The Application of ARIMA and Mean-variance Models on Financial Market
Bibliographic record
Abstract
This study centers on forecasting return and constructing proper portfolios with 5 typical assets rarely focused on the Chinese A-share market. This paper applies the fittest ARIMA models for each of the selected stocks to predict their trend of returns in the next 20 days. Besides, we create the efficient frontier by Monte Carlo simulation under Markowitz’s Mean-Variance framework to focus on two portfolios, i.e., the maximum Sharpe ratio portfolio and the minimum volatility portfolio. The empirical results of the ARIMA model indicate a rational prediction of return for assets in the A-share market. The maximum Sharpe ratio portfolio and the minimum volatility portfolio show that stock of Foshan Haitian Flavouring and Food Company Ltd. and stock of China Merchants Bank Co., Ltd. account for the largest proportion in the two portfolios. Further empirical results show that returns for two portfolios are higher than the market index return, which illuminates the two portfolios outperform the market index. The results in this paper will surely benefit related investors in the financial market.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".