Axyon AI | AI-driven investment Solutions: AI-Based Signals

Axyon AI’s models learn to rank assets according to their expected performance. These AI-based rankings can be used to sort the assets belonging to the investment universe according to their expected performance on a given time horizon in the future (say, a week, a month, or three months).

Tailored for a specified investment universe and timeframe, these rankings can help you distil complex data into actionable insights.

How can we integrate AI signals into existing Investment processes?

BUILD AN AI-DRIVEN STRATEGY
The first way to integrate AI is building out from scratch a fully-fledged AI-based strategy. To build such a strategy, an investor could transform the daily AI-based rankings into target portfolio weights. As an example, a portfolio manager could choose to create a long-only strategy that every week puts equal weight on each of the top 30 assets of the AI-based ranking.

ALTERNATIVE ALPHA GENERATING DATA SOURCE
Another approach is combining AI signals with custom proprietary investment factors to create enhanced quantitative strategies. Quants could literally treat AI signals and rankings as an alternative alpha-generating data source.

AI INTEGRATION
Finally, a portfolio manager could integrate AI-based rankings into an already existing investment process. As an example, a portfolio manager could use predictive rankings to overweight or underweight assets already belonging to his portfolio

The choice between these alternatives depends on the characteristics of the investment team, the business needs, and the constraints an investor has to comply with.

WHY AXYON AI'S AI-BASED SIGNALS

ENHANCED STOCK SELECTION

AI-powered rankings provide a data-driven, objective assessment of assets, aiding managers in selecting stocks or other assets that are predicted to out or under perform, thereby affording the opportunity to improve portfolio performance.

DEEP
INSIGHTS

To identify and analyse the underlying factors driving asset performance, providing deeper insights into market behaviour and asset dynamics.

STRATEGIC ALLOCATION

Understanding the drivers behind performance aids in constructing portfolios aligned with prevailing market trends and economic conditions, enhancing the strategic asset allocation process.

DECISION MAKING EFFICIENCY

Automating the ranking and weighting process with AI reduces time and effort for analysis, allowing managers to focus on strategic decision-making rather than data crunching.

Signals and drivers are essential for quantitative models in portfolio optimisation. Derived from financial metrics, market sentiment, or machine learning, they identify undervalued or overvalued stocks, potential outliers, or trends in market behaviour that traditional models may overlook.

AI-based signals aid factor-based investing, provide discretionary managers with screening tools, and help identify stocks meeting investment criteria. They can also monitor market dynamics and validate research, improving investment decisions.