Machine over Mind? Stock price clustering in the era of algorithmic trading

Das, Sougata; Kadapakkam, Palani‐Rajan

doi:10.1016/j.najef.2018.08.014

Cited by 11 publications

(11 citation statements)

References 26 publications

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“…More recently, Das and Kadapakkam (2018) examined time trends in price clustering for Exchange-Traded Funds (ETFs) and individual stocks, evidencing a substantial reduction in clustering over the sample period (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010). The authors attributed that to the increasing proeminence of algorithmic trading, which seems to be less susceptible to psychological biases.…”

Section: Previous Empirical Evidencementioning

confidence: 99%

Price Clustering in Bank Stocks During the Global Financial Crisis

Lobão

Pacheco

Alves

2019

SAEB

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Market anomalies are one of the most intriguing and fascinating phenomena observed in financial markets. This paper examines the incidence of price clustering in US and European bank stocks during the Global Financial Crisis. The results reveal a significant level of price clustering in European and US banks’ samples, which is difficult to reconcile with the Efficient Market hypothesis. The Attraction hypothesis and the Price Resolution/Negotiation hypothesis seem to be the best explanations for the clustering effect. However, the results also suggest that the uncertainty associated with the crisis did not have a significant impact in the clustering levels, which is at odds with the recently proposed Panic Trading hypothesis. Surprisingly, we observe a tendency to have less price clustering during the period of crisis and banks located in countries mostly affected by the European sovereign debt crisis exhibit lower levels of price clustering. These results are consistent with the idea that investors tend to be more analytical in their appraisals in periods of negative sentiment.

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Section: Previous Empirical Evidencementioning

confidence: 99%

Price Clustering in Bank Stocks During the Global Financial Crisis

Lobão

Pacheco

Alves

2019

SAEB

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“…The first academic paper on the price clustering was written by Osborne (1962), where the author described the price clustering phenomenon as a pronounced tendency for prices to cluster on whole numbers, halves, quarters, and odd one-eighths in descending preference, like the markings on a ruler. Since then, there have been many studies focusing on this phenomenon -from Niederhoffer (1965) to very recent papers of Li et al (2020); Song et al (2020); Das and Kadapakkam (2020) -showing that price clustering is remarkably persistent across various markets:…”

Section: Price Clusteringmentioning

confidence: 99%

“…Mbanga (2019)). Another studies analyzed the panel data by pooled regression (see, Chung et al, 2005;Liu and Witte, 2013;Blau and Griffith, 2016), models with fixed effects (see, Box and Griffith, 2016;Blau, 2019;Das and Kadapakkam, 2020), or random effects (see, Ohta, 2006). Part of the literature models price clustering as a binary variable using logit (see, Aitken et al, 1996;Brown and Mitchell, 2008;Bharati et al, 2012), or probit models (see, Kahn et al, 1999;Sopranzetti and Datar, 2002;Palmon et al, 2004;Ohta, 2006;Alexander and Peterson, 2007;Capelle-Blancard and Chaudhury, 2007;Liu, 2011;Narayan and Smyth, 2013;Lien et al, 2019).…”

Section: Price Clusteringmentioning

confidence: 99%

Modeling Price Clustering in High-Frequency Prices

Holý¹,

Tomanová²

2021

Preprint

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The price clustering phenomenon, i.e. an increased occurence of specific prices, is widely observed and well-documented for various financial instruments in various financial markets. In the literature, however, it is rarely incorporated into price models. We consider that there are several types of agents trading only in specific multiples of the tick size resulting in an increased occurrence of these multiples in prices. For example, stocks on the NYSE and NASDAQ exchanges are traded with precision to one cent but multiples of five cents and ten cents occur much more often in prices. To capture this behaviour, we propose a discrete price model based on a mixture of double Poisson distributions with dynamic volatility and dynamic proportions of agent types. The model is estimated by the maximum likelihood method. In an empirical study of DJIA stocks, we find that higher instantaneous volatility leads to weaker price clustering at the ultra-high frequency. This is in sharp contrast with results at low frequencies which show that daily realized volatility has positive impact on price clustering.

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“…Most ANNs proposed so far are estimated using the classical model, the metrics of which give us the concept of a generalized model, but not understanding the behavior of the system in the real market. One of the ways to assess the real quality of ANN forecasts is to create a hybrid system, which is an automatic trading advisor operating in real time [5,6,7].…”

Section: Similar Systemsmentioning

confidence: 99%

Machine learning for algorithmic trading

2020

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The purpose of the study is to confirm the feasibility of using machine learning methods to predict the behavior of the foreign exchange market. The article examines the theoretical and practical aspects of the implementation of artificial neural networks in the process of Internet trading. We studied the features of constructing automated trading advisors that perform trading operations based on the forecast of neural networks in combination with indicator signals. As a result, a hybrid system has been built that has a high-precision forecast and allows you to make a profit with the correct selection of parameters.

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Machine over Mind? Stock price clustering in the era of algorithmic trading

Cited by 11 publications

References 26 publications

Price Clustering in Bank Stocks During the Global Financial Crisis

Price Clustering in Bank Stocks During the Global Financial Crisis

Modeling Price Clustering in High-Frequency Prices

Machine learning for algorithmic trading

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