Background and Significance of the Study
The democratization of digital financial infrastructure has significantly accelerated retail foreign exchange (FX) market participation across global developing economies (Davison, 2016). In financial environments like Cambodia, digital brokerages grant individual traders immediate, highly leveraged entry into spot currency pairs, commodities, and crypto-assets. Granular, transaction-level platform metadata provides a precise analytic lens into how trading execution parameters—specifically localized trading frequencies and systematic risk controls—impact net performance curves over time (Kaourma et al., 2021).
Statement Problem
Despite widespread market accessibility, a substantial proportion of retail FX market participants experience rapid capital depletion (Davison, 2016). Elevated execution frequencies often manifest as behavioral overtrading, heavily eroding profit margins due to transaction friction, compounding spreads, and underlying psychological biases (Phan et al., 2018). Within the Cambodian retail framework, there remains a critical empirical research gap regarding how systemic risk-management indicators directly interact with trading frequency to affect net results. This dynamic is evaluated here through empirical transaction records retrieved from the dataset file named cleaned ChayMPLs.csv.
Theoretical Framework/Theory of Change
This study integrates Behavioral Finance Theory with Rational Learning Theory. In volatile electronic markets, retail participants frequently fall prey to self-attribution biases, interpreting random market successes as an indicator of individual skill, which leads to increased position sizing and dangerous transaction frequency spikes (Ben-David et al., 2018). The Theory of Change models how structural risk intervention—via platform-enforced parameter rules such as hard-coded stop-loss thresholds—can actively mitigate these cognitive errors, suppress overtrading behaviors, and align a trader’s execution frequency with objective, risk-adjusted performance benchmarks (Hayley & Marsh, 2016).
Assumptions/Hypotheses
- $H_1$: Elevated individual trading frequencies are inversely correlated with overall retail forex trader profitability.
- $H_2$: Consistent implementation of explicit take-profit (
[tp]) and stop-loss ([sl]) conditions significantly mitigates downside portfolio volatility.
Definition of Terms
- Trading Frequency: The aggregate number of transaction entries and exits executed by an individual account holder within a defined temporal scope.
- Platform-Level Data: High-fidelity algorithmic transaction records logging specific metadata (e.g., login ID, contract symbol, volume, closed profits) as structured within the file
cleaned ChayMPLs.csv.
Limitations and Delimitations
- Limitations: The scope of this study is bound by the quantitative nature of the ledger system, lacking direct psychometric variables concerning individual retail trader intentions or background education.
- Delimitations: The investigation isolates its analysis strictly to retail ledger metadata generated within the proprietary Cambodian dataset file
cleaned ChayMPLs.csvtracking activity throughout the early fiscal quarters of 2026.
References
Ben-David, I., Birru, J., & Prokopenya, V. (2018). Uninformative Feedback and Risk Taking: Evidence from Retail Forex Trading. Review of Finance, 22(6), 2009–2036. https://doi.org/10.1093/rof/rfy022
Cited by: 44
Davison, C. (2016). The Retail FX Trader: Random Trading and the Negative Sum Game. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2711214
Cited by: 6
Hayley, S., & Marsh, I. W. (2016). What do retail FX traders learn? Journal of International Money and Finance, 64, 16–38. https://doi.org/10.1016/j.jimonfin.2016.02.001
Cited by: 20
Kaourma, F., Milidonis, A., Nishiotis, G. P., & Panayides, M. A. (2021). News and Intraday Retail Investor Order Flow in Foreign Exchange Markets. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3796753
Cited by: 8
Phan, T. C., Rieger, M. O., & Wang, M. (2018). What leads to overtrading and under-diversification? Survey evidence from retail investors in an emerging market. Journal of Behavioral and Experimental Finance, 19, 39–55. https://doi.org/10.1016/j.jbef.2018.04.001
Cited by: 62
