Quantile recurrent forecasting in singular spectrum analysis for stock price monitoring

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 foreare 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.