The Impact of Cryptocurrencies on Stock Exchange Development: Empirical Evidence on Canadian firms
Bibliographic record
Abstract
Cryptocurrencies are decentralized digital currencies secured by blockchain technology. Their growing popularity has a significant impact on traditional financial markets. The purpose of this paper is to examine the impact of cryptocurrency investment on stock financial development. Our empirical evidence is conducted on (30) Canadian firms during the period August 2017- May 2023. The firms are the most important companies in the financial sector. The results of the VECM estimation show a positive and significative impact of Bitcoin Value on each variable assessing stock market development in long term as Market Liquidity, Market Size, Market Capitalization. In short term, this same relationship is observed with Market Size and Market Liquidity. Bitcoin value has a negative impact on Market Capitalization. The Exchange Rate and Unemployment Rate provide a negative and significant relationship towards stock market development in the long-term. In contrast, the short-term relationship results show that Exchange Rate acts positively only on the Market Capitalization. In contrast, Market Liquidity has a positive impact on the Exchange Rate. Moreover, we find the absence of the impact of Unemployment Rate on Stock Financial Development in short term. But, there is a significant and negative incidence of Market Liquidity and Market Size on Unemployment Rate. Our results demonstrate also the positive and significant impact of Unemployment Rate on Bitcoin Value.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".