Adaptive portfolio analysis based on the trend decomposition of a financial time series: Case study of the moscow exchange

Abstract

Testing an adaptive modification of the portfolio analysis model with a two-level mechanism of return generation is used to identify the temporal structure of efficient frontiers. The stock market is volatile and, although it is consolidated, it follows different trends at different times. Therefore, stock market processes are viewed as multitrend in nature. It particularly applies to the process of generating returns. For the convenience of the analysis, a multitrend process can be presented as a finite decomposition using adaptation principles. As we have already said, adaptive mechanisms are an important factor for the effectiveness of the stock market. Considering these requirements, the most suitable method is adaptive trend decomposition. In our study, we used data analysis and machine learning methods. The article presents a method of portfolio analysis based on the decomposition of efficient sets into temporal components. This allows for a comparative analysis of portfolio sets regarding their efficiency over different time intervals and enables a dynamic analysis of the temporal structure of efficient sets in order to determine the optimal time for holding the portfolio or changing its structure. A family of efficient sets provides a better understanding of investment opportunities. Our calculations also demonstrated that the temporal structure of a family of efficient sets is more likely to remain robust during the prediction period.