The financial crisis effects on asset allocation: Markowitz theory vs. behavioural portfolio theory
Bibliographic record
Abstract
This article focuses on two alternative theories of portfolio optimisation namely the mean variance theory (MVT) of Markowitz (1952) and the behavioural portfolio theory (BPT) of Shefrin and Statman (2000). Using stock prices from the Canadian Stock Exchange database for the 2002-2017 period, we attempt to compare the asset allocations generated by MVT and BPT frameworks by investigating the effect of the financial crisis. Our results indicate the financial crisis caused large drops of the market values of efficient MVT portfolios covering risky securities and the absence of the BPT optimal portfolio. This finding is mainly attributed to the concept of security and fear that characterises BPT and MVT investors. We also found out that the modification of the security parameter was consistent with the way BPT investors perceived risk. Thus, in the case of higher degree of risk aversion induced by BPT investors, we show that the security set is located on the upper right of the mean variance (MV) efficient. However, even if the asset allocations of MVT and BPT coincide, MV investors displaying lower degrees of risk-aversion don't systematically select the BPT optimal portfolios.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".