MétaCan
Menu
Back to cohort
Record W2196988881 · doi:10.2118/0315-011-twa

Winning in Acquisitions and Divestitures: Advice to Young Professionals in a Changing Company Landscape

2015· article· en· W2196988881 on OpenAlexaff
Angela Dang, Andy Rogers

Bibliographic record

VenueThe Way Ahead · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsDivestmentPortfolioWork (physics)BusinessAsset (computer security)MarketingStrategic fitFinanceStrategic planning

Abstract

fetched live from OpenAlex

Technical Leaders Andy Rogers, vice president of business development at Encana, shares his thoughts on the current state of the industry and provides career advice to young professionals amid the acquisitions and divestitures (A&D) landscape. The low commodity prices of 2015 have left some companies financially challenged and others with opportunities for business growth by means of A&Ds. For young professionals working in the industry, this can mean many things, such as the opportunity to work in a new asset or technology area, changing career streams, and even the opportunity to return to school. Professionals are always looking to “work the right asset.” One does not necessarily want to be working an asset that might be divested by a company. Conversely, one does not necessarily want to work at a company that might be acquired by another company. What are some indicators that the asset on which one is working may be acquired or divested? At the working level, you are probably not in tune with the strategic decisions that executive level managers are making. However, if you pay attention to the company’s strategic messages, you can potentially identify the direction in which the company is headed. For example if your company has sent out a strategic plan to “balance their strategic mix,” it is a good indicator that the company is trying to be oilier or gassier, depending on its current portfolio. If the company is trying to become oilier, it does not take a genius to figure out that the company has to sell its gassier assets and/or aggressively develop or buy more oil assets. That is one of the more obvious scenarios one can identify. Alternatively, keeping up with the headlines allows you to identify whether or not a company is financially stressed. Clearly, there have to be some changes in order for that company to continue to survive. In that situation, likely the company will have to sell some assets to sure up their balance sheet. Can there be winners and losers when it comes to A&D? There is only one case where there is a clear winner and loser. In a majority of cases, the goal is a win-win scenario for both the divesting and acquiring company. If you think about why people sell assets, there are probably three or four reasons to engage in a mergers and acquisitions (M&A) transaction.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.030
GPT teacher head0.271
Teacher spread0.241 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueThe Way AheadSame topicPrivate Equity and Venture CapitalFrench-language works237,207