Entropy-Based Technical Analysis Indicators Selection for International Stock Markets Fluctuations Prediction Using Support Vector Machines
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.
Bibliographic record
Abstract
Most of works on stock price forecasting are concerned with the problem of predicting its future value. However, forecasting stock price future fluctuation trend could be easier and interesting for traders and investors to maximize profits. The purpose of this study is to predict CAC40, FTSE, NASDAQ and S&P500 price index up and down fluctuations. In particular, it aims to propose a methodology to forecast regime switches in these markets time series to assist traders and investors in decision making. In the first stage, a large set composed of twenty five technical analysis indicators is formed. They fall into four broad categories namely oscillators, stochastic measures, indexes and indicators. Entropy statistic is employed to rank the initial technical analysis indicators. Finally, in the third stage, polynomial-based kernel support vector machines (SVM) are used for predicting CAC40, FTSE, NASDAQ and S&P500 future upward and downward fluctuations. The forecasting results show that the choice of technical analysis indicators used to predict CAC40 and NASDAQ fluctuations depend on the type of risk-aversion and risk-appetite of the investor. For the S&P500 and FTSE, technical analysis indicators used in our study can detect future downshifts with high accuracy. Thus, they are suitable for market analysis and trading by risk-averse investors on these markets.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it