Early warning on stock market bubbles via methods of optimization, clustering and inverse problems

2017 ◽  
Vol 260 (1-2) ◽  
pp. 293-320 ◽  
Author(s):  
Efsun Kürüm ◽  
Gerhard-Wilhelm Weber ◽  
Cem Iyigun
2020 ◽  
Vol 2 (1) ◽  
pp. 22-31
Author(s):  
Hyang Lee ◽  
Jungyoung Park

By analyzing the financial bubbles, it can be observed that every bubble burst causes financial turmoil and severe economic recession. The paper utilize the technical analysis indicators for providing warning conditions for predicting the stock market bubbles. The significance of the technical analysis indicators are that they can provide early warning system for financial crisis and can help in avoiding such problems. We applied the early warning technical indicators to the stock market of US, South Korea, Brazil, China, Germany, and Japan for the period of 1995 to 2018. We also made comparison of the bubble warning conditions of emerging markets and the mature markets. The standard bubble warning conditions include K value 90, Bias 10%, and RSI value of 90.  Empirical results shows that mature market shows less degree of volatility and lowering the warning baseline can improve the accuracy of bubble predictions. Furthermore, results shows that mature markets bursting time is about 6 months while in emerging market, the time is reduced to only 3 months. These results also indicate that mature market has good antirisk ability compare to the emerging markets.


Author(s):  
Murat Acar ◽  
Dilek Karahoca ◽  
Adem Karahoca

This chapter focuses on building a financial early warning system (EWS) to predict stock market crashes by using stock market volatility and rising stock prices. The relation of stock market volatility with stock market crashes is analyzed empirically. Also, Istanbul Stock Exchange (ISE) national 100 index data used to achieve better results from the view point of modeling purpose. A risk indicator of stock market crash is computed to predict crashes and to give an early warning signal. Various data mining classifiers are compared to obtain the best practical solution for the financial early warning system. Adaptive neuro fuzzy inference system (ANFIS) model was proposed to forecast stock market crashes efficiently. Also, ANFIS was explained in detail as a training tool for the EWS. The empirical results show that the fuzzy inference system has advantages to gain successful results for financial crashes.


2019 ◽  
Vol 121 ◽  
pp. 129-136 ◽  
Author(s):  
Xiao-Li Gong ◽  
Xi-Hua Liu ◽  
Xiong Xiong ◽  
Xin-Tian Zhuang

1995 ◽  
Vol 55 (3) ◽  
pp. 655-665 ◽  
Author(s):  
Eugene N. White

Research in finance is guided by powerful intuitions from models of efficient markets. However, researchers have uncovered a number of puzzles that are not explained by these models. Such anomalies include the excess volatility of stock prices, the closed-end mutual fund paradox, and the mean reversion in stock prices that produces predictable returns for long holding periods.1 Whereas financial economists all recognize the existence of these puzzles, they disagree about how they can be explained. Robert J. Shiller argues, for example, that efficient-markets models cannot hope to explain these anomalies and looks to alternatives that incorporate fads.2 In contrast, John H. Cochrane believes that the puzzles can be explained by improved models of fundamentals.3


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