When it comes to processing massive financial data, machines have a natural advantage over humans.
The human brain did not evolve to process and make sense of vast amount of heterogeneous and sometimes high-frequency financial data. Traditional quant trading models only exploit market inefficiencies (or 'alphas') arising as deviation from simplistic equilibrium models (such as implied by factor analysis) constructed around a simple economic narrative. We believe this approach barely scratches the surface of market inefficiencies.
The more trading becomes electronic, the harder it will be for humans to compete with machines.
The relentless electronification of trading across all asset classes, driven by technological advances, cost reduction, and regulatory pressures, means that virtually every type of market and price-sensitive piece of information will be accessible by computers in real-time in the near-future. This will significantly widen the scope of automation in financial markets. We believe that there is no trading logic or market insight that cannot be learned by an artificial intelligence using the same data as a human being.
We view the automation of trading idea generation as a viable long-term solution to management fee compression.
Investment management and trading idea generation are perhaps the last frontiers of automation in financial markets. As the downward pressure on management fees is here to stay, there is an industry-wide need for more efficiency and scalability in trading idea generation. We believe that artificial intelligence will not only generate investment returns that are expected of active management strategies, but, by utilising the same core technology, artificial intelligence will provide the scalability required to cope with a low fees environment.
We believe that computing has become cheap and powerful enough to enable an AI revolution in finance.
Over the past 50 years, computers have become 10,000 times more powerful per dollar. This development has played a key role in most of the artificial intelligence breakthroughs that have been witnessed over the past decade. Today, specialised hardware such as TPUs and strategic initiatives such as quantum computing, are paving the way for the next artificial intelligence revolution. We believe that the last barrier to an AI revolution in finance is machine learning methodology, and we have set out to break it down.
Our approach is based on a bleeding-edge, unpublished, proprietary machine learning technology.
We have developed a core machine learning technology that is based on a nonconventional quantitative finance approach and novel machine learning techniques. In addition to solving some engineering challenges, we have had to push the boundaries of machine learning methodologies in the process. Early results are very promising.
Going forward, our endeavour will remain guided by the following four principles.
AI agents should be able to detect and exploit long-lived as well as short-lived market opportunities, some of which are so subtle and complex that they do not fit in the standard alpha generation paradigm used by major investment management firms.
Our core technology is not specific to an asset class, a holding period, or an investment objective, and can be deployed to a vast number of markets with little human effort.
AI agents need not be overlaid by human investment judgements, and consequently should be free from cognitive biases.
AI agents should continuously monitor massive data sets to dynamically adapt to changing market conditions.