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Record W2173814641 · doi:10.2469/cfm.v24.n4.4

Financial Analysis at “Jungle Speed”

2013· article· en· W2173814641 on OpenAlex
Michele Armentrout

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCFA Magazine · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsJungleBusinessFinanceEconomicsGeographyArchaeology

Abstract

fetched live from OpenAlex

The CFA Institute Research Challenge wrapped up its seventh competition on 12 April in London with a new global champion: Wroclaw University of Economics. The five-member winning team from Poland included Jan Kasperowicz, Katarzyna Kowalczyk, Piotr Lembas, Kamil Saklaski, and Marta Szudzichowska, who served as the team’s captain. Szudzichowska explained that in addition to extensive preparation for each challenge event, an important element of the team’s winning strategy was their ability to handle stress. To relieve pressure and regain their focus, the team enjoyed playing a card game called “Jungle Speed.” “In the end, before our final presentation, only one thing really mattered: how to deal with the stress,” said Szudzichowska. “The card game really helped us cool off and enabled us to go into the Q&A session with clear heads.” As part of the research challenge competition, students worked in teams to write an initiation-of-coverage report on their assigned subject company. Some teams are then invited to present and defend their “buy,” “sell,” or “hold” recommendation to a panel of industry experts. This is no small task. Students spend hundreds of hours preparing for the competition while enrolled in full course loads (sometimes at the graduate level), working, and managing other personal and professional commitments. Team member Kamil Saklaski learned many lessons from the experience, especially when it came to collaboration. “The research report that we had to prepare on KGHM Polska Miedź S.A. required different expertise, approaches, and ideas. Each of us improved our analytical skills while working on each section, keeping in mind the whole structure and the character of our investment story. On the other hand, we gained a lot of ‘soft skills’ too, like teamwork, time management, task planning, and a broad array of presentation skills.” More than 3,500 students from more than 775 universities participated in local competitions held in 55 countries this year. Winners from each local-level competition, organized by volunteers at CFA Institute member societies, advanced to one of three regionals hosted by CFA Institute: Asia Pacific (held in Kuala Lumpur on 23 February), the Americas (held in Toronto on 21 March), and EMEA (held in London on 11 April). The winners from each regional then advanced to the final competition on 12 April and included:

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.012

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 head0.016
GPT teacher head0.208
Teacher spread0.192 · 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