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This chapter examines the relationship between directors and officers (D&O) liability insurance and stock-price synchronicity by testing competing corporate governance-related monitoring and moral hazard-related agency conflict hypotheses. Testing a sample of stocks listed on the Taiwan Stock Exchange and the Taipei Exchange for 2008–2020, the empirical results of this study indicate that D&O insurance in Taiwan is negatively correlated to stock-price synchronicity. This negative relation is robust to a battery of tests, including those of fixed-effects regression models, alternative sample periods, alternative synchronicity measures, and alternative insurance measures. Further evidence indicates that this negative relationship is more pronounced among firms with greater agency problems, especially during periods of high market uncertainty. Overall, these findings support the corporate governance-related monitoring hypothesis, which posits that firms with greater D&O insurance are likelier to be characterized by better governance structures and information transparency. Additionally, their stock prices are more likely to reflect firm-specific information in a timely and precise manner, and they are more likely to have lower synchronicity with the industry and market.

The purpose of this paper is to address the opposing views of the relationship between directors’ and officers’ liability insurance (D&O insurance) and stock price crash risk in a major Asian emerging stock market.

This paper finds an endogenous relationship between D&O insurance and stock price crash risk. Hence, the two-stage least squares regression analysis is used to address the endogeneity issue when the relationship is examined. Moreover, this paper further controls the quality of other corporate governance mechanisms to investigate whether D&O insurance still has an effect on stock price crash risk.

The effect of D&O insurance coverage is significantly negatively related to firm-specific stock price crash risk in Taiwan. More importantly, even when the quality of other corporate governance mechanisms is controlled, the negative relationship between D&O insurance coverage and firm-specific stock price crash risk remains significant. The evidence supports that D&O insurance serves as an effective external monitoring mechanism, strengthens corporate governance, and thus reduces stock price crash risk.

Emerging Asian markets suffer a dearth of research on the relationship of D&O insurance coverage and the firm-specific stock price crash risk. Investigating the relationship in Taiwan, the present study fills the research void. The findings show that D&O insurance plays an important role in reducing stock price crash risk of Taiwanese firms even when other corporate governance mechanisms are in place.

The aim of this study is to investigate the role of Corporate Social Responsibility (CSR) in assisting firms in their response to public emergency crises under the integrated view of government emergency response.

Using event study and survival analysis method, the authors examine whether CSR can act as a stock price stabilizer for companies from China by splitting the stock price fluctuations into two phases – CSR price insurance, which decrease the shock on stock prices during the emergency crisis, and CSR price recovery, which helps stock prices rebound faster during the postcrisis phase.

The authors’ empirical results confirm the stabilizer role of CSR during crisis and that effective government response can strengthen such effect. Furthermore, the authors examine the different aspects of the government’s response and the impact of multiple waves of public emergency.

This study provides empirical evidence on the topic of CSR and the government’s response to public emergency under the emerging context.

The authors aim to analyze the impact of monetary policy interventions during the financial crisis of 2007-2009 on the stock prices of US insurance firms.

The authors use an event study methodology and a database of 89 policy announcements to analyze if monetary policy interventions could restore stability in the insurance sector. In addition, the authors conduct a second-stage analysis to identify the individual firms’ determinants of their stock market response.

The results indicate that the market reaction depends upon the type of policy intervention as well as the timing of the intervention. A second stage analysis examines firm level determinants of the insurers’ stock price responses and finds various firm specific factors also affect the insurers’ reaction to policy interventions.

First, to the best of the authors’ knowledge, this paper is the first to examine the impact of non-conventional policy announcements on firms from the insurance sector during the financial crisis. Moreover, the authors add to the literature an analysis on how conventional central bank announcements affect insurance firms.

This study aims to investigate whether firms purchase directors' and officers' liability (D&O) insurance when the country-level economic policy uncertainty (EPU) is high.

This study uses D&O insurance data from Chinese listed firms between 2003 and 2019 to conduct regression analyses to examine the association between D&O insurance and EPU.

The results show that government EPU, despite being an exogenous factor, increases the likelihood of firms' purchasing D&O insurance, and this effect is more pronounced when firms are exposed to great share price crash risk and high litigation risk, suggesting that firms intend to purchase D&O insurance possibly due to the accentuated stock price crash risk and litigation risk associated with EPU. In addition, the results indicate that the effect of EPU on the D&O insurance purchase decision is moderated by the provincial capital market development and internal control quality.

The study highlights the role of uncertain economic policies in shareholder approval of D&O insurance purchases.

The study enriches the literature on the determinants of D&O insurance purchases by documenting novel evidence that country-level EPU is a key institutional factor shaping firms' decisions to purchase D&O insurance.

Catastrophe (CAT) events associated with natural catastrophes and man-made disasters cause profound impacts on the insurance industry. This research thus reviews the impact of CAT risk on the insurance industry and how traditional reinsurance and securitized risk-transfer instruments are used for managing CAT risk.

This research reviews the impact of CAT risk on the insurance industry and how traditional reinsurance and securitized risk-transfer instruments are used for managing CAT risk. Apart from many negative influences, CAT events can increase the net revenue of the insurance industry around CAT events and improve insurance demand over the post-CAT periods. The underwriting cycle of reinsurance causes inefficiencies in transferring CAT risks. Securitized risk-transfer instruments resolve some inefficiencies of the reinsurance market, but are subject to moral hazard, basis risk, credit risk, regulatory uncertainty, etc. The authors introduce some popular securitized solutions and use Merton's structural framework to demonstrate how to value these CAT-linked securities. The hybrid solutions by combining reinsurance with securitized CAT instruments are expected to offer promising applications for CAT risk management.

The authors introduce some popular securitized solutions and use Merton's structural framework to demonstrate how to value these CAT-linked securities. The hybrid solutions by combining reinsurance with securitized CAT instruments are expected to offer promising applications for CAT risk management.

This research reviews a broad array of impacts of CAT risks on the (re)insurance industry. CAT events challenge (re)insurance capacity and influence insurers' supply decisions and reconstruction costs in the aftermath of catastrophes. While losses from natural catastrophes are the primary threat to property–casualty insurers, the mortality risk posed by influenza pandemics is a leading CAT risk for life insurers. At the same time, natural catastrophes and man-made disasters cause distinct impacts on (re)insures. Man-made disasters can increase the correlation between insurance stocks and the overall market, and natural catastrophes reduce the above correlation. It should be noted that huge CAT losses can also improve (re)insurance demand during the postevent period and thus bring long-term effects to the (re)insurance industry.

We review and synthesize the existing research on directors' and officers’ (D&O) liability insurance. Our objectives are (1) to examine the institutional forces and regulatory requirements that have influenced the development of D&O liability insurance; (2) to identify the factors that influence firms to purchase D&O liability insurance and explore the consequences associated with its usage and (3) to identify gaps in the current literature and provide recommendations for future research on D&O liability insurance.

We perform a systematic literature review (SLR) using the Preferred Reporting Items for a Systematic Review of Meta-Analysis (PRISMA) guidelines to examine archival studies that investigate the determinants and consequences of D&O liability insurance. Using a Boolean search strategy on the “Web of Science” (WoS) and PRISMA selection criteria, we review 64 published archival research articles and three working papers from 1987 to October 2023.

Our review reveals that disclosing detailed information regarding D&O liability insurance, such as total insurance premiums and coverage limit, is predominantly voluntary, except in Taiwan. Our findings suggest that the decision to purchase D&O liability insurance is influenced by litigation risk, which is determined by factors such as firm size, complexity and corporate governance variables. We also find that D&O liability insurance has implications for financial reporting, audit outcomes, investment behavior and capital market performance.

In the post-COVID era, where firms face pressure due to financial constraints, our research emphasizes the practical importance of carefully considering and understanding the impact of D&O liability insurance, particularly as it concerns the demand for such insurance.

To the best of our knowledge, this study represents the first systematic review of previous research on D&O liability insurance. Our review highlights some research gaps, particularly in relation to the implications for financial reporting practices, auditing outcomes, firm investment behavior and capital market consequences.

Introduction: The insurance industry has unprecedented growth, and the demand for insurance has outgrown in the recent past due to the prevailing pandemic. The companies have a large base of the data set at their disposal, and companies must appropriately handle these data to come out with valuable solutions. Data mining enables insurance companies to gain an insightful approach to map strategies and gain competitive advantage, thus strengthening the profits that will allow them to identify the effectiveness of back-propagation neural network (BPNN) and support vector machines (SVMs) for the companies considered under study. Data mining techniques are the data-driven extraction techniques of information from large data repositories, thus discovering useful patterns from the voluminous data (Weiss & Indurkya, 1998).

Purpose: The present study is performed to investigate the comparative performance of BPNNs and SVMs for the selected Indian insurance companies.

Methodology: The study is conducted by extracting daily data of Indian insurance companies listed on the CNX 500. The data were then transformed into technical indicators for predictive model building using BPNN and SVMs. The daily data of the selected insurance companies for four years, that is, 1 April 2017 to 21 March 2021, were used for this. The data were further transformed into 90 data sets for different periods by categorising them into biannual, annual, and two-year collective data sets. Additionally, the comparison was made for the models generated with the help of BPNNs and SVMs for the six Indian insurance companies selected under this study.

Findings: The findings of the study exhibited that the predictive performance of the BPNN and SVM models are significantly different from each other for SBI data, General Insurance Corporation of India (GICRE) data, HDFC data, New India Assurance Company Ltd. (NIACL) data, and ICICIPRULI data at a 5% level of significance.

Introduction: The insurance industry is one of the lucrative sectors of the economy. However, it is volatile because of the large chunk of data generated by the transactions taking place daily. However, every bit of it is responsible for creating market trends for stock investors to predict the returns. The specialised data mining techniques act as a solution for decision-making, reducing uncertainty in decision-making.

Purpose: There are limited studies that have examined the efficiency and effectiveness of data mining techniques across the companies in the insurance industry to date. To enable the companies to take exact benefit of data mining techniques in insurance, the present study will focus on investigating the efficiency of artificial neural network (ANN) and support vector machine SVM across insurance companies of CNX 500.

Method: For predictive models, various technical indicators were considered independent variables, and change in return, i.e. increase and decrease, was deemed a dependent variable. The indicators were transformed from daily raw data of insurance company’s stock values spanning four years. We formed 90 data sets of varied periods for building the model – specifically six months, one year, two years, and four years for selected six insurance companies.

Findings: The study’s findings revealed that ANN performed best for the ICICIPRULI data model in terms of hit ratio. Whereas the performance of SVM was observed to be the best for the ICICIGI data model. In the case of pairwise comparison among the six selected Indian insurance companies from CNX 500, the extracted data evaluated and concluded that there were eight significantly different pairs based on hit ratio in the case of ANN models and nine significantly different pairs based on hit ratio for SVM models.

Conventional wisdom states that catastrophe risk securities show no or little correlation with stock and bond markets, and offer significant attractions to investors providing a good diversification of risks. This study examines the correlation between catastrophe risk securities and portfolios of other equities by analyzing catastrophe effects on the Japanese stock market.

Using catastrophe data from SwissRe Sigma publications and stock returns from the Pacific‐Basin Capital Markets database, this paper analyzes stock and abnormal returns in the Japanese stock market using event study methodology.

For the Japanese stock market as a whole, there is no significant catastrophe effect. The results indicate a significant negative correlation between catastrophe loss amount and the insurance industry's equity returns and abnormal returns, a significant positive correlation with the construction industry, but no significant correlation with the real estate industry. This paper also analyzes the impact of catastrophe causalities. The results show little evidence on the significance of these variables.

This study provides important insights to the insurance/reinsurance industry in the Japanese risk market for catastrophe property and mortality risk securitization and to investors who are interested in further improvement of their portfolio risk/return profile by including catastrophe risk securities.