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Statistics and Its Interface
Volume 16 (2023)
Number 2
Special issue on recent developments in complex time series analysis – Part II
Guest editors: Robert T. Krafty (Emory Univ.), Guodong Li (Univ. of Hong Kong), Anatoly Zhigljavsky (Cardiff Univ.)
Quantile recurrent forecasting in singular spectrum analysis for stock price monitoring
Pages: 189 – 197
DOI: https://dx.doi.org/10.4310/21-SII720
Authors
Abstract
Monitoring of near real-time price movement is necessary for data-driven decision making in opening and closing positions for day traders and scalpers. This can be done effectively by constructing a movement path based on forecast distribution of stock prices. High frequency trading data are generally noisy, nonlinear and nonstationary in nature. We develop a quantile recurrent forecasting algorithm via the recurrent algorithm of singular spectrum analysis that can be implemented for any type of time series data. When applied to median forecasting of deterministic and shortand long-memory processes, our quantile recurrent forecast overlaps the true signal. By estimating only the signal dimension number of parameters, this method can construct a recurrent formula by including many lag periods. We apply this method to obtain median forecasts for Facebook, Microsoft, and SNAP’s intraday and daily closing prices. Both for intraday and daily closing prices, the quantile recurrent forecasts produce lower mean absolute deviation from original prices compared to bootstrap median forecasts. We also demonstrate the tracing of price movement over forecast distribution that can be used to monitor stock prices for trading strategy development.
Keywords
Forecast distribution, recurrent forecasting, quantile, stock price, trading
2010 Mathematics Subject Classification
37M10, 91B84
Received 23 May 2021
Accepted 24 December 2021
Published 13 April 2023