AI and Machine Learning have emerged as a central aspect of analytics which is applied to multiple domains. AI and Machine Learning, Pattern classifiers and natural language processing (NLP) underpin Sentiment Analysis (SA); SA is a technology that makes rapid assessment of the sentiments expressed in news releases as well as other media sources such as Twitter and blogs. This conference addresses and explains how to extract sentiment from these multiple sources of information and showcases the advances that have taken place in the field of financial innovation.
This conference builds on the findings of the six previous highly-regarded conferences on this topic. It highlights the recent developments in the application of AI and machine learning to trading strategies including automatic and algorithmic trading, quantitative fund management.
Topics to be covered include:
Elijah DePalma, Thomson Reuters
Machine learning algorithms analyze multilingual professional news and unstructured social media to provide meaningful investment signals for both fundamental and quantitative strategies. Furthermore, research applications of News & Social Media Analytics are expanding beyond equities to encompass Global Macro, Systemic Risk, Supply-Chain Economics and FX Markets. We demonstrate the importance of integrating behavioral insights into long-term investment decisions, and we discuss cutting-edge AI applications for short-term trading strategies.
Enza Messina, Professor in Operations Research, University of Milano-Bicocca
In this talk we show how social relationships can be managed to improve user-level sentiment analysis of microblogs, overcoming the limitation of the state-of-the-art methods that generally consider posts as independent data. Early approaches consist in exploiting friendship relations, but since two friends could have different opinions about the same topic, it could however be inappropriate to measure sentiment similarity. We show how combining post contents and approval relations may lead to significant improvements in the polarity classification of the sentiment both at post and at user level.
Merve Alanyali, Data Science Lab, Warwick Business School, University of Warwick
A mammoth amount of data is being generated by our daily interactions with technological devices and online services. Compared to existing approaches, these new forms of data offer faster and cheaper measurements of human behaviour at a global scale. Examples I will showcase range from using online newspapers to quantify the relationship between financial news and the stock market, to analysing photographs shared on Flickr to track protest outbreaks around the world.
Peter Hafez,Chief Data Scientist,RavenPack
The emergence of big data in finance has shifted the alpha focus away from being faster to being smarter than the competition. Access to alternative data sources is considered a key input to such process. Peter Hafez, Chief Data Scientist, will provide an overview of RavenPack’s Big Data Analytics and the future development of the RavenPack product suite. In addition, he will present on his latest work on thematic alpha streams as well as providing an overview of general use cases of RavenPack data across various trading and investment applications.
Tilman Sayer,OptiRisk,Xiang Yu,OptiRisk Systems
The classic Markowitz model considers the standard characteristics: return and volatility. Second order stochastic dominance (SSD) in contrast encompasses the whole distribution of asset returns. The true magic of SSD lies in its choice of portfolio based on the minimisation of downside tail risk. Using this modelling paradigm we have developed an innovative and dynamic trading product for equities. News sentiment is integrated into the system to digest market moods and enhance prediction. Regime switching algorithms are used to detect market shifts. We provide insight into these novel techniques and supply performance results.
Nearly all online “news” sources, which are the traditional sources we know in the likes of Bloomberg and Reuters, are a fraction of the content that is available on the World Wide Web. The remaining content comes from new media sources including Twitter, YouTube, and Facebook generated by individuals who talk about events as they happen. These millions of voices, when structured, can generate insights which can help investors make investment decisions. This presentation will touch on how Sentifi structures and delivers these insights, providing an information advantage for media platforms globally.
Pierce Crosby, StockTwits
The digitizing of disparate data has led to a new wave of “emerging data” in the industry, applicable specifically to existing risk and trading models. Translating these data into repeatable results is the new frontier for many asset managers and hedge funds. The discussion will draw specifically from examples of earnings crowdsourcing, satellite imagery forecasting, and social data as it applies to investor sentiment and volatility modeling. We will also cover the diversity of methods used to deliver these data sources and what is considered the new standard for asset managers when it comes to the consumption of data.
Enza Messina, University of Milano-Bicocca
In this talk we show how social relationships can be managed to improve user-level sentiment analysis of microblogs, overcoming the limitation of the state-of-the-art methods that generally consider posts as independent data. We show how combining post contents and network structure information may lead to significant improvements in the polarity classification of the sentiment both at post and at user level.