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Record W3035304904 · doi:10.36587/wasananyata.v4i1.581

PELATIHAN INDENTIFIKASI INDEKS SAHAM USA UNTUK MEMPREDIKSI FLUKTUASI IHSG: LINCOM ANALYSIS PADA NASABAH TYPE SWINGER RELIANCE SURAKARTA

2020· article· en· W3035304904 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWASANA NYATA · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Analysis and Corporate Governance
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsStock (firearms)Stock marketDividendBusinessPortfolioStock exchangeTechnical analysisFinancial economicsEconomicsMonetary economicsFinance

Abstract

fetched live from OpenAlex

ABSTRACT Capital market practices in the type of stock instruments undergo a shift in the way of analysis. This is not in line with the theory of stock fundamental analysis which explains that fundamental factors become the main variable in determining stock investment decisions for the long term. Fundamental analysis of stocks takes into account various factors including company performance, macroeconomic analysis and the industrial sector. The fundamental variable of stock analysis is used as a consideration of the investment portfolio of the stock for the long term. Users of these variables are usually the owners of capital with the type of investor. Investor type is the owner of capital with the main purpose of buying shares by expecting stock valuations in the long run and dividends, not short-term capital gains. The need for the development of applied science of technical analysis and fundamental analysis of stock investors who have a form of trading activities on a daily basis generally only conduct transactions on the capital market using speculation from each investor. This of course in terms of education that novice stock investors do must have the same time in obtaining maximum income in the trading stock market This service is carried out on customers of PT Reliance Surakarta. There were 13 training participants, namely customers who became stock investors but did not trade every day for a short period of time. The service was held for 1 day. Expected outputs from the event are expected that after attending the training the participants are expected to be able to carry out fundamental and technical analysis of JCI fluctuations on a daily basisKeywords: Stock index, JCI Fluctuation Prediction, Swinger Type

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.035
GPT teacher head0.211
Teacher spread0.177 · 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