Do Institutional and Macroeconomic Factors Matter in IT companies M&As? Evidence from India

2020 ◽  
pp. 227853372096492
Author(s):  
Niti Bhasin ◽  
Amit Soni ◽  
Rabi Narayan Kar
Keyword(s):  
Mergers And Acquisitions ◽  
Negative Binomial ◽  
Random Effect ◽  
Economic Recession ◽  
Institutional Factors ◽  
Binomial Model ◽  
Indian Government ◽  
Macroeconomic Factors ◽  
Institutional Determinants ◽  
Set Up

The liberalisation of the Indian economy, along with targeted policies of the Indian government for the information technology (IT) sector, has led to tremendous growth in this sector. While there has been substantial foreign investment in India in the IT sector through setting up of subsidiaries, joint ventures and acquisitions, Indian firms have also aggressively invested within and outside India through multiple modes. Among different sectors, the IT sector has been the most dominant in terms of mergers and acquisitions (M&As). This article focuses on host country (macroeconomic and institutional) determinants of M&As undertaken by Indian IT firms. Random effect negative binomial model in panel set up was selected to estimate the models. Four models were estimated as per the availability of data for 35–42 countries and for the period 2000–2015. The results for most of the variables in four models were very similar, reflecting robustness of the methodology. Most of the macroeconomic and institutional factors were found to be important determinants of the M&As by Indian IT companies. However, the economic recession of 2008–2009 was found to significantly reduce the M&A activities by Indian IT companies.

scholarly journals What Drives Cross-Border Mergers and Acquisitions and Greenfield Foreign Direct Investment Capital Flows? Reconsideration in the Case of the Selected Former Yugoslav Countries

2021 ◽  
Vol 32 (3) ◽  
pp. 234-246
Author(s):  
Ksenija Denčić-Mihajlov ◽  
Vinko Lepojević ◽  
Jovana Stojanović
Keyword(s):  
Mergers And Acquisitions ◽  
Developing Economies ◽  
Random Effect ◽  
Random Effect Model ◽  
Foreign Direct Investments ◽  
Macroeconomic Factors ◽  
Fdi Inflows ◽  
Cross Border ◽  
Data Framework ◽  
The Impact

Bearing in mind the different nature and the impact of various types of foreign direct investments (FDI) on the one hand, and the specific macroeconomic environment in the post-socialist countries on the other hand, in this paper we reexamine the selected macroeconomic factors that affect the two types of FDI inflows (cross-border mergers and acquisitions and greenfield FDI) in four countries of the former Socialist Federal Republic of Yugoslavia. The study employs the balanced panel data framework and covers twelve-year period (2006-2017). Having performed the Hausman test, we use the random effect model and provide evidence that: (1) the key FDI macroeconomic determinants in stable business conditions, examined in numerous research studies, can have a different impact on FDI in times characterized by unstability and financial crisis, (2) some determinants of FDI inflows have different importance and direction in the case of cross-border M&A and greenfield FDI. Our findings are relevant for policymakers who should reconsider the key factors that fuel the FDI inflows towards their developing economies.

scholarly journals Modeling Traffic Accident Occurrence at Hazardous Road Locations: A Case Study in Tunisia

2018 ◽  
Author(s):  
Fedy Ouni ◽  
Mounir Belloumi
Keyword(s):  
Goodness Of Fit ◽  
Negative Binomial ◽  
Prediction Models ◽  
Pearson Correlation ◽  
Surface Condition ◽  
Random Effect ◽  
Negative Binomial Model ◽  
Empirical Modeling ◽  
Binomial Model ◽  
Model Average

The purpose of the present study is to explore the linkage between Hazardous Road Locations-based crash counts and a variety of geometric characteristics, roadway characteristics, traffic flow characteristics and spatial features in the region of Sousse, Tunisia. For this purpose, collision data was collected from at 52 hazardous road sections including 1397 crash records for a 11-year monitoring period from January 1, 2004 to December 31, 2014 obtained from National Observatory for Information, Training, Documentation and Studies on Road Safety in Tunisia (NOITDRS). The matrix of Pearson correlation was used in order to avoid inclusion of both variables, which were highly correlated. Both the Random Effects Negative Binomial model and the Negative Binomial model were estimated. To evaluate the models, the Random Effect Negative Binomial model improves the goodness-of-fit compared to the Negative Binomial model. Average Daily Traffic volume, Curved alignment, Presence of public lighting, Visibility, Number of lane, Presence of vertical/horizontal sign, Presence of rural segment, Presence of drainage system, Roadway surface condition, Presence of paved shoulder and presence of major road were found as significant variables influencing accident occurrences. Overall, the current research contributes to the literature from empirical, modeling methodological standpoints since it was the first study conducted in Tunisia to use crash prediction models for hazardous road locations, and that portrays Tunisian reality. The research findings present advantageous insights on hazardous road locations in the region of Sousse, Tunisia and present useful planning tools for public authorities in Tunisia.

M&A Movements: An Analysis of Patterns of Indian Trends

2021 ◽  
pp. 097215092110457
Author(s):  
Minakshi Kar ◽  
Rabi Narayan Kar
Keyword(s):  
Mergers And Acquisitions ◽  
Common Factors ◽  
Economic Recession ◽  
Macroeconomic Factors ◽  
Industrial Landscape ◽  
Corporate Decisions ◽  
Cyclical Pattern ◽  
Cyclical Behaviour ◽  
The Waves ◽  
Recession Period

Indian industrial landscape had been completely redrawn by the forces of globalization, deregulation and unprecedented technological advancements for the last three decades. Corporate enterprises have responded to the competitive pressures unleashed by these forces through extensive repositioning activities involving corporate restructuring in general and mergers and acquisitions (M&As) in particular. This article has carried out a survey of Indian M&As for different industry groups by creating a database of 1990–2011 to find the presence of M&As waves in India. Empirical construct revealed the trends of Indian M&As for 24 industry groups and identified three distinct waves of M&As. The survey of Indian M&As has revealed that there was a significant reduction of Indian M&As in international deals than domestic deals during the economic recession period (2008–2009). Using VAR and VECM model, it emerged that M&As waves for different sectors of Indian industry move in the same cyclical pattern. Their behaviour, apparently independent corporate decisions, are most likely affected by the conditions of the economy, which may be changed by various macroeconomic factors which are in line with the findings of several other studies. This study contributes towards finding the answer to this question by establishing the underlying common factors that cause the cyclical behaviour in all the M&A waves. This article also establishes the basic interdependence and co-movements between the waves, and how this interdependence changed over time.

Predicting expressway crash frequency using a random effect negative binomial model: A case study in China

2017 ◽  
Vol 98 ◽  
pp. 214-222 ◽  
Author(s):  
Zhuanglin Ma ◽  
Honglu Zhang ◽  
Steven I-Jy Chien ◽  
Jin Wang ◽  
Chunjiao Dong
Keyword(s):  
Negative Binomial ◽  
Random Effect ◽  
Negative Binomial Model ◽  
Binomial Model ◽  
Crash Frequency
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