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.
Mandie Quartly, Worldwide Lead, Machine Learning and High Performance Analytics software, IBM
There’s a lot of talk about using Artificial Intelligence (AI) to gain business insights, but there are a number of key ingredients required to actually make it happen in a timely fashion. A vital element is the technology which underpins AI and machine learning applications. Come and hear more about "making it happen" for your organisation; learn how to take advantage of rapidly evolving and innovating technologies. The emphasis is very much on real world use cases encompassing retail and financial markets.
Sanjiv Das, Professor of Finance and Data Science, Santa Clara University
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.
Peter Hafez, Head of Data Science, RavenPack
RavenPack Analytics brings the latest innovations in natural language processing - providing deeper textual analysis of unstructured documents, as compared to its predecessor. Focusing on the new event relevance and novelty scores, Peter will show how to take advantage of asymmetries stemming from event group heterogeneity to achieve stronger risk-adjusted performance. Specifically, he’ll highlight various examples that showcase the significant benefits of going beyond the headline when trading European and US equities.
Xiang Yu, Chief Business Development Officer, and Gautam Mitra, CEO/Director, OptiRisk Systems/UCL
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.
Arun Verma, Quantitative Researcher, Bloomberg LP
♦ Extracting actionable information in the high volume, time-sensitive environment of news and social media stories
♦ Using machine learning to address the unstructured nature of textual information
♦ Techniques for identifying relevant news stories and tweets for individual stock tickers and assigning them sentiment scores
♦ Demonstrating that using sentiment scores in your trading strategy ultimately helps in achieving higher risk-adjusted returns
Oded Luria, CITI
Deep Learning has been shown to outperform traditional methods in many learning tasks such as image and voice recognition, but its role in processing financial datasets is yet to be fully discovered. In this talk, Oded shares practical insights about applying Deep Learning for different aspects of market making and discusses some of the unique challenges and tradeoffs of this field.
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).
Moderator: Gautam Mitra; Panellists: Ashok Banerjee, Simon Bigg, Sanjiv Das, Giuliano de Rossi, Peter Hafez
Guillaume Vidal, CEO, Walnut Algorithms
♦ How machine learning fits into systematic strategies
♦ What are the pros and cons of using machine learning in quant finance
♦ Building an infrastructure that enables machine learning research within the company
♦ Debunking the myth of superintelligence in finance
Ashok Banerjee, Professor, Finance & Control, Indian Institute of Management Calcutta
Banks and financial institutions in emerging markets are saddled with a huge proportion of bad loans. Banking regulations require lenders to provide for troubled debt which adversely affects the profitability of banks. The capital market also reacts negatively to such write-offs of big ticket debts. Banks are, therefore, putting significant resources into developing early warning signals to arrest eventual default. The financial institutions use a wide range of default prediction models to estimate the loan loss. These models use data from financial statements and the market. The present study shows that such models fail to provide effective early warning signals. We use annual reports of companies to develop a default model which is predictive and hence has the capability of providing early warning signals. Using information from Directors’ Reports, Audit Reports and notes to accounts, our model successfully discriminates the ‘good’ firms from the ‘bad’ ones.
Enza Messina, Professor in Operations Research, University of Milano-Bicocca
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.
Geoff Horrell, Director of Product Incubation at Thomson Reuters
How can asset managers deliver insight using NLP and Knowledge Graphs? Data Lakes, Data Science, AI, Graphs, Machine Learning are all getting massive attention. How should asset managers approach this area to ensure that business value isn’t lost in the mix of the technology hype?
Tilman Sayer, CIO, 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.
Anders Bally, CEO and Founder, 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.
Stephen Pulman, Professor of Computational Linguistics, Oxford University/ TheySay
A non-technical overview of our work over the last ten years in sentiment analysis and related techniques. I'll also describe various practical applications of these technologies, some successful, some less so, in a variety of different areas: sports gambling, politics, conversational agents, health care monitoring, and financial market prediction.
Richard Peterson, CEO, MarketPsych Data
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.
Moderator: Richard Peterson; Panellists: Anders Bally, Geoff Horrell, Holger Knauer, Enza Messina, Grigorios Papamanousakis, Arun Verma
Christina Erlwein, Senior Quantitative Analyst and Researcher, OptiRisk Systems
We report the outlook of a credit risk assessment tool, in which sentiments of news and social media are 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.
Holger Knauer, CEO/CRO, 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
Grigorios Papamanousakis, Deputy Head, Systematic Asset Solutions, Aberdeen Asset Management
On this presentation we describe how we use Machine learning for forecasting the relative performance of various asset classes (rates, credit, equities, commodities, etc.) from an asset management perspective. How we define the question to the machine based on different client risk profiles, performance targets and machine learning algorithms. We finally emphasize on the market factors selection, data cleansing, signal processing and high performance computing.
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