Artificial Intelligence, Machine Learning and Sentiment Analysis are changing the way in which numerous client services are offered. In particular, Financial Organisations are creating and leveraging such innovation in the domain of wealth management. This trend is now being taken on board by multiple innovators: academia, start-ups, technology companies and financial market participants.
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.
The 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. The focus is on the latest research and includes presentations by leading subject experts from all over the world, as well as a number of applications/case studies.
Sanjiv Das, Professor of Finance and Data Science, Santa Clara University, USA
Information is extracted from big textual data to create a single risk score for the financial system. To do this network analysis is overlaid with mathematical theory to create a systemic risk monitoring dashboard. I will discuss implementations in the US and India. Extensions to stochastic networks will also be presented.
Xiang Yu, Business Development Techno Executive, and Gautam Mitra, CEO/Director, OptiRisk Systems/UCL, UK
We have created an innovative and dynamic trading strategy for equities, with a particular focus on controlling downside risk. The mathematical concept behind the approach is called stochastic dominance, where investment decisions are based on distributions rather than moments. A major contribution of news sentiment is in the prediction of future distributions. Regression analysis on news sentiment and regime switching models are employed to digest market moods and account for changing market situations.
Ashok Banerjee, Departmental Head of Finance and Control, Indian Institute of Management, Calcutta (IIMC), India
Studies at the Finance Lab of IIM Calcutta show that the effect of any news on the market depends on the attention of investors. If the attention of investors were somewhere else, even news carrying strong positive/negative sentiment would go unnoticed or would be penalized. This is particularly true in the case of major non-market attention grabbing events. In other words, attention overwhelms the effect of sentiment. Processing any attention-grabbing event requires effort. If that effort is directed towards some particular information, people are too lazy to put inthe extra effort to process any other information at the same time, no matter how much sentiment that information might carry.
Enza Messina, Professor in Operations Research, University of Milano-Bicocca, Italy
We show how deep learning and ensemble methods can successfully address challenging problems arising in sentiment analysis such as irony detection or domain adaptation. In particular, we propose an unsupervised framework for domain-independent irony detection built upon an existing probabilistic topic model initially introduced for sentiment analysis purposes. Moreover, in order to improve its generalization abilities, we apply Word Embeddings to obtain domain-aware ironic orientation of words. The acquisition of cross-domain high level feature representations through word embeddings combined with the generalization capability of ensemble methods can also be used for addressing the problem of domain adaptation also in the scenario where the testing target domain is completely unlabeled.
Richard Peterson, CEO, MarketPsych Data, USA
Dr. Peterson will describe the unique characteristics of media sentiment data and approaches to financial price prediction with this data. The basics of media sentiment data, various modeling approaches, and their results (including live trading results) will be described in this talk. Viewers will gain an understanding of real-world modeling tips and techniques when dealing with noisy and inconsistent data such as media sentiment streams.
Giuliano De Rossi, Head of European Quantitative Research, Macquarie
We consider a new approach to analyse the vast amount of information available about the portfolio positions of institutional investors over time. Our goal is to use machine learning to analyse the stock picks of active equity funds. Recommender systems have been employed for a wide range of applications: Suggesting books, hotels, scientific papers and even new social connections. Here we aim to identify stocks that are likely to be bought by a given portfolio manager based on his or her own existing stock picks and the recent trading activity of other investors. By aggregating the results we seek to build a new signal related to the institutional demand for a given stock analysed by Koijen and Yogo (2016).
Tilman Sayer, Advanced Logic Analytics
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.
Roland Schweter, Acatis Research; Joerg Wenzel Fraunhofer Institute for Industrial Mathematics ITWM; Christina Erlwein, OptiRisk Systems; Michael Phringer, PS Quant
We report the outlook of credit risk assessment tool, in which sentiments of news and social media included in the risk assessment of corporate and sovereign bonds. Our tool investigates all current country and company information and market sentiment as well as historical time series to enable a quantitative as well as qualitative analysis of bonds’ inherent risk.
Stephen Pulman, Professor of Computational Linguistics, Oxford University/ TheySay Analytics
All sentiment analysis systems can deliver positive/negative/neutral classifications. But there are many other useful signals in text: emotion, intent, speculation, risk, etc. This talk will present a survey of the state of the art in recognising these other dimensions of sentiment in text and describe some practical applications in finance and elsewhere.
Holger Knauer, Catana Capital
♦ How to use Big Data and A.I. to build predictive models and better analyse structured and unstructured data
♦ How to interpret big data to view a real-time gauge of financial markets
♦ Assessing how far big data’s capabilities will impact, reshape and benefit world economies
Anders Bally, Sentifi
In the early 90’s, the majority of financial market participants used news mainly from services like Bloomberg and Reuters to inform themselves. 20 years later, they still do. During the same period, our society went through a communication paradigm shift. Today more than 2 billion people walk around with mobile devices and communicate what they see and think on social media. These billions of voices, when structured, can generate insights which can help investors make better investment decisions. This presentation will touch on how Sentifi structures and delivers these insights, providing an information advantage for media platforms globally.
Peter Hafez, 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.
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