ARIMA-MODELS: MODELING AND FORECASTING PRICES OF STOCKS

Modeling the dynamics and forecasting of financial market indicators is of interest to its participants and analysts, as well as scientific circles. The implementation of the task of researching market indicators involves the selection of appropriate methods, tools and resources. The popularity and a significant number of approbations belongs to technical analysis, based on the visual analysis of time series using the construction of special charts and graphical models (figures). Technical analysis is subjective, therefore, in addition to it, methods are used that involve the use of a mathematical apparatus. ARIMA model has shown its effectiveness in working with different time series and has become a powerful tool for obtaining accurate forecasts. The algorithm for constructing such a model involves performing a number of mathematical calculations, which can cause difficulties. But thanks to modern software capabilities, for example, the R programming language, statistical analysis of time series, namely the construction of an ARIMA model, is implemented quickly and with the ability to obtain graphical and numerical results. That is why building an ARIMA model using the R language for modeling the price of a company's shares is of practical importance, which will allow you to get a forecast and make decisions during asset purchase and sale transactions in the financial market. In this work, the algorithm for constructing an ARIMA model is implemented using the R-studio environment in 3 stages (with the consistent use of the corresponding library functions) using the example of PepsiCo prices of stocks time series (monthly and daily data). The graphs of the series were constructed, the series were tested for stationarity, an assumption was made about the value of the model parameters according to the preliminary analysis, automatic selection of parameters was also used, and the corresponding models were built. All constructed models were tested for adequacy through appropriate tests and criteria, as well as the quality of approximation of the actual data by the model. Forecast values were obtained, presented graphically in comparison with the actual data of the share price, and the accuracy of the forecast was calculated.