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On our quest to fully utilise publicly available data, we were asked whether there is alpha to be gained from the Companies House database. Well, at ‘Companies House, my virtual Russian squatter and me’ (behind a paywall) Cynthia O’Murchu found out about Weight a Minute, a company with an officer who resides in heaven. ThatContinue reading “Companies House and Heaven”
(update January 29, 2021 — after Reddit users claim victory as $13bn hedge fund capitulates on ..) I added a false negative example — see the last colaboratory notebook below — and be careful when using off-the-shelf code! A very hyped use of AI in Finance comes from the application of Sentiment Analysis as a factor to predictContinue reading “Sentiment Analysis in Finance”
As I wrote in Calling AI’s bluff, we are currently living at the ‘peak of inflated expectations’ when ‘mass media hype’ begins and just before the ‘negative press begins’ (click on the previous blog for the famous hype cycle). The media also seems unable to distinguish the limits of AI, even calling it ‘creative’ (e.g. “Machine CreativityContinue reading “AI in Finance: Cutting Through the Hype (With Case Studies)”
Artificial Intelligence is enjoying one of its (regular) ‘peak of inflated expectations’ from Gartner’s well known hype cycle. Examples of ‘Mass media hype’ are: Machine intellectual superiority: “AlphaGo Zero: Google DeepMind supercomputer learns 3,000 years of human knowledge in 40 days”, “AI made this painting”: artificial intelligence is also driving cars, making money, exploring oceans … andContinue reading “Calling AI’s bluff”
and when it might just work. After the Writing like Cervantes appetizer, where a LSTM neural network ‘learnt’ to write in Spanish in under a couple of hours (an impressive result, at least for me), I applied the same technique to Finance. This is what I learnt: View in Colaboratory (the notebook with the code) Time Series predictionContinue reading “Why Financial Time Series LSTM Prediction fails”
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