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The Journal of Portfolio Management

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Using a Mean-Changing Stochastic Processes Exit–Entry Model for Stock Market Long–Short Prediction

Sebastien Lleo, Mikhail Zhitlukhin and William T. Ziemba
The Journal of Portfolio Management November 2022, 49 (1) 172-197; DOI: https://doi.org/10.3905/jpm.2022.1.429
Sebastien Lleo
is a professor of finance at NEOMA Business School in Reims, France
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Mikhail Zhitlukhin
is a research fellow at the Steklov Institute in Moscow, Russia
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William T. Ziemba
is an alumni professor emeritus of financial modeling and stochastic optimization at the University of British Columbia in Vancouver, BC, Canada, and a distinguished visiting research associate in the Systemic Risk Centre at the London School of Economics in London, UK
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Abstract

This article has a unique application to the study of mean-changing models in stock markets. The idea is to enter and exit stock markets like Apple Computer stock (AAPL) and the broad S&P 500 Index (S&P 500) at good times and prices (long and short). Mean estimation is far more important to portfolio success than the estimation of variance or covariance. The idea in the stochastic-process model is to determine when the mean changes and then reverse the position direction. This was applied to AAPL in 2012 when its stock price rallied dramatically and then had a large fall, and to AAPL and the S&P 500 in the extremely difficult and volatile January to June 2020 period of COVID-19 and through 2021.

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The Journal of Portfolio Management: 49 (1)
The Journal of Portfolio Management
Vol. 49, Issue 1
November 2022
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Using a Mean-Changing Stochastic Processes Exit–Entry Model for Stock Market Long–Short Prediction
Sebastien Lleo, Mikhail Zhitlukhin, William T. Ziemba
The Journal of Portfolio Management Oct 2022, 49 (1) 172-197; DOI: 10.3905/jpm.2022.1.429

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Using a Mean-Changing Stochastic Processes Exit–Entry Model for Stock Market Long–Short Prediction
Sebastien Lleo, Mikhail Zhitlukhin, William T. Ziemba
The Journal of Portfolio Management Oct 2022, 49 (1) 172-197; DOI: 10.3905/jpm.2022.1.429
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  • Article
    • Abstract
    • BUBBLELIKE MARKETS
    • CHANGEPOINT DETECTION AND BUBBLE MARKETS
    • BUBBLELIKE MARKETS CHALLENGE OUR ABILITY TO HAVE ACCURATE MEAN ESTIMATES IN PORTFOLIO-SELECTION PROBLEMS
    • A PRIMER ON CHANGEPOINT-DETECTION METHODS IN FINANCIAL TIME SERIES
    • BACKGROUND AND IMPORTANCE OF APPLE COMPUTER STOCK
    • PRICE HISTORY OF AAPL
    • APPLYING THE MODEL TO AAPL IN 2012
    • APPLYING THE MODEL IN THE 2020–2021 COVID-19 PERIOD
    • A TEST OF THE MEAN-CHANGING EXIT MODEL DURING THE JANUARY 1–MAY 31, 2020, COVID-19 PERIOD ON AAPL AND THE S&P 500
    • APPLYING THE MEAN-CHANGING MODEL TO PREVIOUS STOCK MARKET CRASHES: UNITED STATES, CHINA, AND ICELAND
    • THE GREAT CRASH IN 1929
    • THE 1987 CRASH OF THE S&P 500
    • THE 2000–2002 INTERNET BUBBLE CRASH
    • THE 2008 CRASH
    • THE 2007–2009 CRASH IN ICELAND
    • THE 2015 CRASH IN CHINA
    • CONCLUSION
    • REFERENCES
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