Development of Trading Strategies Using Time Series Based on Robust Interval Forecasts

Abstract

The task of time series forecasting is to estimate future values based on available observational data. Prediction Intervals methods are aimed at finding not the next point, but the interval that the future value or several values on the forecast horizon can fall into given current and historical data. This article proposes an approach for modeling a robust interval forecast for a stock portfolio. Here, a trading strategy was developed to profit from trading stocks in the market. The study used real trading data of real stocks. Forty securities were used to calculate the IMOEX. The securities with the highest weight were the following: GAZP, LKOH, SBER. This definition of the strategy allows operating with large portfolios. Increasing the accuracy of the forecast was carried out by estimating the interval of the forecast. Here, a range of values was considered to be a result of forecasting without considering specific moments, which guarantees the reliability of the forecast. The use of a predictive interval approach for the price of shares allows increasing their profitability.