Junyi Liao

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I am a lecturer in finance at Essex Business School, University of Essex. I obtained my PhD in economics from LSE. My current research is on expectations formation and its macro and finance implications. 

Contact: junyi.liao@essex.ac.uk   twitter 

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Working Papers

Over-/Underreaction and Judgment Noise in Expectation Formation Link 

In forecast surveys of aggregate macroeconomic and financial variables, the correlation between forecast errors and forecast revisions is positive at the consensus level, but negative at the individual level. Past literature has interpreted this discrepancy as evidence of underreaction to news at the aggregate level and overreaction at the individual level. In this paper, I challenge this view by arguing that noise in predictive judgment can account for the difference. Using a stylized model, I examine how introducing judgment noise at the individual level changes the interpretation of the correlation coefficients. A negative coefficient at the individual level no longer necessarily means overreaction. Using forecast survey data, I provide evidence that judgment noise is large enough to reconcile the difference between the two coefficients. The structural parameter measuring over-/underreaction mainly points to underreaction, regardless of whether the model matches correlation coefficients at the individual or aggregate level.

Presented at: FIRS 2024, University of Essex 2024, Asia Meeting of Econometric Society 2023, Shandong University 2023, HKUST 2023, Jinan University 2023, Money, Macro and Finance Society 2023, China Financial Research Conference 2023

Adaptive Expectations and Over-/Under-reaction to New Information 

This article first investigates whether adaptive expectation implies over- or under-reaction to new information. It is shown that the occurrence of over- or under-reaction using adaptive expectations is contingent on both the weighting parameter used in forecasts and the persistence of the associated actual variable. Furthermore, compared to the generalized diagnostic expectations model, the adaptive expectations framework can better match the under-reaction to new information for several variables, as measured by the correlations between forecast errors and forecast revisions. This advantage stems from the capacity of adaptive expectations to allow varying degrees of stickiness in expectations, a feature not present in the generalized diagnostic expectations model. Additionally, adaptive expectations account for two empirical phenomena: an increasing degree of over-reaction with forecast horizons and an initial under-reaction followed by over-shooting upon shocks.


EC220 Introduction to Econometrics, London School of Economics   2018-2019

EC400 Mathematics for Macroeconomics and Microeconomics, London School of Economics   2018-2021

EC210 Macroeconomic Principle, London School of Economics   2019-2023

BE334 Financial Market and Monetary Policy, University of Essex   2023-2024

BE364 Global Trading on the Financial Market, University of Essex   2023-2024