‘Tail Hedging in the Age of Machine Learning’: and “don’t start with a new black box”

So said Dr Ernest Chang on yesterday’s Zoom presentation at the STA’s monthly meeting. Known as Ernie among friends, he is charming, witty and speaks perfect English – albeit with a Chinese accent. With my very limited knowledge of all the sciences, I had been worried about covering this for our readers. I need to have done so: his presentation was clear, to the point, and avoided unnecessary jargon.

Introduced by Jeff Boccaccio, who also moderated the questions, both gents did very well with this new format we are all having to get used to. The doctor started at IBM’s famous Watson laboratory where he specialised in Machine learning – hence the title of his talk. He then moved into banking at Morgan Stanley and Credit Suisse; now he’s a Commodity Trading Advisor at QTS Capital Management LLC with 3 books under his belt: Quant Trading, Algo Trading and Machine Trading.

For his presentation he chose to focus on just one of his trading strategies, one based on day-trading e-mini S&P 500 futures – he does not trade options as he believes in simple strategies. The abundance of short term intra-day data is also useful for training purposes. Therefore, he also believes picking up one of the myriad existing black boxes (readily available for Python) and tweaking it via machine learning – which are not market specific.

He acknowledges, and warns, that humans are often much better at watching out for regime change, gauging the contextual background far better than any computer. This matters because there is ‘’strong mean-reversion in calm markets which machines must learn – and find this difficult’’. The chart of the profits based on the specific model discussed clearly show a sharp increase during this year’s market turmoil.

The key is that machine learning can provide probabilities of outcome, as well as the suggestion as to whether to bother with the trade at all. Rather tellingly he also warns: ‘’it’s no good to perform as the average trader – because most traders lose money.’’

Something I didn’t know is that deep learning’s GPT-3 has 175 billion parameters to fit into neural networks! Understandably Chan says: ‘’we are museum curators, not supermarket operators.’’

Links you might find useful:

ernest@nullpredictnow.ai
https://predictnow.ai where he is offering a free trial.

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Posted in Finance, Markets, STA charts, STA news, Technical Analysis Courses, Trading, Trending
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Disclaimer

The views and opinions expressed on the STA’s blog do not necessarily represent those of the Society of Technical Analysts (the “STA”), or of any officer, director or member of the STA.

The STA makes no representations as to the accuracy, completeness, or reliability of any information on the blog or found by following any link on blog, and none of the STA, STA Administrative Services or any current or past executive board members are liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its display or use.

None of the information on the STA’s blog constitutes investment advice.

About Nicole Elliott

Nicole Elliott

A graduate of the London School of Economics and Political Science (BSc Social Psychology) Nicole Elliott has worked in banks in the City of London for the last 30 years. Whether in sales, trading or forecasting technical analysis has always been the bedrock of her thinking. Key expertise lies within all areas of treasury: foreign exchange, money markets, fixed income and commodities.

She has also added to the body of knowledge of the industry writing the first western book on Ichimoku Cloud Charts. Strong media links and a cult following are due to her prescient calls on the markets and often entertaining format.

Nicole can be contacted at trending@sta-uk.org

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