How AI Generates Investment Signals – And the Role of Systematic Investing

How AI Generates Investment Signals

At the heart of AI-led investing is the ability to analyze enormous datasets and generate investment signals—indications of whether to buy, hold, or sell assets. AI models, particularly in the realm of asset management, rely on advanced techniques such as Time Series Forecasting (TSF), pattern recognition, and deep learning to identify trends, correlations, and anomalies in market data. These signals are then used to inform trading strategies.

Here’s how the process typically works:

1. Data Collection and Processing: AI models begin by collecting vast amounts of historical and real-time market data, including stock prices, economic indicators, trading volumes, and even news and social media sentiment. The quality and relevance of this data are crucial, as poor input can lead to unreliable signals.
2. Feature Engineering: The AI processes the raw data to extract meaningful features, such as trends, seasonality, volatility, and many more. These features help the AI understand the structure of the data and identify patterns that might signal future market movements.
3. Pattern Recognition and Forecasting: Based on the processed data, the AI model uses machine learning techniques such as Time Series Forecasting (TSF) or Reinforcement Learning to predict future price movements. The AI can identify patterns that would be difficult for human analysts to detect due to the sheer volume and complexity of the data.
4. Signal Generation: After analyzing the data, the AI generates investment signals. These signals indicate whether the AI believes an asset is likely to increase (long position) or decrease (short position) in value, or whether it’s better to hold off on any action.

Systematic Trading: Transparent and Explainable

While AI plays a critical role in generating investment signals, the execution of trades based on these signals is a fully transparent, systematic process. Once the AI generates a signal, it’s passed through a set of predefined trading strategies, which are designed and overseen by human experts.

At Omphalos Fund, these systematic trading strategies are rule-based and fully explainable. The execution process follows a clear set of guidelines that are:

• Defined by humans: Experts design the trading strategies based on market theory, historical data, and risk tolerance. These strategies determine how much to invest, when to execute trades, and how to manage risk.
• Predictable: The AI-generated signals feed into a transparent process where all decisions are based on predefined rules. This removes the emotional aspect that can affect human traders and ensures consistency.
• Subject to monitoring: Human oversight ensures that the trading process remains consistent with the strategy. If the market conditions change significantly, humans can adjust the strategy accordingly, ensuring that the system remains aligned with the broader investment goals.

The Role of Human Oversight in Systematic Investing

In systematic investing, humans remain responsible for designing and managing the strategies that govern how trades are executed. While the AI is responsible for analyzing data and generating signals, humans ensure that the overall strategy remains sound and that the system behaves as intended.

• Strategy Development: Experts create the investment strategy, defining the rules that govern trade execution based on AI-generated signals. This includes setting risk parameters, deciding asset allocation, and determining when and how to exit positions.
• Consistency Monitoring: After the AI generates a signal and the strategy is executed, humans monitor the performance of the system to ensure that everything is functioning as expected. If the AI produces results that are consistently better or worse than anticipated, human experts will review the model to ensure it’s behaving as expected.

The Transparency of Systematic Trading

A key point of reassurance for investors is that systematic trading is fully explainable and transparent. Even though AI generates signals based on complex data analysis, the actual execution of trades follows clear, predetermined rules.

Here’s a breakdown of how systematic investing provides transparency and replicability:

• Clear Rules for Execution: Every trade is made according to predefined rules, meaning there’s no guessing or ad-hoc decision-making involved. If a signal indicates that an asset is likely to rise by a certain percentage, the system will execute the trade as defined by the strategy, such as buying a set number of shares or adjusting the portfolio’s exposure.
• Full Audit Trail: Every trade, signal, and decision made by the system is recorded, allowing for a complete audit of the entire process. Investors can see exactly why a trade was made, how much was invested, and how the system responded to market conditions.
• Risk Management: Systematic investing also includes built-in risk management strategies that define when to exit trades, limit exposure, and rebalance portfolios. These risk controls are clearly defined and adhered to, providing further transparency and protection for investors.

Addressing Concerns About AI as a “Black Box”

While the AI’s process of generating signals can appear complex, it is not a true “black box.” AI’s predictions are based on clearly defined data and mathematical models. The systematic execution of trades is fully transparent, adhering to predefined rules and offering a complete explanation of every decision.

Moreover, the quality of the input data remains under human control. Experts at Omphalos Fund carefully select the datasets that feed into the AI, ensuring that the model is working with reliable, relevant, and high-quality data. This prevents the system from being led astray by poor or incomplete data and allows for more accurate signal generation.

The Omphalos Approach: AI and Systematic Investing

At Omphalos Fund, we strike a balance between the power of AI and the transparency of systematic investing. AI generates the investment signals based on vast amounts of data, including market prices, economic reports, and even social media sentiment. However, the actual trade execution is governed by human-defined strategies, which are transparent, predictable, and free from emotional bias.

The AI system provides the ability to process enormous datasets and generate insights that humans alone could not. But at the same time, the systematic approach ensures that trades are executed in a fully explainable and controlled manner, providing clarity and accountability at every stage of the investment process.

Furthermore, AI allows for the elimination of human errors and unethical behaviors that can sometimes occur in traditional trading environments. AI can help prevent issues like front-running (traders executing orders ahead of client trades), the use of insider information, fraudulent behavior, or even placing incorrect trade sizes. By adhering to a strict rule-based system, AI removes these risks, ensuring that every trade is executed according to the strategy without personal intervention.

Conclusion: AI Signal Generation vs. Systematic Trade Execution

While AI plays a powerful role in analyzing data and generating investment signals, the execution of trades remains a fully transparent and systematic process. The systematic strategies that guide trade execution are designed by human experts and follow clearly defined rules, ensuring that every trade is explainable and consistent with the broader investment objectives.

By combining AI’s ability to analyze data with human-designed trading strategies, Omphalos Fund ensures both the power of AI-driven insights and the transparency of systematic investing. This hybrid approach offers the best of both worlds, allowing investors to benefit from AI’s advanced analytical capabilities while maintaining full visibility into the trading process.