Abstract
Abstract. Stock price prediction is one of the most relevant aspects in a stock market and world economies. Price is an important variable of concern in a sector where market and economic conditions vary over time. Efficient methods are needed to describe the trends and characteristics of stock prices. The performance of different time series models for analysis of stock prices is provided to determine the feasibility of techniques for the generation of results in the wake of economic decisions. Historical time series of monthly average price of stocks for Callon, Chesapeake, General Electric and Encana in the oil and gas sector of the New York Stock Exchange were analysed for the period 2012-2019. It was discovered that the New York Stock Exchange follows a random walk. A random walk implies uncertainty. Uncertainty implies high risk. Risk is directly related to profit. The fitted autoregressive integrated moving averages model used for forecasting shows that the predicted average stock price for the period 2021-2024 for Callon, Chesapeake, General Electric and Encana may reach United States Dollars 12.14, United States Dollars 7.34, United States Dollars 21.69 and United States Dollars 23.90, respectively. Therefore, cautious trading in the New York Stock Exchange was recommended for high profits to be achieved.
Keywords. Convergence; Integration; Modelling; Stationarity; Variation.
JEL. C5, C22, C32, E27, E32.References
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