Can AI Outperform Human Advisors?

Have you ever wondered what AI (Artificial Intelligence) can’t do? Will it eventually replace our jobs? Will it get to the point that it becomes a danger to our survival?

Whatever happens in our near future, and longer term, AI is already changing the way we do things.

I feel any writeup about AI becomes obsolete in no time. It’s virtually impossible to write AI evergreen content as it changes on a daily basis.

Regarding our investments, a serious question can be asked:

Is ChatGPT, or similar AI models, already better than us at stock picking? Can AI beat experienced traders at their own game?

In many instances, YES. Just like AI is already better and faster than us humans in performing many other tasks.

Why AI Might Be Your Best Financial Advisor

Investing in the stock market has always been both an art and a science. For decades, financial advisors have been the go-to experts for those looking to grow their wealth through smart stock picks. Advances in Artificial Intelligence (AI) have been revolutionizing the way we look at investing.

The Evolution of Stock Picking

Traditionally, stock picking relied heavily on fundamental analysis, technical analysis and a deep understanding of market trends and economic indicators. Human advisors have used their experience and intuition to make informed decisions.

The Rise of AI in Stock Picking

AI is fundamentally changing the game by bringing in advanced data processing capabilities and sophisticated algorithms that can analyze vast amounts of data far more efficiently than any human. This is why it might be superior to traditional methods.

I won't cover what AI is exactly and how it works. Books are written about concepts such as machine learning (ML), natural language processing (NLP), and deep learning techniques, both supervised and unsupervised learning.

Instead we'll go straight to some reasons why AI might be a better stock picker than most human advisors:

Speed and Efficiency

AI can process and analyze vast amounts of data in a fraction of the time it would take a human. This speed allows AI to react to market changes and news events almost instantaneously, providing real-time insights and recommendations.

Elimination of Human Bias

Human advisors are susceptible to biases, such as overconfidence, confirmation bias, and emotional decision-making. AI, on the other hand, makes decisions based purely on data and algorithms, thus eliminating these biases and providing more objective and consistent recommendations.

Continuous Learning and Adaptation

AI systems continuously learn and adapt from new data. As markets evolve and new information becomes available, AI algorithms update their models to reflect the latest trends and patterns. This continuous learning process ensures that AI stays relevant and accurate over time.

Ability to Handle Complexity

Financial markets are complex. We all know that as they're influenced by a myriad of factors. AI's ability to analyze multiple data sources and identify intricate relationships allows it to handle this complexity better than human advisors, same ones who may struggle to process and interpret vast amounts of information simultaneously. (Take chess for example. Machines have exceeded the world's best players).

Cost-Effectiveness

Employing a human advisor can be expensive due to management fees, commissions, and other charges. AI-driven investment platforms, referred to as robo-advisors, offer cost-effective solutions with lower fees, making them accessible to a broader range of investors.

Real-World Applications

There are already several examples of AI funds and strategies that have managed portfolios and have outperformed traditional human investment methods.

Robo-Advisors

Robo-advisors, as already mentioned, are automated platforms that use AI algorithms to manage and optimize investment portfolios. Companies like Betterment, Wealthfront, and Robinhood offer robo-advisory services that provide personalized investment advice based on individual risk tolerance, goals, and preferences.

For instance, Wealthfront uses AI to provide tax-loss harvesting, risk parity, and other advanced strategies that were previously available only to wealthy individuals with access to high-end financial advisors. As of 2023, robo-advisors managed over $1 trillion in assets, highlighting their growing popularity and effectiveness.

Hedge Funds and AI

Several hedge funds have embraced AI to gain a competitive edge. Renaissance Technologies, a pioneer in the field, has been using machine learning models for decades. Their Medallion Fund, one of the most successful hedge funds in history, has achieved annual returns of over 35% net of fees since its inception in 1988.

Similarly, BlackRock, the world's largest asset manager, employs AI to analyze data and make investment recommendations for its clients.

Another notable example is Two Sigma, a hedge fund that leverages AI and big data to drive its investment strategies. Two Sigma's AI models analyze a vast array of data sources, from satellite images to social media trends, in order to predict market movements. This data-driven approach has enabled Two Sigma to outperform many traditional hedge funds consistently.

AI-Powered Research Tools

Several companies offer AI-powered research tools that provide insights and recommendations for individual investors. Platforms like AlphaSense, Kavout, and Zacks Investment Research use AI to analyze financial data, news, and market sentiment to generate stock picks and investment strategies.

Sentiment Analysis Tools

Sentiment analysis tools use NLP to gauge market sentiment from news articles, social media posts, and other textual data. These tools can provide valuable insights into how the market perceives a particular stock or sector. Companies like MarketPsych and StockTwits offer sentiment analysis tools that help investors make informed decisions.

Case Studies: AI vs. Human Advisors

To better understand AI's potential in stock picking, here are a few case studies comparing AI-driven investment strategies with traditional human advisors.

1. Kensho

Kensho, an AI-powered analytics platform, analyzed historical data and identified patterns (notably during the 2016 Brexit movement) that many human analysts overlooked.

2: IBM Watson

IBM Watson, a cognitive computing platform, can analyze unstructured data, such as news articles and social media posts, to gauge market sentiment and predict stock performance. In a trial run, Watson's stock picks outperformed those of human advisors.

3: Numerai

Numerai is a hedge fund that crowdsources AI models from data scientists worldwide. Participants build predictive models using encrypted data, and Numerai combines these models to make investment decisions and has resulted in impressive returns, demonstrating the power of AI and collective intelligence in stock picking.

4: Kavout

AI-driven stock picking has also made waves in individual stock recommendations. Back in 2020, the AI platform Kavout developed Kai, an AI stock selection tool that analyzed millions of data points to identify promising stocks. As it turned out, Kai's stock picks outperformed the S&P 500 by a significant margin.

5: Deloitte

A study by Deloitte found that AI-driven portfolios outperformed human-managed portfolios by an average of 8% annually over a five-year period. The study attributed this outperformance to AI's ability to process and analyze data more effectively, as well as its lack of cognitive biases.

Challenges and Limitations of AI in Stock Picking

While AI holds great promise in revolutionizing stock picking, it's not without its challenges and limitations. Understanding these drawbacks is crucial for investors considering AI-driven investment strategies.

Data Quality and Availability

AI algorithms rely on high-quality, accurate data to make predictions. Inaccurate or incomplete data can lead to erroneous predictions and investment losses. Ensuring data quality and availability is a significant challenge for AI-driven stock picking.

Overfitting

Overfitting occurs when an AI model is too closely tailored to historical data, capturing noise rather than underlying patterns. Overfitted models may perform well on historical data but fail to generalize to new, unseen data. This can lead to poor investment decisions when market conditions change.

Lack of Interpretability

AI models, particularly deep learning algorithms, can be complex and challenging to interpret. Investors may find it difficult to understand how AI arrived at a particular recommendation, leading to a lack of trust in AI-driven decisions. Enhancing the interpretability of AI models is an ongoing area of research.

Market Unpredictability

Financial markets are inherently unpredictable and influenced by numerous factors, including geopolitical events, economic indicators, and investor sentiment. While AI can analyze historical data and identify patterns, it cannot predict future events with certainty. Unexpected market shocks can still lead to significant investment losses.

So What Does The Future Hold?

According to a World Economic Forum report (yeah, that WEF organization), by 2025, over 75% of asset management firms are expected to incorporate AI and machine learning into their investment processes. This growing adoption rate further validates AI's potential to enhance investment performance.

Another survey, this time conducted by Ernst & Young revealed that 62% of investors believe AI will play a significant role in investment advisory in the next decade. Furthermore, 55% of respondents indicated that they would trust AI-driven recommendations as much as, or more than, human advisors.

The future of AI in stock picking will likely involve greater integration with other advanced technologies. For instance, quantum computing, which promises to revolutionize data processing speeds, could further enhance AI's capabilities. Additionally, blockchain technology could provide more transparent and secure data sources for AI models.

Ethical and Regulatory Considerations

As AI becomes more prevalent in financial markets, ethical and regulatory considerations will come to the forefront. Issues such as data privacy, algorithmic transparency, and accountability will need to be addressed to ensure that AI-driven investment strategies are fair and equitable.

Human-AI Collaboration

While AI has the potential to outperform human advisors in many areas, the future of investment advisory will most likely involve a hybrid approach. By collaborating with AI, human advisors can leverage its strengths while adding their unique value.

Conclusion

The rise of AI in stock picking represents a paradigm shift in the financial industry.

Of course, AI is not a magic wand and it has its own limitations. AI models can be overfit to past data, have blind spots, and make mistakes just like humans. They also require high-quality training data and careful monitoring. As a result, the future is likely to be a partnership between human and artificial intelligence. Humans will need to provide oversight, domain expertise, and qualitative judgment - while leveraging AI's quantitative muscle to augment and enhance their capabilities.

Whether you're an investor or not, the rise of AI in finance is a fascinating trend that will likely have major ramifications in the years ahead. Algorithms may soon manage the majority of the world's wealth. So keep an eye on this space as AI continues to reshape investing and the stock market.

Inspiration and full credit for this topic goes to the amazing Ruben Hassid

https://www.dhirubhai.net/posts/ruben-hassid_you-think-chatgpt-is-stupid-well-its-now-activity-7200806750814396417-htwO

Please check out my new YT channel: https://www.youtube.com/@jeremybenisrael?sub_confirmation=1?

Disclaimer:

I encourage you to approach this writeup with an open mind and see it as a resource for further learning. As always, please seek guidance from a licensed financial expert for any investment decisions or tax-related matters you may have. Or just go straight to AI!! ??

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