Mumbai, Maharashtra Apr 30, 2025 (Issuewire.com) - Fidelfolio, an investment management firm leveraging machine learning, has unveiled its rule-based investing framework designed to address longstanding inefficiencies in active fund management. By directly targeting key issues such as human bias, limited analytical capacity, and error-prone processes, the company aims to support scalable and transparent investing grounded in data.
Addressing Structural Industry Challenges
Fidelfolio’s core philosophy is rooted in solving systemic problems within traditional investment research. Insights drawn from years of fund management experience, along with discussions with fund managers, analysts, and wealth advisors, highlighted three dominant issues: psychological biases, limited analytical scope, and frequent human errors.
“Among these, human biases often pose the most significant hurdle to effective decision-making,” a Fidelfolio spokesperson noted. “Despite efforts to maintain rationality, decisions are frequently influenced by behavioral tendencies that are difficult to overcome without external systems.”
While analytical limitations constrain opportunity sets, human errors can lead to critical setbacks in portfolio outcomes. To tackle these, Fidelfolio has developed a hybrid framework combining machine learning algorithms with human oversight, offering both scale and accountability.
Leveraging Modern Data Infrastructure
The firm’s investment in machine learning comes at a time when data infrastructure has advanced considerably. Modern capabilities now allow for data processing at speeds thousands of times faster than in the past decade - making ML and AI not only feasible but highly effective for use in financial research and strategy formulation.
“The technology has progressed from being a support tool to a decision-making engine,” said the spokesperson. “Where earlier tools assisted with calculation or visualization, current ML systems generate insights, analyze historical behavior, and identify patterns beyond human detection.”
Machine Learning in Contemporary Investment Models
Machine learning continues to gain ground across diverse investment applications - from short-term trading to long-horizon fundamental analysis. Traditional models based purely on human analysis, especially those focused on short-term signals, are being replaced by algorithmic approaches capable of absorbing and processing vast data points in real time.
Fidelfolio is part of a growing wave of firms aligning ML with long-term fundamentals, aiming to combine technological efficiency with investment principles that prioritize performance, transparency, and low-churn scalability.
Framework Breakdown: FF Strategy Generator and FF Strategy Analyser
The firm's proprietary framework is anchored by two core modules - the FF Strategy Generator and the FF Strategy Analyser. These components interact in a self-learning loop, with the generator responsible for crafting rules-based strategies and the analyser tasked with backtesting their effectiveness.
For example, one rule developed by the system - labeled ‘13-14-24-20-8-11-C’ - selects companies with a minimum of 20% Return on Equity and 8% growth in Operating Profit for at least 11 of the past 13 years. This rule, applied to 32 years of data, forms a historical portfolio that is then evaluated by the analyser.
Performance metrics for the backtested portfolio included a 20% average annual return, 25% standard deviation, and a Sortino ratio of 1.4x. The best and worst one-year returns stood at 120% and -30%, respectively. Each parameter was benchmarked against the Nifty50 index and assessed for relative performance.
A Self-Learning Investment Cycle
This continuous feedback loop enables the FF Strategy Generator to iterate and enhance its rules over time. Investment teams define overarching objectives, such as risk levels or target durations, and the ML engine outputs multiple strategies aligned to these criteria.
Each strategy undergoes expert validation, qualitative review, and performance analysis before implementation. The process is designed to combine algorithmic consistency with human judgment to ensure clarity, relevance, and accountability.
A Transparent and Scalable Approach
Fidelfolio’s framework integrates human intelligence and machine learning to systematically reduce biases, expand analytical scope, and minimize operational errors. The system emphasizes:
- Interpretability and Transparency – designed to avoid black-box complexity
- Long-Term Investing Based on Fundamentals – not short-term trading
- Scalable Strategy Development – with minimal churn and robust risk controls
By facilitating deep analytics and incorporating human validation, the approach is intended to outperform traditional models while maintaining trust and visibility.
“This hybrid model combines the best of both worlds - automation for scale and speed, and human judgment for insight and responsibility,” the spokesperson added.
About Fidelfolio
Fidelfolio is a machine learning–driven investment management firm that blends algorithmic processing with human expertise. Its rule-based strategies, built on fundamental analysis and trained via a self-learning framework, are designed to deliver long-term, transparent, and scalable investment solutions. For inquiries, please visit: http://fidelfolio.com/contact/
Media Contact
Fidelfolio *****@fidelfolio.com 9741588937 WeWork Vaswani Chambers, 2nd Floor, 264-265, Dr Annie Besant Rd, Worli, Mumbai, Maharashtra 400025 https://fidelfolio.com/
