Current market conditions are in the process of modification. It
is caused by technological innovations, modern approaches in management
and finance, new financial instruments, transformation of the role of
central banks and government. We believe, apart from the standard set of
econometric technics which are losing their effectiveness now, the new
mainstream in the analysis of economic systems and forecasting of
financial markets’ dynamics is emerging. This mainstream is based on the
application of artificial intelligence technics to predict economic
indices. Thus, Dr. Galeshchuk will focus on the development and
successful application of these innovative scientific approaches to
nonlinear analysis and prediction of financial markets with cutting-edge
deep learning methods and agent-based systems.
Over the last decade brain inspired deep learning technologies has been
proven to be a very robust and effective prediction method in a variety
of application domains. Deep networks have had success in time series
forecasting applications and have also been used for financial
predictions. The recent success of deep networks is partially
attributable to their ability to learn abstract features from raw data.
Dr. Galeshchuk will target empirical studies that prove deep neural
networks are significantly better at financial prediction than existing
time series models. In her presentation Dr. Galeshchuk will identify
questions raised by recent her work on foreign exchange prediction that
appears in peer-reviewed journals. She will also provide brief overview
of deep learning architecture types and relevant programming environment
(i.e., Tensorflow, Caffe, Matlab).
Agent-based modelling is a powerful technology to address high
complexity problems in a decentralized way. It has led to the new branch
of Computational Economics. Dr. Svitlana Galeshchuk will introduce the
agent-based modelling methodology and emphasize its application to
simulate and predict currency markets. Design problems occurring when
creating an agent-based financial market will be discussed. Existing
agent-based frameworks for market simulation will be presented.