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Stock Markets, Market Crashes, and Market Bubbles

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Psychological Perspectives on Financial Decision Making

Abstract

The omnipresent and reoccurring market bubbles and crashes have been puzzling both finance professionals and academics. Important economic theories such as the efficient market hypothesis indicate that, rationally, mispricing of assets traded on stock markets should not occur. However, this is not the case in real life. Behavioral and neuroscientific studies have provided an important contribution to explaining the psychological mechanisms driving the behavior of individual stock market players as well as the market in general. This chapter presents the most influential scientific work devoted to stock markets, bubbles, and crashes. It provides a brief overview of the research aiming to explain behavioral phenomena on stock markets. First, it outlines the key terminology and classification of market bubbles and crashes. Second, it reviews the psychological and economic literature devoted to experiencing extreme financial events. Despite a low number of studies, the general conclusion is that experiencing a strongly negative financial event would lead to decreased risk-taking in the future. Next, the reader’s attention is drawn to a range of typical cognitive biases present in the stock market. Group-think, the disposition effect, overconfidence, and home bias are among the most researched biases. Finally, the chapter presents experimental asset markets—a strand of experimental studies investigating coordination on the stock markets. This type of work has gained prominence after the invention of the SSW design—the Nobel Prize winning experimental method. The chapter concludes with a short note on econophysics—a field that deals with simulation and prediction of stock market players’ behavior.

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Notes

  1. 1.

    The quotation comes from the Big Think interview with Vernon Smith. The interview and the transcript are available at https://bigthink.com/videos/big-think-interview-with-vernon-smith.

  2. 2.

    The Nobel Prize, https://www.nobelprize.org/prizes/economic-sciences/2002/summary/ last accessed 01.11.2019

  3. 3.

    Irrational exuberance refers to market overvaluation. The phrase was coined by Alan Greenspan who was a US Federal Reserve chairman in the years 1987–2006. He used this term to refer to the dot-com bubble in the 1990s.

  4. 4.

    Arbitrage is a situation in which an investor can use the mismatch of prices by buying low and selling high. Arbitrage can happen when prices differ across markets or when markets are inefficient.

  5. 5.

    In the Capital Asset Pricing Model, the efficient frontier is the curve that defines the maximum expected return for the possibly lowest risk level.

  6. 6.

    https://www.nytimes.com/2007/09/29/business/29nocera.html?mtrref=undefined&gwh=70AE488A7619DE2C0CBE032AC752A93C&gwt=pay&assetType=REGIWALL

  7. 7.

    The black swan idea was developed by a former trader and risk analyst Nassim Taleb. The theory aims to explain the role of highly unpredictable events and psychological biases that magnify the impact of these extreme events. The name of the theory comes from ancient folklore that assumes that black swans do not exist.

  8. 8.

    A fat-tailed or heavy-tailed distribution is a probability distribution that exhibits a large skewness or kurtosis, assigning relatively high probability weight to events or values that are far from the mean of the distribution.

  9. 9.

    Bullish market patterns are conditions in which stock prices are rising or are expected to rise. The opposite situation is described as bearish. Based on this terminology, many stock markets and investment firms use the imagery of a bull and/or a bear.

  10. 10.

    Traders and investors who use price charts to classify patterns of stock price developments are called chartists. A head-and-shoulder pattern is characterized by two shoulders (the left one containing rising prices and the right one containing decreasing prices) and a head (a peak) in the middle. There are numerous patterns that have been classified. A curious reader is encouraged to simply Google “price chart patterns” to obtain lists and descriptions of these patterns.

  11. 11.

    https://er.ethz.ch/financial-crisis-observatory.html

  12. 12.

    Sornette used the same approach to predict outbreaks of epidemic diseases, earthquakes (Sornette, 2002), and social events (Crane & Sornette, 2008; Gisler & Sornette, 2009, 2010).

  13. 13.

    Trading volume refers to the turnover of stock units on a given market or of a given company.

  14. 14.

    The description-experience gap refers to the fact that people take different decisions when choice option outcomes and their corresponding probabilities are personally experienced or when they are presented in a descriptive fashion. In the experience mode, a respondent would have to sample information about the possible outcomes and the frequencies of which they can occur, before the decision maker expressed their preference for one choice option or another.

  15. 15.

    Insider trading implies trading stocks of a publicly traded company, based on non-publicly available information, such as information about structural changes within the company. Insider trading is illegal and, when identified, can lead to large penalties imposed on the trading agency whose employee committed insider trading.

  16. 16.

    Recent research devoted to incentive schemes and employee performance is summarized in the review by Devers, Cannella, Reilly, and Yoder (2007).

  17. 17.

    FinTech stands for financial technology and refers to technologically inspired tools with application in finance. These tools include, for example, mobile payment apps, robo-advisors, financial data visualization tools, etc.

  18. 18.

    In finance, volatility refers to the price variability across a certain period of time (i.e., monthly or weekly volatility). This measure can be conceived of as the standard deviation of the price. Volatility is the most important factor indicating riskiness of a stock.

  19. 19.

    Mentalizing refers to being able to understand the emotions of others or, in simple words, “to be able to put oneself in the shoes of others.”

  20. 20.

    Hindsight bias refers to the feeling that a certain event was more predictable than it actually was.

  21. 21.

    Randomness bias refers to seeing patterns in random data (i.e., seeing patterns that are not there).

  22. 22.

    Availability bias refers to the tendency to think that the first available thought is more representative than it actually is.

  23. 23.

    A complete market is a market in which the number of securities equals the number of the states of nature.

  24. 24.

    In call markets, transactions happen at predetermined time intervals, and bid (maximum price at which a buyer is willing to pay for an asset) and ask (minimum price at which a seller is willing to sell an asset) orders are aggregated and executed at specific times.

  25. 25.

    A transaction (i.e., a buy or sell order) fee is paid by an investor to the broker for executing it.

  26. 26.

    In a double auction market, buyers and sellers submit their orders simultaneously to an auctioneer or a trading platform.

  27. 27.

    http://veconlab.econ.virginia.edu/da/da.php

  28. 28.

    Herding usually refers to “following the crowd rather than one’s own opinion,” where in this case herding would mean “betting in disagreement with one’s private signal but in favor of the consensus based on prior bets” (Noussair & Tucker, 2013, p. 8).

  29. 29.

    Information cascade is a process of propagation of information from one person to multiple people. This phenomenon is often present in stock markets when pieces of news propagate and impact individual investment decisions.

  30. 30.

    Algorithmic trading refers to the situation in which an algorithm with predefined trading strategies implements transactions that optimize its strategy.

  31. 31.

    Robo-advisors refer to algorithms that make investment suggestions based on a predefined investment optimization strategy and supplied historical market data.

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Andraszewicz, S. (2020). Stock Markets, Market Crashes, and Market Bubbles. In: Zaleskiewicz, T., Traczyk, J. (eds) Psychological Perspectives on Financial Decision Making. Springer, Cham. https://doi.org/10.1007/978-3-030-45500-2_10

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