Ambiguity-Informed Modifications to Multivariate Process Analysis Using Binance Market Data

The growing complexity of the contemporary financial systems requires the emergence of sophisticated computational and statistical methods that are capable of managing uncertainty, lack of normality and structural variability of multivariate data. The TS charts defined by Hotelling are widely applicable but have been observed to be susceptible to asymmetrical distributions and outliers and are therefore inapplicable in a dynamic real-world example, such as cryptocurrency markets. We present a computationally efficient ambiguity-aware framework in this work, which generalizes the robust covariance estimation methods, which are MVE and MCD, into a neutrosophic logic-based framework. This adaptation also allows the proposed charts to model and react to the intrinsic data ambiguity and indeterminacy with improved robustness and additional multivariate process monitoring. The methodology is validated by a combination of simulation experiments and empirical research on high-frequency financial data of the Binance Exchange, with the focus on the BTCUSDT and ETHUSDT trading pairs. The evaluation of the performance is performed based on total and generalized variance measures that give a holistic picture of the sensitivity and adaptability of the method to noise in data and complexities arising in the presence of noise and complexity of data. The results demonstrate that the proposed approach is considerably superior to conventional TS charts and their robust variants, particularly in terms of detecting a small shift and trends of multivariate financial procedures. Thus, it is a contribution to the growing body of knowledge about applying computational statistics and data science to a scalable, uncertainty-sensitive system of high-dimensional process monitoring in volatile financial settings.