Alpha Pulsx isn't built on marketing claims. Our methodology is grounded in published academic research — peer-reviewed studies from the world's leading finance journals. Here's the research that informs our approach.
Forecast Combination
Rapach, Strauss & Zhou (2010) — "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy." Review of Financial Studies.
This landmark study demonstrated that combining independent forecasting models consistently outperforms any single predictor in out-of-sample equity premium prediction. This is the foundation of our six-pillar confluence approach — each dimension is an independent model, and their combination is more robust than any one alone.
Ensemble Methods
Gu, Kelly & Xiu (2020) — "Empirical Asset Pricing via Machine Learning." Review of Financial Studies.
Showed that ensemble methods that combine multiple signals doubled the performance of leading single-signal strategies. This validates the multi-dimensional approach at the core of Alpha Pulsx.
Momentum Premium
Jegadeesh & Titman (1993) — "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency." Journal of Finance.
The foundational momentum study demonstrating that strategies based on 3–12 month price momentum yield approximately 1% per month. This validates the swing-trading timeframe that Alpha Pulsx targets — intermediate-term, not intraday noise.
Options Lead Price Discovery
Pan & Poteshman (2006) — "The Information in Option Volume for Future Stock Prices." Review of Financial Studies.
Demonstrated that options markets contain significant predictive information about future stock returns. Institutional participants often express views through options before equities — which is why options flow analysis is one of our six dimensions.
The Cost of Overtrading
Barber & Odean (2000) — "Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors." Journal of Finance.
Showed that the most active traders underperformed passive investors by 6.5% annually. This is why Alpha Pulsx generates signals once daily (with a 10 AM refresh), designed for swing trading — not intraday chasing.
Behavioral Finance
Kahneman & Tversky (1979) — "Prospect Theory: An Analysis of Decision under Risk." Econometrica.
The foundational behavioral finance study showing that loss aversion leads to systematic biases — including the disposition effect (selling winners too early, holding losers too long). Our signal continuity tracking and clear invalidation rules are designed to counteract these biases.
Day Trading Failure Rates
Chague, De-Losso & Giovannetti (2020) — "Day Trading for a Living?"
Found that 97% of persistent day traders lost money. Combined with the DALBAR QAIB study showing average equity investors underperform the S&P 500 by 5–8% annually, the evidence overwhelmingly supports less-frequent, research-driven decision-making over reactive trading.
Multi-Factor Models
Fama & French (1993, 2015) — Three-factor and five-factor models. Journal of Financial Economics.
Established that multiple independent factors (market risk, size, value, profitability, investment) explain cross-sectional stock returns. Our fundamental quality dimension draws on these factor-based insights.
Fundamental Scoring
Piotroski (2000) — "Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers."
Demonstrated that a simple 9-point scoring system based on financial statement data can separate winning and losing stocks within value portfolios. Our fundamental dimension incorporates these quality metrics alongside valuation.