International Journal of Marketing and Digital Creative.
Vol.
3 No.
https://doi.
org/10.
31098/ijmadic.
Research Paper Profitability of Cryptocurrency Trading Strategies Employed by Investors of a Philippine-Based Online Community: Basis for the Development of an Investor's Guidebook Miriam Belle A.
Abarquez1 .
Ernielyn C.
Esperanza2 .
Jerwin A.
Samson3 .
Heidi C.
Turiano4 .
Genieve Aline A.
Navela5 .
Jesus P.
Briones6 .
Resty P.
Umali6 .
Joanna Paula E.
Verano6 1Laguna State Polytechnic University.
Philippines 2Pag-Ibig Fund - Southern Tagalog.
Philippines 3Department of Trade and Industry.
Philippines 4Banco De Oro Unibank.
Inc.
Philippines 5Sun Life of Canada Philippines Inc.
Philippines 6Southern Luzon State University.
Philippines Received: January 22.
Revised: March 28, 2025 Accepted: June 10, 2025 Online: September 30, 2025 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.
PearsonAos 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 variablesAiparticularly sex, age, income, and professionAisignificantly influenced strategy 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 investorAos 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.
Keywords: Cryptocurrency Markets.
Cryptocurrency Trading Strategies.
InvestorAos Guidebook.
Modern Portfolio Theory.
Philippine-Based Online Community INTRODUCTION Cryptocurrency trading is a rapidly expanding financial activity that presents both lucrative opportunities and considerable risks for investors.
In this dynamic landscape, online communities have become key venues where individuals, often anonymous and geographically dispersed, collaborate asynchronously via digital platforms, fostering shared learning and support (Brainard.
In this context, the relevance of the internet and digital platforms on financial behavior has become increasingly pronounced (Saeed et al.
, 2.
Copyright Holder:
This Article is Licensed Under:
A Abarquez.
Esperanza.
Samson.
Turiano.
Navela.
Briones.
Umali, & Verano, .
Corresponding authorAos email: jpbriones@firstasia.
Corresponding authorAos email: x@x.
International Journal of Marketing and Digital Creative Despite growing interest, many individuals approach cryptocurrency trading with hesitation due to uncertainty, while others engage impulsively without proper preparation.
To address these behavioral patterns, it is essential to equip investors with the ability to assess credible information sources, evaluate their trading motives, and learn from the experiences of more seasoned participants (Hadan et al.
, 2.
However, reality remains sobering: only 44% of investors outperform the market, achieving gains of up to 300% through long-term investments, while the majority either break even or incur losses (Ante et al.
, 2.
For experienced and institutional investors alike, success increasingly depends on the adoption of sound trading strategies, the use of advanced technologies, and a nuanced understanding of regulatory frameworks (Sattarov & Choi, 2.
While academic researchersAo attention has been primarily focused on technical models such as deep learning-based price prediction, less is known about how retail investors navigate strategy in communal settings (Park & Seo, 2.
In the Philippine context, research reveals a wide variation in financial literacy among cryptocurrency investors (Diaz et al.
, 2.
Studies have highlighted the potential profitability of leading cryptocurrencies (Patiyo & Lelis, 2.
, yet many traders still fail to optimize these Moreover, despite continued profits in 2023, trading performance within Philippine online communities has declined compared to prior years (Chainanalysis, 2.
This trend points to the need for improved strategy formulation, particularly for retail investors who rely on intuition, peer guidance, and community discourse rather than institutional-grade tools.
Notably, the collaborative nature of online communitiesAiand their potential to influence trading outcomesAiremains understudied.
While existing literature has examined profitability and individual trading behavior, there is a conspicuous absence of research on how Filipino online investment communities engage with structured financial frameworks such as Modern Portfolio Theory (MPT), which emphasizes diversification and optimal risk-return trade-offs.
This gap is significant because understanding how communal strategies form and evolve in these digital spaces can offer valuable insights into collective decision-making, peer-based financial learning, and the democratization of investment practices.
As retail investors increasingly turn to online communities for guidance, how these groups adoptAior fail to adoptAidisciplined investment frameworks may have profound implications for financial literacy, portfolio performance, and risk management across broader investor populations.
To enable Filipino investors to navigate cryptocurrency markets more effectively, this study aimed to assess the profitability of cryptocurrency trading strategies within a Philippine-based online community.
Specifically, this study identified the demographic profile of cryptocurrency investors within the subject online community.
Additionally, it analyzed the investorsAo practices regarding cryptocurrency trading strategies and the profitability of these strategies, and subsequently examined their relationship.
Furthermore, the study also identified significant differences in trading strategies based on the demographic profile of investors.
Lastly, this study culminated in the development of a chapter of guidelines for a trading strategies guidebook, which summarizes the findings to provide actionable insights for cryptocurrency investors.
Key chapter guidelines, which will serve as the primary thematic sections of the proposed investor's guidebook, will be outlined to provide useful, research-based tactics intended to enhance the confidence, decision-making, and risk management abilities of Filipino cryptocurrency investors.
LITERATURE REVIEW
This literature review focuses on the theoretical framework of the study.
It also examines the applicability of the underpinning theory to cryptocurrency trading strategies, alongside the perceived impacts and challenges of implementing these approaches in the dynamic digital asset International Journal of Marketing and Digital Creative Modern Portfolio Theory (MPT) This study is anchored on MPT, which provides a structured framework for managing risk and maximizing returns through diversification.
According to Yu and Zhang .
, the MPT enables investors to select the optimal portfolio with a predefined risk, reduces the risk of their assets through diversification, and facilitates diversification to optimize their portfolios.
Developed by Harry Markowitz.
MPT remains relevant in todayAos volatile cryptocurrency market (Alidaee et , 2025.
Chen, 2.
Similarly, the current study supports the MPT by framing key trading strategiesAisuch as algorithmic trading, scalping, event-driven trading, and diversificationAias tools to balance short-term volatility with long-term gains.
Several studies have confirmed the relevance of MPT in optimizing cryptocurrency Xia .
demonstrated that including Bitcoin in diversified portfolios enhances efficiency, thereby reinforcing the applicability of MPT to digital assets.
Similarly.
Chen .
affirmed the ongoing relevance of MPT in guiding investors through risk-return trade-offs in volatile cryptocurrency markets.
These studies highlighted the potential for MPT to inform strategic behavior among retail investors.
However, there is no study yet on how MPT principles are applied by investors in online communities, particularly in the Philippine context, where decision-making is often peer-influenced and unstructured.
This underscores a significant research gap, which the current study addresses.
By analyzing the interaction between investor demographics, trading strategies, and profitability within an MPT framework, this study aimed to produce a practical guidebook.
This guidebook will translate theory into actionable strategies, offering scholarly and practical value to Filipino retail investors navigating digital markets.
Trading Strategies Trading strategies are critical components in building the conceptual framework of this study, as they serve both as practical tools for investors and as operational expressions of MPT principles in volatile cryptocurrency markets.
These strategiesAialgorithmic trading, scalping, event-driven approaches, and diversificationAisupport MPTAos emphasis on optimizing risk-return balance through informed decision-making.
Algorithmic trading, which uses mathematical models to predict returns, improves precision and speed (Hatch et al.
, 2.
However, its unequal accessibility raises ethical and technical concerns, as dominant firms exploit these systems, potentially leading to market manipulation and data reliability issues (Sai et al.
, 2021.
Koehler et , 2.
These dynamics underscore the need to assess the real-world effectiveness and accessibility of algorithmic trading in community-based settings.
Scalping aligns with MPTAos principle of dynamic portfolio adjustment, allowing investors to capture quick profits and reduce systemic exposure (Sattarov et al.
, 2.
Yet, its long-term risk-reward profile remains underexplored, particularly in emerging markets like the Philippines.
Diversification is central to MPT, and studies confirm its value in cryptocurrency trading.
Al Halaseh .
and Bakry et al.
found diversified portfolios outperform single-asset strategies.
This reinforces the importance of integrating diverse strategies to stabilize returns and manage riskAian objective directly addressed by this study.
Profitability This section examines the relationship between trading strategies, as viewed through the lens of MPT, and profitability in cryptocurrency markets.
These strategies are not only tools for decision-making but also mechanisms for operationalizing MPT's core principles of diversification and risk-return optimization.
Recent studies have emphasized the strategic selection of trading International Journal of Marketing and Digital Creative approaches as a determinant of profitability.
Li .
and Ma .
underscored that optimal strategy selection directly enhances returns, particularly in volatile markets.
Vital et al.
further validated MPTAos relevance by demonstrating that its application in cryptocurrency trading can outperform conventional methods by over 652% in 16 months, highlighting its potential in high-risk, high-reward environments.
Despite these promising results, critical barriers remain.
The cryptocurrency market's speculative nature, regulatory unpredictability, and extreme price fluctuations may challenge MPTAos assumptions of rational behavior and normally distributed returns (Jeleskovic & Mackay.
Moreover, investor demographicsAisuch as age, education, and experienceAiinteract with strategy selection in complex ways (Holden & Tilahun, 2.
, often diluting the expected profitability outcomes.
These gaps justify the current study's focus on testing MPT-aligned strategies within Filipino online communities, assessing their real-world effectiveness amidst demographic variability and market instability.
In this respect, the interplay between MPT-aligned strategies and demographic influences on cryptocurrency profitability requires evaluation to understand their effectiveness in a volatile market.
Thus, the following hypotheses were formulated:
HO1: There is no significant relationship between profitability and the trading strategies employed by cryptocurrency investors.
HO2: There is no significant difference in cryptocurrency trading strategies among investors grouped by their demographic characteristics.
These hypotheses are grounded in prior studies suggesting a non-significant correlation between profitability and trading strategies (Jin et al.
, 2.
, as well as the similarities of trading strategies across demographics (Hasso et al.
, 2.
By testing these hypotheses, this study will contribute to a clearer understanding of the profitability of the cryptocurrency trading strategies employed by investors of a Philippine-based online community.
Additionally, the findings will inform the creation of a guidebook that offers actionable insights to cryptocurrency investors seeking to optimize their trading strategies and improve overall returns.
RESEARCH METHOD
This study used the descriptive-correlational research design to examine the relationships between variables without manipulating them, an approach commonly used in cryptocurrency trading studies to identify patterns and associations.
This method is crucial for examining how investor behavior and market conditions impact the effectiveness of various trading strategies.
Descriptive-correlational research is appropriate as it enables the assessment of how these strategies correlate with trading outcomes, providing valuable insights to enhance profitability and refine approaches in volatile cryptocurrency markets (Almeida & Gonyalves, 2.
This approach adds rigor to the understanding of the complex factors that influence cryptocurrency trading.
Cryptocurrency investors were the respondents in a study because they had direct experience with the asset class, providing relevant insights into the application of portfolio management techniques in this volatile market.
The study focused on a sample of 100 members drawn from a private Facebook group composed of 134 Filipino cryptocurrency investors, all residing in the same province in the Philippines.
This group was selected due to its nature as a small, niche online community, which allows for a deeper examination of how specialized investment knowledge and strategies are shared and developed among peers.
Membership in the group is exclusively limited to individuals actively engaged in cryptocurrency trading, and all members share similar levels of trading experience, making the group relatively homogeneous in terms of expertise.
This focused sampling International Journal of Marketing and Digital Creative frame was designed to provide insights into the formation of collaborative strategies within tightly knit, interest-based communities.
The sample size was determined using a Raosoft sample size calculator, with a 95% confidence level and a 5% acceptable margin of error.
According to Memon et al.
, this online calculator, which requires inputs for a studyAos population size, confidence level, and margin of error, helps determine sample size for social science research.
Although the sample size of 100 obtained through simple random sampling may limit the ability to detect very fine-grained differences, it is large enough to conduct meaningful subgroup analysis and identify significant patterns within the population.
Data collection took place during the first to second week of October 2024, using a Google Form link published in the subject community's online group.
This group was selected for its ease of use and convenience, ensuring that all group members had an equal opportunity to participate.
A researcher-structured questionnaire consisting of 28 items was rigorously validated by three experts: a cryptocurrency investor, a research professional, and a statistician.
Their comments and suggestions were incorporated into the final version of the questionnaire.
The questionnaire includes five demographic questions, four items on cryptocurrency investor profiles, sixteen statements on trading techniques, and three questions about profitability.
The sixteen statements were assessed on a four-point Likert scale as follows: 1=1.
trongly disagre.
2=1.
3=2.
and 4=3.
trongly agre.
A pilot test was conducted on 10 respondents, who were not part of the study sample.
The Cronbach Alpha reliability coefficient test for the questionnaire ranged from 0.
944 to 0.
991, as shown in Table 1, exceeding the commonly expected composite reliability range of 0.
6 to 0.
9 suggested by Hair Jr.
, indicating a well-designed instrument that effectively measures the variables.
Table 1.
Reliability Statistics Indicators CronbachAos Alpha Algorithmic Trading Event-Driven Trading Scalping Diversification No.
of Items To reassure respondents that the information they provided would only be used for the research, a confidentiality statement was included in the online survey form.
Frequency, percentage, and weighted mean were used to assess the information gathered from the The correlation between profitability and trading strategies was analyzed using PearsonAos r, with prior testing for normality to ensure the appropriateness of the parametric test.
To examine significant differences in trading strategies across demographic groups, independent samples t-tests were conducted, following checks for normality and homogeneity of variances using the Shapiro-Wilk and LeveneAos tests, respectively.
FINDINGS AND DISCUSSION
This section presents the interpretation of data collected from the survey questionnaires distributed to the selected online community members.
A detailed discussion of the tabulated responses was presented and examined in accordance with the studyAos objectives.
Demographic Profile The profile of the investors highlights key demographic, educational, and economic characteristics that provide insights into their potential trading behaviors and capacities.
Table 2 presents the respondentsAo profile information.
International Journal of Marketing and Digital Creative Table 2.
Profile of Study Respondents Indicator Frequency Sex Male Female Age 18 -30 years 31 Ae 40 years 41 Ae 50 years 51- 60 years More than 61 years Highest Educational Attainment Elementary graduate or less Secondary school College undergraduate College graduate (Degree holde.
MastersAo degree holder PhD Holder Employment Status Employed Firm / Project owner Freelance Unemployed Indicative Range of Monthly Income Poor Low-income class Lower middle-income class Middle middle-income class Upper middle-income class Upper-income class Rich Field of Profession Healthcare and Medicine Business and Finance Education Engineering and Technology Skilled Trades and Construction Percentage The majority of cryptocurrency investors are male, aged 18 to 30, with college degrees, and have a moderate disposable income.
This implies that they have possible knowledge and familiarity with financial systems and trading, as most of them work in the business and finance sector.
emphasized by Jariyapan et al.
and Hackethal et al.
, investors with this demographic profile tend to have larger portfolios, utilize the bank's innovative products and services, and exhibit distinct portfolio compositions and habits compared to other investor groups.
This is further supported by Efendi et al.
, who confirmed that investors with generally good International Journal of Marketing and Digital Creative financial literacy would tend to have high access to capital.
Although the research indicates that the majority of cryptocurrency investors come from middle-income categories, previous studies have shown that cryptocurrency investors, which were initially dominated by affluent early adopters, now span a wide range of income levels and are geographically distributed (Aiello et al.
, 2.
This is a positive sign, as indicated by the respondents' profile data, that the online community is committed to diversity and has a membership pool with a relatively stable source of income.
InvestorsAo Profile The demographic profile of cryptocurrency investors focuses on their general characteristics, such as age, gender, and education, while the investorsAo profile delves into their preferences and trading experience.
Table 3.
InvestorsAo Profile Indicator Frequency Number of Cryptocurrency Investment One Two to Five Six to Ten More than Ten Type of Cryptocurrency Bitcoin Ethereum Ripple Extent of Use of Cryptocurrency Never Rarely Occasionally Regularly Frequently Always Trading Experience 0-1 year 2 years 3 years 4 years More than 4 years Percentage Table 3 shows that most respondents hold two to five cryptocurrencies .
%), with Bitcoin being the most popular choice .
%).
A quarter of the respondents .
%) use cryptocurrency regularly, while trading experience is pretty balanced, with 28% having three years of experience or less.
However, research indicates that most investors have between six months and one year of experience (Hadan et al.
, 2.
The data suggest that cryptocurrency investors demonstrate moderate financial literacy, as reflected in their diversified holdings, preference for a widely recognized asset like Bitcoin, and a balanced level of trading experience.
International Journal of Marketing and Digital Creative InvestorsAo Practices on Cryptocurrency Trading Strategies The assessment of investors' trading techniques is applied to the four most widely used trading strategies, including algorithmic trading, event-driven trading, scalping, and diversification.
Algorithmic Trading Algorithmic trading practices should be evaluated to ensure they optimize trade execution, minimize errors, reduce emotional biases, and align with market efficiency and regulatory Table 4 presents the assessment of the trading strategy using algorithmic trading.
Table 4.
Assessment of Trading Strategy using Algorithmic Trading Statement Mean Interpretation In fast-paced markets characterized by high-frequency 2.
Agree trading, relying solely on intuition without utilizing algorithmic techniques can lead to missed opportunities and suboptimal outcomes.
I utilize complex mathematical models to automate trade 2.
Agree executions, making my investment more profitable.
I utilize the automated trading strategies that help me 2.
Agree eliminate emotional biases and ensure consistent adherence to predefined trading rules.
By using automated systems that leverage complex 2.
Agree mathematical models.
I can enhance trade execution efficiency, reduce errors, and improve profitability by minimizing the need for manual intervention.
Average Weighted Mean Agree The study reveals a positive review of algorithmic trading as reflected by the average weighted mean of 2.
The findings highlight respondentsAo acknowledgment of the benefits of algorithmic trading, consistent with the literature that emphasizes efficiency gains and reduced human error (Arnoldi, 2016.
Reznik & Pankratova, 2.
High weighted mean scores reflect trust in automated systems for enhanced decision-making and profitability in dynamic markets.
Event-Driven Trading Event-driven trading practices should be evaluated to ensure they effectively capitalize on market opportunities while managing risks associated with significant events.
Table 5 shows the assessment of investors on their trading strategy using even-driven trading.
Table 5.
Assessment of Trading Strategy Using Event-Driven Trading Statement Mean Interpretation I react swiftly to significant news events and corporate 3.
Agree announcements, which enables me to capitalize on short-term market volatility and profit opportunities.
I rely solely on external factors, such as economic 3.
Agree indicators and geopolitical events, to guide my trading When I execute my trades based on anticipated 3.
Agree reactions to earnings reports and product launches.
I require myself to deeply understand the market International Journal of Marketing and Digital Creative Statement sentiment and have the ability to interpret data in realtime.
Leveraging trading strategies based on significant market events enhances my ability to respond swiftly to potential security breaches, reducing vulnerability and improving overall risk management.
Average Weighted Mean Mean Interpretation Agree Agree The data reveals an average weighted mean of 3.
18, indicating agreement on the usefulness of event-driven trading.
The investors' agreement demonstrates their understanding of the need to respond quickly to market developments, utilize external influences, and implement plans to manage risks and capitalize on market opportunities.
As emphasized by Rizal et al.
, the importance of proactive risk management strategies, such as leveraging market events and robust frameworks, is crucial in determining financial risks, particularly in the context of credit, market, and operational risks.
This is consistent with the idea that leveraging trading strategies based on significant market events can help address risks quickly, such as security breaches, thereby improving risk management.
Scalping The contributing factors for evaluating scalping practices by cryptocurrency investors include leverage use, rapid price movements, and consistent cash flow opportunities.
Table 6 reflects the assessment of trading strategy using scalping.
Table 6.
Assessment of Trading Strategy Using Scalping Statement Mean Interpretation Scalping in cryptocurrency trading can be both 2.
Agree profitable and efficient, as it allows me to capitalize on rapid price movements within short time frames.
I allocate my funds across various cryptocurrency 2.
Agree sectors, including decentralized finance (DeF.
and non-fungible tokens (NFT.
, which provide a balanced exposure to different market trends and potential growth opportunities.
I find that frequent trades can create a more 2.
Disagree consistent cash flow, which is beneficial for me as an active trader.
Engaging in rapid, high-frequency trading with short 2.
Agree holding periods allows me to make quick decisions and minimize prolonged exposure to market fluctuations, which can significantly reduce my stress Average Weighted Mean Agree The findings reveal generally positive views on scalping as a trading strategy, with an average weighted mean of 2.
57, indicating agreement with statements regarding its profitability, efficiency, and stress-reducing aspects.
The results indicate positive perceptions of scalping, supporting Bhatnagar et al.
, who highlighted its efficiency in leveraging minimal price International Journal of Marketing and Digital Creative The stress-reducing aspect aligns with PurserAos .
emphasis on mindfulness, promoting resilience in high-pressure markets.
Diversification Diversification practices by cryptocurrency investors include risk mitigation, exposure to various sectors, and portfolio stability.
Table 7 indicates a favorable assessment of diversification strategies in cryptocurrency trading, reflecting general agreement.
Table 7.
Assessment of Trading Strategy Using Diversification Statement Mean Interpretation I spread my investments across multiple 2.
Agree cryptocurrencies to mitigate the risk of significant losses associated with the volatility of individual digital assets.
I allocate my funds into various cryptocurrency 2.
Agree sectors, such as decentralized finance (DeF.
and non-fungible tokens (NFT.
, that provide a balanced exposure to different market trends and potential growth opportunities.
Investing solely in a single cryptocurrency exposes 2.
Agree me to extreme volatility and the risk of significant losses during market downturns.
By spreading investments across different assets.
I 2.
Agree can reduce my stress by minimizing the impact of losses from any single investment and providing a more stable overall portfolio performance.
Average Weighted Mean Agree The findings indicate generally positive views on diversification as a trading strategy, with an average weighted mean of 2.
75, reflecting agreement with statements about mitigating risk, managing volatility, and reducing stress through diversified trading.
Tenkam et al.
supported these views by demonstrating how diversification, such as incorporating stablecoins into a cryptocurrency portfolio, can reduce risk and enhance stability.
This approach aligns with the findings, where participants value the ability to spread trading across multiple cryptocurrencies and sectors to ensure a balanced portfolio.
Overall, the literature and findings consistently highlight the risk-reducing and stress-minimizing benefits of diversification.
Assessment of Profitability of Cryptocurrency Trading Strategies In this study, assessing profitability through total investment, total gains, and return on investment (ROI) based on respondentsAo selections from predefined options in the researchersAo validated questionnaire measured the efficiency of trading and their potential to generate returns.
Table 8 provides an overview of profitability among participants.
Table 8.
Assessment of Profitability of Cryptocurrency Trading Strategies Indicator Frequency Percentage Total Investment
PHP 9,999 and below
PHP 10,000 - 49,999
International Journal of Marketing and Digital Creative Indicator PHP 50,000 - 99,999 PHP 100,000 - 499,999 PHP 500,000 - 999,999 PHP 1,000,000 and above Total Gains PHP 9,999 and below
PHP 10,000 - 49,999
PHP 50,000 - 99,999
PHP 100,000 - 499,999
PHP 500,000 - 999,999
PHP 1,000,000 and above Return on Investment
0-20%
21-40%
41-60%
61-80%
81-100%
Frequency Percentage The data reveals a wide distribution of investment sizes, with the majority of participants investing between PHP 10,000 and 99,999.
Notably, the ROI range of 81-100% is most common, yet many investors reported minimal gains.
AntipovaAos .
study on cryptocurrency volatility and the need for risk assessment resonates with these findings.
The significant variance in ROI and gains reflects the inherent risks of the market, with some investors achieving high returns while others experience minimal growth.
This alignment underscores the importance of assessing oneAos risk tolerance when engaging in volatile markets, such as cryptocurrency.
Correlation Between Profitability and Trading Strategies The research investigated the relationship between profitability and trading strategies, specifically testing the null hypothesis (HoCA): There is no significant relationship between profitability and the strategies employed by cryptocurrency investors.
Using PearsonAos r test.
Table 9 presents the results of the statistical analysis for the relationship of the study variables.
Table 9.
Correlation between Profitability and the Trading Strategies Relationship of Strategies and Pearson pInterpretation Decision Profitability Total Algorithmic trading Weak Reject Ho1 Investments Event-driven trading 0.
Weak Reject Ho1 Total Gains Return Investment Sig.
Scalping Weak Reject Ho1 Diversification Weak Reject Ho1 Algorithmic trading Moderate Reject Ho1 Event-driven trading Weak Reject Ho1 Scalping Weak Reject Ho1 Diversification Moderate Reject Ho1 Algorithmic trading Weak Reject Ho1 Event-driven trading Weak Reject Ho1 International Journal of Marketing and Digital Creative Relationship of Strategies and Profitability Scalping Diversification Pearson Interpretation Decision Sig.
Weak Reject Ho1 Weak Reject Ho1 Legend: S = Significant The findings reveal a significant correlation between profitability and various trading strategies, with a p-value of less than 0.
05, indicating that trading strategies do indeed have a meaningful impact on profitability.
However, the data further indicate weak Pearson's r values, which may indicate a relatively small association between the variables.
Furthermore, there are two moderate Pearson's r values, indicating a discernible but not strong association between the This finding of a moderately significant correlation between profitability and algorithmic trading aligns with Arumugam's .
study, which demonstrates that algorithmic traders, particularly buy-side algorithmic traders (BAT.
, consistently outperform non-algorithmic traders (NAT.
in terms of profitability, leveraging market volatility and liquidity dynamics to gain a competitive edge.
Jukait and Gudelyt-ilinskien .
also highlighted how correlation analysis can guide the selection of cryptocurrencies suitable for portfolio diversification, aligning with the finding of a moderately significant correlation between total gains and diversification strategy as shown in the table, suggesting that strategic diversification enhances trading outcomes.
In contrast, event-driven trading exhibits a negative correlation .
= -0.
305, p = .
that may stem from the inherent unpredictability and volatility of external events, which can lead to misaligned timing and unfavorable market responses.
Despite the researchers' best efforts, no study found a negative relationship between profitability and event-driven trading.
A negative correlation between ROI and event-driven trading may suggest that reacting to market events introduces higher risk or timing errors, potentially reducing profitability and highlighting the need for more disciplined or data-driven approaches, as emphasized by Diaconau et al.
This finding is crucial to the studyAos objectives, as it informs the development of the guidebook by emphasizing the need for risk-aware strategies that minimize reliance on external events to enhance profitability.
Overall, the findings suggest that algorithmic trading is more likely to yield higher returns.
Since the study results are significant, the researchers have rejected the null Significant Differences in Trading Strategies based on the Demographic Profile This section presents the testing of the null hypothesis.
HoCC: There is no significant difference in cryptocurrency trading strategies when investors are grouped according to demographic The analysis examines how demographic factors, including sex, age, education, employment, income, and profession, impact preferences for various trading strategies, such as algorithmic trading, event-driven trading, scalping, and diversification.
The objective is to understand how these factors shape trading behavior and strategy selection, providing valuable insights into how investors can optimize their approach to cryptocurrency trading for improved Using the t-test.
Table 10 presents the analysis results.
Table 10.
Significant Differences in Trading Strategies based on the Demographic Profiles Category p-value Decision Conclusion Sex Algorithmic trading Reject Ho2 Significant Event-driven trading Reject Ho2 Significant International Journal of Marketing and Digital Creative Category Scalping p-value Decision Accept Ho2 Conclusion Not Significant Diversification Reject Ho2 Significant Age Algorithmic trading Reject Ho2 Significant Event-driven trading Reject Ho2 Significant Scalping Diversification Reject Ho2 Reject Ho2 Significant Significant Algorithmic trading Accept Ho2 Not Significant Event-driven trading Reject Ho2 Significant Scalping Accept Ho2 Not Significant Diversification Accept Ho2 Not Significant Accept Ho2 Reject Ho2 Accept Ho2 Accept Ho2 Not Significant Significant Not Significant Not Significant Reject Ho2 Reject Ho2 Reject Ho2 Reject Ho2 Significant Significant Significant Significant Reject Ho2 Accept Ho2 Reject Ho2 Reject Ho2 Significant Not Significant Significant Significant Educational Attainment Employment Status Algorithmic trading Event-driven trading Scalping Diversification Monthly Income Algorithmic trading Event-driven trading Scalping Diversification Profession Algorithmic trading Event-driven trading Scalping Diversification Table 10 shows that all trading strategies, except scalping, have resulted in significant differences when investors are grouped according to sex, thus rejecting the null hypothesis for all strategies except scalping.
This may suggest that sex influences algorithmic trading, event-driven trading, and diversification trading more, possibly due to differing decision-making processes and risk tolerances.
Research indicates women are more risk-averse, trading less frequently and executing fewer extreme strategies (Melin et al.
, 2.
It also reveals significant differences in all trading strategies across all age groups, thereby rejecting the null hypothesis.
This suggests age influences trading behavior, with younger individuals more willing to take risks and adopt technology, while older investors prefer conservative, long-term strategies.
Research indicates that younger investors tend to favor highfrequency trading, whereas older investors tend to avoid it (Tollefson, 2.
On the other hand, event-driven trading significantly differs based on the educational attainment of investors, while other strategies do not.
This suggests that education may impact the ability to analyze market events, which is crucial for event-driven trading, while other strategies rely more on technical skills.
This aligns with the findings of Tejwin .
, who found that International Journal of Marketing and Digital Creative investors with higher educational qualifications tend to favor high-frequency trading, while those with lower educational attainment prefer more conservative strategies.
Likewise, event-driven trading significantly differs based on investorsAo employment status, while other strategies do not.
This suggests that employment status influences the ability to respond to market events in real-time, which is crucial for event-driven trading.
Thus, investors with full-time employment may not be able to respond to specific market events quickly in realtime.
Research indicates event-driven investing requires a proactive mindset to anticipate and react to market events swiftly (Soriano, 2.
All trading strategiesAialgorithmic, event-driven, scalping, and diversificationAishowed significant differences when investors were grouped according to their monthly income.
Higher income provides greater resources, which in turn influence trading strategies and risk tolerance.
Research indicates that an investorAos financial status plays a crucial role in shaping their risk tolerance and investment decisions (Merrill and Bank of America Private Bank, 2.
Algorithmic trading, scalping, and diversification strategies showed significant differences based on profession, while event-driven trading did not.
This implies that a profession likely influences technical skill-based strategies more than event-driven trading, which relies on timing.
Research suggests that algorithmic trading, scalping, or diversification strategies require technical expertise, whereas event-driven trading focuses on market analysis (Addy et al.
, 2.
Overall, the significant difference results in the demographics playing a significant role in shaping investment strategies within the framework of MPT.
Factors such as gender, age, experience, and education influence how individuals perceive risk and make investment decisions (Onsomu et al.
, 2.
By understanding these traits.
MPT can be applied to create personalized investment strategies and refine risk assessment tools, ensuring portfolios align with each investor's unique profile.
Proposed Chapter Guidelines of a Trading Strategies Guidebook Focusing on the previous tables, which highlight event-driven trading and its negative correlation with ROI, identified by the researchers, they developed a chapter guide in the development of an investorAos guidebook to address and mitigate this gap, as revealed in the survey This aims to provide a structured approach to mastering event-driven trading for new and experienced investors.
Each chapter offers practical advice and strategies, beginning with the basics of event-driven trading and progressing to advanced techniques for scaling success.
This focuses on addressing common challenges faced by beginners, such as impulsive decision-making and inadequate research, while building investorsAo confidence and trading skills.
It is also suggested that investors foster an innovation culture at all times to optimize the profitability of the cryptocurrency trading strategies employed.
According to previous studies (De Ramos & Briones, 2024.
Rivera et , 2.
, individuals who practice the culture of learning, innovation, and continuous improvement tend to outperform their counterparts.
Findings Event-driven trading is correlated with Table 11.
Proposed Chapter Guidelines Chapter Title Description Rethinking This chapter directly responds to the studyAos finding Event-Driven that event-driven trading is negatively correlated with Trading: Risks profitability.
It critically examines why this strategy and Realities may lead to lower returns, such as overreliance on short-term news and timing issues, and provides practical guidance on how investors can refine or avoid such strategies to minimize risk and improve International Journal of Marketing and Digital Creative Findings Chapter Title Avoiding Common Pitfalls for Beginners Moderate financial literacy is common among investors.
Mastering the Fundamentals of Analysis Young investors .
ged 18Ae.
and males are more likely to adopt riskier strategies, such as algorithmic to riskier strategies like algorithmic trading and Building Confidence with Small Wins Scaling Success with Advanced Techniques Description This chapter is grounded in the studyAos observation that new traders often exhibit lower profitability, frequently associated with impulsive decisions and limited strategy diversification.
It draws on data showing that beginners tend to underperform when relying on event-driven strategies or neglecting risk management principles.
The chapter provides evidence-based recommendations to help novice investors develop discipline, improve decision-making, and make better use of market data to enhance longterm outcomes.
This chapter responds to findings indicating that investors with higher returns tend to rely on disciplined research and market analysis rather than impulsive trading.
It equips beginners with foundational skills such as interpreting news, analyzing trends, and calculating risk-reward ratiosAi practices that have been observed to correlate positively with profitable strategies, including diversification and algorithmic trading.
Based on the data showing that overuse of eventdriven trading correlates negatively with ROI, this chapter encourages new traders to adopt safer, lowrisk scenarios for skill development.
It promotes cautious application of event-driven techniques, guiding readers to test and reflect on small trades to build confidence and refine strategy without exposing themselves to excessive risk.
Grounded in the finding that more experienced, higherincome investors tend to favor advanced, profitable strategies, such as algorithmic trading and diversification, this chapter outlines how beginners can gradually adopt these tools.
It covers the use of trading software, automation, and portfolio diversification while emphasizing the importance of maintaining risk-aware, data-informed decisionmaking as proficiency increases.
CONCLUSION
The respondents are primarily young, educated professionals in business and finance, likely possessing moderate disposable income, and have familiarity with financial systems and trading.
The study found that all trading strategies employed were generally effective, with algorithmic trading and diversification showing the strongest positive correlations with profitability.
Algorithmic trading was recognized for enhancing profitability by reducing emotional bias and improving trade execution, while diversification effectively mitigated risk and stabilized portfolios.
International Journal of Marketing and Digital Creative Event-driven trading helped capitalize on market volatility and improved risk management, though its impact on profitability was mixed.
Scalping was perceived as both profitable and stressreducing, yet opinions varied on its consistency in generating a steady cash flow.
These findings highlighted that investors who combine innovation with discipline and tailored strategies to their demographic characteristics tend to perform better in the volatile cryptocurrency market.
Demographic factors such as age, income, and professional experience significantly influenced strategy preference, with younger, wealthier, and more experienced investors favoring sophisticated approaches like algorithmic trading and diversification.
This highlights the importance of personalized financial education and strategy customization tailored to individual investor characteristics.
The wide variation in ROI among investors further illustrates the unpredictable nature of cryptocurrency markets, emphasizing the need for investors to critically assess their risk tolerance and continuously adapt strategies to dynamic market conditions.
This aligns with MPTAos focus on balancing risk and return through diversified, data-driven decision-making.
To address these challenges, the proposed guidebook chapters focus on the complexities of event-driven trading, advocating for disciplined research and sound decision-making.
These practical, step-by-step strategies are tailored to investorsAo demographic characteristics, empowering confident navigation of market complexities and enhancing profitability.
In conclusion, this study demonstrates that effective cryptocurrency trading requires a strategic blend of data-driven techniques, personalized approaches, and disciplined execution.
emphasizes that success in volatile markets depends on both innovation and careful risk management tailored to individual investor profile characteristics.
This study can also serve as a guide to future cryptocurrency investors in optimizing profits from their portfolios.
LIMITATIONS AND FURTHER RESEARCH
This study has several limitations.
The focus on a specific online cryptocurrency community may limit the generalizability of the findings to a broader global investor audience.
The reliance on self-reported data introduces potential bias, which could affect the reliability of the results.
Additionally, the assessment period may not accurately reflect specific market conditions that could impact the long-term effectiveness of the trading strategies examined.
Future research should broaden its scope to include a more diverse range of investor groups across different demographics and regions, thereby enhancing generalizability and relevance.
Longitudinal studies are recommended to assess the performance of trading strategies over time and under varying market conditions.
Incorporating real-time trading data would enhance the accuracy of findings, while qualitative methods, such as interviews, could provide deeper insights into investorsAo motivations and challenges.
Furthermore, future studies should investigate the impact of technological advancements and regulatory changes, as these factors are increasingly influencing cryptocurrency trading dynamics.
Together, these approaches would offer a more comprehensive understanding of the evolving cryptocurrency trading landscape.
REFERENCES