Year 2019 in review

• Successes.
– Developed and implemented first deep-learning applications providing a massive improvement in analytical power by leveraging previous years hardware purchases
– Acceleration in pipeline of third party cutting edge quantitative research to implement
– Impressive performance from improved security selection criteria
– Further improved the fundamental and technical criteria for security selection
– Corrected and refined machine learning methods in asset allocation
– Further improved econometric criteria in asset allocation
– Developed new software to access better data sources
– Now accepting client defined portfolio universes (asset allocation, ESG, religious etc.)
– First venture capital investment
• Fails.
– Single digit net return for year (annualised double digit return over three years)
– Poor asset allocation due to not applying correct procedures for machine learning
– Natural language processing sentiment analysis not developed further

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