Quarterly data on the value and number of mergers, acquisitions and disposals involving UK companies with values of £1 million or more.

The data center sector saw **** major deals worth over a billion U.S. dollars in 2023 and 2024. During that period, the biggest deal with a reported valuation was Blackstone's acquisition of Airtrunk, worth approximately **** billion U.S. dollars. Two other deals surpassed the **** billion threshold — Silverlake's minority investment in Vantage and Brookfield and Ontario Teachers' acquisition of Compass.

Intellizence is an AI platform focused on monitoring growth & sales, risk & distress signals in companies. Intellizence signals help customers to identify new business opportunities & risks as well as make timely strategic & tactical decisions.

Intellizence discovers Merger & Acquisition (M&A) deals from various public sources, de-duplicates, extracts deal details, resolves correct entities, enriches with firmographic details, ensures quality, and delivers through an API. The data is refreshed daily.

Intellizence M&A data is useful for: Monitoring Merger & Acquisition deal trends Getting intelligence about Merger & Acquisition events in customers, competitors, prospects, etc.,

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Tech Companies - Merger and Acquisitions Dataset - Software Companies

This dataset contains the list of acquisitions made by the following companies:

Microsoft, Google, IBM, Hp, Apple, Amazon, Facebook, Twitter, eBay, Adobe, Citrix, Redhat, Blackberry, Disney

The attributes include the date, year, month of the acquisition, name of the company acquired, value or the cost of acquisition, business use-case of the acquisition, and the country from which the acquisition was made. The source of the dataset is Wikipedia, TechCrunch, and CrunchBase.

Interesting Tasks and Analysis Ideas

• Which company makes the acquisitions quickly
• What is the trend of business use-cases among the acquired companies throughout the years
• What can be forecasted for upcoming years in terms of acquisitions
• Predict who is likely to make next acquisitions and when

This statistic presents the leading methods of data analytics application in the mergers and acquisitions sector in the United States in 2018. At that time, ** percent of executives surveyed were using data analytics on customers and markets.

In 2024, approximately ****** merger and acquisition (M&A) deals were completed worldwide. This figure is in line with those registered recently, despite representing a decrease compared to the previous years. PwC., Deloitte, and JPMorgan advised on the largest number of M&A deals in 2023. How has the value of M&A deals varied over the years? The value of M&A deals worldwide fluctuated significantly recently. A peak value of over *** trillion U.S. dollars was recorded in 2021. So-called mega-deals - M&A deals worth *** billion U.S. dollars or more - have a great influence on the market performance: In 2023, nearly 400 megadeals were worth a combined *** billion U.S. dollars. How did the world’s leading financial advisor perform? In 2023, Goldman Sachs was the leading M&A financial advisor in terms of the value of M&A deals, while PwC was leading in terms of the number of deals. While Goldman Sachs dominated the M&A scene in terms of deal value, J.P. Morgan surpassed them as the world’s leading bank in terms of revenue from investment banking in 2024.

Comprehensive database of mergers and acquisitions in the Cloud Data Services industry

In 2024, there were 373 merger and acquisition (M&A) transactions valued at more than one billion U.S. dollars in the United States. The overall number of M&A deals in 2024 amounted to 13,697, down from 15,643 in the previous year. M&A deals in the United StatesMerger and acquisition (M&A) refers to the consolidation of two companies. The value of M&A deals in the U.S. amounted to roughly 1.8 trillion U.S. dollars in 2024. Additionally, during the last quarter of the year, in the United States, the technology services sector accounted for the largest share of merger and acquisition deals as well as the highest overall transaction value. As of 2024, the largest all-time M&A deal in the United States, which was the acquisition of Time Warner by America Online Inc, was valued at 182 billion U.S. dollars. JPMorgan Chase was the leading M&A advisor in the United States in 2024, managing M&A deals worth approximately 751 billion U.S. dollars.

This dataset contains raw, unprocessed data files pertaining to the management activity 'Mergers and Acquisitions' (M&A). The data originates from five distinct sources, each reflecting different facets of the activity's prominence and usage over time. Files preserve the original metrics and temporal granularity before any comparative normalization or harmonization. Data Sources & File Details: Google Trends File (Prefix: GT_): Metric: Relative Search Interest (RSI) Index (0-100 scale). Keywords Used: "mergers and acquisitions" + "mergers and acquisitions corporate" Time Period: January 2004 - January 2025 (Native Monthly Resolution). Scope: Global Web Search, broad categorization. Extraction Date: Data extracted January 2025. Notes: Index relative to peak interest within the period for these terms. Reflects public/professional search interest trends. Based on probabilistic sampling. Source URL: Google Trends Query Google Books Ngram Viewer File (Prefix: GB_): Metric: Annual Relative Frequency (% of total n-grams in the corpus). Keywords Used: Mergers and Acquisitions + Mergers & Acquisitions Time Period: 1950 - 2022 (Annual Resolution). Corpus: English. Parameters: Case Insensitive OFF, Smoothing 0. Extraction Date: Data extracted January 2025. Notes: Reflects term usage frequency in Google's digitized book corpus. Subject to corpus limitations (English bias, coverage). Source URL: Ngram Viewer Query Crossref.org File (Prefix: CR_): Metric: Absolute count of publications per month matching keywords. Keywords Used: ("mergers and acquisitions" OR "mergers & acquisitions") AND ("corporate" OR "strategy" OR "finance" OR "management" OR "deal" OR "implementation" OR "valuation") Time Period: 1950 - 2025 (Queried for monthly counts based on publication date metadata). Search Fields: Title, Abstract. Extraction Date: Data extracted January 2025. Notes: Reflects volume of relevant academic publications indexed by Crossref. Deduplicated using DOIs; records without DOIs omitted. Source URL: Crossref Search Query Bain & Co. Survey - Usability File (Prefix: BU_): Metric: Original Percentage (%) of executives reporting tool usage. Tool Names/Years Included: Mergers and Acquisitions (2006, 2008, 2010, 2012, 2014, 2017). (Note: Some sources list this as Mergers & Acquisitions). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., et al., various years: 2007, 2009, 2011, 2013, 2015, 2017). Note: Tool potentially not surveyed or reported before 2006 or after 2017 under this specific name. Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 2006/1221; 2008/1430; 2010/1230; 2012/1208; 2014/1067; 2017/1268. Bain & Co. Survey - Satisfaction File (Prefix: BS_): Metric: Original Average Satisfaction Score (Scale 0-5). Tool Names/Years Included: Mergers and Acquisitions (2006, 2008, 2010, 2012, 2014, 2017). (Note: Some sources list this as Mergers & Acquisitions). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., et al., various years: 2007, 2009, 2011, 2013, 2015, 2017). Note: Tool potentially not surveyed or reported before 2006 or after 2017 under this specific name. Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 2006/1221; 2008/1430; 2010/1230; 2012/1208; 2014/1067; 2017/1268. Reflects subjective executive perception of utility. File Naming Convention: Files generally follow the pattern: PREFIX_Tool.csv, where the PREFIX indicates the data source: GT_: Google Trends GB_: Google Books Ngram CR_: Crossref.org (Count Data for this Raw Dataset) BU_: Bain & Company Survey (Usability) BS_: Bain & Company Survey (Satisfaction) The essential identification comes from the PREFIX and the Tool Name segment. This dataset resides within the 'Management Tool Source Data (Raw Extracts)' Dataverse.

This dataset contains 1000 cross-border M&A deals capturing key ESG and performance metrics across multiple countries and years. Each row represents a unique M&A deal, with detailed attributes including ESG scores, sentiment, deal value, and post-merger performance.

Description: The dataset provides structured information to analyze the relationship between ESG factors, sentiment, and M&A performance. It is designed for exploratory data analysis, predictive modeling, and performance assessment of deals.

Key Features (Columns):

Deal_ID – Unique identifier for each M&A deal

Country_Source – Country of the acquiring company

Country_Target – Country of the target company

ESG_Score – Overall ESG score

E_Score – Environmental score

S_Score – Social score

G_Score – Governance score

Sentiment_Score – Sentiment associated with the deal

Year – Year of the deal

Deal_Value_USD_M – Deal value in million USD

Post_Merger_ROI – Post-merger ROI

SWO_MNN_Predicted_Perf – Predicted performance by SWO-MNN model

Confidence_Level – Confidence level of model prediction

Performance_Text – Text description summarizing deal performance

The main results of the paper are based on the Bureau van Dijk’s (BvD) Amadeus and Zephyr data. Those data are commercially available. This is the structure of the folder: (1) Code Results.do: This code generates the tables and figures of the manuscript and appendix. It requires the datasets stored in the “Datasubset” folder. It saves the results in the “Output” folder. Data_aggregation_zephyr.do: This code computes our main M&A Activity variables. Data_aggregation_reporting.do: This code computes our mandatory reporting variable. (2) Data-subset This folder contains the datasets needed to run the analyses of the manuscript and appendix. Due to commercial restrictions, we include a subset of the original data. (3) Output This folder is populated after running the “Results.do” file. results_full_dataset.log: This Log file is the result of running “Results.do” with the original data

This dataset provides processed and normalized/standardized indices for the management activity 'Mergers and Acquisitions' (M&A). Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption). The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding M&A dataset in the Management Tool Source Data (Raw Extracts) Dataverse. Data Files and Processing Methodologies: Google Trends File (Prefix: GT_): Normalized Relative Search Interest (RSI) Input Data: Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query "mergers and acquisitions" + "mergers and acquisitions corporate". Processing: None. Utilizes the original base-100 normalized Google Trends index. Output Metric: Monthly Normalized RSI (Base 100). Frequency: Monthly. Google Books Ngram Viewer File (Prefix: GB_): Normalized Relative Frequency Input Data: Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query Mergers and Acquisitions + Mergers & Acquisitions. Processing: Annual relative frequency series normalized (peak year = 100). Output Metric: Annual Normalized Relative Frequency Index (Base 100). Frequency: Annual. Crossref.org File (Prefix: CR_): Normalized Relative Publication Share Index Input Data: Absolute monthly publication counts matching M&A-related keywords [("mergers and acquisitions" OR ...) AND (...) - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly Crossref publications. Deduplicated via DOIs. Processing: Monthly relative share calculated (M&A Count / Total Count). Monthly relative share series normalized (peak month's share = 100). Output Metric: Monthly Normalized Relative Publication Share Index (Base 100). Frequency: Monthly. Bain & Co. Survey - Usability File (Prefix: BU_): Normalized Usability Index Input Data: Original usability percentages (%) from Bain surveys for specific years: Mergers and Acquisitions (2006, 2008, 2010, 2012, 2014, 2017). Note: Not reported before 2006 or after 2017. Processing: Normalization: Original usability percentages normalized relative to its historical peak (Max % = 100). Output Metric: Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak). Frequency: Biennial (Approx.). Bain & Co. Survey - Satisfaction File (Prefix: BS_): Standardized Satisfaction Index Input Data: Original average satisfaction scores (1-5 scale) from Bain surveys for specific years: Mergers and Acquisitions (2006-2017). Note: Not reported before 2006 or after 2017. Processing: Standardization (Z-scores): Using Z = (X - 3.0) / 0.891609. Index Scale Transformation: Index = 50 + (Z * 22). Output Metric: Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]). Frequency: Biennial (Approx.). File Naming Convention: Files generally follow the pattern: PREFIX_Tool_Processed.csv or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_). Consult the parent Dataverse description (Management Tool Comparative Indices) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding M&A dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

In today's increasingly interconnected digital landscape, Cybersecurity Due Diligence for mergers and acquisitions (M&A) has emerged as a critical practice for safeguarding economic interests and ensuring smooth integrations between companies. As cyber threats and data breaches become more prevalent, organizations a

/ Replication Code for Macias, Rau, and Stouraitis, " Solving Serial Acquirer Puzzles" // The main data come from the Compustat, CRSP, and Thomson Reuters databases, all of which are accessible via a subscription // Replication code is split in (i) Serial Acquirer Classification, and (ii) Tables // The datasets are extracts of the main datasets, using just few years either at the deal-level or at the firm-year level // Further detail is found within each replication code.

The M&A Platform market is booming, projected to reach $6.1 billion by 2033, driven by increased deal complexity and cloud adoption. Learn about key market drivers, trends, restraints, and leading companies like Intralinks and DealRoom in this comprehensive analysis. Explore regional market shares and growth forecasts.

ABSTRACT This paper investigates the probability of companies to perform Mergers & Acquisitions (M&As) based on the characteristics of the CEO.. We use a sample of 794 CEOs of nonfinancial firms, which were listed on the B3 from 2000 to 2017. We adopt a descriptive approach and run logistic regressions to examine the probability of a company performing M&As, given the characteristics of its CEO. We show that CEOs with finance backgrounds are less likely to perform M&As. We find no statistically significant correlations for other CEO’s characteristics, such as executive tenure, age, education, participation as chair of the board, previous experience as an entrepreneur or being a shareholder of the acquiring company.

Best virtual data rooms 2024 dataset is created to provide the data room users and M&A specialists with detailed information on the best virtual data rooms. The dataset contains the descriptions of each dataroom solution and their ratings.

Research data used in the paper entitled " Fusões e Aquisições em Períodos de Recessão" published in Revista Brasileira de Gestão de Negócios (RBGN) - V24, n3 (2022).

The value of M&A deals globally has risen over the past decade and tends to mirror the state of the economy overall. Dips can be seen in the years during and following a recession, and M&A activity increases in periods of economic growth. In 2024, the value of global M&A deals amounted to *** trillion U.S. dollars, a slight increase compared to the previous year.

This table contains figures on the number of mergers and acquisitions of companies by company size and legal form. The data are broken down by economic activity, based on the Standard Business Classification 2008 (SBI 2008). The figures are based on the General Business Register (ABR) of Statistics Netherlands (CBS).

This table is published here for the programme 'SMEs and Entrepreneurship'.

Data available from: 2007.

Status of figures: Figures up to 2021 are final and figures for 2022 to 2024 are provisional.

Changes as of 3 May 2024: Preliminary figures for Q1 2024 have been added.

When will there be new figures? The new figures are usually available 1 month after the end of the reporting year or quarter.