Written by Nicolas Huras

Artificial Intelligence (AI) is becoming an increasingly important part of asset management, and its impact on portfolio management is undeniable. From portfolio optimization and risk management to stock selection and asset allocation, AI and its applications are revolutionising the way asset managers construct portfolios. AI can provide portfolio managers with an edge by enabling faster and more accurate decisions, providing access to previously unavailable information, and enabling predictive models to be created.

1. The growth of AI

The use of AI in portfolio management can provide numerous benefits to asset managers:

  • It can improve the portfolio optimization process by providing more accurate estimates of returns and risk.
  • It can also help portfolio managers to better understand the underlying dynamics of the current market conditions, identify opportunities, and make decisions in real-time or close to real-time.
  • It is also capable of predicting market movements, enabling portfolio managers to adjust their strategies appropriately.

By using technological tools, asset managers can gain an edge on their competitors and improve or stabilise their returns. AI-enabled algorithms can be used to construct optimal portfolios, taking into account both risk, return and review tons of data a human would not be capable of conducting in time. These algorithms can also be used to identify undervalued stocks, allowing asset managers to capture so much thought after alpha.

Furthermore, AI can be used to identify trends and correlations, which can then be used to make more informed decisions. By utilizing AI and mostly generative AI, portfolio managers can quickly identify weaknesses in their processes and strategies, allowing them to adjust accordingly in order to improve their overall returns.

Credits Negative Space

2. Examples of AI applications in the investment landscape

Several leading financial institutions and fintech companies are leveraging this AI-powered technology to revolutionise the investment landscape. Robo-advisors, algorithmic trading systems, and AI-based financial planning tools are just a few examples of applications that are transforming the way investors manage their portfolios.

One example of a company using AI in portfolio management is BlackRock, which utilizes AI to improve its portfolio construction process, as well as to identify potential opportunities and manage risk. The firm’s portfolio optimization algorithms use AI to construct and analyse a range of portfolios, while the firm’s AI-enabled analytics Aladdin platform helps the firm to identify potential investment opportunities.

Another example is Bridgewater Associates, one of the world’s leading hedge funds. Bridgewater utilizes AI for stock selection, portfolio optimization, and risk management. The firm’s AI-driven algorithms have allowed it to achieve superior returns, while its AI-based analytics platform helps the firm to identify potential market trends and correlations.

3. Review of AI applications through the trade cycle

AI applications can be used throughout the trade cycle.

3.1 Data Collection and Aggregation

  • Data Sourcing: AI algorithms can automatically scrape multiple data sources, including news articles, social media sentiment, and financial reports, to gather relevant information.
  • Data Cleaning: Machine learning algorithms can clean and preprocess large datasets to ensure accuracy, consistency, and relevancy.

3.2 Data Analysis

  • Pattern Recognition: AI uses machine learning to identify patterns and correlations within large datasets that might not be obvious to human analysts.
  • Predictive Analytics: AI can analyze past data to predict future performance of assets.
  • Anomaly Detection: AI can spot outliers or anomalies in data that might suggest fraudulent activity or market manipulation.

3.3 Portfolio Construction

  • Asset Allocation: Machine learning models can help identify the optimal mix of investment assets based on a range of criteria, including risk tolerance, market conditions, and investment objectives.
  • Strategy Simulation: AI can simulate various investment strategies under multiple scenarios to test their robustness.

3.4 Risk Management

  • Risk Assessment: AI can provide a more nuanced view of risk based on multiple factors, often in real-time.
  • Stress Testing: Using AI, it’s possible to simulate extremely adverse market conditions to understand how they would affect a portfolio.

3.5 Trading

  • Algorithmic Trading: AI algorithms can execute trades at speeds and frequencies impossible for a human to achieve, often without any human intervention.
  • Cost Optimization: Algorithms can identify the optimal timing and trading venue to minimize the costs associated with buying or selling assets.

3.6 Client Interaction

  • Chatbots for Customer Service: AI-driven chatbots can handle routine queries, freeing up human advisors for more complex tasks.
  • Personalization: Machine learning algorithms can analyze a client’s past behavior, risk tolerance, and other personal factors to provide highly personalized investment advice.

3.7 Reporting

  • Automated Reporting: AI can generate detailed reports that analyze performance, risk, and other metrics, often in real-time.
  • Visual Analytics: Advanced AI-driven tools can turn complex data into intuitive visual representations, making it easier for asset managers to interpret.

3.8 Regulatory Compliance

  • Regulatory Reporting: AI can automate the process of generating and submitting reports required by regulatory bodies.
  • Audit Trails: Machine learning algorithms can automatically create detailed audit trails, reducing the chance of human error.

3.9 Continuous Learning

  • Adaptive Algorithms: AI systems can adapt to changing market conditions and learn from new data, improving their performance over time.

Credits Michelangelo Buonarroti

4. How Generative AI (and OpenAI) can revolutionize portfolio optimization?

The advancements in artificial intelligence (AI) and machine learning (ML), are now greater with the use of generative AI. In particular, OpenAI tools, such as GPT-4, are beginning to revolutionise the asset management industry by providing powerful new capabilities that can improve portfolio optimization.

One of the major advantages of these OpenAI tools is that they can generate much better insights about the market and the necessary trade-offs that come with portfolio optimization. GPT-4 specifically is capable of automatically obtaining patterns and relationships from large datasets in order to more accurately predict future price movements. This predictive capability can be leveraged to analyze the entire portfolio and to make decisions that are more informed and better aligned with the overall investment goals.

Those tools can help asset managers explore a greater range of portfolio optimization scenarios in order to identify the most profitable investment mix. By quickly testing different combinations of assets and parameters, asset managers can identify scenarios that maximize returns while minimizing risks. This allows for a more comprehensive and holistic approach to portfolio optimization.

An intriguing application of AI in this field is the use of Generative AI technologies, which allow generating new and valuable information based on the data they were fed.

Generative AI can be incredibly beneficial for portfolio optimisation. It can simulate various market scenarios based on historical economic data, company performances, political events, and many other variables. These simulations help develop robust investment strategies that can withstand different market conditions and ensure favourable returns. They enable investors to see the potential risk and reward in the future without the traditional risk of experiencing it in reality.

Credits Levart Photographer

For instance, GANs or Generative Adversarial Networks can be used to generate realistic, synthetic data that match the statistical properties of the real data. This synthetic data can then be used to test different investment strategies under multiple scenarios, without risking real money.

Reinforcement learning, a type of machine learning where an agent learns to make decisions by interacting with its environment, is another exciting AI technology used in portfolio management. In this case, the ‘environment’ is the financial market, and the ‘agent’ is the AI system making investment decisions. The agent learns from its mistakes and successes over time and continually refines its strategy to maximise the portfolio’s return.

A great example of this is the hedge fund named Numerai which uses AI and crowdsourcing algorithms to manage its portfolio. The fund hosts weekly tournaments where anonymous data scientists compete to create the best predictive models. Their models’ predictions are aggregated to create a meta-model, which the fund uses to trade in the market. This type of portfolio management tool has never been possible until the advent of AI.

5. The future of AI-powered portfolio optimisation is bright

With increasing computing power, availability of high-quality financial data, and advancements in AI algorithms, we will likely see more and more applications of AI in portfolio optimisation.

The ability of AI to identify complex patterns in vast datasets, make accurate predictions, and adapt to changing market conditions makes it a powerful tool for investors. Though it’s still in its early stages, the future of portfolio optimisation using artificial intelligence is bright and full of potential.

The AI-based portfolio optimisation is not without its challenges and to date cannot be taken for granted. Data quality is fundamental for the success of any AI system, and financial data is often messy and inconsistent. Moreover, while AI tools are stellar at recognizing and modelling patterns, they may sometimes fail to provide an accurate prognosis when the market behaves irregularly or unpredictably, such as during financial crises.

Nonetheless, AI is becoming an increasingly important part of the asset management landscape, and its application in portfolio management is revolutionising the way asset managers construct portfolios. As the use of AI in asset management continues to grow, it is clear that AI is providing asset managers with a significant added value in portfolio management.


This article does not contain investment advice or recommendations. Every investment and trading move involves risk, and readers should conduct their own research when making a decision.

The views, thoughts and opinions expressed here are the author’s alone and do not necessarily reflect or represent the views and opinions of the Swiss Finance + Technology Association.


Nicolas Huras started his career in the financial industry in the early 2000s and is a strong proponent of technical innovation in Asset Management and Circular Economy. A leader in asset management, he acts as independent advisor and board member.

Previously a senior director at UBS, responsible for EMEA and APAC activities of Fund Management and a fund platform, he is the Founder of a Venture Philanthropy Forum in Geneva.

Nicolas holds an MBA, Master of International Relations as well as various Executive diplomas from Harvard Business School, University of Cambridge and INSEAD. He is a regular lecturer and industry body executive committee member.