Background

AI, Machine Learning and Sentiment Analysis Applied to Finance Bangalore

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.

Topics Covered Include:

  • Fundamentals and applications of machine learning and deep learning
  • Pattern classifiers, Natural Language Processing (NLP) and AI applied to data, text, and multi-media
  • Sentiment scores combined with neo-classical models of finance
  • Financial analytics underpinned by qualitative and quantitative methods
  • Predictive and normative analysis applied to finance
  • Behavioural and cognitive science
  • The future of AI and its impact on industries

Why participate?

  • Hear from leading subject experts from UK, US, Europe and India/Hong Kong
  • Programme includes the latest state-of-the-art research, practical applications and case studies
  • Expect technical and in-depth presentations and discussions; we like to stimulate your brain cells!
  • Excellent networking opportunities throughout the days with all participants, including presenters, investors and exhibitors.

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Programme

  • -
    Xiang Yu Sentiment Analysis in Bangalore 2017

    Daily Trade Signals using Sentiment Analysis and Stochastic Dominance for Downside Risk Control

    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.

    Speakers:

    Gautam Mitra

    Xiang Yu Sentiment Analysis in Bangalore 2017

    Xiang Yu

  • -

    Predicting Corporate Default using Text of Corporate Filings

    Ashok Banerjee, Departmental Head of Finance and Control, Indian Institute of Management, Calcutta (IIMC), India

    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.

    Speakers:

    Ashok Banerjee

  • -
    Richard Peterson Sentiment Analysis in Bangalore 2017

    Approaches to Market Forecasting with Media Sentiment Data

    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.

    Speakers:
    Richard Peterson Sentiment Analysis in Bangalore 2017

    Richard Peterson

  • -

    I just called to say I’m bullish – Global analyst conference calls and stock returns

    Gurvinder Brar, Macquarie Research

    Recent academic research (and our own work on US data) have found that analyst conference calls convey useful information not contained in earnings numbers and analyst forecasts. The slow reaction of markets to that type of information implies that sentiment, as expressed by analysts and management during the call, predicts returns. This effect is distinct from the well known post earnings announcement drift. We collected call transcripts for global companies from Factset going back to 2002. Using text mining techniques, we measure the tone of the management discussion and Q&A session of each call, with a goal of developing an alpha signal at low frequency.  This presentation describes the strategy and findings of our research.

    Speakers:

    Gurvinder Brar (TBC)

  • -

    Technology and Finance – Advantages and Pitfalls

    Sankarshan Basu, Professor in the Finance and Accounting Area at the Indian Institute of Management, Bangalore (IIMB), India

    Technology has significantly altered the way the world has known finance over the years. While this has brought significant benefits to the system and the economies as a whole, it has thrown up several challenges as well – some having quite disastrous consequences. This talk will dwell upon the benefits that technology has provided to the financial sector in general and the banking sector in particular at the same time highlighting the pitfalls that have sprung up in the process. Part of the talk also looks at how technology can be used in a dynamic environment context to address some of the issues related to the pitfalls and more particularly what, if any, measures can be taken to reduce the pitfalls in the future.

    Speakers:

    Sankarshan Basu

  • -

    Comparing the BG-NBD and Markov Chain Methods to Model Non-Contractual Customer Churn

    Prithwiraj Mukherjee, Indian Institute of Management, Bangalore

    Modelling customer churn is important for retailers, especially when dropout is unobserved. We compare two popular methods used by managers – the BG-NBD model (Fader, Hardie and Lee 2005) that uses recency and frequency as inputs, and Markov Chain models incorporating threshold recency as a dropout assumption. We compare these approaches on parameters like accuracy and computational load across multiple data sets.

    Speakers:

    Prithwiraj Mukherjee

  • -

    Networks are like onions: Practical Deep Learning with TensorFlow

    Barbara Fusinska, Data Scientist

    Deep learning is the area that wins over the field of Artificial Intelligence. By using libraries like TensorFlow, it is now available to the wider audience. In this tutorial, Barbara will walk the audience through the process of creating several types of neural networks. The session will start with explaining key concepts of deep learning and introducing datasets the computation will be performed on. Along the way, attendees will have the practical opportunity to use TensorFlow to build deep networks, train them and evaluate the results. After the session, participants will become familiar with how to use TensorFlow when shaping the architecture of neural networks. By the hands-on form of the tutorial, the audience will have the chance to gain some first hand experience of how to apply deep learning to computer vision and natural language processing tasks.

    Speakers:

    Barbara Fusinska

  • -

    Using Machine Learning to Trade Stock Markets

    Humberto Brandão, Data scientist

    ♦ The importance of a high-quality simulation process;
    ♦ The importance of a good validation process;
    ♦ Showing good and bad results in real stock markets applying Machine Learning;
         • Market makers;
         • Statistical arbitrage;
    ♦ Showing the results of my methods in crowd-sourcing competitions

    Speakers:

    Humberto Brandão

  • -

    TBA

    Sundar Nagalingam, Head - Deep Learning Practice, NVIDIA Graphics Pvt Ltd

    Speakers:

    Sundar Nagalingam

Speakers

Ashok Banerjee

IIM Calcutta

Sankarshan Basu

Indian Institute of Management, Bangalore (IIMB), India

Humberto Brandão

Data scientist

Barbara Fusinska

Data Scientist

 

Gautam Mitra

OptiRisk Systems

Prithwiraj Mukherjee

Indian Institute of Management, Bangalore

Sundar Nagalingam

Head – Deep Learning Practice, NVIDIA Graphics Pvt Ltd

Richard Peterson

MarketPsych, USA

 

Xiang Yu

OptiRisk Systems

Knowledge Partners

Organised by

 
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Previous Programme

  • 09:30 -
    gautam-mitra

    Welcome and Introduction – Professor Gautam Mitra, OptiRisk Systems

  • 09:35 -
    ajit-balakrishnan

    Keynote 1: The 21st Century Marketing Armory

  • 10:10 -
    Nitish Sinha Sentiment Analysis in Bangalore 2017

    Keynote 2: News versus Sentiment: Predicting Stock Returns from News Stories (forthcoming Financial Analyst Journal)

  • 10:50 -

    Coffee

  • Finance Stream – Industry Focus
    Location: Auditorium
    Chairperson: Ashok Banerjee, Indian Institute of Management, Calcutta (IIMC), India -

  • 11:20 -
    gautam-mitra
    Xiang Yu Sentiment Analysis in Bangalore 2017

    Daily Trade Signals using Sentiment Analysis and Stochastic Dominance for Downside Risk Control

  • 11:50 -
    Richard Peterson Sentiment Analysis in Bangalore 2017

    Keynote: Approaches to Market Forecasting with Media Sentiment Data

  • 12:30 -
    Ashok Banerjee Speaker in Sentiment Analysis Conference

    Predicting Corporate Default using Text

  • 13:00 -

    Lunch

  • Chairperson: Gautam Mitra, OptiRisk Systems -

  • 14:00 -
    enza-messina

    Keynote: Deep Learning and Ensemble Methods for sentiment analysis

  • 14:40 -
    deepak-mishra

    Driving Customer Retention and Growth using Media data

  • 15:10 -

    Tea

  • 15:40 -
    praloy-majumder

    Building up an Early Warning Signals ( EWS) for Indian borrowers based on social media and web oriented triggers

  • 15:55 -
    prateek-agrawal
    surangama-agarwal

    Recommendation System using Collaborative Filtering – Applications in Finance

  • 16:20 -
    vivek-bajaj

    Modern Technology for Financial Research

  • 16:45 -
    niraj-s-kakkad

    AI – Impact on Indian Financial Markets – Mutual Funds

  • 17:10 -
    gautam-mitra

    Discussion and Q&A

  • 17:30 -

    Close

  • Finance Stream – Research & Academic Focus
    Location: Auditorium
    Chairperson: Sankarshan Basu, Indian Institute of Management, Bangalore (IIMB), India -

  • 09:30 -
    Ashok Banerjee Speaker in Sentiment Analysis Conference

    Attention and Sentiment

  • 10:00 -
    arup-ganguly

    Textual Disclosure in SEC Filings and Litigation Risk

  • 10:30 -

    Coffee

  • 11:00 -
    dummyimage
    gaurav-singh
    kingshuk-banerjee
    pavinder-monga
    prithwiraj-mukherjee

    PLENARY Panel -– 90 minutes – Location: Auditorium

  • 12:30 -

    Lunch

  • Finance Stream – Research & Academic Focus
    Location: Auditorium
    Chairperson: Sankarshan Basu, Indian Institute of Management, Bangalore (IIMB), India -

  • 13:30 -
    svetlana-borovkova

    Media Sentiment, Systemic Risk and New Investment Factors

  • 14:15 -

    An Experimental Analysis on One Step Ahead Forecasting of Intraday Values

  • 15:00 -

    Tea

  • 15:30 -
    keshav-sehgal

    Deep Learning for Stock Prediction

  • 16:15 -
    Prof Sankarshan Basu Sentiment Analysis in Bangalore 2017

    Technology and Finance – Advantages and Pitfalls

  • 16:45 -

    Summing up; Close of conference

  • Consumer Stream – Industry Focus
    Location: Classroom
    Chairperson: Kingshuk Banerjee, IBM Global Business Services -

  • 09:30 -
    madhu-gopinathan

    From Keywords to Concepts for Sentiment Analysis

  • 10:00 -
    gaurav-gaba

    Risk Management and Financial Crime Compliance (FCC) using AI & Machine Learning

  • 10:30 -

    Coffee

  • 11:00 -
    dummyimage
    gaurav-singh
    kingshuk-banerjee
    pavinder-monga
    prithwiraj-mukherjee

    PLENARY Panel -– 90 minutes – Location: Auditorium

  • 12:30 -

    Lunch – Location: MDC

  • Consumer Stream – Industry Focus
    Location: Classroom
    Chairperson: Prithwiraj Mukherjee, Indian Institute of Management, Bangalore -

  • 13:30 -
    robin-paniker

    Applying AI to Complement Decision Making

  • 14:00 -
    shabbir-tayabali

    Consumer Market and Decision Trees

  • 14:30 -
    krishma-singla

    Cognitive Roadmap for Finance - a case study

  • 15:00 -

    Tea

  • 15:30 -
    prateek-agrawal
    surangama-agarwal

    Recommendation System using Collaborative Filtering – Applications in Consumer & Retail Markets

  • 16:00 -
    suman-singh

    Role of Machine learning and Big Data Technology on Digital Platform for Retail Bank To Improve Customers’ Experience and Grow Profitability

  • 16:30 -
    vishesh-nigam

    Conversational Bots Gratifying Customer’s Engagement

  • 17:00 -

    Summing up; Close of conference

Previous Speakers

Surangama Agarwal

Ivy Professional School

Prateek Agrawal

Ivy Professional School

Vivek Bajaj

Director, Kredent Ventures

Ajit Balakrishnan

CEO, Rediff.com, India

Ashok Banerjee

IIM Calcutta

Kingshuk Banerjee

IBM Global Business Services

Sankarshan Basu

Indian Institute of Management, Bangalore (IIMB), India

Svetlana Borovkova

Vrije Universiteit Amsterdam, Netherlands

Nishant Chandra

R&D Scientist, AIG

Gaurav Gaba

AVP at Societe Generale Global Solution Centre

Arup Ganguly

University of Pittsburgh, U.S.A

Madhu Gopinathan

Vice President, Data Science, MakeMyTrip

Anup Gunaseelan

Manager, LatentView Analytics

Avadhoot Jathar

Senior Statistician, Analytics Quotient

M Jeevananthan

Thiagarajar School of Management

Niraj S Kakkad

InvestAscent Wealth Advisors Pvt Ltd

Praloy Majumder

Disseminare Consulting

Arun Mallavarapu

Fedo

Enza Messina

University Milano Bicocca

Deepak Mishra

Thomson Reuters

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Venue

  • IIM Bangalore, Bannerghatta Road, Bilekahalli, Bengaluru, Karnataka 560076
  • +44 (0) 1895 256 484
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