Algorithmic Trading + Behavioral Finance

Аннотация

The study is devoted to identifying and OPEN ACCESS analyzing the synergistic interaction between the SUBMITTED 31 July 2025 theoretical principles of behavioral finance and applied ACCEPTED 08 August 2025 PUBLISHED 21 August 2025 methodologies for developing high-r eturn algorithmic VOLUME Vol.07 Issue 08 2025 strategies in the digital asset segment. In conditions where the efficient market hypothesis demonstrates CITATION limitations in its applicability, especially in environments COPYRIGHT with increased volatility and underdeveloped © 2025 Original content from this work may be used under the terms infrastructure—such as cryptocurrency markets and of the creative commons attributes 4.0 License. decentralized finance (DeFi) ecosystems—behavioral biases emerge as important determinants of market inefficiency. The paper presents a framework that combines the targeted exploitation of cognitive patterns, including the disposition effect and the phenomenon of herd behavior, with the application of advanced technological solutions. Based on four original case studies—ranging from the development of a proprietary backtesting mechanism incorporating elements of chaotic process modeling to the construction of a predictive risk management system for DeFi—the practical implementation of the proposed approach is demonstrated. The results obtained confirm the superiority of the hybrid architecture over traditional methods: from effectively reducing crash risk in DeFi carry trade strategies to maintaining portfolio resilience under market stress conditions and generating ultra-high returns (CAGR exceeding 200% with MDD of 30%). The study’s findings reinforce the validity of the adaptive markets hypothesis and confirm the applied value of the synthetic methodology for modern algorithmic trading. The information reflected in the study will be of interest to asset managers, quantitative fund specialists, and researchers focused on creating next-generation algorithms. Keywords: algorithmic trading, behavioral finance, prospect theory, herd behavior, risk management, The American Journal of Management and Economics Innovations 129 https://www.theamericanjournals.com/index.php/tajmei decentralized finance (DeFi), backtesting, chaos modeling chaotic dynamics and managing specific risks modeling, portfolio rebalancing, cryptocurrencies. inherent in digital assets. Introduction To achieve this aim, the following objectives are defined: The modern architecture of financial markets is shaped by the influence of two interconnected yet seemingly - to systematize the main behavioral anomalies opposite megatrends. On the one hand, there is the characteristic of digital assets, based on prospect theory intensive development of algorithmic trading, artificial and the concept of herd behavior; intelligence (AI), and machine learning (ML) methods, - to analyze the methodological limitations of which enable the processing and interpretation of vast classical backtesting approaches and to propose an arrays of market information with unprecedented speed improved model that accounts for the nonlinear and and accuracy. On the other hand, there is a growing chaotic properties of the market, thereby enhancing the interest within the research community in behavioral robustness of algorithms; finance, which challenges the universality of the - to propose a conceptual model of dynamic risk Efficient Market Hypothesis (EMH) and demonstrates management for carry trade strategies in the DeFi that investors’ cognitive biases influence the price ecosystem, ensuring proactive responses to the formation process. This methodological dualism is most deterioration of collateral asset quality; clearly manifested in environments with a high degree of uncertainty and volatility, such as decentralized - to demonstrate, using proprietary case studies, finance (DeFi). In 2024, the DeFi market volume was the practical implementation and empirical estimated at USD 21.04 billion, with a projected increase effectiveness of the integration framework. from USD 32.36 billion in 2025 to approximately USD The scientific novelty of the study lies in the 1,558.15 billion by 2034, reflecting average annual comprehensive synthesis of theoretical-behavioral growth of approximately 523.9% over the 2025-2034 approaches with proprietary engineering solutions in period [19]. Such dynamics simultaneously create the field of algorithmic trading. unique opportunities for generating excess returns and shape an unprecedented spectrum of risks. The research hypothesis is that the systematic exploitation of market inefficiencies caused by The behavioral paradigm acquires particular significance behavioral anomalies (in particular herd behavior and in relation to digital financial ecosystems. The the disposition effect), combined with engineering cryptocurrency and DeFi segments, characterized by the solutions that model the chaotic nature of the market dominance of retail participants,