Master Thesis Fall 2020.pdf
Bibliographic record
Abstract
In this thesis we seek to examine how modern forecasting approaches can improve estimationsof stock pair correlations, and derived from this, contribute to making portfolios more stable.Volatility of financial markets have experienced increases due to the ongoing global pandemic.This amplifies the issues that investors face when assessing the risk related to theirinvestments. We construct a hybrid model consisting of an ARIMA component to explain thelinear tendencies of correlation, and a Long Short-Term Memory component to explain thenon-linear tendencies. Our approach is populated by data from constituents of Oslo StockExchange ranging a time span from 2006 through the third quarter of 2020. Our results indicatethat modern approaches to forecasting accrue stronger predictive performances than theconventional methods. Across all test periods our proposed hybrid model achieves an RMSEof 0.186 compared to an average benchmark RMSE of 0.237. However, the implications ofthese findings are ambiguous as the increase in predictive performance cannot be said todefinitively outweigh the increase in cost of implementation. Our thesis contributes to theexisting literature by exhibiting the untapped potential of how modern approaches toforecasting can improve accuracy of quantitative inputs for decision making.
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How this classification was reachedexpand
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 | 0.001 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.897 | 0.060 |
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; both teacher heads agree on what is shown here.
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".