AI, Machine Learning and Sentiment Analysis Applied to Finance London
Artificial Intelligence is deemed to be the main driver of the 4th Industrial Revolution. Investment in AI has grown at a phenomenal rate with companies investing $26-39bn in 2016. Adoption in 2017, however, remains low. As a result, this has spurred companies from every industry to seize the trend and innovate – from virtual assistants to cyber security to fraud detection and much more. The majority of C-level executives have identified and agree that AI will have an impact on their industry. However, only 20% of C-level executives admit they have already adopted AI technology in their businesses, according to research conducted by McKinsey. So, there is plenty of scope for change and improvement. The Finance industry is anticipated to lead the way in adoption of AI with a significant projected increase in spending over the next three years.
Until recently, practitioners have faithfully relied upon neo-classical models to measure performance, whether it’s in financial organisations or marketing corporations. AI is the new technology that offers an automated solution to these processes. It has the capability to replicate cognitive decisions made by humans and also remove behavioural bias adherent to humans.
Machine learning and sentiment analysis are specific techniques that are applied in AI. These techniques are maturing and rapidly proving their value within businesses. In order to process and understand the masses of data out there, machine learning and sentiment analysis have become essential methods that open the gateway to data analytics. To keep up with the ever-expanding datasets, it is only natural that the techniques and methods with which to analyse them must also improve and update.
This conference will help you to demystify the buzz around AI and differentiate the reality from the hype. Learn about how you can benefit from the unprecedented progress in AI technologies at this conference. Participants will be presented with real insights on how they can exploit these technological advances for themselves and their companies.
Attend this event and earn GARP/CPD credit hours.
Unicom has registered this program with GARP for Continuing Professional Development (CPD) credits. Attending this program qualifies for 14 GARP CPD credit hours. If you are a Certified Financial Risk Manager (FRM®), please record this activity in your Credit Tracker.
Topics Covered Include:
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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
The emergence of big data in finance has shifted the alpha focus away from being faster to being smarter and more efficient than the competition. Access to alternative data sources is considered a key input to such a process. During his talk, Peter Hafez will provide an overview of the changing investment landscape and provide the “winning formula” for successful quant investing. He’ll discuss issues related to unstructured content, crowd-sources alpha, and proprietary vs. public content.
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.
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, Data Scientist, Citi Technology Innovation Lab TLV
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).
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.