  Stock prices are influenced over time by underlying macroeconomic factors.
Jumping out of the box of conventional assumptions about the unpredictability
of the market noise, we modeled the changes of stock prices over time through
the Markov Decision Process, a discrete stochastic control process that aids
decision making in a situation that is partly random. We then did a "Region of
Interest" (RoI) Pooling of the stock time-series graphs in order to predict
future prices with existing ones. Generative Adversarial Network (GAN) is then
used based on a competing pair of supervised learning algorithms, to regenerate
future stock price projections on a real-time basis. The supervised learning
algorithm used in this research, moreover, is original to this study and will
have wider uses. With the ensemble of these algorithms, we are able to
identify, to what extent, each specific macroeconomic factor influences the
change of the Brownian/random market movement. In addition, our model will have
a wider influence on the predictions of other Brownian movements.
