TY - JOUR

T1 - The Stability of Trend Management Strategies in Chaotic Market Conditions

AU - Мусаев, Александр Азерович

AU - Григорьев, Дмитрий Алексеевич

PY - 2025/1/15

Y1 - 2025/1/15

N2 - This study investigates the stability of trend management strategies under stochastic chaos conditions, with a focus on speculative trading in the Forex market. The primary aim is to evaluate the feasibility and robustness of these strategies for asset management. The experimental setup involves sequential optimization and testing of trend strategies across three EURUSD observation intervals, where each subsequent interval alternates between training and testing roles. Methods include numerical data analysis, parametric optimization, and the use of both conventional and bidirectional exponential filters to isolate system components and improve trend detection. Observations reveal that while trend strategies optimized for specific intervals yield positive results, their effectiveness diminishes on unseen intervals due to inherent market instability. The results show significant limitations in using linear trend-based strategies in chaotic environments, with optimized strategies often leading to losses in subsequent periods. The discussion highlights the potential of integrating trend statistics into multi-expert decision systems, leveraging fuzzy solutions based on fundamental analysis to enhance decision-making reliability. In conclusion, while standalone trend strategies are unsuitable for stable asset management in chaotic markets, their integration into hybrid systems may provide a pathway for improved performance and resilience.

AB - This study investigates the stability of trend management strategies under stochastic chaos conditions, with a focus on speculative trading in the Forex market. The primary aim is to evaluate the feasibility and robustness of these strategies for asset management. The experimental setup involves sequential optimization and testing of trend strategies across three EURUSD observation intervals, where each subsequent interval alternates between training and testing roles. Methods include numerical data analysis, parametric optimization, and the use of both conventional and bidirectional exponential filters to isolate system components and improve trend detection. Observations reveal that while trend strategies optimized for specific intervals yield positive results, their effectiveness diminishes on unseen intervals due to inherent market instability. The results show significant limitations in using linear trend-based strategies in chaotic environments, with optimized strategies often leading to losses in subsequent periods. The discussion highlights the potential of integrating trend statistics into multi-expert decision systems, leveraging fuzzy solutions based on fundamental analysis to enhance decision-making reliability. In conclusion, while standalone trend strategies are unsuitable for stable asset management in chaotic markets, their integration into hybrid systems may provide a pathway for improved performance and resilience.

KW - asset management strategies

KW - dynamic instability

KW - fuzzy solutions

KW - multi-expert system

KW - numerical data analysis methods

KW - statistical uncertainty

KW - stochastic chaos

KW - trend forecasting technologies

KW - trend management strategies

UR - https://www.mendeley.com/catalogue/2bba8595-ee4f-36af-a4bf-d12daa5da97c/

U2 - 10.3390/jrfm18010033

DO - 10.3390/jrfm18010033

M3 - Article

VL - 18

JO - Journal of Risk and Financial Management

JF - Journal of Risk and Financial Management

SN - 1911-8066

IS - 1

M1 - 33

ER -