Data for: Application of online multitask learning based on least squares support vector regression in the financial market
Bibliographic record
Abstract
These historical transaction data of financial market are downloaded from the official website of Investing, and which constitute the following stock index dataset, bond index dataset, forex index dataset, and gold index dataset, where the web address is (https://cn.investing.com/). The stock index dataset is mainly composed of 1220 historical opening index values of the four China’s stock indices, which are the Shanghai Securities Composite Index (SSEC), the SZSE Component Index (SZI), the Growth Enterprise Index (CNT), and the SSE SME Composite Index (SZSMEPI). The time period is from January. 1st, 2014 to December. 31th, 2018. The bond index dataset is mainly composed of 1219 historical opening price values of the four China’s bond indices, which are the Shanghai Securities National Bond Index (SSEBI), the Shanghai Securities Company Bond Index (SSECBI), the Shanghai Securities Enterprise Bond Index (SSEEBI), and the Shanghai Securities 5-year Term Credit Bond Inde (SSE5YCB). The time period is from January. 1st, 2015 to December. 31th, 2019. The forex index dataset mainly consists of 1043 historical data of the exchange rate between the four currencies and RMB, which are the United States Dollar to RMB (USD-CNY), the Canadian Dollar to RMB (CAD-CNY), the Euro to RMB (EUR-CNY), and the Swiss Franc to RMB (CHF-CNY). The time period is from January. 1st, 2016 to December. 31th, 2019. The Gold index dataset is mainly composed of 1213 historical opening price values of the four precious metal spots, which are the London gold (XAU), trading-delayed gold (AUTD), London silver (XAG), and trading-delayed silver (AGTD). The time period is from January. 1st, 2015 to December. 31th, 2019. Since the four data sets are all financial time series, they can be used to verify the financial time series model. At the same time, the time series in each data set have a strong correlation, so it can be used to verify the multi-task learning model.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.024 |
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".