Unpacking the Shift From Traditional to AI-Based Quantitative Investment Strategies

Did you know quantitative investment strategies generated $370 billion in trading volume in mid-2022? It’s no wonder Morgan Stanley described them as a “bright spot” in an otherwise dull year marred by traditional investment strategies failing to deliver in the wake of global geopolitical and economic instability.

Today, quant investing offers a unique alternative to traditional investment strategies, backed by powerful machine learning tools and algorithms.

For example, advancements in AI have made it possible to develop powerful AI-based asset rank prediction and performance tools that enable better investment decisions than traditional methods. These tools also use substantial computing power to quickly process high volumes of data and can be scaled up or down. They also mitigate biases and excel in risk management against traditional investment strategies.

With these AI-based strategies now capturing the attention of investors worldwide, we look at four key reasons driving the shift to this alternative approach to investing.

High-Volume Data Processing

One of the biggest catalysts driving the shift from traditional to AI-assisted quant investing is high-volume data processing. AI-assisted quant investment tools use sophisticated algorithms and substantial computing power to process and analyse vast and complex datasets. This results in groundbreaking insights that would take far too long to uncover solely through human logic.

The ability to process high volumes of data using sophisticated AI algorithms opens a world of possibilities for portfolio managers. For instance, they can use AI to detect and exploit inefficiencies by dynamically adjusting asset weights. This results in AI-powered multi-asset tactical allocation that auto-adjusts to enhance portfolio performance.

Scalability

For managers interested in quant investing at scale, it’s time to harness the power of AI. AI-based quant investment models can be adjusted to match multiple asset classes, regions, or portfolios. This results in more consistent and efficient large-scale investment decisions compared to traditional investing.

That said, research shows that AI-assisted quant investing is only scalable if the model is built to accommodate high-performance computing (HPC) systems and operates on flexible system architecture. So, asset managers looking for large-scale AI-powered quant investment solutions should consider companies with a rich history of AI and HPC development.

High computational speed

Why settle for a slow pace to investing when you can capitalise on opportunities lightning-quick using highly efficient AI-powered quant models?

These models contain multiple processors that work in tandem to run complex programs and generate insights from diverse datasets at breakneck speeds. They also use machine learning to improve future accuracy and detect disruptions to fundamentals without compromising on computational speed.

This is why AI-powered prediction models are ideal for volatile markets. They forecast volatility in the blink of an eye by identifying and adjusting for sudden changes in investor preferences and market conditions.

Improved risk management

An AI-based approach can also excel in risk mitigation.

According to the IMF, machine learning tools facilitate risk prediction and management by accurately forecasting macroeconomic and financial variables. AI-based quant investing models take a similar approach – they use mathematical models and optimisation algorithms to assess risk factors, evaluate diversification opportunities, and optimise portfolio allocations based on predefined objectives and constraints.

This contrasts starkly with traditional quant investment strategies that are rigid in the face of changing market conditions and susceptible to bias.

Looking to the future

According to Morgan Stanley, 80% of investment professionals think AI won’t replace human guidance. At Axyon AI, we agree –  the future of investing isn’t about replacing us with AI, but about how we utilise AI-powered quant predictive tools alongside our logic and intuition.

AI minimises human bias and intervention while remaining dynamic in the face of changing market conditions. And as this technology continues to evolve at breakneck speed, it won’t be long before we start to rewrite the definition of ‘traditional investing’ to one which includes AI alongside humans.

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Daniele Grassi is CEO and Co-Founder, Axyon AI

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The views expressed in this article are those of the author and do not necessarily reflect the views of AlphaWeek or its publisher, The Sortino Group