Student Theses and Dissertations

Date of Award


Document Type


Degree Name

Bachelor of Arts (BA)

Honors Designation


Program of Study




First Advisor

Sebastiano Manzan


Technical analysis, or the forecasting of asset price movements using past prices, is commonly practiced in financial markets but poorly explained by mainstream economic theory. I show that a technical rule can have predictive power when an asset’s payoffs are subject to Knightian uncertainty, defined as variation that cannot be described probabilistically (Knight, 1921). I present an asset-pricing model in which asset payoffs undergo periodic shifts in trend, and agents form expectations about these payoffs using a constant gain least squares (CGLS) rule. I investigate whether a second CGLS rule, operating on price, can provide a more accurate forecast of payoffs during the periods following a trend shift. I estimate the model using corporate earnings data from the S&P 500, and present simulation results that show support for the usefulness of technical analysis. Because technical analysis may influence the behavior of asset markets, this finding has potential implications for investment, risk management and financial policy.


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