Behavioral Economics and Machine Learning Methods in Managing a Hybrid Investment Portfolio with Virtual Assets

Behavioral Economics and Machine Learning Methods in Managing a Hybrid Investment Portfolio with Virtual Assets

Blog Article

The article explores the prospects for using machine learning methods and data mining to predict the dynamics of the market for virtual assets such as cryptocurrencies, tokens, and NFTs.Given the high volatility, unstable correlation with traditional assets, and the significant impact of extra-economic factors, the alternative asset market requires innovative approaches to analysis and Gift Cards forecasting.The relevance of the study is due to the growing popularity of virtual assets among investors and the complexity of managing hybrid portfolios that combine traditional and alternative instruments.The application of machine learning methods such as Random Forest, XGBoost, LSTM and neural networks allows you to take into account non-linear dependencies between market indicators, integrate data from social sentiment, news and technical indicators, as well as find hidden patterns in large-scale data.Particular attention is paid to adapting models to dynamic market conditions by using forecasting methods such as GARCH and DCC-GARCH, which take into account volatility and correlation variability.

The key challenges associated with forecasting the dynamics of the virtual asset market are highlighted, in particular, the lack of a stable regulatory framework, the complexity of assessing the impact of emotional and cognitive factors on investor behavior, as well as the need to process large amounts of heterogeneous data.Examples of using clustering methods to group assets by similar characteristics, anomaly analysis to identify market spikes in activity, and sentiment analysis tools to assess the emotional impact on the market are considered.The obtained results show that the integration of machine learning methods with traditional financial models and behavioral conceptions allows to increase the accuracy of forecasts, reduce investment risks, and create adaptive portfolio management strategies Waist in an unstable financial environment.Further research in this area may focus on the development of hybrid forecasting models, the integration of big data with cognitive approaches, and the automation of investment decision-making processes.