Forecasts on Best Investment Portfolio for Healthcare Companies Based on ARIMA and GARCH Models
Bibliographic record
Abstract
Healthcare stocks have increased due to the testing and treatment costs and many other factors caused by the COVID-19 pandemic since 2020.This paper constructs portfolios to minimize the risk and maximize the returns of healthcare stocks.Moreover, to enhance the performance of the initial portfolio, this paper uses time series analysis to forecast the stock price and verify the forecasting outcomes.The paper investigates the stock price of top healthcare companies in the United States using time series analysis to predict their performance and organize an optimal portfolio.Specifically, this research paper first employs the Auto Regressive Integrated Moving Average (ARIMA) and Auto Regressive Conditional Heteroskedasticity (ARCH) models to determine the 30 days stock price forecasting.After that, according to the historical data and forecasts, it evaluates the portfolio's efficient frontier based on Monte Carlo simulations (MCOS), which determines the minimum volatility, maximum Sharpe ratio, and the most suitable portfolio.The results show that the return of the optimal portfolio performs a more significant expected return and has less volatility than the portfolio with equal weights, which proves the validity of our 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".