forecasting crashes

Doug Henwood dhenwood at panix.com
Sat Jan 8 10:23:05 PST 2000


[all you connoisseurs of chaos out there!]

"Forecasting Crashes: Trading Volume, Past Returns and

Conditional Skewness in Stock Prices"

BY: JOSEPH CHEN

Stanford University

Graduate School of Business

HARRISON G. HONG

Stanford University

JEREMY C. STEIN

Massachusetts Institute of Technology (MIT)

Harvard Business School

Document: Available from the SSRN Electronic Paper Collection:

http://papers.ssrn.com/paper.taf?abstract_id=194948

Other Electronic Document Delivery:

http://www.stanford.edu/~chenjs/files/Forecasting_Cras

hes.pdf

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Date: December 1999

Contact: JOSEPH CHEN

Email: Mailto:chen_joseph at gsb.stanford.edu

Postal: Stanford University

Graduate School of Business

Stanford, CA 94305-5015 USA

Phone: (650) 723-4877

Fax: (650) 725-7462

Co-Auth: HARRISON G. HONG

Email: Mailto:hghong at leland.stanford.edu

Postal: Stanford University

518 Memorial Way

Stanford, CA 94305-5015 USA

Co-Auth: JEREMY C. STEIN

Email: Mailto:jcstein at mit.edu

Postal: Massachusetts Institute of Technology (MIT)

50 Memorial Drive

Cambridge, MA 02142 USA

ABSTRACT:

This paper is an investigation into the determinants of

asymmetries in stock returns. We develop a series of

cross-sectional regression specifications which attempt to

forecast skewness in the daily returns of individual stocks.

Negative skewness is most pronounced in stocks that have

experienced: 1) an increase in trading volume relative to trend

over the prior six months; and 2) positive returns over the

prior thirty-six months. The first finding is consistent with

the model of Hong and Stein (1999), which predicts that negative

asymmetries are more likely to occur when there are large

differences of opinion among investors. The latter finding fits

with a number of theories, most notably Blanchard and Watson's

(1982) rendition of stock-price bubbles. Analogous results also

obtain when we attempt to forecast the skewness of the aggregate

stock market, though our statistical power in this case is

limited.

JEL Classification: G1



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