  This paper proposes a forecast-centric adaptive learning model that engages
with the past studies on the order book and high-frequency data, with
applications to hypothesis testing. In line with the past literature, we
produce brackets of summaries of statistics from the high-frequency bid and ask
data in the CSI 300 Index Futures market and aim to forecast the one-step-ahead
prices. Traditional time series issues, e.g. ARIMA order selection,
stationarity, together with potential financial applications are covered in the
exploratory data analysis, which pave paths to the adaptive learning model. By
designing and running the learning model, we found it to perform well compared
to the top fixed models, and some could improve the forecasting accuracy by
being more stable and resilient to non-stationarity. Applications to hypothesis
testing are shown with a rolling window, and further potential applications to
finance and statistics are outlined.

Statistical Finance;Econometrics;Trading and Market Microstructure;Applications