How do we achieve 18% annual returns with AI-driven portfolio management?

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How do we achieve 18% annual returns with AI-driven portfolio management?

At Analytical Platform, we are well aware of the benefits that AI-powered investment strategies can bring. In the past 3 years, our Interactive Brokers portfolio based on stocks from the S&P 100 index has gained 76%, with a staggering 18.4% annual returns.

With the ability to process vast amounts of data and identify trends that humans might miss, AI systems are quickly becoming a powerful tool in the world of investing. I shared a bit more on the topic in a previous article describing Stock Analysis with The Power of AI. But as with any tool, there are risks involved. Without proper oversight, AI-powered investing can lead to algorithmic bias and other issues that can harm investors. In this article, I will explore the importance of human oversight in AI-driven investing and how it can help to mitigate risks.

Investment Management

Overall, investment management requires a solid understanding of finance, statistics, technology, and data management. If you decide to do your own analysis, please, please do it properly. Set a schedule, where you review and rebalance your portfolio so you catch possible problems on time. No analysis is valid forever. Find a time to set up a constant surveilling and monitoring of your investments. Unexpected events happen and you can lose a lot of money because you were preoccupied elsewhere. Set up a news feed regarding your investment, so you won’t miss important information.

Investment management is not a “set it and forget it” solution.

Factor investing

When constructing a portfolio, we rely on multi-factor asset pricing models based on the idea that an asset’s returns can be predicted using the relationship between the asset’s expected return and a number of micro and macroeconomic variables (factors) that capture systematic risk. It is a useful tool to identify securities that may be temporarily mispriced before the market eventually corrects and securities move back to their fair value.

Portfolio construction

Portfolio construction in multi-factor analysis involves combining the factor scores or weights obtained from the factor analysis to determine the allocation of capital across individual securities or portfolios.

Depending on your investment strategy and requirements, here are some key considerations for portfolio construction:

  • Weighting scheme: Determine the weighting scheme to combine the factor scores. The choice of weighting scheme depends on your investment objectives, risk appetite, and beliefs about the efficacy of each factor. Some common weighting schemes include:
    • Equal weighting: Assign equal weights to each security or portfolio in the investment universe. This approach assumes that each factor contributes equally to the portfolio’s performance.
    • Risk-based weighting: Assign weights based on the risk contribution of each security or portfolio. This can be achieved by considering the factor volatility or covariance matrix and allocating more weight to securities or portfolios with lower risk contributions.
    • Optimization: Use mathematical optimization techniques to maximize the portfolio’s expected returns while considering factor exposures, risk constraints, and any other specific requirements. Optimization can help find an optimal balance between factors and diversification.
  • Constraints and considerations: Incorporate any additional constraints or considerations in the portfolio construction process. These may include:
    • Position limits: Set limits on the maximum or minimum allocation to individual securities or portfolios to manage concentration risk or maintain desired exposures.
    • Sector neutrality: Ensure the portfolio is sector-neutral or has controlled sector exposures to avoid unintended biases.
    • Transaction costs: Account for transaction costs associated with buying or selling securities, which can impact portfolio performance.
    • Liquidity considerations: Take into account the liquidity of securities when constructing the portfolio to ensure the practical execution of trades.

  • Rebalancing: Determine the frequency and methodology for portfolio rebalancing. Regularly monitor the performance of the multi-factor portfolio and adjust the weights as needed to maintain desired factor exposures or adapt to changes in market conditions. Rebalancing can be done on a fixed time interval or triggered by specific events or thresholds.
  • Risk management: Consider risk management techniques to control the overall risk of the portfolio. This may involve setting risk limits, incorporating risk models, or implementing stop-loss mechanisms to mitigate downside risk.
  • Backtesting and Simulation: Prior to implementation, perform extensive backtesting and simulation of the constructed portfolio. Evaluate the historical performance of the portfolio using relevant metrics, analyze factor exposures and their contribution to returns, and assess risk characteristics. This helps gauge the effectiveness of the multi-factor portfolio construction methodology.
AI Investment Management

One of the key advantages of investing with AI is the ability to automate many of the tedious tasks involved in managing a portfolio. AI systems can automatically do factor analysis, and factor weighting, take into account considerations and limits, and rebalance portfolios.

This saves investors time and allows them to focus on other aspects of their lives while still ensuring that their investments are being managed effectively. 

All of these benefits are being applied in a growing number of AI-powered investment funds in order to focus on the performance side rather than on the management side of the fund. Such funds are then able to provide better performance, while still keeping management costs down.

Oversight in AI Investing

To mitigate the risks associated with AI-powered investing, it is essential to implement proper oversight. This can be done in a number of ways, including the use of human analysts to review investment decisions made by AI systems, the use of algorithms to detect and correct algorithmic bias, and the use of security measures to protect against hacking and other security threats.

One of the key tools for implementing oversight in AI-powered investing is machine learning explainability. This is the ability to understand how an AI system arrived at a particular decision. By understanding how an AI system makes decisions, analysts can identify potential biases and take steps to correct them.

How to make sure AI is working correctly

One of the great tools we are using is part of backtesting when we “roll back time” and do walk-forward test on various time intervals. Based on the multi-factor analysis our model determines those assets that outperform the market and those that underperform. Then we split them into 5 groups from top performing to the least performing and simulated their investment on real market data. If our top group has the highest returns and the bottom group has the lowest returns,  our model is able to successfully find assets that outperform the market as well as those that underperform. We continue with this testing regularly, to make sure our AI is still working correctly.

AI Investment Strategies – Diversification and Risk Management

One of the key strategies for successful AI-powered investing is diversification. By investing in a range of different stocks and other investment opportunities, investors can spread their risk and potentially earn higher returns. Additionally, by using AI-powered systems to manage their portfolios, investors can identify opportunities to rebalance their portfolios and take advantage of market trends.

Another important strategy for successful AI-powered investing is risk management. By using machine learning algorithms to identify potential risks, investors can take steps to mitigate these risks and protect their investments. This might involve identifying potential market trends that could harm an investor’s portfolio and taking steps to adjust their investments accordingly.

AI investment management in action

At Analytical Platform we have taken the AI path, where we try to automate everything. As a result, we are producing AI-driven strategies that outperform the market. We can easily adjust strategies based on diversification requirements and accepted risk profiles, while still focusing on the outperformance of portfolios rather than their management. 

You can use our existing S&P 100 strategy for your own investment to earn up to 18% annually.

You can also use our AI to create your own custom strategy.

In any case, Contact us today to start with smart algorithmic investing.