How Do Investors Determine Stock Prices after Large Price Shocks?

Brady, Kevin P.; Premti, Arjan

doi:10.1080/15427560.2018.1511563

Cited by 6 publications

(5 citation statements)

References 38 publications

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“…Chan (2003) and Savor (2012) have shown that the returns to a portfolio after price shocks depend on whether or not there was specific news accompanying a price shock. Brady and Premti (2019) have also taken note of the range of contradicting evidence on returns from managerial heuristics following price shocks. These investigators emphasize anchoring and adjustment theory (Tversky and Kahneman, 1974) and predict that investors tend to overweigh (anchor to) what they consider to be the most salient information in decisions under uncertainty and commonly underestimate the true value of information that underlies a shock.…”

Section: Mf 505mentioning

confidence: 99%

Financial market shocks and portfolio rebalancing

Silver,

Raseta

2023

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PurposeThe intention of the empirics is to contribute to the general understanding of investor responses to market price shocks. The authors review assumptions about investor behavior in response to price shocks and investigate alternative rebalancing heuristics.Design/methodology/approachThe authors use market data over 40 years to define market shocks. Portfolio rebalancing implements constrained Markowitz mean-variance (MV) heuristics.FindingsMomentum rebalancing in portfolio management outperforms contrarian rebalancing in the study interval. Sensitivity analysis by decade, sector constraints and proportion of security holdings bought or sold continue to support momentum rebalancing.Research limitations/implicationsThe results are consistent with under-responding to price shocks at consensus levels in financial markets. The theoretical background provides a basis for experimental lab studies of shocks of different magnitudes under conditions in which participants have information on the levels of other participants and a condition in which they can only observe their previous estimates.Practical implicationsManaging portfolios in the face of price disturbances of different magnitudes is informed by empirical studies and their implications for investor behavior.Originality/valueThis is the first study the authors can locate that uses market data with alternative rebalancing heuristics to estimate price returns from the respective heuristics over a time interval of 40 years. The authors support the results with sensitivity estimates and consider implications for the underlying agent heuristics in light of background studies.

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Section: Mf 505mentioning

confidence: 99%

Financial market shocks and portfolio rebalancing

Silver,

Raseta

2023

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“…As most large stock price shocks are not accompanied by publicly available information, investors rely on reference points and their private information signals (Brady & Premti, 2019). An error, however, could occur when the process commences with an incorrect starting point: such as, for example, an incorrectly determined or inflated price‐to‐earnings ratio (Statman, 2019) used within an individual's investment analysis; or revisions to previous earnings forecasts (Amir & Ganzach, 1998).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the absence of publicly available information, much literature refers to the 52‐week high/low as a reference point for shares (Baker et al, 2012; Tsao et al, 2017). Shares closer to their 52‐week high (52‐week low) have negative (positive) returns in the days that follow a large negative (positive) price shock (Brady & Premti, 2019). Riley et al (2020) demonstrate that highs and lows attained during the last 52 weeks play a strong role in reference‐point formation over longer time periods.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Their findings indicated that the closeness of the current price to an anchor (the 52‐week high price) can successfully explain price dynamics. Brady and Premti (2019) find that future returns drift in the direction of the shock when the share's abnormal returns before the shock are in the opposite direction. George and Hwang (2004) also suggest that the anchoring effect may be more powerful than existing theories based on overconfidence, conservatism, or the slow diffusion of information.…”

Section: Literature Reviewmentioning

confidence: 99%

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Buy and buy again: The impact of unique reference points on (re)purchase decisions

Willows

Richards

2022

Int Rev Finance

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Behavioral finance has uncovered that investor engage emotionally when trading. We investigate how three psychological factors influence purchase and repurchase decisions: representativeness, the influence of prior gains, and reference points. Using trading data of 7200 UK investors we find that purchase decisions are influenced by representative heuristic and repurchase decisions are influenced by both representative heuristic and prior profitability. Further survival analysis showed that investors use the prior selling price as a unique reference point. Investors are more likely to repurchase a stock when trading above its reference point, but more likely to initiate the repurchase when trading below. Investors are influenced by previous experience and engage learning behavior when they seek to reinforce past success. As reference points are inferred but infrequently researched, this research adds to the literature and provides important and robust results for those engaging with financial planning clients.

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“…Finally, for illustrative purposes we set the magnitude of the price shock δ = 0.25, following the guidelines on the trading cost perspectives by [14]. (For the detailed overview on the trader-defined choice of δ and associated investment strategies see [3,10,6]. )…”

Section: Experimental Settingsmentioning

confidence: 99%

Dissecting Ethereum Blockchain Analytics: What We Learn from Topology and Geometry of the Ethereum Graph?

Li¹,

Islambekov²,

Akcora³

et al. 2020

Proceedings of the 2020 SIAM International Conference on Data Mining

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Blockchain technology and, in particular, blockchain-based cryptocurrencies offer us information that has never been seen before in the financial world. In contrast to fiat currencies, all transactions of crypto-currencies and cryptotokens are permanently recorded on distributed ledgers and are publicly available. As a result, this allows us to construct a transaction graph and to assess not only its organization but to glean relationships between transaction graph properties and crypto price dynamics. The ultimate goal of this paper is to facilitate our understanding on horizons and limitations of what can be learned on cryptotokens from local topology and geometry of the Ethereum transaction network whose even global network properties remain scarcely explored. By introducing novel tools based on topological data analysis and functional data depth into Blockchain Data Analytics, we show that Ethereum network (one of the most popular blockchains for creating new crypto-tokens) can provide critical insights on price strikes of crypto-tokens that are otherwise largely inaccessible with conventional data sources and traditional analytic methods.

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How Do Investors Determine Stock Prices after Large Price Shocks?

Cited by 6 publications

References 38 publications

Financial market shocks and portfolio rebalancing

Financial market shocks and portfolio rebalancing

Buy and buy again: The impact of unique reference points on (re)purchase decisions

Dissecting Ethereum Blockchain Analytics: What We Learn from Topology and Geometry of the Ethereum Graph?

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