Rational or Irrational? A Comprehensive Studies on Stock Market Crashes

2017 ◽  
Author(s):  
Tai Ma ◽  
Kuo Hsi Lee ◽  
Chien Huei Lai ◽  
Yang Shen Lee
Keyword(s):  
Stock Market ◽  
Market Crashes ◽  
Comprehensive Studies

Rational or Irrational? A Comprehensive Studies on Stock Market Crashes

2017 ◽  
Author(s):  
Tai Ma ◽  
Kuo Hsi Lee ◽  
Chien Huei Lai ◽  
Yang Shen Lee
Keyword(s):  
Stock Market ◽  
Market Crashes ◽  
Comprehensive Studies

Rational Panics and Stock Market Crashes

1999 ◽  
Author(s):  
Pietro Veronesi ◽  
Gadi Barlevy
Keyword(s):  
Stock Market ◽  
Market Crashes

scholarly journals Market shocks and professionals' investment behavior – Evidence from the COVID-19 crash

2020 ◽  
Author(s):  
Christoph Huber ◽  
Juergen Huber ◽  
Michael Kirchler
Keyword(s):  
Stock Market ◽  
Risk Aversion ◽  
Risk Taking ◽  
Investment Behavior ◽  
Controlled Experiments ◽  
Price Expectations ◽  
Market Crashes ◽  
Changes In Risk ◽  
Risk Taking Behavior ◽  
Market Shocks

We investigate how the experience of stock market shocks, such as the COVID-19 crash, influences risk-taking behavior. To isolate changes in risk taking from other factors during stock market crashes, we ran controlled experiments with finance professionals in December 2019 and March 2020. We observe that their investments in the experiment were 12 percent lower in March 2020 than in December 2019, although their price expectations had not changed, and although they considered the experimental asset less risky during the crash than before. Thus, lower investments are driven by higher risk aversion, not by changes in beliefs.

Prediction of Bubbles, Crashes, and Antibubbles

2017 ◽  
Author(s):  
Didier Sornette
Keyword(s):  
Stock Market ◽  
Case Studies ◽  
Power Law ◽  
Statistical Tests ◽  
Statistical Significance ◽  
Large Market ◽  
Simple Power ◽  
Market Crashes ◽  
Successes And Failures

This chapter examines how to predict stock market crashes and other large market events as well as the limitations of forecasting, in particular in terms of the horizon of visibility and expected precision. Several case studies are presented in detail, with a careful count of successes and failures. After providing an overview of the nature of predictions, the chapter explains how to develop and interpret statistical tests of log-periodicity. It then considers the concept of an “antibubble,” using as an example the Japanese collapse from the beginning of 1990 to the present. It also describes the first guidelines for prediction, a hierarchy of prediction schemes that includes the simple power law, and the statistical significance of the forward predictions.

Modeling Financial Bubbles and Market Crashes

2017 ◽  
Author(s):  
Didier Sornette
Keyword(s):  
Stock Market ◽  
Hazard Rate ◽  
Market Price ◽  
Imitative Behavior ◽  
Rational Model ◽  
Basic Principles ◽  
Market Prices ◽  
Speculative Bubbles ◽  
Financial Bubbles ◽  
Market Crashes

This chapter considers two versions of a rational model of speculative bubbles and stock market crashes. According to the first version, stock market prices are driven by the crash hazard that may increase sometimes due to the collective behavior of “noise traders.” The second version assumes the opposite: the crash hazard is driven by prices that may soar sometimes, again due to investors' speculative or imitative behavior. The chapter first provides an overview of what a model is before discussing the basic principles of model construction in finance. It then describes the basic ingredients of the two models of speculative bubbles and market crashes, along with the main properties of the risk-driven model. It also examines how imitation and herding drive the crash hazard rate and concludes with an analysis of the price-driven model, how imitation and herding drive the market price, and how the price return drives the crash hazard rate.

Designing an Early Warning System for Stock Market Crashes by Using ANFIS

2011 ◽  
pp. 109-127
Author(s):  
Murat Acar ◽  
Dilek Karahoca ◽  
Adem Karahoca
Keyword(s):  
Stock Market ◽  
Early Warning ◽  
Fuzzy Inference System ◽  
Early Warning System ◽  
Fuzzy Inference ◽  
Warning System ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Inference System ◽  
Market Crashes

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.

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