Past Meetup

Hong Kong Machine Learning Meetup Season 1 Episode 8

This Meetup is past

88 people went

Every 3rd Wednesday of the month

Location image of event venue

Details

Refinitiv (previously known as Thomson Reuters) will be hosting the meetup. Thanks to them!

For security reasons, you will be granted access to Refinitiv office if and only if you have an ID and registered your full name beforehand.

For those wanting to attend, please send me a full name so that I can compile a list that I will send to Refinitiv security check.

**Please, feel free to contact us if you have something interesting to share! Looking for a third speaker.**

Summary of the previous meetups:

- HKML S1E1 http://marti.ai/hkml/2018/07/18/hkml-s1e1.html
- HKML S1E2 http://marti.ai/hkml/2018/08/21/hkml-s1e2.html
- HKML S1E3 http://marti.ai/hkml/2018/10/09/hkml-s1e3.html
- HKML S1E4 http://marti.ai/hkml/2018/11/21/hkml-s1e4.html
- HKML S1E5 https://gmarti.gitlab.io/hkml/2018/12/19/hkml-s1e5.html
- HKML S1E6 https://gmarti.gitlab.io/hkml/2019/01/23/hkml-s1e6.html
- HKML S1E7 https://gmarti.gitlab.io/hkml/2019/02/20/hkml-s1e7.html

We now have a LinkedIn Page: https://www.linkedin.com/company/hong-kong-machine-learning/ where you can share news or relevant information to the community.

Programme:

Tan Li - Topological Data Analysis --Application in Finance II
Topic: Machine-Learning-based Market Crash Early Indicator and How TDA Can Boost the Performance at High Cut-offs

Eva Liping Zhao - Bidirectional Long Short-Term Memory network for financial market prediction

This paper dissects bidirectional Long Short-Term Memory network (LSTM), a state-of-the-art technique for time series prediction. I implement and analyze the effectiveness of bidirectional LSTM in predicting out-of-sample stock directional movements in Chinese
stock market from[masked]. Utilizing only five dimensional features: daily stock price, daily turnover, daily volatility, deviation from the daily average price, percentage change from opening to closing price, my model achieved significantly good performance in predicting next day stock movement. A long strategy uses the prediction from the
bidirectional LSTM outperforms traditional Random Forest and Logistic regression with an average annual return of 28% and a Sharpe Ratio of 0.8 after transaction cost.

Julian Beltran - Alternative Data for empirical pricing of Alternative Assets (Bitcoin and co.)

Litghtning pitch: Unlocking Data for AI
by Sheridan Johns, Community Engagement, Ocean Protocol & Trent McConaghy, Co-Founder, Ocean Protocol, https://oceanprotocol.com/