Why We Built a Transparent Entry Timing Model
Most stock screeners display a single "entry score" with no explanation of how it's computed. At Blank Capital, we believe that any quantitative signal shown to investors should be grounded in peer-reviewed research and fully transparent.
Our Entry Timing component doesn't produce a single score. Instead, it evaluates 5 independent factor signals, each backed by seminal papers from the Journal of Finance and the Journal of Financial Economics. The result: a transparent alignment count (e.g., "4 of 5 factors bullish") that tells you exactly what the data says — and cites why.
The 5 Factor Signals
1. Time-Series Momentum
Paper: Moskowitz, T., Ooi, Y.H., & Pedersen, L.H. (2012). "Time Series Momentum." Journal of Financial Economics, 104(2), 228-250.
What it measures: Whether the stock's current price is above or below its 200-day simple moving average (SMA200).
The research: Moskowitz et al. documented that assets exhibiting positive returns over the past 12 months tend to continue generating positive returns. This "time-series momentum" effect is distinct from cross-sectional momentum and persists across equities, bonds, commodities, and currencies. We implement this by comparing the current price to SMA200 — a 10-month lookback that aligns with the paper's findings.
Signal direction: Price above SMA200 → Bullish. Price below SMA200 → Bearish.
2. Cross-Sectional Momentum
Paper: Jegadeesh, N. & Titman, S. (1993). "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency." The Journal of Finance, 48(1), 65-91.
What it measures: How a stock's recent momentum ranks relative to its peers (percentile ranking).
The research: This foundational paper demonstrated that stocks that have performed well over the past 3-12 months tend to outperform, while recent losers tend to underperform. The effect generates statistically significant abnormal returns even after adjusting for risk. We use a cross-sectional percentile rank to measure where any given stock falls within the full universe.
Signal direction: Above 60th percentile → Bullish. Below 40th percentile → Bearish. 40-60 → Neutral.
3. Short-Term Reversal
Paper: Jegadeesh, N. (1990). "Evidence of Predictable Behavior of Security Returns." The Journal of Finance, 45(3), 881-898.
What it measures: The stock's return over the past month.
The research: Jegadeesh showed that stocks exhibit mean reversion at very short horizons (1 month). Recent losers tend to bounce back, and recent winners tend to pull back. This effect is opposite to the momentum effect and operates at a different time scale. For entry timing, a recent pullback (negative 1-month return) can signal a better entry point.
Signal direction: 1-month return < -3% → Bullish (pullback = opportunity). 1-month return > +5% → Bearish (extended). Between → Neutral.
4. 52-Week High Anchor
Paper: George, T.J. & Hwang, C.Y. (2004). "The 52-Week High and Momentum Investing." The Journal of Finance, 59(5), 2145-2176.
What it measures: How close the stock's current price is to its 52-week high.
The research: This is perhaps the most counterintuitive factor. George and Hwang demonstrated that stocks near their 52-week highs tend to keep going up, not reverse. The 52-week high serves as a psychological anchor — traders are reluctant to buy at or above the 52-week high due to anchoring bias, creating underreaction that leads to continuation. The paper found that nearness to the 52-week high explains a large portion of the momentum effect.
Signal direction: Price above 90% of 52-week range → Bullish (continuation expected). Below 30% → Bearish. Between → Neutral.
Key insight: Many retail tools treat proximity to the 52-week high as bearish ("stock is expensive"). The academic evidence says the opposite — this is a common mistake we've corrected.
5. Trend Following (Golden/Death Cross)
Paper: Han, Y., Yang, K., & Zhou, G. (2013). "A New Anomaly: The Cross-Sectional Profitability of Technical Analysis." Journal of Financial and Quantitative Analysis, 48(5), 1433-1461.
What it measures: Whether the 50-day SMA is above or below the 200-day SMA.
The research: Han, Yang, and Zhou provided rigorous academic validation that moving average crossover signals — often dismissed as "technical analysis" — have genuine predictive power. The Golden Cross (SMA50 > SMA200) systematically identifies stocks in positive trends, generating abnormal returns that cannot be explained by standard risk factors.
Signal direction: SMA50 above SMA200 → Bullish (Golden Cross). SMA50 below SMA200 → Bearish (Death Cross).
Why Equal Weighting?
Paper: DeMiguel, V., Garlappi, L., & Uppal, R. (2009). "Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio?" Review of Financial Studies, 22(5), 1915-1953.
We deliberately use equal weights (1/N) across all five factors rather than optimized weights. DeMiguel et al. showed that, in practice, simple equal-weight portfolios consistently outperform optimized portfolios on an out-of-sample basis — because estimation error in parameters often outweighs the theoretical gains from optimization. By using equal weights, we avoid overfitting and ensure the model remains robust across different market environments.
What We Show (And Don't)
- We show: Each factor's current direction (bullish, bearish, or neutral), its actual value, the academic citation, and a brief interpretation.
- We show: An alignment count: "X of 5 factors bullish." This tells you how many independent signals agree.
- We don't show: A single composite "score" that obscures how the number was reached.
- We don't show: Arbitrary confidence intervals or probabilities that imply false precision.
Methodology Disclosure
All factor signals are computed from the latest available market data. Time-series signals (price, SMA50, SMA200) use daily closing prices. Cross-sectional momentum uses our proprietary percentile ranking across the full stock universe. All thresholds and directions are based on the published research cited above — not backtested or optimized for appearance.
This is not investment advice. Factor signals describe current market conditions and historical patterns — they do not predict future returns. Past performance, including the performance of these academic factors, does not guarantee future results.
