Profitability of Cryptocurrency Trading Strategies Employed by Investors of a Philippine-Based Online Community: Basis for the Development of an Investor's Guidebook
Bibliographic record
Abstract
The volatile and unpredictable nature of the cryptocurrency market poses significant challenges for making profitable trading decisions. This study investigated the profitability of cryptocurrency trading strategies employed by investors of a Philippine-based online community. A descriptive-correlational design was used, employing a Google Forms survey administered to 100 investors who are members of a private online cryptocurrency trading group based in a specific province in the Philippines. Data analysis included frequency, percentage, weighted mean, Pearson’s r, and t-test. Results indicated that most investors are young, educated professionals with employment in finance-related fields, suggesting a familiarity with trading systems. All trading strategies were perceived as generally effective in their contribution to profitability, with algorithmic trading and diversification showing the strongest positive correlations. Demographic variables—particularly sex, age, income, and profession—significantly influenced strategy choice. Younger, wealthier, and more professionally experienced individuals favored more advanced approaches, such as algorithmic trading and diversification. Based on the study's findings, the researchers proposed chapter guidelines for an investor’s guidebook, offering step-by-step strategies tailored to demographic characteristics to help optimize profitability and manage risk. Study limitations include the small and geographically limited samples, as well as the absence of longitudinal data. Future research should involve broader samples and the use of other research methods to validate these findings and explore the effectiveness of long-term cryptocurrency trading strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".