Brief Review of Research on the Noise Trader Approach

2013 ◽  
Author(s):  
Po-Keng Cheng
Keyword(s):  
Noise Trader

scholarly journals The Impact of Serious Safety Incidents on Airline Stocks

2021 ◽  
Vol 12 (4) ◽  
pp. 1
Author(s):  
Jeffery A. Born
Keyword(s):  
Trading Volume ◽  
Shareholder Wealth ◽  
Noise Trader ◽  
High Level ◽  
The Impact

The impact of commercial airplane crashes on the shareholder wealth of US-listed airline stocks has been the focus of many prior studies, but none have explored the concomitant impact on trading volume. We expand the scope of prior studies to include near crashes. We examine 262 ‘incidents’ from 1962 to 2018 (220 with return evidence) and document a significant (negative) wealth impact for crashes with fatalities and casualties, and an insignificant impact for incidents with no casualties. We find that log-transformed trading volume spikes upward in the three-day crash-period window and that trading volume remains abnormally high in the three plus weeks that follow the crash when casualties occur. We interpret the high level of post-event trading to be consistent with a noise trader hypothesis: naïve trading hoping to take advantage of airline stock over-reaction – which we do not detect.

GARCH in spinor field

2019 ◽  
Vol 16 (07) ◽  
pp. 1950099
Author(s):  
Richard Pincak ◽  
Kabin Kanjamapornkul
Keyword(s):  
Time Series ◽  
Gauge Group ◽  
Spinor Field ◽  
Mirror Symmetry ◽  
Euclidean Plane ◽  
Financial Time Series ◽  
Equivalent Form ◽  
Yang Mills ◽  
Noise Trader ◽  
Financial Time

We extend generalized autoregressive conditional heteroscedastic (GARCH) errors in the Euclidean plane of the scalar field to the tensor field and to the spinor field [Formula: see text], the so-called spinor garch, S-GARCH. We use the model of S-GARCH to explain the stylized fact in financial time series, the so-called volatility cluster, by using hyperbolic coordinate with induced complex lag of delay time scale in mirror symmetry concept. As the result of this theory, we obtain an equivalent form of Yang–Mills equation for financial time series as the interaction between the behavior of traders, the so-called, fundamentalist, chatlist and noise trader, by using volatility in spinor field with invariant of the gauge group [Formula: see text], the so-called modeling of the financial market in icosahedral supersymmetry gauge group.

scholarly journals The Noise Trader Approach to Finance

1990 ◽  
Vol 4 (2) ◽  
pp. 19-33 ◽  
Author(s):  
Andrei Shleifer ◽  
Lawrence H Summers
Keyword(s):  
Financial Markets ◽  
Investor Sentiment ◽  
Efficient Markets ◽  
Risky Assets ◽  
Noise Trader ◽  
Security Returns

This paper reviews an alternative to the efficient markets approach that we and others have recently pursued. Our approach rests on two assumptions. First, some investors are not fully rational and their demand for risky assets is affected by their beliefs or sentiments that are not fully justified by fundamental news. Second, arbitrage—defined as trading by fully rational investors not subject to such sentiment—is risky and therefore limited. The two assumptions together imply that changes in investor sentiment are not fully countered by arbitrageurs and so affect security returns. We argue that this approach to financial markets is in many ways superior to the efficient markets paradigm.

Noise Trader

2015 ◽  
pp. 1-3
Author(s):  
Richard W. Sias
Keyword(s):  
Noise Trader

Noise Trader Risk in Financial Markets

1990 ◽  
Vol 98 (4) ◽  
pp. 703-738 ◽  
Author(s):  
J. Bradford De Long ◽  
Andrei Shleifer ◽  
Lawrence H. Summers ◽  
Robert J. Waldmann
Keyword(s):  
Financial Markets ◽  
Noise Trader

An application of the information-adjusted noise model to the Shenzhen stock market

2016 ◽  
Vol 12 (1) ◽  
pp. 71-91 ◽  
Author(s):  
Xiaoming Xu ◽  
Vikash Ramiah ◽  
Imad Moosa ◽  
Sinclair Davidson
Keyword(s):  
Stock Market ◽  
Noise Model ◽  
Chinese Stock Market ◽  
Content Type ◽  
Asset Pricing Model ◽  
Noise Trader ◽  
Pricing Errors ◽  
Trading Activities ◽  
The Relationship ◽  
Information Pricing

Purpose – The purpose of this paper is to: first, test if information-adjusted noise model (IANM) can be applied in China; second, quantify noise trader risk, overreaction, underreaction and information pricing errors in that market; and third, explain the relationship between noise trader risk and return. Design/methodology/approach – The authors use a behavioural asset pricing model (BAPM), CAPM, the information-adjusted noise model and model proposed by Ramiah and Davidson (2010). Findings – The findings show that noise traders are active 99.7 per cent of the time on the Shenzhen A-share market. Furthermore, our results suggest that the Shenzhen market overreacts 41 per cent of the time, underreacts 18 per cent of the time and information pricing errors occur 40 per cent of the time. Originality/value – Various methods have been applied to the Chinese stock market in an effort to measure noise trading activities and all of them failed to account for information arrival. Our study uses a superior and alternative model to detect noise trader risk, overreaction and underreaction in China.

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