Asset price deviation from fundamentals can be caused by traders’ behavioral
weaknesses, such as overconfidence, during a period of growing successes. The authors
propose a model that can identify the misvaluation of stocks because of these weaknesses,
and they suggest a trading strategy based on the model.
The authors aim to identify the misvaluation of stocks as a result of behavioral weaknesses
shown by traders. They propose a model, based on an extension of the market model, that
finds cases in which the asset valuation differs from fundamentals because of trader biases.
Their model also allows the magnitude of these misvaluations to be measured. A trading
strategy based on the model is proposed and backtested. The authors conclude that following
the strategy can lead to superior returns; however, because the model does not predict
turning points in the valuation of assets, it is more suited to a long-term strategy.
How Is This Research Useful to Practitioners?
The proposed model is a tractable model that practitioners can use as an input for the
investment decision-making processes. The model helps identify situations in which stocks
are misvalued because of cognitive biases exhibited by traders. It is an extension of the
market model, augmented by a composite error term, that captures systematic errors in asset
The results obtained from applying the model suggest that it can identify and measure the
magnitude of misvaluation in stocks. In addition, the authors suggest a practical trading
strategy that can be applied using the model. Results from back testing the strategy show
that there is potential for earning above-average returns even after adjustments are made
for risk and transaction costs.
Practitioners will be able to leverage the model for identifying stock misvaluation in
their own analysis. The model can also estimate the magnitude of the misvaluation, which
allows stocks to be compared.
How Did the Authors Conduct This Research?
The market model describes an individual stock’s return as a function of the market
return, sensitivity to the market return (beta), unsystematic return (alpha), and a single
error term to account for the “white noise” element of stock returns. Under the
assumptions of the market model, this error term has an expected mean of zero.
The model proposed by the authors extends the market model to allow for systematic errors
caused by behavioral biases exhibited by traders. This extension is achieved by including a
dual error term: one traditional error term with an expected mean of zero and one term that
is driven by behavioral bias. Using a maximum likelihood method, the authors estimate the
coefficient of the error term, by which undervaluation or overvaluation can be
Once defined, the authors apply the model to Toyota Motor Corporation stock, which is stock
that is known to have been misvalued and subsequently corrected. Between 2002 and early
2010, Toyota grew to become the largest automobile manufacturer in the world, leading to a
pronounced rise in its stock price. But after the accelerator pedal recall in 2010, the
stock price suffered. The authors confirm that their model could identify the initial
misvaluation followed by a return to the fundamental valuation. There is evidence of
overvaluation leading up to the product recall; after the recall, there is a significant
correction, after which the model shows no significant bias. The coefficients of the error
term give an estimate of the magnitude of the bias.
Using the companies in the Dow Jones Industrial Average between 2005 and 2010, the authors
further test the robustness of the model by back testing a trading strategy based on the
model. The strategy is to equally invest in all of the companies that are identified as
significantly undervalued on an annual basis. The authors find that even after adjusting for
transaction costs and risk, the strategy can earn returns superior to a passive investment
in the market over the same period.
The authors’ model provides a method for traders to identify stocks that deviate from
their fundamental valuations, leading to an opportunity for superior returns. The model
itself is tractable and practical to apply, which increases the likelihood of its use by
practitioners. As noted by the authors, the limitation of such a trading strategy is that no
model can predict a market turning point.