Philippe Jordan, President, CFM on the application of science to investment
Biography: Philippe Jordan is president of fund manager CFM International, which specialises in systematic alpha strategies and alternative beta strategies. Over half of the firm’s employees are data scientists and its data team collects, cleans, and manages terabytes of incoming data every day. Continue reading “Profile : Approaching investment with scientific scepticism”
Investors want to see simplified risk models and lower costs, creating the need for change.
Over the past few years, active asset managers have been under intense scrutiny for charging high fees but delivering disappointing performance. Passive funds have been the beneficiaries of inflows which has forced these buyside firms to up their game. They are not only having to re-evaluate their pricing structures and strategies but also talent pool and technology in order to enhance their investment decision making and operational processes. Continue reading “Industry evolution : Why asset managers need to become more systematic : Lynn Strongin Dodds”
The increased use of models for investing could create herding in risk management strategies, which may be less visible but just as dangerous.
Some stocks and strategies are hard to resist, even for fund managers who claim not to follow the crowd. Yet the hedging of these strategies can be equally crowded, putting pressure on liquidity and capital that can, in turn, create systemic risk. Continue reading “Risk management : Still following the crowd? : Gill Wadsworth”
How to build a portfolio based on sustainable principles.
Sustainable investing needs to create a comparable measure of on-the-ground impact from decisions made by portfolio managers, i.e. medicines supplied, emissions reduced, income levels raised, if investors are to choose between funds with any clarity. Continue reading “Modelling responsible investing : Chris Hall”
Using reinforcement learning for portfolio management has great potential, but asset managers need to think carefully about its weaknesses as well as its strengths. Vineet Naik writes.
A new paper, ‘Adversarial Deep Reinforcement Learning in Portfolio Management’ has suggested reinforcement learning could be used to help with portfolio management by investment firms. The research, conducted at Sun Yat-sen University in China, used the machine learning paradigm to model investing in the Chinese stock market. It found that, used correctly it could deliver positive results, however there are considerable risks that portfolio managers need to be aware of. Continue reading “AI for investors : Reinforcement learning for portfolio managers : Vineet Naik”
Can artificial intelligence boost broker-dealers’ client coverage? Simple automation will not work for complex bond markets; tools that learn to find patterns are needed in order to better support sales trading.
Broker-dealers need to do something to make their operations more efficient. In the summer of 2018 buy-side firms are reporting that the sell side is taking less risk than ever before. Many analysts predict volatility will increase over the latter half of the year putting trading capacity under pressure. Using junior sales-traders to replace senior sales-traders is a false economy; it might lower costs, but it also reduces service levels for the buy side. Continue reading “Technology : How smart is dealer AI? : Dan Barnes”