With the rise of quantitative investing, more and more investors are looking to build systematic investing frameworks based on first principles. By establishing a set of core assumptions about how markets work and developing models based on these, investors can make investment decisions in a structured, replicable way. However, building a robust framework requires deep understanding of markets, advanced modeling skills and rigorous backtesting. Key factors include identifying predictive signals, managing risk, and tuning for different market regimes. For individual investors, framework investing also requires balancing quantitative rules with human oversight. Overall, framework investing holds promise to overcome behavioral biases, but requires dedication and expertise to execute successfully.
start with core assumptions about how markets work
The foundation of any investing framework lies in having clear assumptions about market behavior. For example, the efficient market hypothesis argues prices reflect all available information. In contrast, behavioral finance sees inefficiencies driven by cognitive biases. Other tenets like risk-return tradeoffs, momentum effects, mean reversion all impose structure for model building. Investors should extensively research market theories to develop a viewpoint, while remaining flexible as new data emerges. Academic literature, books by veteran investors, studying crises are rich sources of insight. With robust first principles, investors gain strong theoretical basis for how to profitably exploit markets.
use predictive signals based on demonstrated alpha
A key part of framework design is identifying metrics with demonstrated predictive power for future returns. Valuation ratios like P/E, P/B have long been staples for fundamental investors. Technical traders examine price action, volatility, volume for signals. With alternative data explosion, new datasets around web traffic, satellites, sensors are being explored. Big data techniques help surface complex nonlinear relationships between thousands of factors. However, spurious patterns are easy to overfit so rigorous out-of-sample testing is critical. Signals must persist across different time periods, market regimes, geographies. A handful of proven, fundamental drivers of risk and return make for robust models.
optimize frameworks to different market conditions
Markets continuously evolve, so investors must vigilantly update and optimize their frameworks. Periods of high volatility call for tighter risk controls and shorter holding periods. Rising correlation across assets requires more diversification. Prolonged low interest rate environments affect discount rate assumptions. Major events like COVID can radically reshape economic landscapes. Maintaining performance means constantly monitoring portfolio behavior, identifying regime changes, and tuning models accordingly. This includes adjusting factor exposures, return assumptions, risk limits, trading Frequency. Organizations with deep resources have an advantage in framework agility.
balance quantitative rules with human judgment
While frameworks aim to systematize the investing process, human oversight remains crucial. Computers can miss subtle market shifts that humans intuitively grasp. Strict rules inevitably encounter unprecedented scenarios requiring bespoke judgement. Frameworks also face implementation challenges around friction costs, liquidity constraints. Moreover, data inputs and coding involve risks of errors and bugs. Wise investors stay vigilant for model degradation and limits of automation. With experience, investors learn when to override models, and when to adhere to frameworks without discretion. Domain expertise and intuition complement data-driven decisiveness.
In summary, framework investing holds promise for achieving consistent returns, but requires significant skill in designing robust first principles, predictive signals, risk management, and implementation. Combining data-driven models with human perspective can overcome limitations of discretionary and pure quant investing. Framework investing enables proactive responses to evolving markets, but must be rigorously tested and constantly improved to maintain edge.