AI Asset Manager: Humans Remain Most Important, Founder Says
- The late 1990s marked the dawn of the mainstream internet, but for Miro Mitev, the focus was on a technology decades away from widespread adoption: Artificial Intelligence.
- Mitev recognized the power of neural networks for financial forecasting, stating, "I fell in love with these kinds of possibilities." His 25-year career has been dedicated to forecasting...
- SmartWealth's latest fund, IVAC (Bright Value Allocation Core), is currently targeting $2 billion in assets under management (AUM).
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AI-Driven Asset Management: SmartWealth’s $2 Billion Bet
Table of Contents
The Rise of AI in Finance
The late 1990s marked the dawn of the mainstream internet, but for Miro Mitev, the focus was on a technology decades away from widespread adoption: Artificial Intelligence. now a seasoned asset manager, Mitev became an early adopter of AI in finance, captivated by the potential of neural networks during his studies at the Vienna University of Economics adn Business in 1997.
Mitev recognized the power of neural networks for financial forecasting, stating, “I fell in love with these kinds of possibilities.” His 25-year career has been dedicated to forecasting for prominent institutions like banks and tech giant Siemens. This experience culminated in the founding of SmartWealth Asset Management, a firm distinguished by its complete reliance on AI systems for investment decisions.
SmartWealth’s IVAC Fund: Performance and Ambition
SmartWealth’s latest fund, IVAC (Bright Value Allocation Core), is currently targeting $2 billion in assets under management (AUM). The fund aims to deliver an annualized return of 14-15%, a figure that reflects the firm’s confidence in its AI-driven approach. This ambitious target positions IVAC as a significant player in the evolving landscape of AI-powered investment strategies.
The Human Element in AI-Driven Investing
Despite the absence of human intervention in the AI’s actual trading decisions, Mitev emphasizes the crucial role humans play in the process. “Humans are the most critically important part of the equation,” he asserts, explaining that individuals are responsible for selecting training data, defining input variables, establishing model parameters, and continuously refining the system. This highlights that AI in finance isn’t about replacing humans, but rather augmenting their capabilities.
Mitev’s “golden rule” is to trust the model once its created, cautioning against interference after initial advancement. He believes that constant adjustments can introduce bias and undermine the AI’s objective analysis. This underscores the importance of rigorous testing and validation before deploying an AI-driven investment strategy.
Understanding Neural Networks in Financial Forecasting
Neural networks, the core technology powering SmartWealth’s investment decisions, are computational models inspired by the structure and function of the human brain. They excel at identifying complex patterns in large datasets, making them well-suited for financial forecasting. Here’s a simplified breakdown of how they work:
- Data Input: Financial data (stock prices, economic indicators, news sentiment, etc.) is fed into the network.
- Layers of Neurons: The data passes through multiple layers of interconnected “neurons” that perform mathematical calculations.
- Pattern Recognition: The network learns to identify relationships and patterns within the data.
- Prediction: Based on these patterns, the network generates forecasts or investment recommendations.
The key advantage of neural networks is their ability to adapt and improve over time as they are exposed to more data. This allows them to potentially outperform customary forecasting methods, especially in volatile and complex markets.
The Growing Trend of AI in Asset Management
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