Abstract
The article is devoted to the comparative analysis to forecast models of exchange-traded investment funds dynamics. The exchange traded investment fund was chosen for the study as a modern investment instrument, which managed to combine the best features of stocks and mutual investment funds. However, despite the advantages of this financial instrument, the main task of the investor is the ability to predict its dynamics. To date, there are many forecast models, but the ability to create a reliable and accurate forecast remains extremely important.Two exchange traded funds were selected for forecasting: SPDR S&P 500 ETF TRUST (the stock ticker is SPY) and VanEck Vectors Gold Miners (the stock ticker is GDX). Based on daily prices for the period from January 2016 to January 2020, forecast models of two types were built: linear and nonlinear. Namely, linear models of moving average and exponential smoothing (Holt model) were selected. The neural network model was chosen as a nonlinear model. It turned out that the quality assessment of all models is quite high. However, the constructed forecasts showed that despite the high quality of the obtained statistical models of moving average and exponential smoothing, forecasting with their help is possible only for the forecast horizon, which is one trading day. The neural network model, conversely, shows a worse forecast for the first forecast value, but captures the dynamics and direction of price changes in both the exchange-traded investment fund SPY and GDX. That is, with the help of training, the neural network is able to establish hidden nonlinear patterns of price dynamics. But the horizon of the neural network forecast is also limited: in research it is established that the forecast on the basis of a neural network model it is expedient to build no more than for one exchange week.
Highlights
For the study as a modern investment instrument, which managed to combine the best features of stocks and mutual investment funds
The basic is the Gold Miners Index, and the shares dynamics of the exchange fund follows the dynamics of gold, but may differ due to the fact that gold mining companies are often engaged in several areas of precious metals, for instance silver or platinum
As a result of the study, statistical dynamics models based on the method of moving average, simple exponential smoothing and Holt's exponential smoothing, as well as a model of nonlinear dynamics based on a neural network for two exchange traded investment funds were built: SPDR S&P 500 ETF TRUST and VanEck Vectors Gold Miners
Summary
Stock markets are an integral part of any modern economic system, without which the efficient allocation of resources is impossible. With the development of information technology in the stock market, new modern financial instruments that meet modern requirements and demands of investors. One such tool is exchange traded funds. The main advantages of exchange traded funds as a portfolio investment instrument are low commission costs, wide choice of funds by portfolio composition, low entry threshold with high diversification of the portfolio and high liquidity (investor can buy or sell the fund's shares during the exchange day). Despite the advantages of this financial instrument, the main task of the investor is the ability to predict its dynamics. Today, existing forecasting models can be divided into two types: linear, based on statistical methods, and nonlinear. Nonlinear models include: tree-based models, genetic algorithms, neural network models, and so on. The quality of the forecast and the investor's profit directly depend on the choice of the model type, so the assessment of the forecast quality of different types models and their comparison is an important practical task
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
