Introduction
Systematic investment models can broadly be categorized into fundamental-based strategies (driven by company financial data and economic fundamentals) and technical-based strategies (driven by price and volume patterns). Professional investors, from hedge funds to asset managers, have long debated the merits of these approaches. Both aim to exploit market inefficiencies or risk premia through rules-based methods, but their mechanics, theoretical underpinnings, and performance characteristics differ markedly.
Fundamental-Based Systematic Strategies: Mechanics & Rationale
Fundamental systematic strategies select investments based on economic and company-specific fundamentals, such as valuation ratios, profitability, growth metrics, or balance sheet strength. The concept of intrinsic value lies at the heart of fundamental analysis, which represents the true or inherent worth of an asset, independent of its prevailing market price. Quantitative factor investing is a prime example: portfolios are tilted toward stocks scoring well on factors like:
- Value (e.g. low price-to-book or price-to-earnings),
- Quality (high profitability, low leverage, etc.),
- Size (small-cap tilt), Dividend Yield, or Low Volatility.
The theoretical basis is that these characteristics capture long-term risk premia or behavioral anomalies that cause mispricing. The core belief is that market prices will eventually reflect intrinsic value, creating opportunities for investors to identify undervalued or overvalued assets.
Mechanically, fundamental quant models evaluate periodic financial data and rankings.
- Rebalancing is typically done monthly or quarterly after earnings reports, since fundamentals evolve relatively slowly. Positions are sized to maximize factor exposure while controlling risk. For example, a systematic value strategy might go long the cheapest 30% of stocks and short the most expensive 30%, market-neutral. A long-only implementation (common in smart beta indexes) might overweight cheap stocks relative to a capitalization-weighted index.
- Portfolio construction often combines multiple fundamental factors to diversify return sources - e.g., a composite of value, quality, and momentum signals.
Technical-Based Systematic Strategies: Mechanics & Rationale
Technical systematic strategies, by contrast, ignore “fundamental value” and focus purely on market-generated data like past prices, returns, and trading volume. The most prominent technical strategy is trend-following (time-series momentum), which systematically buys assets that have been rising and sells (or shorts) assets that have been falling. Another is cross-sectional momentum, which ranks securities by recent performance and bets on relative winners versus losers. Other technical signals include mean-reversion indicators, chart pattern recognition, volatility breakout signals, and statistical arbitrage on price relationships. These strategies stem from technical analysis traditions, but in a systematic context, they are rules-based and scientifically tested rather than reliant on ad-hoc chart reading.
Mechanically, technical models often operate on higher-frequency data and respond faster to market changes. A classic trend-following system might compute moving averages or past 12-month returns for each asset and generate long/short positions accordingly, updating positions daily or weekly.
The theoretical justification for technical strategies is often rooted in behavioral finance and market microstructure. Since pure price-based models cannot rely on fundamental valuation, they exploit patterns believed to arise from investor behavior: herding, fear and greed cycles, and slow information diffusion.
Trend-following’s success is frequently attributed to behavioral biases that cause persistent price trends, for example, initial underreaction to new information followed by momentum chasing, or central bank liquidity fueling prolonged moves. In practitioner literature, trend following was once dismissed by efficient-market theorists as an illegitimate strategy (“the black sheep of investment styles” as one article quips). Yet decades of performance challenge that view: trend strategies consistently deliver positive returns uncorrelated with stocks and bonds, suggesting they capture genuine patterns or risk premia.
Other technical approaches have similar rationale. Cross-sectional momentum (relative strength) in stocks, though based on price, can be partly explained by fundamentals (companies with improving fundamentals often have rising prices). However, momentum crashes – sudden reversals when market trends turn – highlight the non-fundamental nature of these bets. For example, the momentum factor in equities has historically offered one of the highest Sharpe ratios among factors, but also the worst drawdowns when trends snap back.
Market Regime Behavior Comparison
Conclusion
Modern quantitative investment management no longer sees fundamental and technical strategies as an either-or choice, but rather as complementary tools. Advanced research and real-world track records show that each approach has distinct strengths. Fundamental strategies are grounded in economic intuition and can tap into persistent drivers of return like value, quality, and growth, albeit with cyclical ups and downs. Technical strategies are agnostic to fundamentals but excel at capturing trends and turning points, offering diversification and tail protection that fundamental strategies alone typically cannot. As Evie Liu referring to Cliff Asness of AQR wrote on factors and trend-following “two great tastes can go great together,” each adding something vital to a portfolio.
For practitioners, the insights are clear: use fundamental models to anchor long-term investments in sensible, valuation-driven ideas, and use technical models to adapt to market dynamics and hedge against the unforeseen. By studying white papers and reports from leading quant firms, we see empirical evidence – from Sharpe ratio improvements to crisis period outperformance – that a blended systematic approach can yield superior risk-adjusted returns.
At 9823 Capital, we take this integration a step further, avoiding off-the-shelf Fama-French factors in our models and instead employing unique variations of traditional fundamental and technical factors to uncover more idiosyncratic characteristics and hedge out the appropriate amount of factor risk. In an era of rapidly shifting market regimes (as witnessed in the past few years), the appropriate mix of fundamental value and technical trend-following perspectives helps counteract the challenges of volatile markets, fostering robust investment performance. As the research suggests, investors don’t have to pick a side in the fundamental vs technical debate; the best outcomes often arise from integrating both, harnessing the theoretical rigor of fundamental analysis and the adaptive, diversifying power of technical strategies.
