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Record W2416117824 · doi:10.2118/0315-022-twa

The Price Is Right: What Makes an M&A Deal Go Round, and What You Can Do When Companies Combine

2015· article· en· W2416117824 on OpenAlexaff
C. T. Frenette, Islin Munisteri, Samuel Ighalo, R. Rueda Terrazas, Alan Tambosso, Jeffrey T. Dodson, Lucas K. Law

Bibliographic record

VenueThe Way Ahead · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsCenovus Energy (Canada)
Fundersnot available
KeywordsCash flowMergers and acquisitionsDatabase transactionCashBusinessFinanceAsset (computer security)Free cash flowLimited partnershipEconomicsGeneral partnership

Abstract

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Forum This year and the last have been significant for acquisitions in the energy sector. At the corporate level, we are seeing the Halliburton takeover of Baker Hughes, the Shell buyout of BG Group, and Repsol’s acquisition of Talisman. At the asset level, there was the Woodside-Apache liquefied natural gas project, Nigerian companies’ acquisition of western companies’ interests in the country, and various master limited partnership mergers in the US. The TWA Forum team talked with some of the top minds in the energy mergers and acquisitions (M&A) arena about what is driving the market, and how to prepare yourself if you are employed on either side of a transaction. This article will provide an overview of what happens behind closed doors before a merger or an acquisition takes place. What Gets the Ball Rolling? What are the key drivers of M&A activity in the energy sector? Alan Tambosso (AT): The key drivers are currently cash flow, cash flow, and cash flow! Assets that are currently making money are in demand. Previously, assets were traded on a reserves basis. Transactions were very often based on a proved plus probable reserves value at a specified discount rate, say 15%. However, the focus has changed now as the market demands a current cash flow. The shift in M&A drivers from reserves to cash flow occurred in the early 1990s when junior companies would buy underdeveloped assets with lots of reserves and then put capital in to accelerate the cash flow of the asset. This in turn provided more capital to develop more reserves, which created growth. The market reacted by funding these companies, as markets will always allocate capital toward companies with high growth potential. Recently, low commodity prices have highlighted the focus on cash flow. Assets must be producing positive cash flow or the company holding the assets will not be able to sustain itself. Other drivers of transactions are debt, including operational obligations, such as abandonment liabilities. Companies with high levels of debt are less likely to be able to acquire a company or asset. In addition, acquirers may not want to purchase a target with high debt load.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0050.005
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.047
GPT teacher head0.255
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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