Institutional Non-Linear AI Factor Models & Systematic Execution
Welcome to Systematic AI Investing Portfolios!
We replace legacy linear factor scoring with non-linear decision tree ensembles (LightGBM & ExtraTrees) analyzing 175+ point-in-time metrics across FactSet data pipelines.
How This Publication Serves Your Allocation:
Free Subscribers: Public research deep dives on US secular bull market dynamics, non-linear machine learning architectures, and point-in-time factor normalization.
Self-Directed Quants (Paid Tier): Direct access to our Research Group https://www.portfolio123.com/app/group/348/home featuring 3 out-of-sample strategy books (Small, Mid/Large, and Large Cap). Requires a Portfolio123 subscription (Screener or Backtest level minimum).
First-Generation Family Offices: Private B2B signal licensing and dedicated sub-advisory models for capacity-capped Mid, Large, and Small Cap Strategies ($125,000/yr minimum base fee).
Automated Exchange Investors (Coming Soon): Turn-key, tax-deferred execution via our public Exchange Traded Instrument (ETI) listed on Börse Stuttgart / EUWAX.
Disclaimer & Legal Notice / Haftungsausschluss
1. Informational Purposes Only (No Financial Advice)
The content, calculations, models, and research provided by Andreas Himmelreich, QuantStrike GmbH, and AI Factor Research GmbH (collectively, “the Authors & Entities”) in this blog, video, publication, or associated materials are provided strictly for educational, informational, and quantitative research purposes. Nothing contained herein constitutes investment, financial, legal, or tax advice, nor does it represent an offer, solicitation, or recommendation to buy, sell, or hold any security, financial instrument, or structured product.
2. Individual Responsibility & Advisor Consultation
Investment decisions should never be made based solely on the information presented. Investors must independently evaluate their financial objectives, risk tolerance, liquidity needs, and personal tax status. Before executing any transaction, investors are strongly advised to consult qualified independent legal, financial, and tax advisors.
3. Data Accuracy & Source Limitations
Information presented is derived from internal research, point-in-time data (including FactSet via Portfolio123), and third-party sources believed to be reliable. However, the Authors & Entities do not guarantee the completeness, accuracy, or timeliness of such data. Content may be condensed, simplified, or updated without prior notice.
4. Model-Driven & Simulated Backtest Performance
Performance figures, capital curves, factor returns, and strategy metrics displayed reflect hypothetical, simulated backtests and out-of-sample quantitative model research. They do not represent audited track records or real-time trading executed in live brokerage accounts. Past, simulated, or out-of-sample performance is no indicator or guarantee of future trading performance. Actual market conditions, execution slippage, bid-ask spreads, and transaction costs will alter real-world returns.
5. Limitation of Liability & Risk of Loss
Trading and investing in equity markets, quantitative factor models, and financial instruments carry inherent market risks, up to and including the total loss of invested capital. The Authors & Entities, QuantStrike GmbH, and AI Factor Research GmbH accept no liability whatsoever for any direct, indirect, consequential, or incidental losses or damages arising from reliance on, or use of, the information, signals, models, or materials provided in this publication.
