Do Predictive Power of Fibonacci Retracements Help the Investor to Predict Future? A Study of Pakistan Stock Exchange
Bibliographic record
Abstract
There are number of ways like Technical Analysis, Fundamental Analysis and The Efficient Market Hypothesis that helps to know the behaviour of investor which helps to predict future. In this study author used Fibonacci numbers/series analysis which consider for forecasting future stock prices trends. For the purpose of this study four listed companies are selected at random by convenience sampling from Cement Sector for the period of 1st quarter of 2017. Closing prices of open days of market are taken from Karachi Stocks and graphs are made. This study concluded that in four companies of cement sector, there were total 63 support levels out of which 17 (27%) and total 66 resistance level from which 24 (36%) were followed Fibonacci retracements. So the study findings accept the hypothesis that the trend reversals in Cement sector listed in PSX follow Fibonacci retracements to some extent. Analysis of this study showed that there is at least one strong support and one strong resistance in every company and some small resistant and support levels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".