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SPACs are reshaping the world of digital entrepreneurial finance. Firms in the digital sector often need access to public markets for long-term competitiveness. SPACs offer a viable solution for these entities to collect capital and transition to public ownership quicker than IPOs. In this context, the paper aims to analyse and compare the performance of SPACs with those of IPOs in the post-business combination phase. The objective is to provide novel insights into the determinants of SPAC operating and market performance by considering firm-specific and deal-specific characteristics and the broader implications of market uncertainty.

The analysis applies univariate and multivariate OLS regressions to a sample of 96 SPACs to investigate the drivers affecting SPACs' performance vis-a-vis IPOs.

The study finds that SPACs underperform the matched group of IPOs on both operating and stock market performance (buy-and-hold strategy). The time to execute a business combination negatively correlates with SPAC performance, and proximity to the 80% deal threshold negatively affects share price performance and EBITDA margin.

The objective is to offer insights for institutional investors to effectively select prime targets within the SPAC framework.

This study strengthens the findings related to the drivers influencing the long-term performance of SPACs that were previously identified in prior research.

Share repurchase programs are the most important form of payout, yet the implications of incomplete share repurchase programs have not been examined in previous literature. This study tests whether incomplete share repurchase programs are seen as a positive or as a negative signal by investors.

The perception of incomplete share repurchase programs by algorithmic traders, institutional investors and analysts is analyzed with structural equation models, seemingly unrelated regressions, propensity score matching and buy-and-hold abnormal returns on data from share repurchase programs in the United States. In contrast to previous literature, algorithmic trading is appropriately estimated as a latent variable, leading to more reliable results. Furthermore, decisions about share repurchases and dividends are appropriately modeled simultaneously and iteratively, based on findings from previous literature.

The results show that sophisticated investors such as algorithmic traders, institutional investors and financial analysts avoid incomplete share repurchase programs over a long-term investment horizon. Thus, incomplete share repurchase programs are interpreted as negative signals. Additional analyses reveal that share repurchase programs are not completed due to insufficient cash flow, as a result of financial difficulties. Overall, this implies that financial managers should be careful to announce share repurchase programs they know cannot be completed, similar to dividends that cannot be maintained over a long-term horizon.

This study is the first to consider incomplete share repurchase programs. The findings are of interest to scholars and practitioners, as this study goes beyond narrow repurchase program announcement windows, and instead focuses on the longer-term investment horizon over the life of the share repurchase program, which is often ignored in prior research.

This research investigates the differential impact of information technology (IT) investments based on their features, such as investment in data management capability, security improvement, IT outsourcing or new IT infrastructure. The Long-Horizon Event Study (LHES) is essential for providing a more appropriate measure of the value of IT investments because firms' strategic decisions often set long-horizon and large-scale organizational goals, and there is inherent uncertainty regarding future cash flows resulting from these investments. Therefore, the authors aim to analyze how announcements of IT investments affect the firm's abnormal stock returns over the long term and to compare the differential impact of different features of IT investment.

The authors gathered IT investment announcements and stock data of listed firms in Korea between 2000 and 2018, and the monthly stock market returns over the 5 years after the announcements. To measure the differential impact of IT investments based on the investment features, the authors separate announcements data into five groups. A LHES is used to estimate the long-term effects of IT investment announcements.

The results indicate that announcements of IT investments had a long-term positive effect on firm performance. Additionally, the findings reveal differential effects of IT investments across industries and investment features. Notably, news of self-developed IT investments and IT investments in the manufacturing industry had significantly positive effects. However, contrary to common belief, announcements of investments in so-called essential IT areas such as data, security, or new IT infrastructure did not yield significant effects.

Although the need for LHES has been emphasized in information systems research, few follow-up studies have been conducted since Barua and Mani (2018). This is primarily due to the challenges associated with collecting large-scale abnormal stock returns data over a long horizon. This research represents the first LHES to investigate the differential impact of IT investments based on their features. By doing so, this study can provide valuable insights for decision-makers within firms, helping them understand the time horizon of market outcomes of IT investments based on their features. Furthermore, this work extends the scope of LHES to comprehend the differential impacts of investment features. For instance, managers need to grasp that so-called essential IT investments, such as data management, security enhancements or new IT infrastructure, may not necessarily generate long-term market value.

This study aims to investigate how stock markets responded to corporate dividend policy changes during the COVID-19 pandemic in the Gulf Cooperation Council (GCC) countries. Likewise, it explores how efficiently market prices incorporate the news by examining the speed of stock price adjustment to various dividend announcements.

The sample includes 741 dividend announcements from 2017 to 2021 made by 326 firms listed in the stock markets of the GCC countries. A series of regression analyses examine how dividend announcements influence the market reaction during the COVID-19 pandemic, controlling for other well-documented firm characteristics.

This study reveals an adverse stock price reaction to all the dividend announcements in most GCC markets. The findings also show strong asymmetric effects of COVID-19 on how the markets react to different dividend changes. Likewise, the authors show that investors tend to underreact to the good news of dividend increases amid hard times of crises due to prevailing uncertainty and bearish sentiment. Besides, regression results reveal that firms with dividend reductions during the pandemic experience less adverse market reactions than dividend-decreasing firms prepandemic.

For firms, the findings confirm the role that corporate dividend policy can play in conveying signals to investors, especially during hard times of crises and turbulences, thereby affecting their share price. For policymakers, the results substantially affect market efficiency and firm valuation in the GCC markets.

This study is not only one of the first few attempts to scrutinize how the pandemic has affected the market reaction to changes in corporate dividend policies but also, to the best of the authors’ knowledge, it is the first to examine how corporate dividend policy could affect stock markets during COVID-19 in the context of GCC markets.

This study investigates the motivations and consequences of classification shifting from cost of sales to research and development (R&D) in high-technology industries.

This study conducts a multivariate analysis using logistic and ordinary least squares regression methods on panel data of high-technology firms for the period 1988–2012 to examine the effect of R&D classification shifting on gross margin benchmarks and future performance.

The results show that R&D classification shifting increases the likelihood of meeting or beating gross margin benchmarks. They also show that firms engaged in R&D classification shifting exhibit lower future R&D productivity, stock returns, and operating performance. The findings indicate that the short-term benefits of achieving gross margin benchmarks are offset by the long-term negative impact of R&D misclassification.

This paper provides insights that can help regulators develop clearer guidelines for the appropriate classification of R&D costs.

Moving beyond the core earnings management paradigm, this study demonstrates the use of R&D classification shifting as a tool to manipulate gross profits and R&D in high-technology industries. Most prior studies focused on the determinants of R&D classification shifting, while few investigated the impact of the practice. The findings in this study provide initial evidence of the consequences of R&D classification shifting for future R&D productivity and firm performance in high-tech industries. Using five methods, this study also validates R&D classification shifting and addresses the alternative explanation of R&D overinvestment.

Stirred by scant regard for market phases in portfolio performance assessments, the current paper investigates the active versus passive investment strategies under the bull and bear market conditions in emerging markets focusing on South Africa as a case study.

Methodologically, the measures of Jensen's alpha and Treynor index are applied to the monthly returns of 20 funds from January 2010 to June 2022.

The results are enlightening; though they contradict developed market evidence, they are consistent with emerging market trends. The findings show that actively managed funds outperform the market benchmark and passive investing style under bear and normal market conditions. Passive investment strategy outperforms both market benchmark and actively investing style under bull market conditions.

In the face of improved market efficiency, increased liquidity and recent technological impact, the findings of this study have practical application. The study outcomes should inform and update global investors, especially asset managers interested in emerging markets; however, the limitations of the study should also be considered.

While limited studies consider market conditions when comparing and contrasting the performance of passive versus active investing, such consideration is lacking in emerging markets. The current study corrects this literature imbalance.

Prior studies have found that managers adjust operational activities to tackle climate risk. However, the effects of climate risk on accounting practices are largely ignored in the literature. This paper investigates whether and how climate risk influences managers’ decision-making on the level of accounting conservatism and explains the results based on two competing channels: valuation demand and contracting demand.

Using firm level climate risk measures, we build a modified Basu (1997) model to conduct our econometric tests. In the baseline model, we use earnings before extraordinary items as the dependent variable, referred to as the earnings model. We control for different levels of fixed effect to identify the shocks of climate risk and mitigate potential concerns on endogeneity and bias in the model. A series of robustness tests provide supporting evidence for our baseline results and our explanation.

Using a sample of 35,832 firm-year observations on listed US firms over the period 2002 to 2019, we find that the perception of climate risk drives managers to choose the less conservative accounting policies. We conclude that the results are consistent with the valuation demand explanation but inconsistent with the contracting demand explanation.

The study provides additional evidence on how managers respond to climate risk by adjusting their corporate polices, specifically accounting policies. Our findings contradict the results of prior studies. We explain our results from a unique perspective. Overall, the study provides valuable insights for academics, investors, managers and policymakers.

This study examines the types, quality, and financial effects of explanations for non-compliance (NCEs) with corporate governance codes.

This study used content analysis to examine various types of NCEs and developed an NCE index (NCEI) to assess their quality and degree of informativeness. Static and dynamic multivariate panel data regression models were used to analyze the relationship between NCEI and firm performance (FP) of 38 non-financial listed Mauritian firms from 2009 to 2019.

Listed Mauritian firms do not provide explanations for all non-compliance, and the most common type of NCE is momentary deviation. The NCEI is 0.243, which implies that the overall quality of the NCEs is poor or uninformative. The NCEI varies according to the listing status and industry type. NCEI has a negative and insignificant relationship with both ROA and Tobin’s Q. The results are inconsistent with the agency, stakeholder, stewardship, and resource dependency theories. Sensitivity analysis indicated that the findings were robust.

Multiple theoretical frameworks offer a deeper understanding of corporate governance practices than a single theory does. A decline in the NCEI in 2019 indicates that the move from the “comply or explain” to the “apply or explain” principle does not necessarily result in enhancements in the degree of informativeness. Regulators should develop guidelines on how to disclose NCEs better. Investors appear to be more concerned about “comply/apply or perform” than the “comply/apply or explain” approach.

This study adds to the extant literature by providing new evidence on the types and quality of NCEs as well as their relationship with FP in emerging economies, where such studies are rare.

We study the role of having an audit committee (AC) as a signal of firm quality and as a monitoring device of the information quality contained in the listing prospectus.

Ordinary Least Squares regressions are used to examine the association between the presence of an audit committee and (1) the initial return (IR), and (2) the earnings forecast error in the listing prospectus in a sample of 55 Real Estate Investment Trusts that went public on the BME Growth market during 2013–2022. Heckman two-step estimation procedure to correct for endogeneity and bootstrap are used for robustness.

We show that IR and earnings forecasts are significantly affected by the presence of AC. The IR is higher and the earnings forecast included in the prospectus are of higher quality in firms with AC.

Our research provides (1) managers with new tools when deciding on their corporate governance structure in the listing process, (2) specific evidence for regulators on the role played by ACs in the process of going public, which may be useful in the context of the ongoing regulatory changes regarding admission processes in Europe, and (3) society with a sign that AC can enhance investor and public confidence in financial markets and foster a more stable and transparent investment environment.

The adoption of an AC is voluntary in this market, so this discretionary decision provides an exceptional opportunity to conduct such an analysis. Additionally, this issue has not been previously analysed in Europe.

我們擬探討審計委員會在作為是公司質素的一個信號, 以及在作為是監控上市招股書內資料質素的一個機制所扮演的角色。

研究人員使用普通最小二乘法回歸模型, 去探討審計委員會的存在與 (一) 初期回報、和 (二)上市招股書內收益預測的錯誤兩者之間的關聯。研究的樣本為55個於2013年 至 2022年間在 BME Growth 市場上市的不動產投資信託。研究人員使用自助法和可幫助修正內生性問題的赫克曼兩階段回歸, 以達數據的穩健性。

研究結果顯示, 審計委員會的存在會顯著地影響初期回報和收益預測。若公司採用審計委員會, 初期回報則會較高; 而且, 招股書內的收益預測也顯示較高的質素。

由於採用審計委員會與否在這研究的有關市場內純基於自主的決定、而非屬強制性, 因此, 與這決定有關的課題為學者提供了特殊的研究機會。再者, 在歐洲至今似仍未有學者曾嘗試探討這個課題。

本研究帶來以下實務方面的啟示: (一) 當經理需決定其在上市過程中的公司治理結構時, 本研究為他們提供了新的工具給他們使用; (二) 本研究為調控者就公司上市過程中審計委員會所扮演的角色提供具體的證據。而這因歐洲正進行更改與批核上市有關的規管而可能使其產生用處; (三) 本研究為社會提供一個信號, 顯示審計委員會不但可增強投資者和大眾對金融市場的信心, 而且還能培育更穩定和透明的投資環境。

公司治理、審計委員會、上市、初期回報、盈餘預測、不動產

The paper aims to investigate differences in non-professional and professional stock investors’ trust in and tendency to follow financial analysts’ buy and sell recommendations.

Online experiment conducted in Sweden in March 2022 comparing non-professional private investors (n = 80), professional investors (n = 33), and master students in finance (n = 28). Information was presented about four company stocks listed on the New York stock exchange. Two stocks were buy-recommended and two stocks sell-recommended by financial analysts. For one stock of each type, the recommendation was presented to participants. Dependent variables were predictions of the stock price after three months, ratings of confidence in the predictions and choices of holding, buying or selling the stock. Ratings were also made of the importance of presented stock-related information as well as trust in analysts’ skill and integrity.

More positive return predictions were made of buy-recommended than sell-recommended stocks. Non-professionals and to some degree finance students tended to trust financial analysts more than professional investors did and they were more influenced by the presentation of the buy recommendations. All groups made too optimistic return predictions, but the professionals were less confident in their predictions, more likely to sell the stocks and lost less on their investments.

A new finding is that non-professional stock investors are more likely than professional stock investors to trust financial analysts and follow their recommendations. It suggests that financial analysts’ recommendations influence non-professional investors to take unmotivated investment risks. Non-professionals in the stock market should hence be advised to exercise more caution in following analysts’ recommendations.