UNCOVERING ALPHA OPPORTUNITIES WITH AI
We help investment managers improve their performance with AI-powered asset signals and optimized model strategies on equities, futures and ETFs.
OUR SOLUTIONS
High-quality, normalized & ready-to-use factors leveraging Al insights.
Al-generated alerts to stay informed about market developments, emerging trends, and potential risks that complement quant analysis
Al-based assets performance signals that identify under and outperformers within a given investment universe & time horizon
Off-the-shelf and bespoke Al-powered model strategies and indices on several asset classes and investment universes
COVERAGE
TECHNOLOGY
Breakthrough automated ML & AI technologies for portfolio management
Understand how our AI engine works under the hood
Click on the images to explore how we harness data and transform it into powerful investment decisions:
Understanding The Clients' Investiment Needs
At Axyon AI we focus on developing the best deep learning solutions to boost clients' investment strategies.
Hence, our very first step is to understand clients' needs in regards to the investment objective like the investment universe, the investment horizon, the benchmark and the risk management constraints and anything else that needs to be considered.
Data Research
Our database is continuously updated with new high-quality predictive and alternative sources of data.
Why is it important?
This step is important to ensure a structured data research process as new, and original data sources can improve alpha generation thanks to our AI models*.
*AI modelling is the creation of a logical decision-making system based on a certain set of available data.
Dataset Preparation
Data is at the centre of every AI engine. And data preparation is one of the most critical phases.
It starts with collecting data such as stock prices, fundamental data, indexes futures, currencies, interest rates, macroeconomic indicators, analysts' estimates, and technical indicators from our data providers.
During the data preparation step, our Quants and Data Engineers validate, identify and rectify inconsistencies, and deal with outliers, anomalies, structural changes and any missing data. After the data preparation, the data is clean and ready to be transformed and engineered to create features.
Why is it important?
Data preparation helps us to get the most out of the data. In this step, the quality and the quantity of data influence directly how good our predictive model will be. Without this phase, we can end up spending a lot of time looking at bad AI results. The better the data, the more outstanding the results.
What is the output?
Output: clean datasets, ready to be used to train the AI models.
In our case, for each investment product (Equities, Futures, Commodities), there is a different dataset containing features.
AI Model Development
INTRO: This step may be considered the heart of the Axyon IRIS technology stack.
💡If our products were cars, this is the step where we produce the engines. Here, our Machine Learning Team will use our proprietary AutoML engine as a factory to train and optimize forecasting models, based on multiple state-of-the-art AI technologies.
More specifically, the clean dataset generated in the previous step will be used to train supervised machine learning models with the goal to forecast the ranking of target financial assets in terms of expected return.
Training the models means defining an objective function that they will have to maximise (e.g. log-likelihood) and letting them autonomously learn how to do so by trial-and-error from historical data.
💡This is analogous to how you would slowly learn a new language by attempting to translate short random sentences and receiving feedback from a teacher: you would slowly learn what works and what doesn't, and gradually improve at this task.
During the training, each ML model learns how to rank financial assets based on the expected relative performance over a given time horizon after processing millions of historical data points. Besides optimizing individual models, our Platform is able to automatically tune hyperparameters** and perform feature selection, thanks to advanced Evolutionary Computation*** techniques.
AI-Signal Analysis
After the model deployment, our proprietary AutoML engine produces AI-predictive signals that allow investors to access AI algorithms insights.
The signals are transformed into rankings based on the assets' predictive performances given a time horizon. Those AI signals are then monitored and analysed to determine whether or not they have helped address the client's business needs.
Why is it important?
This is important to evaluate the overall quality and stability of the signals and to understand the relationship between known investing factors and ML signals.
What is the output?
Output: reports that contain detailed information about the historical signals
AI Rankings & AI-Powered Strategies
The final outputs of the model discovery process are relative performance rankings that can be used to build different investment strategies (according to the client's requirements).
These rankings identify predicted outperformers and underperformers within a given investment universe and time horizon.
The first way to integrate AI into an existing process is directly trading a fully-fledged AI-based strategy.
To build such a strategy, an investor could transform the daily ranking into target portfolio weights that guide the portfolio allocation. 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-generated ranking.
A different alternative to integrating AI into an existing process is treating AI signals and rankings as an alternative alpha-generating data source. This approach can be illustrated using predictive rankings for overweight or underweight assets already traded by discretionary asset managers.
Click on the images to explore how we harness data and transform it into powerful investment decisions:
Understanding The Clients' Investiment Needs
At Axyon AI we focus on developing the best deep learning solutions to boost clients' investment strategies.
Hence, our very first step is to understand clients' needs in regards to the investment objective like the investment universe, the investment horizon, the benchmark and the risk management constraints and anything else that needs to be considered.
Data Research
Our database is continuously updated with new high-quality predictive and alternative sources of data.
Why is it important?
This step is important to ensure a structured data research process as new, and original data sources can improve alpha generation thanks to our AI models*.
*AI modelling is the creation of a logical decision-making system based on a certain set of available data.
Dataset Preparation
Data is at the centre of every AI engine. And data preparation is one of the most critical phases.
It starts with collecting data such as stock prices, fundamental data, indexes futures, currencies, interest rates, macroeconomic indicators, analysts' estimates, and technical indicators from our data providers.
During the data preparation step, our Quants and Data Engineers validate, identify and rectify inconsistencies, and deal with outliers, anomalies, structural changes and any missing data. After the data preparation, the data is clean and ready to be transformed and engineered to create features.
Why is it important?
Data preparation helps us to get the most out of the data. In this step, the quality and the quantity of data influence directly how good our predictive model will be. Without this phase, we can end up spending a lot of time looking at bad AI results. The better the data, the more outstanding the results.
What is the output?
Output: clean datasets, ready to be used to train the AI models.
In our case, for each investment product (Equities, Futures, Commodities), there is a different dataset containing features.
AI Model Development
INTRO: This step may be considered the heart of the Axyon IRIS technology stack.
💡If our products were cars, this is the step where we produce the engines. Here, our Machine Learning Team will use our proprietary AutoML engine as a factory to train and optimize forecasting models, based on multiple state-of-the-art AI technologies.
More specifically, the clean dataset generated in the previous step will be used to train supervised machine learning models with the goal to forecast the ranking of target financial assets in terms of expected return.
Training the models means defining an objective function that they will have to maximise (e.g. log-likelihood) and letting them autonomously learn how to do so by trial-and-error from historical data.
💡This is analogous to how you would slowly learn a new language by attempting to translate short random sentences and receiving feedback from a teacher: you would slowly learn what works and what doesn't, and gradually improve at this task.
During the training, each ML model learns how to rank financial assets based on the expected relative performance over a given time horizon after processing millions of historical data points. Besides optimizing individual models, our Platform is able to automatically tune hyperparameters** and perform feature selection, thanks to advanced Evolutionary Computation*** techniques.
AI-Signal Analysis
After the model deployment, our proprietary AutoML engine produces AI-predictive signals that allow investors to access AI algorithms insights.
The signals are transformed into rankings based on the assets' predictive performances given a time horizon. Those AI signals are then monitored and analysed to determine whether or not they have helped address the client's business needs.
Why is it important?
This is important to evaluate the overall quality and stability of the signals and to understand the relationship between known investing factors and ML signals.
What is the output?
Output: reports that contain detailed information about the historical signals
AI Rankings & AI-Powered Strategies
The final outputs of the model discovery process are relative performance rankings that can be used to build different investment strategies (according to the client's requirements).
These rankings identify predicted outperformers and underperformers within a given investment universe and time horizon.
The first way to integrate AI into an existing process is directly trading a fully-fledged AI-based strategy.
To build such a strategy, an investor could transform the daily ranking into target portfolio weights that guide the portfolio allocation. 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-generated ranking.
A different alternative to integrating AI into an existing process is treating AI signals and rankings as an alternative alpha-generating data source. This approach can be illustrated using predictive rankings for overweight or underweight assets already traded by discretionary asset managers.