Identifying the Transition from Efficient-Market to Herding Behavior: Using a Method from Econophysics

Torrecillas, María José Muñoz; Yalamova, Rossitsa; McKelvey, Bill

doi:10.1080/15427560.2016.1170680

Cited by 12 publications

(3 citation statements)

References 182 publications

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“…Continuing the line of investigation in the literature on log-periodic power law models, Cajueiro et al (2009) investigate whether crashes in the Brazilian stock market could be identified using intraday observations. Another econophysics approach presented by Munoz Torrecillas et al (2016) suggests that the dot-com bubble can be identified. Within this framework, the primary focus is to find the moment in stock trading at which the transition between efficient market behavior and herding behavior occurs.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

When to Hedge Downside Risk?

Giannikos,

Guirguis,

Kakolyris

et al. 2024

Risks

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Hedging downside risk before substantial price corrections is vital for risk management and long-only active equity manager performance. This study proposes a novel methodology for crafting timing signals to hedge sectors’ downside risk. These signals can be integrated into existing strategies simply by purchasing sector index put options. Our methodology generates successful signals for price corrections in 2000 (dot-com bubble) and 2008 (global financial crisis). A key innovation involves utilizing sector correlations. Major price swings within six months are signaled when a sector exhibits high valuation alongside abnormal correlations with others. Utilizing the price-to-earnings ratio for identifying sectors’ high valuations is more beneficial than the bond–stock earnings yield differential. Our signals are also more efficient than those of standard technical analyses.

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Section: Introduction and Literature Reviewmentioning

confidence: 99%

When to Hedge Downside Risk?

Giannikos,

Guirguis,

Kakolyris

et al. 2024

Risks

View full text Add to dashboard Cite

show abstract

“…Herding behavior exhibited by an investor takes his decision to rely on others, which results in resentment about the return on investment. If herding behavior is not revealed by investors they are competent of succeeding independent investment choices and all they achieve their investments elevate their confidence as well (Munoz Torrecillas, Yalamova, & McKelvey, 2016). Theory of prospects describe the emotional bias in loss aversion behaviour.…”

Section: Introductionmentioning

confidence: 99%

The Role of Behavioral Factors on Investment Decision Making: Moderating Role of Financial Literacy

Butt,

Sajjad,

Awan

et al. 2023

Pak. J. Humanit. Soc. Sci

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The purpose of this paper is to examine the impact of behavioral finance factors on stock market investment behavior. Moreover, it also determines the moderating role of financial literacy in behavioral finance factors and stock investment in Pakistan. Studies have been conducted on Pakistan Stock market investors. Data has been collected through a structured questionnaire. The survey was performed among 165 investors in the Pakistan stock market. SPSS and Smart PLS 3.0 were used to test the hypotheses. The study concluded that Overconfidence, Herding, Loss Aversion, and Risk Perception significantly impacted the performance of Stock Investment decision-making. Moreover, Stock Investment decision-making was also moderated by Financial Literacy except for risk perception. The findings of this study would help to comprehend the behavior of investors in the Pakistan stock market. Moreover, to achieve favorable outcomes, investors must possess financial literacy about the stock market.

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“…The intuition behind this extension is the emersion of modern socioeconomics systems which are highly interdependent with an intricate financial structure. Recent studies conducted by econophysicists have reported that the complex dynamics of stock returns contain many applications of complex system analysis (Huang, Yao, Zhuang, & Yuan, 2017;Lillo, Farmer, & Mantegna, 2003;Mantegna & Stanley, 2000;McCauley, 2006;Muñoz Torrecillas, Yalamova, & McKelvey, 2016;Pan & Sinha, 2007;Yang & Yang, 2008). For instance, it is shown that the return behavior has sudden trend changes for a large number of stocks or, in an unsynchronized fashion, effecting only a few stocks at the same time (see (Hirshleifer & Shumway, 2003;Olson, 2006)).…”

Section: Introductionmentioning

confidence: 99%

Market Moods and Network Dynamics of Stock Returns: The Bipolar Behavior

Ajirlou

Esmalifalak

et al. 2019

Journal of Behavioral Finance

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We show that a simple mood-separable preference in a network study of stock returns captures a variety of stylized facts regarding stocks' provisional (ab)normal behavior. These behaviors are articulated in a multi-state complete Euclidean network model that specifies the existence, direction and magnitude of a self-organized dynamics for each individual stock during abnormal market moods. In the empirical setting, we apply suggested model along with two established visual approaches (MDS and AHC) for benchmark purposes. Results reveal different levels of erratic return dynamics for each stock and the entire market in different abnormal market moods. We model and interpret these self-organized dynamics as evidence of stocks' and market's bipolar behavior.

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Identifying the Transition from Efficient-Market to Herding Behavior: Using a Method from Econophysics

Cited by 12 publications

References 182 publications

When to Hedge Downside Risk?

When to Hedge Downside Risk?

The Role of Behavioral Factors on Investment Decision Making: Moderating Role of Financial Literacy

Market Moods and Network Dynamics of Stock Returns: The Bipolar Behavior

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