Using machine learning to predict market volatility
The financial markets never cease to amaze us with their complexity and dynamism. As we've observed from our work at Permutable AI, predicting market volatility has long been the holy grail of trading – and for good reason. Let's explore why this matters more than ever in today's rapidly evolving financial landscape.
Why traditional methods are no longer enough
Much of the world takes for granted the sophisticated tools we now have at our disposal. Indeed, the traditional approaches to predicting market volatility – heavily reliant on historical data and human intuition – served us well for many years. But while these methods laid the groundwork for modern trading, they're increasingly insufficient for today's complex and highly volatile markets.
The trouble with this was clear: human analysts, no matter how skilled, simply cannot process the vast amounts of data that modern markets generate. This is the result of both the speed and complexity of today's trading environment. It has emerged that we need something more sophisticated, more responsive, and more comprehensive.
The machine learning revolution in trading
Here's what we've learned from our own experience: machine learning isn't just another tool in the trader's arsenal – it's a fundamental shift in how we approach market analysis. Take a look at the remarkable way these systems - which is what our flagship Trading Co-Trading is built upon - can process multiple data streams simultaneously:
A cynic might observe that we've heard similar promises before. But now, though, the technology has finally caught up with the vision. And it is this, combined with our deep understanding of trading needs, that led us to develop our Trading Co-Pilot , giving every trader access to world-class news and current affairs analysis and the ability to process high-volume of live data for optimal decision making.
We live in unprecedented times
Pressing global themes such as geopolitical, economic and physical weather events are impacting global trading across the board. Let's take a closer look at the issues at play here.
High-volume and fast moving data is causing buyers to make suboptimal decisions with affected global trading flows expected to be around a staggering $7 trillion per day. Meanwhile, unprecedented rates of change create high volatility in markets and make trading hard and risky.
It's perhaps obvious to say that good trading decisions rely on insight and analysis drawn from vast quantities of news and current affairs data, but this is the crux of things. So then you might ask, what happens f most trading houses do not have access to teams of analysts that can provide these insights precisely when they are needed?
This is why we created the Trading Co-Pilot
And so it is this reason that we turned our attention - and technology - to creating practical solutions for real traders. Through our conversations with traders and institutions, we discovered that the challenge wasn't just about predicting market volatility – it was about making those predictions actionable in real-time whilst reducing the noise in decision making.
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The approach to solving this problem had to be different. Our Trading Co-Pilot emerged from a simple observation: traders need more than just data – they need contextual intelligence that helps them make better decisions faster.
And so we built our Trading Co-Pilot which searches through a high-volume of live data cutting out 80-90% of noise, summarising information into story trends. We built a cutting-edge tool that provides insights and impact analysis, saving user 90% of time by reading and analysing all of today’s news and historical context, providing a real-time market view.
And the feedback from the sector we've had so far is that it's exactly what's needed.
Real-world impact
It's natural that some might question whether machine learning can truly make a difference in trading outcomes. Here's what we've observed from our early clients as well as through trading our own baskets internally:
Bear in mind that this isn't about replacing human judgment – it's about augmenting it. The picture we are seeking to paint here is one of harmony between human expertise and technological capability.
Looking ahead
And what of the future? It's a remarkable time with thrilling possibilities ahead. Of course, many of us in the financial technology sector are working to push the boundaries of what's possible. At Permutable, we're particularly excited about:
Much of the world takes for granted the complexity of financial markets, but we at Permutable are constantly reminded of it's incredible potential as we continue to refine and enhance our Trading Co-Pilot as a tool for navigating and indeed, predicting market volatility.
Whether you're a seasoned trader or new to the field, we believe that better tools for predicting market volatility can help create more efficient, more stable markets for everyone. What are your thoughts on the future of market volatility prediction? We'd love to hear your perspectives and experiences in the comments below.
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