Abdul Hakeem is the managing director of Exp (Quant) Asia Pte Ltd (Singapore) and Exp (Quant) Sdn Bhd (Malaysia).
THE artificial intelligence (AI) symphony is cementing its presence in the ever-evolving world of investment.
The symphony, orchestrated by data-driven algorithms, has permeated every corner of data-driven domains, none more profound than the financial world.
As investors are drawn to dance to the rhythm of the orchestra, we are on the cusp of even more profound transformations in the world of investment.
Across the US, Europe, and major Asian capital markets, over 60% of trading volume flows through algorithmic trading — a phenomenon that promises unprecedented efficiency and sophistication.
While emerging economies like India and in Asia may see lower numbers of around 30 per cent, the impact of AI on global investment practices is undeniable.
The dawn of AI in investing marks a quantum leap — a moment of machine-learning algorithms transcending human limitations.
Investors now navigate vast datasets, unveiling intricate patterns and making decisions steeped in data-driven insights.
AI’s ability to process information in ways previously unimaginable empowers investors to respond swiftly to market dynamics and attain a nuanced understanding of risk and return dynamics.
The financial world anticipates its own AlphaGo moment, where AI’s ingenuity and creativity surpass human capabilities.
As we step into the era of Generative AI, capable of learning from unlabelled datasets, the possibilities are boundless.
With a touch of fine-tuning, these models can handle tasks ranging from text translation to medical image analysis. It is already evident that AI is being integrated into asset management, both in current applications and in emerging trends.
Affluent individuals now have access to wealth management thanks to robo-advisory platforms.
Investors now make informed, data-driven decisions tailored to their risk tolerance and investment goals.
Platforms like StashAway use advanced algorithms to create and manage diversified portfolios, offering a cost-effective and hands-off approach to wealth management.
This paradigm shift empowers individuals to navigate the complexities of the financial market with greater ease and efficiency.
Institutional investors are increasingly turning to AI tools to enhance investment decisions and develop advanced trading strategies.
Blackrock, a financial industry leader, employs Natural Language Processing and large language models to analyse vast amounts of textual data, including earnings call transcripts. The result is an earnings call model that consistently predicts an edge on post-earning market reactions.
By providing quantitative insights for institutional investors, AI allows them to make more informed and strategic decisions.
Morgan Stanley has embraced augmented intelligence to empower its financial advisers. Leveraging on Open AI’s GPT-4, the firm’s advisers access real-time data and research through a live chat format.
This innovation has significantly improved the speed and efficiency of client interactions. Financial advisers can now get immediate insights into market trends, stock outlooks, and investment strategies.
This transformation redefines the client-adviser relationship, setting new standards for real-time information access in the financial industry.
In order to understand the evolving investment landscape, it is essential to distinguish between algorithmic trading, quant trading, and artificial intelligence trading.
Algorithmic trading relies on predefined rules, quant trading involves mathematical models and statistical analyses, while AI trading encompasses a broader spectrum, leveraging machine learning for adaptive decision-making.
Quantitative investing, including forecasting, factor-based approaches and statistical arbitrage, has seen a resurgence with the integration of AI.
Machine-earning methodologies, such as recurrent neural networks and transformer models, have refined forecasting approaches, providing a more sophisticated understanding of market dynamics.
Factor-based investing, however, has faced scrutiny, partly due to external market influences, such as quantitative easing, which disrupts correlations between assets and factors.
Contrary to this, renowned firms like Renaissance Technologies and DE Shaw already employ machine learning to navigate volatile markets.
AI trading stands on the precipice of a new era, with the Transformer model as its foundation for a renewed approach to data sequences.
In addition to textual data, these models can also evaluate time-series financial data, identifying trends with an understanding of a security’s relationship to overall market trends.
Trading strategies based on AI models trained on extensive datasets are expected to be developed in the near future.
The AI revolution represents a quantum leap in efficiency, democratisation of wealth management, and transformative impact on institutional intelligence. They are all aligned with the pioneering spirit of quantitative finance.
Visionaries like Jim Simons of Renaissance Technologies, who recognised the potential of quantitative models in predicting financial markets three decades ago, would marvel at the current landscape.
Exp (Quant) is a quantitative trading algorithm research and development firm using state-of-the-art machine learning methodologies.
This article first appeared in Star Biz7 weekly edition.
