Third-Party Research & Methodology Only

This section shares summaries of third-party academic research and descriptions of quantitative models. The content represents the findings of the original researchers, not the opinions or recommendations of Foxholm Financial. Foxholm Financial does not publish hypothetical or backtested performance metrics on its quantitative research pages. All content is restricted to methodology, signal construction, factor logic, and risk architecture. SEC rules require that investment advisers not present misleading performance data, and our methodology-only approach reflects that standard and the firm's fiduciary obligations.

Quantitative Research

Investment Methodology, Engineered

Quantitative analysis adds structure to investment decisions through statistical models, factor research, and systematic risk measurement. These pages cover the models, concepts, and research behind quantitative portfolio management.

Models
How quantitative models work: trading strategies, portfolio building, risk measurement, and compliance.
Concepts
The building blocks behind quantitative investing: how risk is measured, what factors are, and how to test whether a strategy actually works.
Research Reviews
Plain-language summaries of published academic papers on factor investing, momentum, volatility, and behavioral finance.
Models

Quantitative Models

How quantitative models work: trading strategies, portfolio building, risk measurement, and compliance.

Momentum Scoring
Uses price trends, trading volume, and company data to rank stocks by the strength of their recent performance.
Quantitative Stock Screener
A step-by-step filtering process that narrows thousands of stocks down to a short list using financial health, quality, and trend criteria.
Mean Reversion
Methods for spotting when prices have moved too far and may be due to snap back.
ML Stock Prediction
Machine learning approaches to predicting stock price movements and generating trading signals.
Value at Risk (VaR)
Estimates the worst-case portfolio loss you might expect over a given time period.
Monte Carlo Simulation
Runs thousands of random simulations to show the range of possible outcomes for a portfolio or retirement plan.

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Concepts

Core Concepts

The building blocks behind quantitative investing: how risk is measured, what factors are, and how to test whether a strategy actually works.

Overfitting and Data Snooping
When a model mistakes noise for signal by fitting too closely to past data, making it useless for the future.
Risk-Adjusted Returns
Metrics like the Sharpe ratio that measure how much extra return a strategy earns for each unit of risk taken.
How Factor Models Are Built
How researchers isolate characteristics like value, momentum, and quality from raw market data.
Backtesting Pitfalls
Common mistakes that make a strategy look better in testing than it actually is, including survivorship bias and look-ahead bias.
Signal Decay
Why trading signals lose their edge over time as more investors discover and act on the same patterns.
Transaction Costs and Execution
The hidden costs of trading, including commissions, bid-ask spreads, and the price impact of large orders.

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Research Reviews

Academic Research Reviews

Plain-language summaries of published academic papers on factor investing, momentum, volatility, and behavioral finance, with practical takeaways for portfolio construction.

DeMiguel, Garlappi & Uppal (2009)
Research showing that simply splitting money equally across investments performed competitively against more sophisticated methods.
Moskowitz, Ooi & Pedersen (2012)
Documents momentum in the time dimension: assets that have been rising tend to keep rising across stocks, bonds, currencies, and commodities.
Asness, Moskowitz & Pedersen (2013)
Value and momentum work across stocks, bonds, currencies, and commodities worldwide, and the two strategies offset each other's risks.
Novy-Marx (2013)
Shows that profitable companies earn higher stock returns than unprofitable ones, even when they look expensive by traditional measures.
Harvey, Liu & Zhu (2016)
A study of hundreds of claimed stock market patterns that argues most are false discoveries caused by data mining.
Fama & French (1993)
The landmark paper that identified company size and cheapness as two key drivers of stock returns beyond overall market performance.

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New to the terminology? Start with the glossary for plain-language definitions of key quantitative finance terms.
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Disclaimer

This content is for educational and informational purposes only and does not constitute an offer to sell or a solicitation of an offer to buy any securities. Nothing herein constitutes investment advice or recommendations tailored to your individual situation. All investments involve risk, including the potential loss of principal. Past performance is no guarantee of future results. Information presented is believed to be factual and up-to-date, but Foxholm Financial does not guarantee its accuracy and it should not be regarded as a complete analysis of the subjects discussed. Before making investment decisions, consult with a qualified financial advisor who can evaluate your specific circumstances.