Market Crowd Trading Conditioning and Its Measurement

Shi, Leilei; Han, Liyan; Wang, Yiwen; Chen, Ding; Piao, Yan; Gou, Chengling

doi:10.2139/ssrn.1661515

Cited by 8 publications

(12 citation statements)

References 75 publications

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“…This is most likely due to the limited availability of datasets that cover a wide range of selling events and market environments. Using price and volume information as well as data from Chinese stock markets, Shi et al (2011) provided a theoretical model based on conditioning to explain investor behavior. Their model shows that investors can be either overconfident or panicked based on price momentum.…”

Section: Panic Sellingmentioning

confidence: 99%

When Do Investors Freak Out? Machine Learning Predictions of Panic Selling

Elkind¹,

Kaminski²,

Lo³

et al. 2021

JFDS

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Using a novel dataset of 653,455 individual brokerage accounts belonging to 298,556 households, we document the frequency, timing, and duration of panic sales, which we define as a decline of 90% of a household account's equity assets over the course of one month, of which 50% or more is due to trades. We find that a disproportionate number of households make panic sales when there are sharp market downturns, a phenomenon we call 'freaking out.' We show that panic selling and freak-outs are predictable and fundamentally different from other well-known behavioral patterns such as overtrading or the disposition effect. Highlights• We define a heuristic to identify investors who sell off a substantial proportion of their risky assets abruptly, or panic sell. We find that investors panic sell more often when there are large market movements.• Panic selling is suboptimal if executed in an improving market, but it is beneficial as a stop-loss mechanism in rapidly deteriorating markets.• Artificial intelligence techniques can identify individuals at risk of panic selling in the near future when given the demographic characteristics of the investor, their portfolio histories, and current and past market conditions.

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Section: Panic Sellingmentioning

confidence: 99%

When Do Investors Freak Out? Machine Learning Predictions of Panic Selling

Elkind¹,

Kaminski²,

Lo³

et al. 2021

JFDS

View full text Add to dashboard Cite

show abstract

“…It is an indirect cause-and-effect or functional explanation. In the study of the trading action, a functional explanation might refer to not only the information and news-heuristics and representativeness, but also the outcomes of prior trading-adaptation by return reinforcement or punishment (Tversky and Kahneman, 1974;Staddon, 2010;Shi et al, 2010). The former functional explanation is classified into template learning, and the latter is called as reinforced learning (Staddon, 2010).…”

Section: Two Kinds Of Explanationmentioning

confidence: 99%

“…It is a new method in mathematical finance. However, the implications in the differential equation have not yet been understood for economists and finance people, for examples, trading volume probability, stationary equilibrium 2 , liquidity utility hypothesis, trading conditioning (Shi et al, 2010), and the least trading price variation to generate optimal choice and preference structure in interactive and competitive trading action etc. We explain them and thus form the subject of the present study.…”

Section: Introductionmentioning

confidence: 99%

“…Except for the internal, covert, and biological elements and factors of excessive trading volume 5 , a long list of external, overt, and environmental elements and factors, from which market crowd infer and predict the probability of gain and loss, elicit market crowd's response and cause them to trade in risky choice, for examples, dividend (Shiller, 1981), size and book-to-market ratio (Fama and French, 1995), economic fundamentals (He and Wang, 1995), private information (Amihud, Mendelson, and Pedersen, 2005;Chordia, Roll, and Subrahmanyam, 2008), news announcement (Jia, Wang, and Xiong, 2013), a technical analysis cue (Lo, Mamaysky, and Wang, 2000;Zhu and Zhou, 2009), entertainment (Dorn and Sengmueller, 2009;Hoffmann and Shefrin, 2012), social interaction (Shiller, 1984 andHan and Yang, 2013;Han and Hirshlerfer, 2013), linguistic diversity (Chang, Hong, Tiedens, and Zhao, 2013), and so on. Past return, reference price effect, tax-loss selling, and the size of the holding period capital gain or loss etc affect trading frequency (Grinblatt and Keloharju, 2001;Griffin, Nardari, and Stulz, 2006;Shi et al, 2010). According to prospect theory (Kahneman and Tversky, 1979), investors tend to frame investment choices in terms of gain and loss, the outcome of prior trading action.…”

Section: Introductionmentioning

confidence: 99%

“…We extend Shi's price-volume differential equation (Shi, 2006), study the volume uncertainty associated with price, and attempt to solve these questions. We incorporate subjective thinking into the equation, measure the frequency of market crowd's trading action by trading volume probability in terms of principles in behavior analysis (Shi et al, 2010), and test two kinds of adaptive trading hypotheses relevant to the volume uncertainty associated with price. They are: 1) market crowd's adaptive behavior in reference to an outcome, an agreement price, or a stationary equilibrium price on a trading day 6 ; and 2) market crowd's adaptive behavior to gain and loss by trading volume increase or decrease in any two consecutive trading days.…”

Section: Introductionmentioning

confidence: 99%

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