Systelligence, LLC

Systelligence, LLC is a Chicago, Illinois-based quantitative investment management firm that applies systematic, data-driven methodologies to equity portfolio construction and asset allocation. The firm files quarterly 13F reports with the SEC under CIK #0001882691, a registration that dates to the early 2020s, placing Systelligence among a new generation of technology-forward asset managers that have entered the institutional reporting landscape with quantitative investment processes as their foundational operating framework. The firm's name itself — a portmanteau combining "systematic" and "intelligence" — signals an investment culture built at the intersection of algorithmic rigor and analytical insight. The firm is led by Michael Steliaros, who serves as Founder and Chief Executive Officer. Steliaros brings a background rooted in quantitative finance and systematic investment strategies, informing a firm culture that privileges empirical evidence, statistical validation, and disciplined process execution over subjective discretionary judgment. This quantitative pedigree distinguishes Systelligence from traditional fundamental advisory practices, positioning it within the increasingly influential segment of the asset management industry that leverages computational power, data science, and algorithmic portfolio construction to generate investment outcomes. Chicago serves as a natural headquarters for a quantitative investment firm. The city has long been a global center for derivatives trading, quantitative finance, and systematic investment management — home to major exchanges, proprietary trading firms, and a deep talent pool of quantitative researchers, engineers, and financial technologists. This ecosystem provides Systelligence with access to the human capital, technological infrastructure, and intellectual community that support the development and continuous refinement of systematic investment strategies. The concentration of quantitative finance expertise in Chicago creates a competitive and collaborative environment that drives innovation in algorithmic portfolio management. The firm's organizational structure reflects the lean, technology-driven model characteristic of systematic investment managers, where the ratio of technology infrastructure and research capability to headcount is significantly higher than in traditional fundamental advisory practices. This capital-efficient operating model enables the firm to process vast quantities of market data, execute complex portfolio optimization algorithms, and manage diversified portfolios with a precision and consistency that would be impractical through purely discretionary processes.

Systelligence employs a systematic, quantitative investment strategy that utilizes algorithmic models and data-driven processes to construct and manage equity portfolios. The firm's approach represents a deliberate departure from traditional fundamental analysis, instead relying on statistical models, factor research, and computational optimization to identify investment opportunities, determine position sizing, manage risk, and execute portfolio rebalancing with minimal human discretionary intervention. Examination of the firm's 13F Portfolio Composition reveals a portfolio structure consistent with systematic equity management — typically featuring a large number of positions distributed across multiple sectors and market capitalizations. This broad position count is a hallmark of quantitative strategies that seek diversification across many individual alpha signals rather than concentrating capital in a small number of high-conviction fundamental ideas. The systematic framework evaluates each position as a component within a broader portfolio optimization — where the contribution of each holding to expected return, risk, factor exposure, and correlation is continuously assessed and adjusted through algorithmic processes. The sector allocation reflects systematic broad-market exposure rather than concentrated thematic positioning. Technology, healthcare, financials, consumer discretionary, industrials, and other sectors are represented in proportions driven by the model's factor signals and optimization constraints rather than top-down macroeconomic views or sector specialist research. This model-driven allocation can produce sector weights that deviate meaningfully from capitalization-weighted benchmarks when the underlying factor signals identify concentrated opportunities in specific market segments — but these deviations are systematically generated and risk-managed rather than discretionarily imposed. The Sector Allocation History across sequential quarterly filings is expected to exhibit more variability than typical fundamental buy-and-hold strategies, reflecting the dynamic nature of systematic signal generation. As factor signals evolve — with momentum, value, quality, volatility, and other quantitative characteristics shifting across the equity universe — the portfolio's sector composition adjusts accordingly. This dynamic allocation is a feature of systematic management rather than evidence of strategic inconsistency, as each allocation shift is driven by the same underlying model logic applied to continuously updated market data. Portfolio turnover is moderate to high, consistent with the regular rebalancing cadence that systematic strategies require to maintain alignment between the portfolio's actual composition and the model's optimal portfolio targets. Quantitative strategies typically rebalance on defined schedules — daily, weekly, or monthly — generating turnover that reflects the continuous evolution of factor signals across the equity universe. The elevated turnover is a structural characteristic of systematic management, not a behavioral deficiency, and the associated transaction costs are incorporated into the model's optimization framework as explicit constraints. The quantitative methodology may incorporate multiple factor dimensions including but not limited to: momentum (price trend persistence), value (fundamental cheapness relative to intrinsic measures), quality (profitability, earnings stability, and financial strength), low volatility (risk-adjusted return optimization), and size (small-cap premium capture). The specific factor weights, interaction effects, and implementation details constitute the firm's proprietary intellectual property — the algorithmic equivalent of a fundamental manager's research edge. The systematic framework enables simultaneous processing of thousands of securities across these multi-dimensional factor spaces, identifying portfolio-level optimal combinations that would be computationally infeasible through discretionary analysis. INVESTMENT STRATEGY — SYSTEMATIC ALPHA GENERATION The fundamental value proposition of systematic investing rests on the premise that disciplined, emotion-free execution of empirically validated investment signals generates superior risk-adjusted outcomes relative to discretionary processes that are vulnerable to cognitive biases, inconsistent application, and behavioral errors. Systelligence's systematic framework codifies this premise into an operational reality — translating research insights into executable algorithms that apply consistently across all market conditions without the performance variance introduced by human judgment under stress.

Systelligence, LLC represents the systematic, quantitative approach to equity portfolio management — applying algorithmic precision, factor-driven signal generation, and computational portfolio optimization to construct diversified equity portfolios. Under Michael Steliaros's leadership, the firm has established a clear identity within the Chicago quantitative investment community, building an investment process that prioritizes empirical evidence, disciplined execution, and emotion-free portfolio management. Key analytical dimensions observable through the firm's 13F filing record include broad position diversification consistent with systematic portfolio construction; dynamic sector allocation driven by evolving factor signals; moderate-to-high turnover reflecting regular algorithmic rebalancing; and multi-factor exposure profiles that distinguish the strategy from both passive indexing and concentrated fundamental approaches. The Sharpe Ratio derived from historical replication of the disclosed portfolio provides the single most important diagnostic for evaluating a systematic strategy's effectiveness — revealing whether the algorithmic process generates sufficient risk-adjusted return premium to justify the complexity, turnover costs, and model risk inherent in quantitative management. The firm's 13F filing history, accessible through backtesting service platforms and portfolio simulator tools, enables researchers to reconstruct the real-world capital trajectory produced by the systematic process across actual market conditions. This empirical reconstruction provides validation evidence that complements — and potentially contradicts — the backtested performance typically presented for quantitative strategies, offering a crucial reality check on the strategy's out-of-sample effectiveness. The Simulated Growth Chart derived from sequential 13F filings is particularly diagnostic for systematic strategies, as it embeds the cumulative impact of every algorithmic rebalancing decision, every factor signal rotation, and every portfolio optimization output executed through real market conditions. Unlike fundamental strategies where the growth chart primarily reflects stock selection skill, a systematic strategy's growth trajectory is a direct measure of the model's predictive validity and implementation efficiency. The Alpha Generation vs S&P 500 metric provides the definitive test of whether Systelligence's systematic process adds measurable value above what investors could achieve through low-cost passive exposure. For quantitative strategies, alpha generation must be evaluated net of the elevated turnover costs, model maintenance expenses, and technology infrastructure investments that systematic management requires — ensuring that gross alpha translates into genuine net-of-cost value creation. A foundational diagnostic question for ongoing evaluation: As quantitative investment strategies proliferate and factor-based approaches become increasingly commoditized through low-cost smart-beta ETFs and factor index products, does Systelligence's proprietary systematic framework demonstrate sufficient differentiation in signal generation, execution efficiency, and adaptive capability to sustain alpha production in an environment where the marginal returns to commonly harvested factors are compressed by growing capital allocation — or does the democratization of quantitative tools inevitably erode the informational edge that systematic managers historically exploited?

The risk architecture of Systelligence's portfolio is shaped by the systematic nature of its investment process, creating risk characteristics that are meaningfully distinct from both traditional fundamental strategies and passive index approaches. The Volatility Profile is expected to be carefully managed through the model's optimization framework, which explicitly incorporates risk constraints alongside return objectives. Systematic strategies have the ability to target specific volatility levels, maximum position sizes, sector concentration limits, and factor exposure bounds — engineering portfolio-level risk characteristics with a precision that discretionary managers cannot consistently replicate. The resulting volatility may be either above or below benchmark levels depending on the strategy's specific risk targets, with the critical distinction being that the realized volatility is intentional and controlled rather than an emergent consequence of discretionary decisions. The Max Drawdown Depth observable through historical replication of the firm's 13F disclosures provides an essential diagnostic for evaluating the strategy's behavior during market stress. Systematic strategies face a unique risk dynamic during crisis periods: models calibrated on historical data may underperform when market behavior deviates sharply from historical patterns — a phenomenon known as model risk or regime change risk. The 2022 rate-driven bear market and the AI-catalyzed rally of 2023 each represented distinct regime shifts that tested systematic models' ability to adapt to rapidly changing market dynamics. Factor crowding represents an important risk dimension for quantitative strategies. As the systematic investment management industry has grown, many firms employ similar factor signals — creating the potential for correlated positioning across the quantitative manager community. When multiple systematic managers simultaneously attempt to exit similar factor exposures during stress periods, the resulting selling pressure can amplify drawdowns beyond what individual position-level fundamentals would suggest. This crowding risk is difficult to detect in advance and can produce nonlinear loss patterns during liquidity events. The Downside Capture Ratio derived from historical replication would reveal whether the systematic framework provides genuine asymmetric risk management — reducing participation in market declines more effectively than it captures market advances — or whether the strategy's factor exposures create symmetric market sensitivity that tracks benchmark drawdowns proportionally. The answer to this question is central to understanding the strategy's value proposition within a broader multi-manager portfolio. Model overfitting risk warrants consideration in evaluating any quantitative strategy. Models optimized on historical data may capture noise rather than genuine signal, producing in-sample performance that does not replicate out-of-sample. The relatively recent filing history provides a limited out-of-sample validation window, making ongoing monitoring of the strategy's real-time performance versus backtested expectations particularly important. Transaction cost management is a critical risk factor for systematic strategies with moderate-to-high turnover. The gap between gross and net returns — the implementation shortfall — determines whether the strategy's theoretical alpha survives the friction of real-world execution. Systematic firms operating in Chicago's quantitative finance ecosystem typically invest heavily in execution technology and market microstructure research to minimize this implementation drag.