Portfolio Management: All The Ways AI Is Transforming Modern Asset Strategiesby@kustarev
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34,759 reads

Portfolio Management: All The Ways AI Is Transforming Modern Asset Strategies

by Andrey KustarevApril 25th, 2024
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The rise of AI has significantly impacted various industries, and the finance industry is among those most affected. In recent decades, AI has been implemented in different sectors of the finance industry. In the back office, ML algorithms are used to find anomalies in execution logs, detect suspicious transactions, and manage risks, leading to increased efficiency and security. In the front office, AI helps segment customers, automate customer support processes, and optimize derivatives pricing. However, the most intriguing aspect is AI's capabilities for the buy-side of finance — identifying predictive signals amid market noise by analyzing significant amounts of data as quickly as possible. Fields of application for AI include portfolio optimization, fundamental analysis, textual analysis, trading activities, investment advisory services, risk management, etc. Examples of implemented techniques and tools are machine learning algorithms, natural language processing, quantitative trading strategies, and explainable AI (XAI), among others.
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The rise of AI obviously has impacted various industries, and the finance industry is among the ones that have been impacted the most. For example, the public launch of models like GPT-3.5 last year has increased interest in utilizing AI to help increase fund managers' abilities in analysis, risk management, and decision-making.

Thus, AI tools are implemented to make market assessments more accurate and manage risks more effectively. Portfolio managers are expected to make a clearer assessment of the market movements, narrow down on appropriate investment choices, and manage risks when they apply machine learning algorithms, natural language processing, and artificial intelligence tools in their trading.

The integration of machine learning algorithms, as well as natural language processing tools into key players' trading strategies, helps them to increase the efficiency of these processes and gain a competitive advantage with faster and more accurate investment decisions and predictive analytics.

In the last decades, AI has been implemented in different sectors of the finance industry. In the back office, ML algorithms are used to find anomalies in execution logs, detect suspicious transactions, as well as manage risks, leading to increased efficiency and security. In the front office, AI is helping to segment customers, automate customer support processes, and optimize derivatives pricing.

However, the most intriguing part of it is the AI capabilities for the buy-side of finance - identifying predictive signals amid market noise by analyzing significant amounts of data as fast as possible. For example, such applications might include time series forecasting, segmenting markets, and of course, managing asset portfolios. AI's opportunities to process and analyze vast datasets help to find subtle patterns that traditional methods will probably miss.

Portfolio optimization has been a common practice for several decades, evolving significantly under the development of data science and the implementation of advanced computational techniques. Сlassical approaches, such as Markowitz's Modern Portfolio Theory (1952) and Capital Asset Pricing Model (1964) were introduced more than 50 years ago but still remain relevant. However, their limitations in handling non-linear risk and dependence on historical data are becoming more and more obvious by the day.

Practices like risk modeling, scenario analysis, and quant trading, implemented widely by key players, such as Renaissance Technologies, DE Shaw, and Two Sigma Investments have led to the implementation of more complex and advanced algorithms. In addition, the industry has been highly impacted by AI in recent years, as machine learning and artificial intelligence have made predictive analytics more accurate, and did the same to personalized investment strategies and automated complex decision-making processes.

This AI-driven transformation has enabled portfolio managers to process vast arrays of data in real-time and solve the three main challenges:

  • Scalability: Managing and analyzing large-scale data from multiple assets and global markets is now more easy to do.

  • Complex Decision-Making: AI can “keep in mind” more factors, including psychological and behavioral analytics, in decision-making processes.

  • Adaptability: AI systems can learn non-stop and adapt to new market conditions, helping managers to quickly adjust strategies.

Source: Global Market Insights

According to Global Market Insights, AI in the Asset Management market was valued at USD 2.5 billion and is expected to grow at a CAGR of 24% over the next 10 years. Interestingly, Portfolio Optimization leads in Global market segmentation by application, followed by Data Analysis, accounting for 25% of the market share.

Increasing adoption and investment in asset management solutions powered by AI and highlighting the practical use of AI in portfolio optimization.

Source: Global Market Insights

AI Adoption in Portfolio Management:

AI adoption within the asset management industry is not a new trend; it has seen growth in recent years but is still limited to a small number of market players namely hedge funds, quantitative management offices, large research departments, and financial institutions using IT services.

There are many fields of application for AI already:

Portfolio Optimization

AI significantly betters the process of portfolio construction optimization. For instance, the classical approach of Markowitz's Modern Portfolio Theory, which relies on convex optimization concepts, serves as a precursor to contemporary AI-driven methodologies. The reason this foundational theory is so pivotal is that it forms the basis from which AI algorithms can further change and refine investment strategies.

Nowadays, AI expands upon this theory by exploring new dimensions of data and integrating advanced analytical techniques. This expanded data capability allows for more nuanced and informed decision-making - a practice that has been widely used in the industry.

Fundamental Analysis

Certain AI techniques are perfectly compatible with quantitative management, using large volumes of data about company fundamentals, the macroeconomic environment, or market conditions. Machine learning algorithms can find complex non-linear relationships between different variables and, of course, detect trends that analysts can’t.

Textual Analysis

Textual analysis is another application of AI in fundamental analysis. Using natural language processing (NLP), AI processes and analyzes textual sources such as corporate earnings reports, central bank press releases, and financial news. Through NLP, AI can extract economically and financially important information from this unstructured data. By doing so, it provides a quantitative and systematic measure that improves and helps human interpretations.

Trading Activities

AI's powers are extremely useful in trading, where the complexity of transactions and the need for speed are at a balance. AI supports algorithmic trading by automating many stages of the process, improving the efficiency of transactions managed in financial markets.

Investment Advisory Services

AI has opened an opportunity for a wider offering of personalized investment advisory services at a lower cost. These systems use complex algorithms to process real-time market data, coming up with the most suitable strategies for individual client needs based on their return objectives and risk profiles.

Risk Management

In risk management, AI assists by modeling various 'likely but undesirable' scenarios, which in turn, enhance traditional practices that focus only on mostly probable outcomes.

Artificial Intelligence (AI) Techniques and Tools in Portfolio Management

Machine Learning Algorithms:

Classical Machine Learning methods are still very popular in Portfolio Management, and they are: Linear Models, including Ordinary Least Squares, Ridge Regression, and Lasso Regression. These are frequently combined with the Mean-Variance Optimization procedure and matrix decomposition techniques such as Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), which are foundational in understanding asset relationships and optimizing portfolio allocations.

Situated between these classical approaches and more modern methods are Support Vector Machines (SVMs). Although SVMs are used in practice, they are not as commonly deployed but play a significant role, particularly, in classification tasks aimed at forecasting stock performance.

These tasks usually include predicting whether a stock will experience a profit or a loss, using historical financial data including stock price fluctuations and trading volumes to put assets into categories and forecast their performance.

Talking about more modern methods, neural networks show major advancements in machine learning for portfolio management and offer improved capabilities for modeling complex non-linear patterns that are difficult to capture with traditional models. Besides neural networks, other classical approaches such as supervised and unsupervised learning further improve and refine data analysis, making the discovery and exploitation of subtle market signals possible.

Newer approaches, such as Reinforcement Learning and Deep Q-Learning bring these qualities into fast-paced decision-making environments, where portfolios can be adjusted in real-time to optimize financial outcomes based on the system learning from market feedback.

Natural Language Processing (NLP):

Natural Language Processing techniques like sentiment analysis can help pick and choose common opinions from things like newspaper articles, social media posts, and analyst reports. Additionally, portfolio managers can also analyze the language used in financial media, including firms' earnings reports, to feel out investor sentiment and predict market movements, all of which are crucial info in the decision-making process.

Quantitative Trading Strategies:

Firms that specialize in high-frequency trading (HFT), like those that employ AI-powered quantitative trading algorithms, make money on inefficiencies that occur just for a moment in the market. These firms use machine learning technologies to analyze relevant market information at extremely high speeds and place orders with precision timing for as short as a millisecond.

Such rapid execution allows them to benefit from arbitrage opportunities and maximize profits by taking action on price discrepancies faster than competitors. While Renaissance Technologies is known for its quantitative trading approaches, it is important to keep in mind its broader strategy encompassing various holding periods from traditional HFT practices, which are mainly focused on speed.

Explainable AI (XAI):

LIME (Local Interpretable Model-agnostic Explanations) is a prominent XAI method used to make the outputs of complex machine learning models more understandable. In portfolio management, this method can be very valuable for interpreting how black-box models make predictions. By using input data and analyzing the impact on model outputs, LIME helps portfolio managers and data scientists define which features influence investment decisions more than others.

This process helps enhance the transparency of AI-boosted decisions and supports efforts to verify and improve how easy to understand these models can be. However, while LIME improves our understanding of model behavior, assessing the overall reliability of the models involves additional validation techniques.

AI in Compliance and Monitoring:

AI tech plays a major role in ensuring compliance with regulatory frameworks and monitoring investment restrictions within the financial industry. By automating these processes, AI systems help financial firms stick to legal standards more efficiently, more accurately, and not get into trouble. This technology is very valuable in monitoring compliance across large volumes of transactions and diverse portfolio activities, where it can quickly (instantly, in fact) identify deviations from regulatory requirements or internal guidelines.

Moreover, the use of AI minimizes the risk of human error, which is crucial in high-stakes regulatory environments where mistakes can lead to legal and financial consequences.

Portfolio Rebalancing:

AI applications in automated rebalancing are crucial for maintaining the ideal asset allocations over time. They can adjust portfolios in response to market changes or shifts in an investor’s risk profile, which ensures alignment with strategic investment goals.

On a Broader View

In addition to applications that are specifically designed for investment, the potential for the development of artificial intelligence inside the asset management business appears to be extensive. However, despite the fact that we instinctively see the possibility of automating specific jobs at various stages of the operational chain, it is still difficult to fully anticipate the disruptive power of artificial intelligence. This is because AI is expected to give rise to new sectors of application as additional advances are developed.

We must be mindful of the limitations of artificial intelligence as well as the hazards that it poses for some aspects of portfolio management, despite the fact that it has made it possible for technological advancements and productivity gains using artificial intelligence. In the first place, artificial intelligence and machine learning approaches rely on data that is used to feed the learning algorithms.

It’s necessary that this data is of high quality in terms of updates, accuracy, completeness, and representativeness.

In addition to the requirement for a very large volume of data, which is not always available, it is the case that this data must be of good quality. In any other case, the findings that are obtained using predictive models are not reliable or resilient.

Moreover, the algorithms can also make false assumptions by picking out irrelevant trends from the dataset that is analyzed, which may lead to erroneous conclusions. This may result in gross-scale grabbing, jumps that are too sharp, and the tiniest possible crashes. Loss of market competition may happen due to the fact that many market operators managing the same AI algorithms could commit the wrong decision simultaneously or react in a similar way to a real-time circumstance. Such a risk could become fatal.

Despite the potential benefits of AI in portfolio management, as in any field, there are plenty of challenges that we have to keep in mind and eventually – address. One of the main difficulties is the possible lack of transparency and interpretation issues of AI models, which can make it challenging for managers to explain the results of their collaboration with AI. This use complexity may be one of the reasons why the adoption of AI in European funds is relatively low. As of September 2022, only 65 out of 22,000 funds based in the European Union claimed to use AI in their investment processes.

The European Financial Markets Authority (ESMA) has identified factors that may contribute to the low adoption rate, such as a lack of clear regulatory frameworks and AI skills among fund managers. However, the challenge of explaining AI outcomes because of model complexity may also be one of the factors justifying the low adoption rate. I guess we will find out with time.

At this point, it appears that artificial intelligence is still a long way from totally replacing real people in the asset management industry. That being said, transparency, a relationship of trust, and contact between clients and management experts continue to be crucial characteristics, now more than ever.

Yet, we can’t deny that artificial intelligence brings with it new and exciting tools that can be used in the value chain, and the potential of these tools could really change the way the industry looks today.