Abstract:

Abstract Financial time series (FTS) is chaotic which contributes to dynamic and difficult predictability in turn. This research employs the new hybrid model for the prediction of FTS which comprises Long Short-Term Memory (LSTM), Polynomial Regression (PR), and Chaos Theory. First of all, FTS is tested in this hybrid for chaos. Later, using Chaos Theory, the time series is modelled with the chaos existence. The model time series will be entered in LSTM for initial forecasts. The sequence of errors derived from LSTM forecasts is PR appropriate for error predictions. Error forecasts and original model forecasts are applied to produce the final hybrid model forecasts. Performance testing of the hybrid model (Chaos+LSTM+PR) is conducted using three categories of foreign exchange, commodity price and stock-market indices. The hybrid model proposed in this study, in compliance with MSE, Dstat and Theil’s U is proved superior to the individual models like ARIMA, Prophet, LSTM and Chaos+LSTM. However, this study of the hybrid method in the stock market indices of Nifty 50 result is equated with the Covid pandemic results using descriptive statistics and also compared with the stock price of Nifty 50 using random forest method from which the result of proposed method gets better prediction than the existing methods from the comparison. The execution of these various hybrid proposed methods is done using mainly in Python, additionally, the authors used Gretl® and R for some methods respectively. Ultimately, the final result of this hybrid model describes with a better result than the existing prediction models and it is proved using various types of FTS like Foreign exchange rates, commodity prices, and stock market indices respectively. Hence, the Diebold Mariano test is carried out to compare the existing and proposed models, certainly that the Chaos+LSTM+PR is better than the existing models on the nine datasets, respectively.

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