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This study aims to contribute to the debate on goodwill accounting by examining the information content of impairment losses recognized in half-yearly reports. Half-yearly reports provide a suitable context to examine the effectiveness of the impairment process. Due to IFRIC 10 requirements, indeed, managers may have incentives to avoid recognizing impairment losses at the interim reporting date.

The study adopts an archival approach. Based on the traditional Ohlson’s model (1995), it explores the information content of half-yearly impairment losses in the European context over the period 2007–2017.

Findings confirm the relevance of half-yearly reports and suggest that half-yearly impairment losses are significantly associated with stock prices. In particular, investors positively value companies that recognized goodwill impairment losses at the interim reporting date.

The study contributes to the academic debate on goodwill and the effectiveness of the impairment procedure. In particular, it provides empirical evidence on the recognition of goodwill write-offs when it is possible to avoid the impairment test in the absence of indications of impairment.

Findings of this study can support the current debate on accounting for goodwill also in the light of the recent proposals of the IASB on the need to improve the effectiveness of the impairment test.

This study provides original empirical evidence on the goodwill impairment test in half-yearly reports, extending previous research that typically examines this issue in annual reports.

This study investigates the efficiency of value relevance and faithful representation when stock market price derivates from its firm value to the investigated IT companies listed in FTSE Taiwan 50. The empirical investigation reveals one financial indicators: Return on equity (ROE) has explanatory ability among seven financial indicators, earnings per share (EPS), book value (BV), dividend yield (Div.), price–earnings ratio (P/E), ROE, return on assets (ROA), and return on operating asset (ROOA) to both sampled companies, United Microelectronics Corporation, UMC, (2303) and Taiwan Semiconductor Manufacturing Company Limited, TSMC, (2330). Furthermore, the empirical results indicate that the higher order moments, skewness and kurtosis, of price deviation do not provide a reliable prediction or explanatory power for stock price trends.

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.

Corporate social responsibility (CSR) has gained great attention among regulators, stock market authorities, and firms' stakeholders for many decades. In this chapter, we first review the main regulations, standards, and laws issued by UAE federal authorities namely the Company Commercial Law of 2015, the Abu Dhabi Stock Exchange (ADX) disclosure guidance of 2019, Abu Dhabi Sustainability Week, and UAE CSR platform. Second, we present a summary of the empirical research on CSR issues in UAE context, namely in the following four fields: (1) CSR determinants both at the micro and macro levels, (2) CSR measures in the three pillars (environmental, social, and governance), (3) the impact of CSR policy and practices on financial performance/market value, (4) and the role of some mediating/moderating variables such as leadership and board gender diversity. Results show greater compliance to CSR standards among different industries and institutions but heterogenous empirical findings in the four explored fields. While there is crucial alignment with both social and environmental standards as evidenced by numerous empirical studies, additional efforts should be deployed to highlight the governance pillar through firms' discretionary reporting. Our survey provides useful directives and outcomes as it portrays both legal aspects coupled with some empirical evidence of CSR issues in the UAE context. Our study helps corporations to comply with local standards on sustainability reporting and highlights the potential economic benefits and advantages for firms adopting CSR strategy. Furthermore, it can be considered as the cornerstone for regulatory bodies in the United Arab Emirates when issuing/enhancing new standards/rules on CSR practices.

Over the last few years, the European Central Bank (ECB) has adopted unconventional monetary policies. These measures aim to boost economic growth and increase inflation through the bond market. The purpose of this paper is to study the impact of the ECB’s quantitative easing (QE) on the investor’s behavior in the stock market.

First, the authors theoretically identify the transmission channels of the QE shocks to the stock market. Then, the authors empirically assess the financial market’s responses to QE shocks in a data-rich environment using a factor augmented VAR (FAVAR).

The results show that the ECB’s unconventional monetary policy positively affects the stock market. A QE shock leads to an increase in stock prices and a drop in the realized volatility and the implied risk premium. The authors also suggest that the ECB’s QE is transmitted to the stock market through five main channels: the liquidity, the expectation, the portfolio reallocation, the interest rates and the risk premium channels.

The findings help to better understand the behavior of stock market assets in a data-rich economic context and guide investors and policymakers in the presence of unconventional monetary tools. For instance, decision-makers and investors should consider the short-term effect of the QE interventions and the changing behavior of the financial actors over time. In addition, high stock market returns can increase risk appetite. This can lead investors to underestimate the market risk. Decision-makers and market participants should take into consideration the impact of the large injection of money through the QE, which may raise the risk of a speculative bubble in the financial market.

To the best of the authors’ knowledge, this is the first study that incorporates a theoretical and empirical analysis to explore QE transmission to the stock market in the European context. Unlike previous studies, the authors use the shadow rate proposed by Wu and Xia (2017) to quantify the effect of the ECB’s QE in a data-rich environment. The authors also include two key risk indicators – the stock market risk premium and the realized volatility – to capture investors’ behavior in the stock market following QE shocks.

This study aims to investigate how firms of different sizes respond to competitive pressure from peers.

This study employs machine learning techniques to measure competitive pressure based on management discussion and analysis (MD&A) documents and then utilises the constructed pressure indicator to explore the relationship between competitive pressure and corporate risk-taking behaviours amongst firms of different sizes.

We find that firm sizes are positively associated with their risk-taking behaviours when firms respond to competitive pressure. Large firms are inclined to exhibit a high level of risk-taking behaviours, whereas small firms tend to make conservative decisions. Regional growth potential and institutional ownership moderate the relationships.

Utilising text mining techniques, this study constructs a novel quantitative indicator to measure competitive pressure perceived by focal firms and demonstrates the heterogeneous behaviour of firms of different sizes in response to competitive pressure from peers, advancing research on competitive market pressures.

Environmental, social and governance (ESG) issues have become the cornerstone of investment decisions in firms today. With that, publicly traded ESG indices (like the BSE ESG 100 index in India) have come into existence. The existing literature signifies that ESG generates financial implications and induces stability. The current study aims to test whether the firms listed on the ESG index (ESG-sensitive firms) face less financial distress than those not listed on such an index.

The study applies panel data difference-in-differences (DID) regression by considering ESG as an unstaggered treatment to 74 non-financial firms listed on India's Bombay Stock Exchanges (BSE) 100 index. In total, 42 firms are ESG treated as they got listed on the BSE ESG 100 index, formed in 2017. The remaining 32 firms form the control group. The confidence intervals and standard errors are estimated using clustered robust errors and the Donald and Lang method.

Listing on the ESG index matters for financial stability; differences in financial distress are significant on financial distress. ESG-sensitive firms face less financial distress than non-ESG firms (or firms not perceived as ESG-sensitive). The results are consistent across two financial distress measures, Altman z-scores for emerged and emerging markets. Thus, the DID in distress status between ESG-sensitive and non-ESG firms matter.

The study creates vibrant implications for practitioners using ESG to reduce financial distress.

The study is one of its kind to test the treatment effects of ESG on firm value and quantify treatment effects on financial distress.