Artificial Intelligence and Machine Learning in Trading (Part 2)

Investor Education & Capacity Building Programmes

23
Nov 2022

8:30 pm - 10:30 pm

SIDC LAMS

Speakers : Yi Peng

5 CPE

MYR FOC *Fee is not inclusive of 6% SST

Derivatives Programme for Professionals

This initiative is supported by the Capital Market Development Fund (CMDF)

The economic significance of artificial intelligence (AI) is a fundamental question investors face. With the world’s top hedge funds using machine learning (ML) to find new investment opportunities, it is imperative for investors to know what kinds of financial applications may be leveraged by this constantly evolving technology. With the exponential growth in data, AI is arguably the best tool to ingest, decipher and learn the patterns of the financial markets.

In the second part of the programme, we get our hands dirty and create our own neural networks. We cover a relatively mature subfield in AI, Natural Language Processing (NLP) that can classify the sentiment of financial text; an emerging one, Time Series Classification/Regression, which has previously been dominated by simpler machine learning methods; and lastly perform unsupervised clustering of stocks to identify anomalies/trading opportunities/risk management.

We will go through the end-to-end pipeline of building these models – from data collection/pulling to modelling and testing so the participant will be able to apply the templates to their own datasets/use cases.

   

Programme Objective

This programme is designed to provide participants with hands-on experience in applying AI and ML techniques in trading and its other significant use in the capital market.

 

Mode

Live Webinar

 

Target Audience

  • Capital Market and Services Representative License (CMSRL) holders
  • Professionals Trader and Investor

Learning Outcomes

Upon completion of the programme, participants will be able to:

  • Enlist the critical components and processes involved before and during Machine Learning research
  • Apply code that leverages cutting-edge Natural Language Processing tools to classify the financial sentiment of news/financial documents
  • Implement code that utilises deep learning to classify/predict time series
  • Utilise code that cluster stocks with similar risk characteristics

Competencies

  1. Foundational (Product) – Capital Market Products (Level 3)
  2. Functional (Technical)  – Digital Technology Application (Level 3)
  3. Functional (Process) – Derivatives Dealing (Level 3)
8.30 pm – 10.30 pm How to start your own ML research

  • Hardware
  • Software and programming environment
  • Obtaining and cleaning data
    • Text data sources
    • Financial price sources
  • Model training and validation
  • Model deployment

Demonstrations/Applications of ML in Financial Markets

  • Applying/Fine-tuning a systematic and accurate financial sentiment analysis NLP model
  • Building a deep learning model to classify/regress stock returns/volatility
  • Unsupervised clustering of stocks to identify risk/trading opportunities

About the Speaker

Yi Peng

Senior Quantitative Strategist

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*The SIDC reserves the right to amend the programme as deemed appropriate without prior notice

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