Research Foundation

Scientific Foundation Behind EdgeTools Models

Our quantitative trading models are built upon decades of peer-reviewed academic research from leading economists and financial scientists. Every indicator incorporates proven theories and methodologies from top-tier academic institutions.

150+

Academic Papers

25+

Years of Research

50,000+

Citations

Academic Research Categories

Six core areas of financial research that power our quantitative models.

Commodity Economics

Structural econometric models for oil, gold, and copper markets based on supply-demand dynamics and shock identification theory.

Key Papers:

Hamilton, J. D. (1983). "Oil and the Macroeconomy since World War II". Journal of Political Economy, 91(2), 228-248.

Kilian, L. (2009). "The Price of Oil and the Price of Carbon". Journal of Applied Econometrics, 24(7), 1069-1087.

Baumeister, C., & Kilian, L. (2016). "The Role of Inventories and Speculative Trading in the Global Market for Crude Oil". Journal of Applied Econometrics, 31(3), 454-478.

Focus: Supply shocks, demand elasticity, inventory dynamics

Monetary Economics

Central bank policy analysis using Taylor Rule framework and modern monetary policy transmission mechanisms.

Key Papers:

Taylor, J. B. (1993). "Discretion versus Policy Rules in Practice". Carnegie-Rochester Conference Series on Public Policy, 39, 195-214.

Clarida, R., Galí, J., & Gertler, M. (2000). "Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory". Quarterly Journal of Economics, 115(1), 147-180.

Bernanke, B. S. (2015). "Measuring the Natural Rate of Interest". Brookings Papers on Economic Activity, 2015(1), 1-87.

Focus: Interest rate policy, inflation targeting, employment

International Finance

Exchange rate determination models based on interest rate parity, purchasing power parity, and capital flow analysis.

Key Papers:

Taylor, M. P., & Taylor, A. M. (2004). "A Panel Unit Root and Cointegration Test of the PPP Hypothesis". Journal of International Economics, 64(1), 73-96.

Engel, C., & West, K. D. (2005). "Exchange Rate Pass-Through and Monetary Policy". Journal of Monetary Economics, 52(3), 485-507.

Rossi, B. (2013). "Currency Carry Trade Regimes: Beyond the Fama Regression". Journal of International Money and Finance, 33, 113-130.

Focus: Exchange rates, capital flows, policy divergence

Behavioral Finance

Market sentiment analysis using behavioral finance theories and crowd psychology patterns for market turning points.

Key Papers:

Baker, M., & Wurgler, J. (2006). "Investor Sentiment and the Cross-Section of Stock Returns". Journal of Finance, 61(4), 1645-1680.

De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). "Noise Trader Risk in Financial Markets". Journal of Political Economy, 98(4), 703-738.

Shleifer, A., & Vishny, R. W. (1997). "The Limits of Arbitrage". Journal of Finance, 52(1), 35-55.

Focus: Market sentiment, behavioral biases, crowd psychology

Asset Pricing Theory

Multi-factor models for risk assessment and return prediction using modern portfolio theory and factor analysis.

Key Papers:

Fama, E. F., & French, K. R. (2004). "The Capital Asset Pricing Model: Theory and Evidence". Journal of Economic Perspectives, 18(3), 25-46.

Fama, E. F., & French, K. R. (1993). "Common Risk Factors in the Returns on Stocks and Bonds". Journal of Financial Economics, 33(1), 3-56.

Frazzini, A., & Pedersen, L. H. (2014). "Betting Against Beta". Journal of Financial Economics, 111(1), 1-25.

Focus: Risk factors, return prediction, portfolio optimization

Machine Learning in Finance

Modern quantitative techniques including neural networks, ensemble methods, and time series analysis for financial forecasting.

Key Papers:

Gu, S., Kelly, B., & Xiu, D. (2020). "Machine Learning for Asset Management: New Developments and Financial Applications". Journal of Financial Economics, 138(2), 270-299.

Gu, S., Kelly, B., & Xiu, D. (2020). "Empirical Asset Pricing via Machine Learning". Review of Financial Studies, 33(5), 2223-2273.

Kelly, B. T., Pruitt, S., & Su, Y. (2019). "Autoencoder Asset Pricing Models". Journal of Econometrics, 209(2), 429-450.

Focus: Neural networks, ensemble methods, time series forecasting

Our Methodology

Rigorous Selection

We only implement theories and models that have been published in top-tier, peer-reviewed journals and have stood the test of time through extensive academic scrutiny.

Practical Implementation

Academic theories are carefully adapted and optimized for real-world trading applications while maintaining their theoretical integrity and statistical validity.