Subject The stock market is characterized by a significant variety of economic processes. Classical methods for modeling the time series to analyze and forecast processes in the stock market often produce unsatisfactory results. The article investigates and encourages new approaches to projections in the stock market in the face of uncertainty. Objectives The aim is to develop a technique to examine rank decisions in portfolio analysis. Methods In the research process, I used data analysis and machine learning methods. Results I examined the procedure for ranking portfolio solutions. This procedure rests on replacing optimization with a preference analysis commonly used in expert data processing. The replacement is possible due to the use of a special equation that includes the probability of positive yield as a factor. Conclusions I suggest using probabilistic preference technique based on the auto predictive model, instead of optimization approach to portfolio selection. The main result of this technique is rank portfolio formation that expands opportunities of portfolio analysis.
Keywords: probability, portfolio, binary choice
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