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Detecting market bubbles: A generalized LPPLS neural network model

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  • Ma, Juntao
  • Li, Chenchen

Abstract

To enhance bubble detection capabilities, we introduce two significant improvements to the Log-Periodic Power Law Singularity (LPPLS) model: (1) a novel fitting approach, which yields more accurate predictions of critical price distributions within a single sample window; (2) a restructured neural network approach further enhances the estimations of the probability distributions of the critical points across both time and price dimensions, and it can be fine-tuned with real-world data. The simulation and practical applications to typical asset price bubbles in cryptocurrencies, commodities, and equity indices demonstrate that our refined model, the Generalized-LPPLS Neural Network (G-LPPLS-NN), outperforms all other models we examined in terms of predictive accuracy for critical point distributions.

Suggested Citation

  • Ma, Juntao & Li, Chenchen, 2024. "Detecting market bubbles: A generalized LPPLS neural network model," Economics Letters, Elsevier, vol. 244(C).
    • Handle: RePEc:eee:ecolet:v:244:y:2024:i:c:s0165176524004877
    DOI: 10.1016/j.econlet.2024.112003
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    References listed on IDEAS

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    1. Dilip Abreu & Markus K. Brunnermeier, 2003. "Bubbles and Crashes," Econometrica, Econometric Society, vol. 71(1), pages 173-204, January.
    2. Anders Johansen & Olivier Ledoit & Didier Sornette, 2000. "Crashes As Critical Points," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(02), pages 219-255.
    3. John M. Griffin & Amin Shams, 2020. "Is Bitcoin Really Untethered?," Journal of Finance, American Finance Association, vol. 75(4), pages 1913-1964, August.
    4. Can Gao & Ian W. R. Martin, 2021. "Volatility, Valuation Ratios, and Bubbles: An Empirical Measure of Market Sentiment," Journal of Finance, American Finance Association, vol. 76(6), pages 3211-3254, December.
    5. John M. Griffin & Jeffrey H. Harris & Tao Shu & Selim Topaloglu, 2011. "Who Drove and Burst the Tech Bubble?," Journal of Finance, American Finance Association, vol. 66(4), pages 1251-1290, August.
    6. Joshua Nielsen & Didier Sornette & Maziar Raissi, 2024. "Deep LPPLS: Forecasting of temporal critical points in natural, engineering and financial systems," Swiss Finance Institute Research Paper Series 24-33, Swiss Finance Institute.
    7. H. K. Moffatt, 2000. "Euler's disk and its finite-time singularity," Nature, Nature, vol. 404(6780), pages 833-834, April.
    8. Didier SORNETTE & Guilherme DEMOS & Zhang QUN & Peter CAUWELS & Vladimir FILIMONOV & Qunzhi ZHANG, 2015. "Real-Time Prediction and Post-Mortem Analysis of the Shanghai 2015 Stock Market Bubble and Crash," Swiss Finance Institute Research Paper Series 15-32, Swiss Finance Institute.
    9. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    10. Lin, L. & Ren, R.E. & Sornette, D., 2014. "The volatility-confined LPPL model: A consistent model of ‘explosive’ financial bubbles with mean-reverting residuals," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 210-225.
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