Factor investing has been a key strategy in the finance sector for years. The premise is based on the fact that there are a variety of factors that can influence market movement. These factors are used to drive investment decisions. By studying these factors, investors can make better more informed decisions about stocks.
This approach dates back to the 1970s. When studies began to challenge the market assumptions used to make investments at that time. Prior to the 70’s, the widely held belief was that it was impossible to beat the market consistently, because everyone has access to relevant information, and acts on that information rationally. Factor investing was the first modern attempt to attain alpha!
Factor investing challenged the conventional approach. However, while effective, it’s not without its challenges. Initially, this investing strategy was meant to break the investing mould. Today, the market is just as blended as ever. So many are now following a factor-based approach that we are back to many people saying ‘it’s harder than ever to outperform the market’.
This article will take a look at the challenges that modern factor investing faces, and how it can overcome them in the future. Deep factor frameworks could be the solution to the increasingly homogenized stock market.
The sustained growth in passive investing strategies means that all investment managers and advisors are using and analysing the same factors. In part, this is why passive investing is so popular. The risk is relatively low and returns are sustainable. Breaking the mould has always been challenging.
This shared approach means that all investors and advisors conduct the same analysis, gather the same takeaways and form the same conclusions. As a result, there is a “race to the middle”, in the current investment market.
In fact, critics have gone as far as to say that investing in funds that track an index can only underperform the market, considering the resulting costs. Active managers can make smarter decisions that drive increased returns, versus the rest of the crowd.
Needless to say, it’s effectively impossible to out perform when everyone is doing the same thing. For true alpha performance and increased returns, it’s time to try something new.
In the past, the premise of factor investing was a fairly simple one. There were key factors that would impact investment decisions. These included factors like value and variation in earnings.
However, digitization has taken over the world, and with it comes an influx of data. There has been an explosion in additional factors, due to the continued increase of available market data.
Companies are now publishing more information than ever before, meaning that there are more individual components that could contribute to investment decisions and market movement. Company information, combined with third-party data, has made big data a key driving force in investment success.
A rational person has to understand every single element or important factor before making critical investment decisions. That’s just not possible with the volume of data that is now readily available on the market. Investors and advisors cannot do their day job, alongside carrying out deep big data analysis.
So, how do you make sure you’re making the most of the available information, when you only have the same 24 hours in a day as everyone else?
At 3AI, we’re pioneers in the Deep Factor Framework. We believe that this is the key to truly innovative investing for maximum returns. But first, we need to understand what Artificial Intelligence (AI) is, and how it works for the finance sector.
We know that data is the fuel on which AI runs. It needs massive amounts of data to provide statistically significant results, regardless of the use case. Therefore, massive amounts of data are needed to train a Machine Learning (ML) algorithm. Thankfully, the investment industry can provide that data.
As mentioned, investors are facing considerable challenges in managing big data. A person alone cannot decipher each individual data point to find meaningful reasoning. AI and ML algorithms are well suited to the investing industry, where there is no shortage of historical data to train the system.
Not only is access to historical data ideal for ML training purposes, but it’s also particularly critical for AI and ML testing. Ultimately, we can test a system's forecast ability by running historical data and comparing AI conclusions with actual historical events.
However, it’s important to note that you cannot just give an AI solution access to large amounts of data and expect results. Not all data are relevant, you need curated vast amounts of data to train an AI system correctly.
Just as we are overwhelmed by the hordes of financial data available in the current market, AI can be too. The wrong data is just as inefficient as no data at all. Unstructured, random noise, as the data appears in its original form, is useless for AI tech.
This is why the best AI solutions in the investing space are built using the expertise of a data scientist, as well as AI developers. You need a fully rounded solution. Tech cannot perform on its own.
This is proven by the success, or rather lack of, of other AI solutions in this space. Too many try to give their systems all the available data, without considering which data is the right data for the technology. It’s understandable to think that maximum data, means maximum accuracy, but this isn’t always true for ML. Ultimately, overloading AI with random data leads to Black Box AI technology.
The best AI investment tools are building deep factor frameworks. This means training AI on hundreds of factors and thousands of stocks over thousands of cumulative years. In training AI and ML algorithms effectively, they can contextualize data, providing results that are bound in reasoning for investor use.
At 3AI, our system analyses based on 326 factors for 20,000 stocks. We provide our technology with over 1 million years of cumulative data, to effectively train the solution for efficient analysis.
So, you might be thinking, that sounds great, but does it really work? Other AI solutions have yet to prove fruitful enough for widespread integration, so why invest time into deep factor AI technology?
Our AI software, led by deep factor framework analysis, has repeatedly outperformed the MSCI Global Index since2001.
In the run up to 2022, our software was more bearish than many investors and analysts. This shows not only that it forecasts correctly, but that it can forecast downturns, as well as growth.
The truth is that deep factor frameworks, like ours, make better sense of the data overload, and harnesses that data to unlock investors’ potential. We’re keen to help you move away from passive investing and drive forward in the quest for alpha results.
If you’d like to learn more about our deep factor framework analysis and AI investment technology, or if you’re interested in a data sheet to dive into our performance, get in touch! We’re more than happy to weigh our performance against a variety of other indices.
Deep factor frameworks aren’t necessarily the answer to the future of investment on their own. After all, how can you trust a technology that analyses data, but isn’t transparent about it?
In the past, there has been a considerable lack of trust in AI technology. This comes down to either a simple fear of AI solutions, bred from some of the best that Hollywood has had to offer, or, a lack of trust in its abilities and the concern of following it blindly.
What we mean by this is that AI can assess and harness hordes of data in seconds. For the human user, that seems impossible. It’s difficult to understand and trust something that isn’t tenable.
This is why explainable AI technology, or XAI, is so critical for success in the financial sector. Transparency is the key to faith in all walks of life. The more we can understand, the more trust we are willing to put into something.
Explainable AI prioritizes transparency. This technology is about utilizing AI to achieve results, while making the data and decision-making accessible to users. The basic premise is that end-users are more willing to act on forecasts if they can understand the reasoning.
This is where deep factor frameworks contribute to the explainability of AI in the investments industry. The best AI technology will use the accessible data to make forecasts and recommendations, while also providing clarity about its processes.
3AI’s investment solution identifies the exact frameworks, of the 326 available, that the AI used to reach a conclusion and then shows the user which factors led to the result. This allows investors to make informed decisions and ask questions about each factor before taking any kind of investment risk.
At 3AI, we didn’t set out to become an AI company. The company was born out of an ambition to understand the markets and how they work.
We found that AI technology was the best way to achieve this. Our deep factor framework allows us to translate big data into tenable and actionable forecasts and predictions.
We want to empower investors to outperform, and achieve positive alpha returns. Our priority is to provide AI solutions that can be adopted by investors without fear.
Keen to learn more about this innovation in the financial sector? Get in touch with us today!
Do we have more figures we have used the MSCI a lot?