Modeling and forecasting of stock market processes

Abstract

Stock market valuation uses a variety of indicators, such as indices and ratings, to reflect its state and movement. For example,
a stock exchange index reflects activity on a stock exchange and is calculated using specific formulas. The calculation of indices is
based on statistical data on securities and helps to assess the risks of investments. These indices reflect market conditions. The
methodology for forming stock indices includes four stages: sampling, weighting of shares, calculation of the average, and
conversion to the index form. Two types of sampling are used: deterministic and floating-power sampling. The weighting
coefficients are determined by the price criterion and market capitalization. The studied approaches to stock market modeling allow
identifying functional dependencies in the data and developing forecasts. In particular, the methods of approximation and modeling
by the Wiener process are allocated. Stock market forecasting using the multi-layer architecture of Long Short-Term Memory in the
Keras library is investigated. The overall results confirm that an intelligent information system for automated trading decisions is
effective, providing traders with competitive advantages and reducing risks.