Search results

The coronavirus disease 2019 (COVID-19) pandemic adversely affected small and medium-sized enterprises (SMEs) in India. Amongst the challenges faced were the adjustments required in leadership skills to address pandemic-induced technological changes and the necessity for employee skill upgrading. This study examined the factors that influenced organisational performance in Indian SMEs, particularly in the context of the digital transformations that were brought about by the pandemic.

The study employed a cross-sectional design to investigate a set of hypotheses that were formulated to understand the relationships amongst digital leadership, digital training, empowerment and organisational performance. The data were collected during the pandemic from 487 employees who were working in various SMEs in India. Questionnaires were distributed through email and social media platforms, and electronic consent was obtained from each participant.

The study's findings indicated positive associations amongst digital leadership, digital training, empowerment and organisational performance. They also highlighted the mediating role of empowerment in these relationships. Furthermore, organisational resilience was found to positively moderate the relationship between empowerment and performance.

The study stands amongst the pioneers in exploring the role of digital leadership and digital training during the pandemic and their impact on employee empowerment amongst SMEs in a developing country.

Emerging market multinational companies have been vigorous in pursuing inorganic growth through cross-border acquisitions (CBAs). The fundamental studies till now have portrayed that rapid internationalization through CBAs tends to create value for these emerging market firms (EMFs) in the short term. However, there is an ambiguity about whether these firms endure better performance in the long term. The purpose of this study is to assess the long-term (ex-post) financial and operating performance of EMFs involved in overseas acquisitions before the COVID-19 pandemic hit the world economy.

CBAs completed by Indian and Chinese companies constitute the sample of the study. The performance has been analysed during the pre-COVID period spanning 17 years from 2001 to 2017. A comprehensive set of 14 financial ratios has been used to represent change (improvement/decline) in enterprises’ post-acquisition operating performance; these ratios have been divided into four broad groups: profitability, efficiency, solvency and liquidity ratios.

The performance of Indian companies has deteriorated significantly after the acquisition. However, there has been no change (deterioration/improvement), subsequent to CBAs, in the profitability of Chinese firms.

The findings of the study support that firms from emerging economies exploit CBAs as a “springboard” to obtain strategic assets including intangible resources and brands rather than to achieve synergies through economies of scale and scope. Apparently, outbound acquisitions by emerging economy firms are not driven by cost-reduction or revenue-generation activities.

None of the studies, to the best knowledge of the authors, has carried out performance analysis using a comprehensive set of financial ratios. The comparative study of two emerging economies is another valuable addition to the existing literature. The study holds the potential to serve as the benchmark to assess the performance of CBAs executed after COVID-19.

The purpose of this study is to ascertain how foreign and domestic Exchange Traded Funds (ETFs) investing in Indian equities affect their return volatility and pricing efficiency. Further, we investigate how the difference in market timings affect the impact of ETFs on their constituents. Lastly, we examine how these effects vary during tranquil and turmoil periods in the ETF markets.

The study is based on quarterly data for stocks comprising the CNX Nifty 50 Index from 2009Q1 to 2019Q3. The data on holdings of 45 domestic and 196 foreign ETFs in the sample stocks were obtained from Thomson Reuters' Eikon. The paper employs a panel-regression methodology with stock and time fixed effects and robust standard errors.

Foreign ETFs from North America and the Asia Pacific largely have an adverse impact on stocks' return volatility. In times of turmoil, stocks with higher coverage of European, North American and Domestic funds are susceptible to volatility shocks emanating from these regions. European and Asia Pacific ETFs are associated with improved price discovery while North American funds impound a mean-reverting component in stock prices. However, in turbulent markets, both positive and negative impacts of ETFs on pricing efficiency coexist.

To the best of the authors' knowledge, this is the first study that examines the impact of domestic as well as foreign ETFs on the equities of an emerging market. Furthermore, the study is unique as we investigate how the effects of ETFs vary in turbulent and tranquil markets. Moreover, the paper examines the role of asynchronous market timings in determining the ETF impact. The paper adds to the growing literature on the unintended consequences of index-linked products.

The stock market generates massive databases of various financial companies that are highly volatile and complex. To forecast daily stock values of these companies, investors frequently use technical analysis or fundamental analysis. Data mining techniques coupled with fundamental and technical analysis types have the potential to give satisfactory results for stock market prediction. In the current paper, an effort is made to investigate the accuracy of stock market predictions by using the combined approach of variables from technical and fundamental analysis for the creation of a data mining predictive model.

We chose 381 companies from the National Stock Exchange of India's CNX 500 index and conducted a two-stage data analysis. The first stage is identifying key fundamental variables and constructing a portfolio based on that study. Artificial neural network (ANN), support vector machines (SVM) and decision tree J48 were used to build the models. The second stage entails applying technical analysis to forecast price movements in the companies included in the portfolios. ANN and SVM techniques were used to create predictive models for all companies in the portfolios. We also estimated returns using trading decisions based on the model's output and then compared them to buy-and-hold returns and the return of the NIFTY 50 index, which served as a benchmark.

The results show that the returns of both the portfolios are higher than the benchmark buy-and-hold strategy return. It can be concluded that data mining techniques give better results, irrespective of the type of stock, and have the ability to make up for poor stocks. The comparison of returns of portfolios with the return of NIFTY as a benchmark also indicates that both the portfolios are generating higher returns as compared to the return generated by NIFTY.

As stock prices are influenced by both technical and fundamental indicators, the current paper explored the combined effect of technical analysis and fundamental analysis variables for Indian stock market prediction. Further, the results obtained by individual analysis have also been compared. The proposed method under study can also be utilized to determine whether to hold stocks for the long or short term using trend-based research.

Utilizing the expectation states theory in sociology, this study probes into the influence of the board's informal hierarchy on the quality of enterprise innovation, originating from the perspective of internal directorial interactions, while analyzing the boundary effects exhibited by the nature of property rights and the intensity of geo-culture.

The study selects China's A-share listed companies from 2008 to 2021 as the research sample, employing the Tobit regression analysis method to scrutinize the hypotheses presented in the text.

The regression results demonstrate a positive correlation between the board's informal hierarchy and the enterprise innovation quality (EIQ). Upon introducing variables specific to property rights and geographical culture, the authors found that in comparison to non-state-owned enterprises (non-SOEs), the influence of the board's informal hierarchy on the quality of corporate innovation is diminished in SOEs. Conversely, the intensity of geo-culture across Chinese provinces enhances their mutual positive influence. In the additional analysis, the authors also found that the elevation of corporate risk tolerance is a significant pathway for the positive effect of the board's informal hierarchy on EIQ. Moreover, this positive influence is more profound in high-tech enterprises, businesses implementing equity incentive plans and companies that have subscribed to director and officer liability insurance.

The findings not only deepen the understanding of how the board's internal status characteristics influence corporate decision-making but also enrich the application scope of expectation states theory. Furthermore, this study offers valuable guidance for optimizing innovation decision-making by adjusting the personnel structures of corporate boards.

This study aims to provide the best estimate of a stock's next day's closing price for a given day with the help of the hidden Markov model–Gaussian mixture model (HMM-GMM). The results were compared with Hassan and Nath’s (2005) study using HMM and artificial neural network (ANN).

The study adopted an initialization approach wherein the hidden states of the HMM are modelled as GMM using two different approaches. Training of the HMM-GMM model is carried out using two methods. The prediction was performed by taking the closest closing price (having a log-likelihood within the tolerance range) to that of the present one as the closing price for the next day. Mean absolute percentage error (MAPE) has been used to compare the proposed GMM-HMM model against the models of the research study (Hassan and Nath, 2005).

Comparing this study with Hassan and Nath (2005) reveals that the proposed model outperformed in 66 out of the 72 different test cases. The results affirm that the model can be used for more accurate time series prediction. Further, compared with the results of the ANN model from Hassan's study, the proposed HMM model outperformed 24 of the 36 test cases.

The study introduced a novel initialization and two training/prediction approaches for the HMM-GMM model. It is to be noted that the study has introduced a GMM-HMM-based closing price estimator for stock price prediction. The proposed method of forecasting the stock prices using GMM-HMM is explainable and has a solid statistical foundation.

Automatically extracting stance information from natural language texts is a significant research problem with various applications, particularly after the recent explosion of data on the internet via platforms like social media sites. Stance detection system helps determine whether the author agree, against or has a neutral opinion with the given target. Most of the research in stance detection focuses on the English language, while few research was conducted on the Arabic language.

This paper aimed to address stance detection on Arabic tweets by building and comparing different stance detection models using four transformers, namely: Araelectra, MARBERT, AraBERT and Qarib. Using different weights for these transformers, the authors performed extensive experiments fine-tuning the task of stance detection Arabic tweets with the four different transformers.

The results showed that the AraBERT model learned better than the other three models with a 70% F1 score followed by the Qarib model with a 68% F1 score.

A limitation of this study is the imbalanced dataset and the limited availability of annotated datasets of SD in Arabic.

Provide comprehensive overview of the current resources for stance detection in the literature, including datasets and machine learning methods used. Therefore, the authors examined the models to analyze and comprehend the obtained findings in order to make recommendations for the best performance models for the stance detection task.

The purpose of this study is to investigate the effect of international expansion of emerging markets multinationals (EMNEs) on the home country nonmarket political strategy and why some EMNEs intensify this political tie more than others.

We test our theoretical framework using longitudinal data, with 16 years of observations, in Multilatinas and state loans from Brazil, one of the main outward foreign direct investment (OFDI) players in the world and the OFDI leader in Latin America.

Theoretically grounded on the institution-based view of strategy, it can be postulated that international expansion is a driver of home country nonmarket political strategy. It can also be hypothesized that political tie intensity is affected by the capacity of EMNEs to deal with international expansion issues without having to depend on relationship with homes country nonmarket political actors. The results provide support for the hypotheses presented.

This paper contributes to the EMNE internationalization literature by extending the understanding of the underlying motivations and forces shaping the home country nonmarket political strategy of multinationals from emerging markets and, thus, helping understand why some EMNEs tend to be more politically active than others. Likewise, the study contributes to advancing understanding regarding the home country strategic responses adopted by Multilatinas and the forces behind the nonmarket political strategies they employ in their international expansions, especially during turbulent times.

The purpose of this study is to examine the differential impact of qualitative and quantitative informational signals within the merger and acquisition (M&A) press releases on deal completion and duration. A significant percentage of deals by emerging market acquirers get abandoned before completion, and those that are completed have a longer duration. The limited information about the operations of acquirers from emerging markets creates suspicion among the stakeholders involved in deal resolution, hindering the completion of deals. Thus, using the signal-feedback paradigm, authors investigate how informational signals in the M&A press release impact the deal resolution.

The study employs content analysis on M&A press releases announced by firms from five emerging economies: Brazil, Russia, India, China and South Africa. The technique is applied based on the exploration-exploitation framework developed by March (1991) to categorize the announced deal motives (qualitative information). Next, the authors identify the percentage of relevant quantitative information disclosed in the press release, following which results are obtained using logistic and ordinary least square regressions.

The study reports that deals with declared exploratory motives take longer to complete. Additionally, deals disclosing higher percentage of quantitative disclosure exhibit lower completion rate and increased deal duration.

This is the first study to provide evidence that familiarity bias impacts deal duration as relative to exploitation deals that are familiar to the stakeholders; exploratory deals take longer to conclude. Further, our analysis indicates that a greater percentage of quantitative disclosure may not always reduce information risk but rather be interpreted negatively in the form of the acquirer’s overconfidence in the deal’s potential.

This study investigates the connection between corporate governance and zombie firm’s exit time.

With a sample of 2,794 French zombie firms, the analysis focuses on four aspects of corporate governance: board size (BS), managerial ownership (MO), director turnover (DT) and ownership concentration, using tobit regression.

Dimensions of corporate governance have an important role in determining zombie firms’ exit time. MO and ownership concentration increase zombie firm exit time, whereas larger BSs and DT reduce it.

To the best of the authors’ knowledge, this study is the first to include corporate governance as a characteristic relevant to zombie firms’ exit time. It provides new insights on why some zombie firms remain in the market longer than expected.