It is widely recognized that news, as well as social media and other types of data, play a key role in financial markets. With the rapid growth of financial data sources and volume, nowadays, few fields generate as many data as the financial industry. Big data is fueling a transformation of finance and the world of business in yet unpredictable ways. This poses many challenges to us. For example, we need to fuse different kinds of data, from quite distinct sources, and with different degrees of reliability. We also need to transform unstructured data into structured intelligence to enable high end analytics. In this process, many issues need to be addressed by applying big data, machine learning and NLP technologies, such as automatic data collection, entity extraction, classification, clustering, search, filtering, sentiment analysis, event novelty detection, news relevance/significance identification, modeling and aggregation. To make informed decision making, ideally, these modules and information also need to be connected to financial analytics models for trading, investment management, asset pricing and risk control. 

Deep learning and cognitive computing have shown to be promising; we are also interested in their applications in financial domain. 

The goal of this workshop is to bring together researchers and industry practitioners working on big data mining and financial related data to share their ideas and best practices. It will feature paper presentations and invited talks or panel discussion on topics and research directions on big data for financial industry. Papers about original and ongoing research and those that describe systems and practices are welcome.