Efficiency of Using Cryptocurrencies as an Investment Asset
Why this work is in the frame
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Bibliographic record
Abstract
The study of the effectiveness of using cryptocurrencies as an investment resource was conducted on the basis of testing the hypothesis that the introduction of leading cryptocurrencies that are components of the CRIX index into the investment portfolio improves its quality (efficiency). Cryptocurrency investment opportunities are explored on the basis of statistics for July 2016-June 2019. An average annual return on investment (ROI), which is adjusted for passive income on an investment asset (PI), is used to evaluate investment performance. In this study, cryptocurrencies are compared with the following alternative investment areas: Forex market, equities (companies with the highest weights in Nasdaq 100, Euro STOXX 50), commodities, government bonds, real estate. The criteria were determined by the increase in the Sharpe ratio of the investment portfolio and its average annual return. Optimization of investment portfolios without cryptocurrencies and with them was performed on the basis of the Markowitz model. The result shows the confirmation of the hypothesis: the introduction of 3 cryptocurrencies – Bitcoin, Ripple, Litecoin – in the proportions of 2.31%, 1%, 1%, respectively, increased the Sharpe ratio of the investment portfolio by 3.29 points, and the coefficient of return by 9.42 percentage points while increasing the risk by only 0.51 percentage points. This result indicates that the quality (increase in efficiency) of the investment portfolio due to the introduction of cryptocurrencies and the ability to control the investment risk of the portfolio despite the high volatility of cryptocurrencies. This proves the investment (speculative) function of crypto-assets, which can be the basis for developing a model of accounting for crypto-assets.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it