Journal of Financial Management, Markets and Institutions

In this study, the key areas and current trends in the field of big data applications in the insurance industry are identified, along with suggestions for future research initiatives. We identified the most prominent authors, journals, organizations, and countries based on their total publications and citations, showing their significance within the network, using bibliometric analysis on a sample of 191 articles retrieved from Scopus from 1976 to 2021. VOSviewer and R-Biblioshiny tools were used to generate the bibliometric output on these retrieved papers. The findings showed that although while a good number of writers from other parts of the world contributed to the literature on big data applications in the insurance industry, during this time, most research papers have listed the United States, India, and China as their affiliated countries. The yearly publication was either one or two, with some discontinuity, from 1976 to 2011, but since 2012, it has increased, exhibiting an exponential growth tendency. The three journals “Risks,” “Applied Stochastic Models in Business and Industry,” and “Expert Systems with Applications” are the most popular for including a sizable number of papers in the field of big data technologies in the insurance sector. Each of the top 10 authors in this field published two research papers during these 46 years. Seven areas, including fraud detection and prevention, risk assessment, pricing & rate making, technology utilization, risk management, claim processing & prediction, and finally digitalization, were the major focus of research papers on bigdata applications in the insurance business. The human-centered AI system development, adoption of wearable technology, personalization, and other topics were found to have received very little attention in this study. As a result, the researchers may now direct future research in this area. This study is completely new of its kind in the domain of insurance though few documents are available on the broad concept of finance.

Academic literature and market practitioners have always devoted great attention to the analysis of asset management products, with particular regard to fund classification and performance metrics. Less attention has been paid to rating methodologies and to the risk of attributing positive ratings to underperforming asset managers. The most widespread rating criterion is the ordinal one, which is based on the assumption that the best asset managers are those who have performed better than their competitors regardless of their ability to achieve a given threshold (i.e. a positive overperformance against the benchmark). Our study, after a description of the most common risk-adjusted performance measures, introduces the idea of attributing the rating on a cardinal basis, setting in advance a given threshold that should be achieved to receive a positive evaluation (i.e. a rating equal to or higher than 3 on a scale of 1–5). The empirical test conducted on a sample of funds (belonging to the main equity and bond asset classes) made it possible to quantify the effects of the cardinal approach on the attribution of the rating and on the probability of assigning a good rating to underperforming funds. Empirical analysis also highlighted how the cardinal method allows, on average, better performance than the ordinal one even in an out-of-sample framework. The differences between the two methodologies are particularly remarkable in efficient markets such as the North American equity market. The two rating assignment systems were also analyzed using contingency tables to test the ability to anticipate the default event (underperformance relative to the benchmark). The policy suggestion emerging from our study concerns the significant impact of the rating criterion in reducing the risk of recommending funds that, despite a good rating, have failed to perform satisfactorily and are unlikely to do so in the future either.

The European Union Taxonomy on Sustainable Finance is the first comprehensive classification designed to identify the extent to which an economic activity is environmentally sustainable. This paper investigates how the EU Taxonomy affected corporate investments for Taxonomy-eligible companies compared to non-eligible ones. We applied a difference-in-difference model using a dataset of 130,918 firms from 27 EU Member States, using 2019 as a threshold. Our findings show that the introduction of the EU Taxonomy per se did not result in an increase in corporate investments among Taxonomy-eligible companies; rather, firms’ size and Taxonomy-eligibility uncertainty played a key role in explaining variation in corporate investments. The implications are twofold: the participatory and communication approach of the European Commission implemented with the EU Taxonomy is effective in nudging corporate investments aimed at sustainability. However, this only happens when the regulation provides an unambiguous sector specification. Thus, it is important that the regulatory interventions clearly define what is sustainable.

We examine how changes in research and development expenditures (R&Ds) at the beginning of the 2008 financial crisis affected firms’ market performance immediately after the crisis. Our sample contains 2470 firms listed on NYSE and NASDAQ. We find that firms that increased their investment in R&Ds had higher risk-adjusted returns in 2009. The highest risk-adjusted returns were among firms that decreased dividends but increased their R&Ds and are associated with the firms’ managerial efforts before and during the crisis. These findings underscore the importance of investment in innovations even during severe cash constraints and document that firms benefit from preserving cash by cutting other than R&D expenses.

We examine the stock return implications of corporate-defined benefit pension plans in innovative U.S. firms and in R&D- and patent-sorted portfolio specifications. We find that investors underreact to firms increasing off-balance-sheet liabilities. Pensions represent material off-balance-sheet liabilities: in our extensive and large sample (1985–2017, 2541 firms for 26,522 observations), entities with pension plans are 38% more levered when we integrate pension liabilities and assets into the firms’ capital structure. We find that R&D-intensive firms increasing the size of their pension liability subsequently underperform their benchmark returns. Through six alternative R&D-market capitalization portfolios, we also find that this association is stronger for smaller firms. Finally, the relationship remains persistent over a long horizon. These findings are robust to endogeneity concerns addressed through instrumental variables, propensity score matching, and Heckman correction.