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Record W6920572367 · doi:10.6068/dp15bc04e002e47

TREND: Xignite. FactSet Corporate Fundamentals: Beta | Stock Symbol: UPS | Symbol Name: United Parcel Service Inc, 01/04/2010 - 04/28/2017. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 016-005-038

2017· other· en· W6920572367 on OpenAlexaboutno aff

Bibliographic record

VenueData Planet · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsStock (firearms)Stock marketBETA (programming language)Volatility (finance)Stock market bubbleStock market index

Abstract

fetched live from OpenAlex

Xignite. FactSet Corporate Fundamentals: Beta | Stock Symbol: UPS | Symbol Name: United Parcel Service Inc, 01/04/2010 - 04/28/2017. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 016-005-038 Dataset: Reports a coefficient that measures the volatility of a stock's returns relative to the market (S&P 500). It is based on a minimum of 30 days to a maximum of three years’ (however much is available) historical regression of the daily returns on the stock onto the daily returns on the S&P 500: Ri = a + b(Rm) + e where Ri is the monthly total returns on the stock, a is the stock's Alpha, b is the stock's Beta (this fundamental), Rmis the monthly total returns on the market (S&P 500), and e is a random error term. A beta of 1 means that the market and the stock move up or down together, at the same rate. That is, a 5% up or down move in the market should theoretically result in a 5% up or down move in the stock. A beta coefficient of 2 suggests that the stock will tend to fluctuate twice as much as the market; ie, if the market moves up 5%, then the stock should move up 10%. A beta coefficient of 0.5 indicates that the stock will move one-half as much as the market, either up or down. A negative beta indicates the stock tends to move in the opposite direction from the general market; ie, the stock price declines when the overall market is rising, or rises when the overall market is declining. Negative beta stocks are rare. A beta of 0 means there is no correlation between the stock and the market. Note that this value may be different than the other Beta values. Exchange Traded Fund (ETF): The sensitivity of the returns of the fund to the movement of the FactSet designated 'segment benchmark'. Beta of 1.0 means magnitude of fund returns equals that of segment benchmark returns. Closed-End Fund (CEF): Returns the market weighted average beta for equities in the fund's portfolio. Benchmarks are local to the security's trading region (S&P 500 in the USA). This database provides stock fundamentals data for active United States and Canadian listed companies. Companies are identified by symbol and name. Equities, common stock, and some ETFs and preferred stocks are covered. Exchanges include BATS, NASDAQ, NYSE ARCA, NYSE Market Equities, and limited OTCBB and TSX Venture coverage (liquid companies only). Data points include earnings and dividends, ratios and sales, and stock price. Note that not all metrics are available for all stocks. http://www.xignite.com/product/company-fundamentals Category: Industry, Business, and Commerce, Banking, Finance, and Insurance Subject: Stock Indexes, Corporations, Stock Markets, Financials, Publicly Traded Companies, Stock Prices Source: Xignite Xignite provides on-demand financial market data covering global equities, commodities, currencies, options, fixed income, mutual funds, derivatives, and OTC (“over-the-counter” or those traded in a context other than a formal exchange) instruments. http://www.xignite.com/

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.145
Threshold uncertainty score0.485

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.007
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0040.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.1450.286

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 head0.082
GPT teacher head0.311
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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
Published2017
Admission routes1
Has abstractyes

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