By Daniele Grassi, CEO and Co-Founder, Axyon AI
Did you know quantitative investment strategies generated $370 billion (£283bn) in trading volume in mid-2022?
It is hardly surprising that Morgan Stanley praised them as a “bright spot” in an otherwise lacklustre year, marked by the underperformance of traditional investment strategies due to geopolitical and economic turbulence.
Despite the effects of the recent turbulences on the economy, quantitative investing is a rapidly growing strategy that has demonstrated reliance by posting a healthy positive total return of 3.9%, according to the SG Alternative Risk Premia Index.
Today, quant investing offers a viable alternative to traditional investing by leveraging complex computer models, including AI-powered algorithms, to make trading decisions.
There is no doubt advancements in AI have transformed the development of quantitative models for investment strategies. In fact, research has shown that machine learning and AI-based quantitative strategies outperform traditional investments in enhanced efficiency. Additionally, these techniques offer a multitude of benefits such as rapidly processing large datasets, assistance with asset allocation, and robust risk management.
Given how these AI-based strategies are 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
A key characteristic of AI-powered algorithms is that they can process large volumes of data and uncover patterns that traditional strategies would overlook.
As such, AI-based algorithms can provide enhanced forecasting abilities through the development of highly complex models that can analyse market trends and whose accuracy increases once exposed to more data.
This transformative aspect of AI is a game-changer for investors and portfolio managers alike, allowing them to draw insights from high-volume data in real-time and make better, more informed investment decisions as a result.
Interested in quant investing at scale as a manager? Well, there is no better time to seize the potential of AI than now.
AI-powered quant investment models can be customised to adapt to various asset classes, geographic areas and portfolios. In contrast to traditional techniques, AI-powered investments are more reliable and effective when making large-scale investment decisions as they can accommodate diverse investment needs.
However, research indicates that in order to unlock the full scalability of AI-assisted quant investment, asset managers must understand two important factors: high-performance computing (HPC) systems and flexible system architecture. For this reason, it is important for managers in search of comprehensive AI-powered quant investment strategies to consider working alongside companies with a strong background in AI and HPC development.
In doing so, managers can confidently implement scalable quant strategies and elevate their investment strategies to new heights.
High computational speed
The era of slow-paced investing is a thing of the past. Managers and investors must now embrace the potential for lightning-quick opportunities using AI-powered quant models.
What sets these models apart is their ability to leverage numerous processors that collaborate to run sophisticated programs and rapidly draw insights from various datasets. Without sacrificing computational speed, they also harness the power of machine learning to enhance future accuracy and identify disruptions to fundamentals.
In volatile markets, these AI-powered algorithms prove invaluable by identifying and correcting for sudden changes in investor preferences and market conditions.
Improving risk management
Risk management is a critical aspect of any investment strategy, as it helps investors to mitigate potential losses and optimise returns. Through the power of AI, the development of more robust risk management strategies within quantitative models is made possible by identifying correlated exposures that may go unnoticed by traditional strategies and the human eye.
Additionally, managers can take solace in the fact that when properly developed, AI-based strategies show reduced susceptibility to human error. This is attributed to the inherent nature of algorithms that operate without the presence of biases and emotions that can inadvertently undermine investment decisions.
The future of quant investment strategies
According to Morgan Stanley, 80% of investment professionals think AI won’t replace human guidance. At Axyon AI, we share this sentiment, recognising that the future of investing isn’t about replacing people with AI, but about how we utilise AI-powered quant predictive tools alongside human logic and intuition.
By incorporating AI into our investment strategies, we can effectively mitigate human bias while remaining dynamic in response to evolving market conditions. As this technology continues to evolve, it won’t be long before we redefine the concept of ‘traditional investing’ to encompass the harmonious integration of AI and human expertise.
This paradigm shift offers investors the dual benefits of diversification and capital appreciation, serving as a beacon of hope in even the most challenging market
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