  With the increasing volume of high-frequency data in the information age,
both challenges and opportunities arise in the prediction of stock volatility.
On one hand, the outcome of prediction using tradition method combining stock
technical and macroeconomic indicators still leaves room for improvement; on
the other hand, macroeconomic indicators and peoples' search record on those
search engines affecting their interested topics will intuitively have an
impact on the stock volatility. For the convenience of assessment of the
influence of these indicators, macroeconomic indicators and stock technical
indicators are then grouped into objective factors, while Baidu search indices
implying people's interested topics are defined as subjective factors. To align
different frequency data, we introduce GARCH-MIDAS model. After mixing all the
above data, we then feed them into Transformer model as part of the training
data. Our experiments show that this model outperforms the baselines in terms
of mean square error. The adaption of both types of data under Transformer
model significantly reduces the mean square error from 1.00 to 0.86.
