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Journal articles on the topic 'Price clustering'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

Wang, Qiming. "Evolution of integer price clustering of IPOs in the aftermarket." Nankai Business Review International 5, no. 4 (2014): 365–81. http://dx.doi.org/10.1108/nbri-01-2014-0008.

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Purpose – The purpose of this paper is to, using a large sample of NASDAQ initial public offerings (IPOs), examine the evolution of integer price clustering of IPOs in the aftermarket trading. Design/methodology/approach – Consistent with Harris’s (1991) costly negotiation hypothesis, clustering on integer prices is a positive function of price level and various stock valuation uncertainty proxies, and it is a negative function of trading activities for IPOs and seasoned stocks. Findings – It was found that, after controlling for price level, daily return volatility, number of trades, trading

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2

Wafula, Lugongo Maurice, and Dr Sifunjo E. Kisaka. "AN EMPIRICAL STUDY OF PRICE CLUSTERING ON THE NAIROBI SECURITIES EXCHANGE." International Journal of Finance and Accounting 2, no. 2 (2017): 23. http://dx.doi.org/10.47604/ijfa.295.

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Purpose: The purpose of this study was to empirically investigate price clustering phenomenon on the Nairobi Securities Exchange for the period 2009 to 2013.Materials and methods: The study used secondary sources of data obtained from the Nairobi Securities exchange. The study revealed that there has been a preference by investors for stock whose prices end with the digit 5 and this accounted for 67.88 percent of all the stocks examined and was followed by stocks whose prices ended with the digit 0 which accounted for 4.55 percent. In order to establish the determinants of this observed behavi

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3

Baig, Ahmed, Benjamin M. Blau, and Jie Hao. "Accounting Information Quality and the Clustering of Stock Prices." American Business Review 23, no. 2 (2020): 182–210. http://dx.doi.org/10.37625/abr.23.2.182-210.

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The foundation of economic theory is based on the premise that prices will converge to their equilibrium value. However, prior research has documented that stock prices cluster on round pricing increments. In this study, we develop and test the hypothesis that audit quality and the management of earnings—both of which affects the information environment of the firm—influence the degree of price clustering. Results show that firms with Big 4 auditors have less clustering in their stock prices while firms with higher abnormal audit fees, more discretionary accruals, and firms that tend to manipu

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4

Chen, Tao. "Price Clustering and Price Barriers: International Evidence." Nang Yan Business Journal 3, no. 1 (2014): 1–16. http://dx.doi.org/10.1515/nybj-2015-0001.

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Abstract In this paper, I try to complement the existing literature by empirically examining the effect of price clustering and price barriers based on the international stock market. Evidence suggests that a strong effect of clustering and barriers is observed on last digit 0. Such effect is not robust and persistent on last digit 5. In addition, the cross-country analysis shows that price clustering and barriers become intensified in countries with a more transparent and open environment.

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5

Urquhart, Andrew. "Price clustering in Bitcoin." Economics Letters 159 (October 2017): 145–48. http://dx.doi.org/10.1016/j.econlet.2017.07.035.

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6

Baig, Ahmed S., Omair Haroon, and Nasim Sabah. "Price Clustering After the Introduction of Bitcoin Futures." Applied Finance Letters 9 (April 23, 2020): 36–42. http://dx.doi.org/10.24135/afl.v9i0.200.

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Economic theory suggests that introduction of derivative contracts can improve the informational efficiency of the underlying asset prices (Danthine, 1978). In this study, we examine the impact of the introduction of Bitcoin futures on price clustering in Bitcoin. Our findings suggest that price clustering in Bitcoin meaningfully decreases post the introduction of its futures contracts.

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7

Blau, Benjamin M., and Todd G. Griffith. "Price clustering and the stability of stock prices." Journal of Business Research 69, no. 10 (2016): 3933–42. http://dx.doi.org/10.1016/j.jbusres.2016.06.008.

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8

Mola, Simona, and Tim Loughran. "Discounting and Clustering in Seasoned Equity Offering Prices." Journal of Financial and Quantitative Analysis 39, no. 1 (2004): 1–23. http://dx.doi.org/10.1017/s0022109000003860.

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AbstractAn analysis of 4,814 SEOs during 1986–1999 indicates that the average offering ofnew shares is priced at a discount of 3% from the closing price on the day before the issue. Discounts have risen steadily over time, sharply increasing the indirect costs of issuing seasoned equity. There is evidence of increased clustering of offer prices at integers, and of greater importance in the analyst coverage provided by underwriters. Adjusting for other factors, we find that issues with integer offer prices, and underwriters with highly regarded analysts, are increasingly associated with larger

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9

van Kralingen, Marc, Diego Garlaschelli, Karolina Scholtus, and Iman van Lelyveld. "Crowded Trades, Market Clustering, and Price Instability." Entropy 23, no. 3 (2021): 336. http://dx.doi.org/10.3390/e23030336.

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Crowded trades by similarly trading peers influence the dynamics of asset prices, possibly creating systemic risk. We propose a market clustering measure using granular trading data. For each stock, the clustering measure captures the degree of trading overlap among any two investors in that stock, based on a comparison with the expected crowding in a null model where trades are maximally random while still respecting the empirical heterogeneity of both stocks and investors. We investigate the effect of crowded trades on stock price stability and present evidence that market clustering has a c

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10

Harris, Lawrence. "Stock Price Clustering and Discreteness." Review of Financial Studies 4, no. 3 (1991): 389–415. http://dx.doi.org/10.1093/rfs/4.3.389.

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11

Blau, Benjamin M. "Price Clustering and Investor Sentiment." Journal of Behavioral Finance 20, no. 1 (2018): 19–30. http://dx.doi.org/10.1080/15427560.2018.1431887.

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12

Narayan, Paresh Kumar, Seema Narayan, Stephan Popp, and Michael D'Rosario. "Share price clustering in Mexico." International Review of Financial Analysis 20, no. 2 (2011): 113–19. http://dx.doi.org/10.1016/j.irfa.2011.02.003.

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13

Deng, Hua Ling, and Yǔ Qiàn Sūn. "Soybean Price Pattern Discovery Via Toeplitz Inverse Covariance-Based Clustering." International Journal of Agricultural and Environmental Information Systems 10, no. 4 (2019): 1–17. http://dx.doi.org/10.4018/ijaeis.2019100101.

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The high volatility of world soybean prices has caused uncertainty and vulnerability particularly in the developing countries. The clustering of time series is a serviceable tool for discovering soybean price patterns in temporal data. However, traditional clustering method cannot represent the continuity of price data very well, nor keep a watchful eye on the correlation between factors. In this work, the authors use the Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data (TICC) to soybean price pattern discovery. This is a new method for multivariate time series clu

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14

Lien, Donald, Pi-Hsia Hung, and I.-Chun Hung. "Order price clustering, size clustering, and stock price movements: Evidence from the Taiwan Stock Exchange." Journal of Empirical Finance 52 (June 2019): 149–77. http://dx.doi.org/10.1016/j.jempfin.2019.03.005.

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15

Syahputra, Ahmad Agung Zefi, Annisa Dwi Atika, Muhammad Adam Aslamsyah, Meida Cahyo Untoro, and Winda Yulita. "Smartphone Price Grouping by Specifications using K-Means Clustering Method." Jurnal Teknik Informatika C.I.T Medicom 13, no. 2 (2021): 64–74. http://dx.doi.org/10.35335/cit.vol13.2021.98.pp59-68.

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The use of smartphones in the industrial era 4.0 had become more frequent and widespread in various circles of Indonesian society. In addition, the COVID-19 pandemic that had not end yet also made high school and college students obliged to carry out online learning. This research aimed to cluster the price from smartphones using the specifications of the smartphone. K-Means Clustering was used as a method in this research. This algorithm was a data mining algorithm with unsupervised learning as data grouping and could group the price of a smartphone into several clusters based on the similari

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16

Brooks, Robert, Edwyna Harris, and Yovina Joymungul. "Price clustering in Australian water markets." Applied Economics 45, no. 6 (2013): 677–85. http://dx.doi.org/10.1080/00036846.2011.610747.

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17

Baig, Ahmed, Benjamin M. Blau, and Nasim Sabah. "Price clustering and sentiment in bitcoin." Finance Research Letters 29 (June 2019): 111–16. http://dx.doi.org/10.1016/j.frl.2019.03.013.

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18

Miles, William. "Clustering in U.K. Home Price Volatility." Journal of Housing Research 20, no. 1 (2011): 87–101. http://dx.doi.org/10.1080/10835547.2011.12092031.

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19

Chen, Tao. "Trade-size clustering and price efficiency." Japan and the World Economy 49 (March 2019): 195–203. http://dx.doi.org/10.1016/j.japwor.2018.12.002.

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20

Chueh, Horace. "Price Clustering in the Nikkei 225 Stock Index Futures Contract on the SIMEX: An Intraday Empirical Analysis." Review of Pacific Basin Financial Markets and Policies 03, no. 04 (2000): 519–33. http://dx.doi.org/10.1142/s0219091500000273.

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Price clustering in financial markets has been identified by previous studies. However, few studies have examined the phenomenon in the futures market. This paper presents price clustering for the Nikkei 225 stock index futures contract on the SIMEX. An extremely low percentage of odd-tick trades appears at the opening for the first trading session, while moderately low percentage occurs at the opening and the closing for the second trading session. GARCH estimation results document that the degree of price clustering increases in the periods with high volatility, bid-ask spreads, and transact

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21

Kumar, Arya, and Uma Sankar Mishra. "Volatility Clustering and Price Dependency of Turmeric amongst the State of India." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 228–35. http://dx.doi.org/10.5373/jardcs/v11sp10/20192795.

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22

Lobão, Júlio, and Luís Pacheco. "PRICE CLUSTERING IN BANK STOCKS DURING THE GLOBAL FINANCIAL CRISIS." Scientific Annals of Economics and Business 66, no. 4 (2019): 465–86. http://dx.doi.org/10.47743/saeb-2019-0043.

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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 c

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23

Xu, Zhiling, Hualing Deng, and Qiufeng Wu. "Prediction of Soybean Price Trend via a Synthesis Method With Multistage Model." International Journal of Agricultural and Environmental Information Systems 12, no. 4 (2021): 1–13. http://dx.doi.org/10.4018/ijaeis.20211001.oa1.

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Soybean is an important crop, so it is very important to forecast soybean price trend, which can stabilize the market. This paper presents a Synthesis Method with Multistage Model (SMwMM) in order to identify and forecast soybean price trend in China. In the previous work,Toeplitz Inverse Covariance-based Clustering(TICC) has been applied to cluster the prices of four variables. The research have found that there are four patterns in soybean market price, which could be explained by economic theory. This paper consider four patterns as market risk levels. Based on the clustering results, we us

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24

Trang, Le Hong, Tran Duong Huy, and Anh Ngoc Le. "Clustering helps to improve price prediction in online booking systems." International Journal of Web Information Systems 17, no. 1 (2021): 45–53. http://dx.doi.org/10.1108/ijwis-11-2020-0065.

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Purpose Pricing on the online booking systems is a difficult task for the host, the systems usually set the prices that are lower than the general premises and quality, and that only gives benefits to the system by easily attracting the customer to use the service. The setting price of the new accommodation is often based on location, the number of beds, type of house and so on. The main problem is to predict the most reasonable price for the host. This paper aims to study the use of machine learning and sentiment analysis for predicting the price of online booking systems. Design/methodology/

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25

Szenderák, János. "Correlation clustering: analysis of major agricultural commodity markets." International Journal of Engineering and Management Sciences 3, no. 3 (2018): 288–302. http://dx.doi.org/10.21791/ijems.2018.3.24.

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The aim of this article is to compare the clusters formed by the correlation distances between the agricultural and the energy commodity price returns in different periods of time. The energy and agricultural markets have become more interlinked in the past ten years, which can be attributed partly to the increased usage of biofuels. According to the results of this research, after the global financial and economic crisis of 2008/09, the relationship has become tighter between the agricultural commodity prices and the price of the crude oil. Based on the hierarchical clustering, the relationsh

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26

Gwilym, Owain ap, and Thanos Verousis. "Price Clustering in Individual Equity Options: Moneyness, Maturity, and Price Level." Journal of Futures Markets 33, no. 1 (2012): 55–76. http://dx.doi.org/10.1002/fut.21547.

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27

Horan, Stephen M. "Stock Price Clustering on Option Expiration Dates." CFA Digest 36, no. 2 (2006): 101. http://dx.doi.org/10.2469/dig.v36.n2.4133.

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28

Aitken, Michael, Philip Brown, Christine Buckland, H. Y. Izan, and Terry Walter. "Price clustering on the Australian Stock Exchange." Pacific-Basin Finance Journal 4, no. 2-3 (1996): 297–314. http://dx.doi.org/10.1016/0927-538x(96)00016-9.

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29

Sopranzetti, Ben J., and Vinay Datar. "Price clustering in foreign exchange spot markets." Journal of Financial Markets 5, no. 4 (2002): 411–17. http://dx.doi.org/10.1016/s1386-4181(01)00032-5.

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30

Aşçıoğlu, Aslı, Carole Comerton-Forde, and Thomas H. McInish. "Price Clustering on the Tokyo Stock Exchange." Financial Review 42, no. 2 (2007): 289–301. http://dx.doi.org/10.1111/j.1540-6288.2007.00172.x.

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31

Box, Travis, and Todd Griffith. "Price Clustering Asymmetries in Limit Order Flows." Financial Management 45, no. 4 (2016): 1041–66. http://dx.doi.org/10.1111/fima.12136.

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32

Geoffrey Booth, G., Juha-Pekka Kallunki, Ji-Chai Lin, and Teppo Martikainen. "Internalization and stock price clustering: Finnish evidence." Journal of International Money and Finance 19, no. 5 (2000): 737–51. http://dx.doi.org/10.1016/s0261-5606(00)00022-x.

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33

Hu, Bill, Christine Jiang, Thomas McInish, and Haigang Zhou. "Price clustering on the Shanghai Stock Exchange." Applied Economics 49, no. 28 (2016): 2766–78. http://dx.doi.org/10.1080/00036846.2016.1248284.

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34

Li, Xin, Shenghong Li, and Chong Xu. "Price clustering in Bitcoin market—An extension." Finance Research Letters 32 (January 2020): 101072. http://dx.doi.org/10.1016/j.frl.2018.12.020.

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35

Palao, Fernando, and Angel Pardo. "Assessing price clustering in European Carbon Markets." Applied Energy 92 (April 2012): 51–56. http://dx.doi.org/10.1016/j.apenergy.2011.10.022.

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36

XIAOYANNI, S., N. PEARSON, and A. POTESHMAN. "Stock price clustering on option expiration dates." Journal of Financial Economics 78, no. 1 (2005): 49–87. http://dx.doi.org/10.1016/j.jfineco.2004.08.005.

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37

Kordlouie, Hamid Reza, Mehrnoush Ebrahimi, Narges Mohseni Dehkolani, and Azam Zare Jafar Kolaei. "Price clusters and stock price stability." Revista Eletrônica em Gestão, Educação e Tecnologia Ambiental 24 (April 8, 2020): 33. http://dx.doi.org/10.5902/2236117043410.

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Understanding the factors affecting stock return volatility, for analysts, investors and company executives, it is critical. In this study, using a traditional approach, we identify the factors influencing volatility and how price friction is formed on stock price stability, and in particular, examining the clustering test for price increases. This study was carried out by examining the price clusters and stock price stability in the stock market and the OTC market between 2009 -2010. Econometric software was used to investigate the research variables. In this study, we tried to study stock pr

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38

ŞAHİN, Cumhur. "EXAMINATION OF VOLATILITY STRUCTURE BETWEEN TURKISH STOCK MARKET AND COMMODITY MARKETS: A PERSPECTIVE FOR THE PERIOD OF 2015-2019." Business & Management Studies: An International Journal 8, no. 1 (2020): 351–70. http://dx.doi.org/10.15295/bmij.v8i1.1418.

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Commodity markets, both in the past and in modern times, have had an extraordinary economic impact on individuals and societies. Although it is not known exactly when and where commodity markets started, it is thought that it started about 6000 years ago with rice trade in China. Commodities, as raw material providers used in production, have an intensive usage area. This study aims to examine the global commodity prices such as gold ounce price, silver ounce price, copper price, Brent crude oil price, and natural gas prices, and the volatility structure in the Borsa Istanbul 100 index, repres

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39

Meng, Lei, Thanos Verousis, and Owain ap Gwilym. "A substitution effect between price clustering and size clustering in credit default swaps." Journal of International Financial Markets, Institutions and Money 24 (April 2013): 139–52. http://dx.doi.org/10.1016/j.intfin.2012.11.011.

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40

Zhang, Guangyong, Lixin Tian, Wenbin Zhang, Xu Yan, Bingyue Wan, and Zaili Zhen. "A Study on the Similarities and Differences of the Conventional Gasoline Spot Price Fluctuation Network between Different Harbors." Sustainability 12, no. 2 (2020): 710. http://dx.doi.org/10.3390/su12020710.

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According to the fluctuation series of the conventional gasoline spot prices (CGSP) in New York Harbor (NYH) and U.S. Gulf Coast (GC), this paper defines the fluctuation modes by the coarse-grained method based on the CGSP series in the two harbors. The fluctuation series are converted into the characters by means of the sliding window, where five symbol series is used as a fluctuation mode, one day was used as a step to slide in the data window, and the conventional gasoline spot prices fluctuation network (CGSPFN) is constructed in the two harbors. Then the evolutionary rule of the new nodes

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41

Martinez Álvarez, F., A. Troncoso, J. C. Riquelme, and J. M. Riquelme. "Discovering patterns in electricity price using clustering techniques." Renewable Energy and Power Quality 1, no. 05 (2007): 174–81. http://dx.doi.org/10.24084/repqj05.245.

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42

Chiao, Chaoshin, and Zi-May Wang. "Price Clustering: Evidence Using Comprehensive Limit-Order Data." Financial Review 44, no. 1 (2009): 1–29. http://dx.doi.org/10.1111/j.1540-6288.2008.00208.x.

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43

Wang, Qiming. "Price clustering of IPOs in the secondary market." Applied Economics Letters 17, no. 13 (2010): 1285–92. http://dx.doi.org/10.1080/00036840902881843.

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44

Calantone, Roger J., and C. Anthony Di Benedetto. "Clustering product launches by price and launch strategy." Journal of Business & Industrial Marketing 22, no. 1 (2007): 4–19. http://dx.doi.org/10.1108/08858620710722789.

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45

ap Gwilym, Owain, and Thanos Verousis. "Price clustering and underpricing in the IPO aftermarket." International Review of Financial Analysis 19, no. 2 (2010): 89–97. http://dx.doi.org/10.1016/j.irfa.2010.01.007.

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46

Narayan, Paresh Kumar, Seema Narayan, and Stephan Popp. "Investigating price clustering in the oil futures market." Applied Energy 88, no. 1 (2011): 397–402. http://dx.doi.org/10.1016/j.apenergy.2010.07.034.

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47

Fleishman, Larisa, Yury Gubman, and Aviad Tur-Sinai. "Dwelling Price Ranking versus Socioeconomic Clustering: Possibility of Imputation." Journal of Official Statistics 31, no. 2 (2015): 205–29. http://dx.doi.org/10.1515/jos-2015-0014.

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Abstract In order to characterize the socioeconomic profile of various geographic units, it is common practice to use aggregated indices. However, the process of calculating such indices requires a wide variety of variables from various data sources available concurrently. Using a number of administrative databases for 2001 and 2003, this study examines the question of whether dwelling prices in a given locality can serve as a proxy for its socioeconomic level. Based on statistical and geographic criteria, we developed a Dwelling Price Ranking (DPR) methodology. Our findings show that the DPR

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48

CHANG, IKSOO, DIETRICH STAUFFER, and RAS B. PANDEY. "ASYMMETRIES, CORRELATIONS AND FAT TAILS IN PERCOLATION MARKET MODEL." International Journal of Theoretical and Applied Finance 05, no. 06 (2002): 585–97. http://dx.doi.org/10.1142/s0219024902001584.

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Modifications of the Cont-Bouchaud percolation model for price fluctuations give an asymmetry for time-reversal, an asymmetry between high and low prices, volatility clustering, effective multifractality, correlations between volatility and traded volume, and a power law tail with exponent near 3 for the cumulative distribution function of price changes. Combining them together still gives the same power law. Using Ising-correlated percolation does not change these results. Different modifications give log-periodic oscillations before a crash, arising from nonlinear feedback between random flu

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49

KRAWIECKI, A. "MICROSCOPIC SPIN MODEL FOR THE STOCK MARKET WITH ATTRACTOR BUBBLING AND HETEROGENEOUS AGENTS." International Journal of Modern Physics C 16, no. 04 (2005): 549–59. http://dx.doi.org/10.1142/s0129183105007285.

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A multi-agent spin model for changes of prices in the stock market is considered, based on the Ising-like cellular automaton with global, randomly varying in time interactions between traders. The presence of "fundamentalists" is taken into account by introducing additional term in the local field acting on each agent, dependent on the ratio between the actual stock price and its fundamental value. Numerical multi-agent as well as mean-field simulations show that for properly chosen parameters, the time series of logarithmic price returns exhibit bursting typical of volatility clustering, due

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50

Madhi Hamzh Al Rubaie, Evan. "Crypto-Currency Price Trend Prediction using k-Means Clustering." Journal of Engineering and Applied Sciences 14, no. 9 (2020): 10594–99. http://dx.doi.org/10.36478/jeasci.2019.10594.10599.

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