Bill Hortz, February 2025 (Institute For Innovation Development)
This Q&A sits down with NextFolio’s Co-Founders and Chief Investment Officer reveal to explores NextFolio’s journey—from years of research and development to the active investment challenges they’re solving today.
NextFolio, March 2025
This document explains how combining multiple models in machine learning improves accuracy, reduces bias and variance, and enhances robustness, with a focus on NextFolio’s application in active investing.
NextFolio, March 2025
A quick snapshot of our firm, ensemble active investment approach, and the value we bring to firms and clients.
Prof. David Goldsman: H. Milton Stewart School of Industrial & Systems Engineering, 2024 (Georgia Institute of Technology)
An independent validation of Turing’s January 2024 white paper, Ensemble Active Management: AI’s Transformation of Active Management, confirms the soundness of its methodology, statistical rigor, and portfolio construction process. This executive summary examines the integrity of Turing’s approach to fund selection, performance analysis, and bias mitigation—reinforcing the credibility of its findings.
Klaas P. Baks, Jeffrey A. Busse, and T. Clifton Green, March 2006 (Emory University)
Research shows that mutual fund managers who take big bets on a select few stocks outperform their more diversified peers, highlighting the power of a more focused investment approach.
Russ Wermers, April 2003 (University of Maryland)
A 26-year study reveals that mutual fund managers who take larger active bets demonstrate superior stock-picking skills, despite the average manager underperforming benchmarks.
Miguel Antón, Randolph B. Cohen, and Christopher Polk, April 2021 (Harvard Business School)
Best Ideas research reveals a powerful truth: active managers’ highest-conviction stocks outperform the rest of their portfolios. Could fewer, more concentrated bets be the key to unlocking true alpha?
Alexey Panchekha, CFA, October 2019 (CFA Institute)
Research uncovers a paradox at the heart of active management: while high-conviction overweights are the sole source of stock-selection alpha, most managers dilute their best ideas—sabotaging performance in the process.
If you're interested in Ensemble Active, reach out!
We'd be happy to provide a customized comparative analysis to your current fund lineup, covering all nine Morningstar® style boxes to align with your investment goals.
Find answers to common questions about NextFolio and Ensemble Active.
NextFolio aims to redefine active management by offering data-driven strategies that overcome the limitations of traditional single-manager approaches.
Our founders, with over 60 years of combined wealth management experience, spent nearly five years evaluating the Ensemble Active approach and recognized its breakthrough potential. In 2024, they launched NextFolio to deliver institutional-grade Ensemble Active Portfolios to banks, RIAs, broker-dealers, institutions, and family offices.
NextFolio offers ready-to-implement strategies designed for banks, RIAs, broker-dealers, institutions, and family offices, helping them drive asset growth through differentiation and scale that no other firms can provide.
NextFolio addresses the challenge of traditional active management in four ways:
Data science is at the core of how NextFolio develops its strategies, accessing machine learning to analyze real-time holdings and build strategies based on high-conviction stock picks of multiple mutual fund managers. Machine learning powers Ensemble Active’s ability to access and analyze the real-time holdings and weights of mutual funds (information typically not publicly available), offering unmatched visibility into high-conviction stock picks.
The primary source of holdings data comes from form N-Port filings, which registered investment companies (RICs) are required to file quarterly with a 60-day lag (and just for the last reported month of the fund’s fiscal quarter). Those quarterly holdings are “converted” to daily holdings through machine learning techniques applied by NextFolio’s technology partner. NextFolio’s technology partner creates daily replication portfolios of mutual fund holdings using machine learning that has been able to produce over a 99% correlation with daily fund NAV changes. Through this replication technology, NextFolio accesses a “best estimate” of how a fund’s holdings evolve over time. For ETFs, which report their holdings daily, no replication is required; data can be accessed directly.
Ensemble methods (or learning) involve multiple models, often referred to as base models or weak learners, combining their predictions to harness the power of collective knowledge and multiple viewpoints.
Ensemble Active is the process by which NextFolio tracks the daily holdings of mutual fund/ETF managers across the U.S., using this data to construct portfolios that reflect real-time, high-conviction stock selection from a curated set of 10-15 competitively-advantaged institutional-quality managers.
NextFolio addresses the challenge of traditional active management in four ways:
Ensemble Alpha™ represents the excess returns achieved through using ensemble methods to identify and invest in only high-conviction stocks. This is measured by comparing the return generated by a NextFolio Ensemble Active Portfolio with the underlying basket of funds from which high-conviction stocks are drawn.
In this manner, Ensemble Alpha™ seeks to capture collective skill while avoiding dilutive overdiversification.No. Ensemble Active Portfolios are composed solely of long-only, U.S. domestic equity securities, drawn directly from corresponding benchmarks. This ensures that all portfolios are style-pure, meaning a Large Core portfolio, for example, contains only stocks from the Russell 1000 benchmark.
Fund of Funds combine entire funds without considering overlap in stock selection. This compounds the overdiversification drawbacks already present in single funds that dilute alpha. Ensemble Active Portfolios only use high-conviction ideas sourced across multiple funds, determining which of those stocks to own directly.
Fund of Funds rely on entering into sub-advisory agreements with fund managers and paying them fees for their service. Ensemble Active Portfolios use only publicly available information to inform their stock selection process, providing greater flexibility in hiring and firing decisions.
Yes, while fund selection and portfolio construction overlap conceptually, they are distinct processes.
Ensemble Active Portfolios require a curated set of competitively advantaged managers. It’s not just about picking strong funds; it’s about how they fit together. The process emphasizes high-conviction ideas across funds, making factors like holdings count, active exposure, and top-ten concentration crucial.NextFolio offers strategies across all nine Morningstar style boxes. The inception dates for each are as follows:
For the ensemble process to effectively capture high-conviction signals across funds, 10-15 strategies are the desired range. Too few funds lead to insufficient diversification on multiple levels, while too many funds overdiversify the number of signals needed to achieve effective ensemble construction.
NextFolio’s key differentiator is its focus on utilizing multiple funds and only high-conviction stocks across those funds to form portfolios. Unlike traditional firms that offer single-manager products, NextFolio's Ensemble Active process uses mutual funds as models to identify investment insights, focusing on high-conviction ideas as predictive signals.
Another major distinction is that NextFolio applies this approach across all nine Morningstar style boxes, a breadth few firms offer.Ensemble methods combine the strengths of individual models by leveraging their diverse perspectives. This approach:
It has, in areas like asset allocation and risk management, but not effectively or at all in stock selection. Historically, mutual fund holdings have been disclosed only with significant delays, making it difficult to apply this approach.
With proprietary new technology that provides real-time access to holdings and weights, Ensemble Methods can now be used effectively in portfolio construction.
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