Title: Aggregating multiple types of complex data in stock market prediction: A model-independent framework,
Abstract: The increasing richness in volume, and especially types of data in the
financial domain provides unprecedented opportunities to understand the stock
market more comprehensively and makes the price prediction more accurate than
before. However, they also bring challenges to classic statistic approaches
since those models might be constrained to a certain type of data. Aiming at
aggregating differently sourced information and offering type-free capability
to existing models, a framework for predicting stock market of scenarios with
mixed data, including scalar data, compositional data (pie-like) and functional
data (curve-like), is established. The presented framework is
model-independent, as it serves like an interface to multiple types of data and
can be combined with various prediction models. And it is proved to be
effective through numerical simulations. Regarding to price prediction, we
incorporate the trading volume (scalar data), intraday return series
(functional data), and investors' emotions from social media (compositional
data) through the framework to competently forecast whether the market goes up
or down at opening in the next day. The strong explanatory power of the
framework is further demonstrated. Specifically, it is found that the intraday
returns impact the following opening prices differently between bearish market
and bullish market. And it is not at the beginning of the bearish market but
the subsequent period in which the investors' "fear" comes to be indicative.
The framework would help extend existing prediction models easily to scenarios
with multiple types of data and shed light on a more systemic understanding of
the stock market.