Возможности прогнозирования динамики фондового индекса S&P 500 с помощью нейросетевых и регрессионных моделей
The article presents a comparative analysis of neural network modeling and regression analysis for forecasting the S & P 500 index. Initially, the forecast of the absolute value of the index is provided, then we justify the use of stationery data, that is, the return of S & P 500. The comparison of two methods is carried out in two stages. Firstly methods are compared by the coefficient of determination on the periods of three and twelve months, and by the quality of trend predictions. Note that the choice of model and its testing is performed at different time intervals (the so-called in-sample and out-of-sample periods). Taking into account the fact that the primary desire of a typical trader is to gain a profit at the second stage we have chosen such trading criteria as profit and profit, weighted on risk (drawdown). On a longer time interval (12 months) regression shows the best results, but in terms of economic gains neural network win. When we consider a shorter period (3 months) neural network has better results. Thus, neural networks are able to assess the dynamics of the stock due to its flexibility and ability to find non-linear patterns.