempts to make up a massive shortfall in investigators. To the best of the authors’ knowledge, this paper presents the first attempt to explain the challenges the police faced in this context, thematically. This study’s focus on systems and on human behaviour is intended to inform a wide audience beyond British policing.

Since the development of the COVID-19 vaccinations, questions surrounding race have been prominent in the literature on vaccine uptake. Early in the vaccine rollout, public health officials were concerned with the relatively lower rates of uptake among certain racial/ethnic minority groups. We suggest that this may also be patterned by racial/ethnic residential segregation, which previous work has demonstrated to be an important factor for both health and access to health care.

In this study, we examine county-level vaccination rates, racial/ethnic composition, and residential segregation across the U.S. We compile data from several sources, including the American Community Survey (ACS) and Centers for Disease Control (CDC) measured at the county level.

We find that just looking at the associations between racial/ethnic composition and vaccination rates, both percent Black and percent White are significant and negative, meaning that higher percentages of these groups in a county are associated with lower vaccination rates, whereas the opposite is the case for percent Latino. When we factor in segregation, as measured by the index of dissimilarity, the patterns change somewhat. Dissimilarity itself was not significant in the models across all groups, but when interacted with race/ethnic composition, it moderates the association. For both percent Black and percent White, the interaction with the Black-White dissimilarity index is significant and negative, meaning that it deepens the negative association between composition and the vaccination rate.

The analysis is only limited to county-level measures of racial/ethnic composition and vaccination rates, so we are unable to see at the individual-level who is getting vaccinated.

We find that segregation moderates the association between racial/ethnic composition and vaccination rates, suggesting that local race relations in a county helps contextualize the compositional effects of race/ethnicity.

Unlike other types of corporate disclosure, corporate political disclosure (CPD), which is the disclosure of corporate political contributions and the related governing policies and oversight mechanisms, does not provide completely new information to stakeholders. Some of the information disclosed in CPD is available from other public records (e.g. the Federal Election Committee website or OpenSecrets website). Given this unique feature of CPD, it is interesting to investigate the cost and benefit tradeoff for firms of altering their CPD practice in response to policy and political uncertainty.

This study employs recently developed indexes of aggregate economic policy uncertainty (EPU) and a novel dataset of CPD transparency to examine the impact of EPU on CPD transparency and how the proprietary cost of corporate political activities moderates this association. The sample consists of S&P 500 companies from the 2012 to 2019 period.

The authors document that firms mitigate the heightened information asymmetry associated with higher aggregate EPU by increasing CPD transparency. The positive association between EPU and CPD is less pronounced for firms that are more sensitive to EPU, for firms that more actively manage EPU through corporate political contributions or lobbying activities and for firms that are followed by more analysts. The authors also find that more transparent CPD helps to mitigate the information asymmetry caused by heightened EPU. This study’s results hold when the authors control for other types of voluntary corporate disclosure.

This study contributes to the emerging literature on the determinants of CPD transparency by identifying EPU's positive impact on CPD transparency. This study also provides empirical evidence that the proprietary costs arising from the controversial nature of corporate political activities dampen firms' incentives to provide transparent CPD in response to heightened EPU, and that information on corporate political activities gathered and processed by financial analysts seems to lower the marginal benefit to companies of publicizing CPD on their own website.

This paper sheds light on the impact of market risk measures on systemic risk. Market risk, which is captured by the volatility of stock market returns, is also decomposed into systematic and idiosyncratic risks.

The author uses the five-factor asset pricing model and systemic risk methodologies to derive market and systemic risk measures, respectively. Using a sample of 2,667 US banks for over 30 years and employing panel data estimation techniques, the author tests the said relationship.

It is shown that idiosyncratic risk can surge systemic risk, while systematic risk plays a less important role. Results survive a battery of tests, including different systemic risk measures, controlling causality and interacting with bank size, market fear and crisis periods.

These findings call for regulatory intervention, especially for large banks with high idiosyncratic risk.

This is the first paper that provides a more granular picture of the relationship between market and systemic risk from the US banking industry for more than 30 years.

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.

The study measures the total systemic risks and connectedness across commodities, stocks, exchange rates and bond markets in Africa during the Covid-19 pandemic.

The study uses the Diebold-Yilmaz spillover and connectedness measures in a generalized VAR framework. The author calculates the net transmitters or receivers of shocks between two assets and visualizes their strength using a network analysis tool.

The study found low systemic risks across all assets and countries. However, we found higher systemic risks in the forex market than in the stock and bond markets, and in South Africa than in other countries. The dynamic analysis found time-varying connectedness return shocks, which increased during the peak periods of the first and second waves of the pandemic. We found both gold and oil as net receivers of shocks. Overall, over half of all assets were net receivers, and others were net transmitters of return shocks. The network connectedness plot shows high net pairwise connectedness from Morocco to South Africa stock market.

The study has implications for policymakers to develop the capacities of local investors and markets to limit portfolio outflows during a crisis.

Previous studies have analyzed spillovers across asset classes in a single country or a single asset across countries. This paper contributes to the literature on network connectedness across assets and countries.