The human vs algorithms race is a qualitative stomping ground, where results will determine our appetite for AI. While we are far from mainstream adoption, consumers are considering autonomous vehicles (AVs) while already speaking to AI chatbots or even Alexa at home.
At 2-3% of market share, AVs are leading the AI adoption with a strong year-on-year growth. We’ve seen early tipping points like this before across the digital landscape.
eCommerce jumped from a few percent in 2010 to almost 20% of all retail sales in 2021.
AI heavily relied on in equity trading
In advertising, AI places almost 100% of impressions. When markets are perform well, people get praised for performance. After a correction, market pundits blame the AI.
This is an obvious ‘have your cake and eat it too’ scenario. Notwithstanding bias being programmed into algorithms, AI is held back by human error during inception. In academia, the pursuit of the AI holy grail is ongoing. Algorithms are far more prolific than we realise.
Dr. Nitin Yadav of Melbourne University and Brain, Mind and Markets Laboratory, says “We teach Algorithmic Trading to students. From the basic models and game theory, we add complexity with known risks and a few “states”, the world quickly becomes non-linear.”
“At first students feel overwhelmed but when they continue they feel more empowered.”
Algorithms execute 80-90% of equity according to HFR Inc. and the World Bank. In 2018, “quant” funds managed $932B, a mere 1.2% of the total value of stocks traded, $77.6T.
Unhedged, the “easy to access” trading AI
Preqin also identified that 23% of Hedge Funds already use AI to generate trading strategies. While funds are bounding into AI, where does that leave the everyday consumers?
Quantitative investing has historically been incredibly expensive, with their PhD teams and supercomputers: investments that made most Quant Funds unreachable as the funds dealt with big chunks of capital, rather than retail grubstakes. On top of that, most regulators have designated Hedge Funds as a “rich people’s playground” and limit retail investors.
Thanks to the warp speed that AI and machine learning have developed, alongside greater accessibility to computing power, retail investors are getting a look into AI automated trading.
One company leading the charge is Melbourne and Sydney based, Unhedged. The team walks and talks like a global capital fund; an in-house quant team that researches and delivers investing algorithms with one big difference – it’s available in the app store.
- Impressive results track over and above the market
- First to introduce performance based pricing to Retail
- In-house quant team that researches and delivers investing algorithms
- Proprietary algorithms that run defensive states to mitigate loss while aiming to outperform the market when markets go up
Unhedged’s impressive display of market muscle
Led by co-founders Peter Bakker (ex-Google), Glenn Vanbavinchove (Ex-CERN) and Saskia Albers (ex-Volt), they raised $3.3 million in equity to launch their platform to consumers.
Their results are impressive, dominating well above the market, +0.65% compared to -3.33% benchmark (S&P500) for January 2022 for their beta release. The appeal for automated investing is resonating loudly, Unhedged had over 8000 people on the waitlist.
“Algorithmic investing is out there, but not yet easily accessible to everyday investors who arguably would benefit the most from its knowledge and scalability. Democratisation in this area is inevitable” says Saskia Albers, Chief Operating Officer of Unhedged.
“While other robo’s were stuck with their predetermined positions and rode the market down last month, among other proprietary processes, our algorithms run defensive states to mitigate loss” says Glenn Vanbavinckhove, PhD MBA, the CTO and Chief quant of Unhedged.
Algorithmic trading strategy researcher Dr Thomas Starke who is an advisor to Unhedged said, “It used to be a space that was all about absolute returns and High-Frequency Trading. Now, it’s accelerating towards quant strategies for long term investment”.
Individual trading is beyond most consumers, due to multiple barriers to entry.
Research and market uncertainty leads to emotional decisions including fear and greed, so newbies tend to sell and buy too late or too early. If you want to ensure that a stable process manages your money, use algorithms. They don’t get emotional and never quit.
As financial literacy is being aggressively sought out by consumers, results will dictate where people invest. The big picture opportunities of AI are at a tipping point and Unhedged is primed to help investors navigate how AI fits into their financial independence.
While the AI versus people races are being run in many sectors, it’s obvious more investment decisions will be trusted to AI. We’ll likely see traditional funds continue to lead from the top down, and robo-trading will expand market share from the bottom up.
