Price Clustering Research Articles

Price Clustering and Price Barriers: International Evidence

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.

Understanding Irrigator Bidding Behavior in Australian Water Markets in Response to Uncertainty

Water markets have been used by Australian irrigators as a way to reduce risk and uncertainty in times of low water allocations and rainfall. However, little is known about how irrigators’ bidding trading behavior in water markets compares to other markets, nor is it known what role uncertainty and a lack of water in a variable and changing climate plays in influencing behavior. This paper studies irrigator behavior in Victorian water markets over a decade (a time period that included a severe drought). In particular, it studies the evidence for price clustering (when water bids/offers end mostly around particular numbers), a common phenomenon present in other established markets. We found that clustering in bid/offer prices in Victorian water allocation markets was influenced by uncertainty and strategic behavior. Water traders evaluate the costs and benefits of clustering and act according to their risk aversion levels. Water market buyer clustering behavior was mostly explained by increased market uncertainty (in particular, hotter and drier conditions), while seller-clustering behavior is mostly explained by strategic behavioral factors which evaluate the costs and benefits of clustering.

Evolution of integer price clustering of IPOs in the aftermarket

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 volume, number of market makers and the effect of price support, the integer price frequency of IPOs converge to that of seasoned stocks immediately, and whether IPOs have integer offer prices does not affect their integer price clustering in the aftermarket trading after the effect of price support is controlled for. Originality/value – These results suggest that the IPO pricing process significantly reduce the differences between integer priced IPOs and non-integer priced IPOs in pre-offering valuation uncertainty.

Tick size reduction and price clustering in a FX order book

We investigate the statistical properties of the EBS order book for the EUR/USD and USD/JPY currency pairs and the impact of a ten-fold tick size reduction on its dynamics. A large fraction of limit orders are still placed right at or halfway between the old allowed prices. This generates price barriers where the best quotes lie for much of the time, which causes the emergence of distinct peaks in the average shape of the book at round distances. Furthermore, we argue that this clustering is mainly due to manual traders who remained set to the old price resolution. Automatic traders easily take price priority by submitting limit orders one tick ahead of clusters, as shown by the prominence of buy (sell) limit orders posted with rightmost digit one (nine).

Clustering of Trade Prices by High‐Frequency and Non–High‐Frequency Trading Firms

AbstractWe examine clustering of transaction prices in a sample that contains high‐frequency trading firms’ transactions. We separate our sample into four categories: transactions with a high‐frequency trading firm on both sides of the transaction, on only one side of the transaction (either liquidity provider or liquidity demander), or on neither side of the transaction. We find that transaction price clustering is less frequent when a high‐frequency trading firm is on both sides of the transaction than when a high‐frequency trading firm is on only one side of the transaction or if a high‐frequency trading firm is not involved in the transaction. Further, we find that transactions where the liquidity providing order, which more likely dictates the price, is submitted by a high‐frequency trading firm cluster less than when the liquidity providing order is submitted by a non–high‐frequency trader. We thus conclude that the tendency of prices to cluster appears to be driven by a distinctly human bias.

Volatility Clustering, Risk-Return Relationship, and Asymmetric Adjustment in the Canadian Housing Market

In this study, we apply a Lagrange multiplier (LM) test for the autoregressive conditional heteroscedasticity (ARCH) effects and an exponential generalized autoregressive conditional heteroscedasticity-in-mean (EGARCH-M) model to assess whether regional house prices in Canada exhibit financial characteristics similar to stock indices. Volatility clustering, positive risk-return relationships, and leverage effects are empirically shown to exist in the majority of provincial housing markets of Canada. These volatility behaviors, which reflect regional idiosyncrasies, are further found to differ across provinces. More densely populated provinces exhibit stronger volatility clustering of house prices. The existence of these volatility patterns similar to stock indices has important implications ranging from proper portfolio management to government policy.

Price Clustering and Round Number Effects - Evidence from the German Market

Price clustering was found to be a major artifact of securities trading over many decades. In the literature several conjectures about the origin of these effects have been put forward. However, most explanations are not applicable anymore since today a great share of the trading activity is due to automated trading systems which are assumed to be unaffected by any number bias. I analyze round number effects in the German market and thereby contribute to existing literature by showing that round number effects are also present in the Germany, but exhibit local stylized facts. Further, new determinants of round number effects are identified. In accordance with increasing algorithmic trading activities the strength of effects is decreasing over the observation period. On the other hand, an analysis of retail investor trading reveals that the magnitude of human round number biases remains stable over time and is mainly induced by the usage of limit orders.

The Optimal Division of the Energy Market into Zones: Comparison of Two Methodologies Under Variable Wind Condtions

We compare two competing methodologies of market zones identification under the criterion of social welfare maximization: (i) consensus clustering of Locational Marginal Prices over different wind scenarios and (ii) congestion contribution identification with congested lines identified across variable wind generation outputs. We test the division of market into zones based on each of the two methodologies using a welfare criterion, i.e., comparing the cost of supplying energy on uniform market (including readjustments made on a balancing market to overcome the congestion) with cost on k-zone market. A division which maximizes the welfare is considered as the optimum.

Forecasting oil prices: Smooth transition and neural network augmented GARCH family models

The study focuses on a new class of nonlinear volatility models based on neural networks and STAR type nonlinearity. Accordingly, LSTAR–LST–GARCH family and LSTAR–LST–GARCH–NN family of models will be evaluated to analyze petrol prices with economic applications. The nonlinear behavior and leptokurtic distribution are discussed in many studies. The study aims proposing augmentation of linear GARCH, fractionally integrated FI–GARCH and Asymmetric Power APGARCH models with LSTAR type nonlinearity modeling. Further, the proposed models will be augmented with neural networks to benefit from well known learning and forecasting capabilities. The multilayer perceptron (MLP) neural network model and LSTAR model have significant similarities in terms of their architecture. The proposed LSTAR–LST–GARCH family and ANN augmented LSTAR–LST–GARCH–MLP models are evaluated for modeling petrol prices. Empirical findings of the study are: (1) Fractionally integrated and asymmetric power improvements among the GARCH family models provide better forecasting capability for petrol prices; better captured long memory and high volatility characteristics of petrol prices. (2). LSTAR–LST–GARCH model family results in even better gains in out-of-sample forecasting. (3) Donaldson and Kamstra (1997) based MLP–GARCH family provided similar results with the LSTAR–LST–GARCH family models. One exception is for MLP–FIGARCH and MLP–FIAPGARCH models; FI and AP augmented models proposed in this study. (4) Volatility clustering, asymmetry and nonlinearity characteristics of petrol prices are best captured with the LSTAR–LST–GARCH–MLP model family. Forecasting capabilities of neural network techniques are promising. Among the evaluated models, the LSTAR–LST–APGARCH–MLP model provided the best performance overall. With a political perspective, in addition to the highly volatile structure, the long memory characteristics of petrol prices requires that the economic policy interventions should be kept at the modest levels to avoid persistent impacts of shocks.

Has political instability contributed to price clustering on Fiji's stock market?

The goal of this article is to examine evidence of stock price clustering on the South Pacific Stock Exchange, located in Fiji, and explore its determinants. We find that stock prices cluster at the decimal of 0 and 5, with almost half of prices settling on these two decimals. Upon investigating the determinants of price clustering on the South Pacific Stock Exchange we find that price level and volume of trade have a statistically significant positive effect on price clustering. We also propose and test a ‘panic trading’ hypothesis which states political instability induces price clustering. We find evidence that political instability in Fiji induces price clustering behaviour.

Psychological Barriers and Price Clustering in Energy Futures

WTI and Brent futures are tested for the presence of psychological barriers around $10 price levels applying a multiple hypothesis testing approach for robustness. Psychological barriers are present in Brent pricing but not in WTI pricing, which is argued, based on recent behavioural finance research, to be due to the greater uncertainty inherent in Brent fundamental value determination. Particularly Brent displays significant resistance when breaching from below a $10 barrier level. Similar patterns are present at the $1 barrier level for the WTI-Brent spread. A range of reaction windows are applied with the main finding being that the trading potential around such psychological barrier levels is primarily in the immediate 1-2 days following a breach. The research contributes to the scant existing research on psychological influences on energy market traders, and suggests strong potential for further application of behavioural finance theories to improving understanding of energy markets pricing dynamics.

Price clustering in Australian water markets

The finding of clustering in financial prices on particular digits is common across a broad range of financial markets. This article explores whether price clustering is also present in the case of the weekly market for seasonal water in rural Victoria, Australia. We find a similar degree of clustering in the seasonal water market. This suggests that the trading activities of the market produce characteristics that are similar to more sophisticated and deeper financial markets.

Price Clustering in the U.S. Dollar/Taiwan Dollar Swap Market

AbstractPrice clustering in financial markets is pervasive. Using transaction‐level data from the world's largest financial market, this study is the first to examine price clustering behavior in the foreign exchange swap market. In addition to existing hypotheses, we investigate new determinants of price clustering including the expected return, contract liquidity, and trader's identity. The results support both negotiation and price resolution hypotheses. We find a positive effect from the level of expected return on price clustering. Markets with greater liquidity experience reduced clustering. Transactions involving domestic banks have less clustering suggesting an information advantage over foreign banks.

Volatility Clustering, Risk-Return Relationship and Asymmetric Adjustment in Canadian Housing Markets

This study applies a Lagrange Multiplier (LM) test for the AutoRegressive Conditional Heteroskedasticity (ARCH) effects and an Exponential Generalized Autoregressive Conditional Heteroskedasticity-in-Mean (EGARCH-M) model to assess whether regional house prices in Canada exhibit financial characteristics similar to stock indices. Volatility clustering, positive risk-return relationships, and leverage effects are empirically shown to exist in the majority of provincial housing markets of Canada. These volatility behaviors, which reflect regional idiosyncrasies, are further found to differ across provinces. More densely populated provinces exhibit stronger volatility clustering of house prices. The existence of these volatility patterns similar to stock indices has important implications ranging from proper portfolio management to government policy.

Share Price Clustering on the Shanghai Stock Exchange: A Reconsideration of Cultural and Rational Explanations

Share Price Clustering on the Shanghai Stock Exchange: A Reconsideration of Cultural and Rational Explanations

A substitution effect between price clustering and size clustering in credit default swaps

In a perfectly liquid market, investors’ optimal allocation decisions refer to maximizing all three dimensions of liquidity, namely immediacy, width and depth. To the extent that investors fail to accommodate size (depth) along with price (width) in their optimal allocation decisions, their overall costs may increase. This paper focuses on the substitution of width and depth by investigating the simultaneous determination of price clustering and size clustering in the credit default swap (CDS) market. We report strong evidence that when traders round prices they tend to quote more refined sizes, and vice versa. The findings highlight a clear trade-off between price clustering and notional amount in the CDS market, and contribute to the emerging literature on size clustering.

Size Clustering in European Carbon Markets

This paper documents empirical evidence of size clustering behavior in the European Carbon Futures Market and analyzes the circumstances under which it happens. Our findings show that carbon trades are concentrated in sizes of one to five contracts and in multiples of five. We have observed the existence of price clustering of prices ending in digits 0 or 5, and we have also proved that more clustered prices have more clustered sizes. Finally, the analysis of the key factors of the size clustering reveals that carbon traders use a reduced number of different trade sizes to simplify their trading process when uncertainty is high, market liquidity is poor, and the desire for opening new positions is very strong.

Nonlinear Volatility Models in Economics: Smooth Transition and Neural Network Augmented GARCH, APGARCH, FI-GARCH and FIAGARCH Models

Recently, Donaldson and Kamstra (1997) proposed a class of NN-GARCH models which are extended to a class of NN-GARCH family by Bildirici and Ersin (2009). The study aims to analyze the nonlinear behavior and leptokurtic distribution in petrol prices by utilizing a newly developed family of econometric models that deal with these concepts by benefiting from both LSTAR type and ANN based nonlinearity. With this purpose, the study proposed several LSTAR-GARCH-NN family models. It is noted that the multilayer perceptron (MLP) neural network and LSTAR models have significant architectural similarities. Accordingly, linear GARCH, fractionally integrated FI-GARCH, asymmetric power APGARCH and fractionally integrated asymmetric power APGARCH models are augmented with a family of Neural Network models. The study has following contributions: i. STAR-GARCH and LSTAR-GARCH are extended to their fractionally integrated asymmetric power versions and STAR-ST-FIGARCH and STAR-ST-APGARCH, STAR-ST-FIAPGARCH models are developed and evaluated. ii. By extending these models with neural networks, LSTAR-LST-GARCH-MLP family models are developed and investigated. These models benefit from LSTAR type nonlinearity and NN based nonlinear NN-GARCH models to capture time varying volatility and nonlinearity in petrol prices. ANN augmented versions of LSTAR-LST-GARCH models are as follows: LSTAR-LST-GARCH-MLP, LSTAR-LST-FIGARCH-MLP, LSTAR-LST-APGARCH-MLP and LSTAR-LST-FIAPGARCH-MLP. Empirical findings are collected as follows. i. To model petrol prices, fractionally integrated and asymmetric power versions provided improvements among the GARCH family models in terms of forecasting. ii. LSTAR-LST-GARCH model family is promising and show significant gains in out-of-sample forecasting. iii. MLP-GARCH family provided similar results with the LSTAR-LST-GARCH family models, except for the MLP-FIGARCH and MLP-FIAPGARCH models. iv. Volatility clustering, asymmetry and nonlinearity characteristics of petrol prices are captured most efficiently with the LSTAR-LST-GARCH-MLP models benefiting from forecasting capabilities of neural network techniques, whereas, among the newly developed models, LSTAR-LST-APGARCH-MLP model provided the best performance overall.

TRADE SIZE AND PRICE CLUSTERING: THE CASE OF SHORT SALES AND THE SUSPENSION OF PRICE TESTS

AbstractIn this article, we compare trade size and price clustering of short sales with regular trades. We find that short sales cluster less on round sizes and round prices than do nonshort trades. When price tests are suspended, both trade size and price clustering markedly increase for short sales although the difference between shorts and nonshorts remains significant during the postsuspension period. These results are consistent with the idea that because of execution uncertainty caused by price tests, short sellers are less concerned with cognitive processing costs, negotiations costs, and the costs associated with revealing information through trade sizes.

Price Clustering in Individual Equity Options: Moneyness, Maturity, and Price Level

Equity options have a significant influence on the price discovery process. This study presents unique evidence of substantial price clustering in individual equity options contracts. A particular contribution arises from investigating competing hypotheses on the roles of moneyness and maturity as determinants of option price clustering. We assert that options price clustering can be decomposed to price level, moneyness, and maturity effects. After controlling for other factors, price clustering has an inverse relation with time‐to‐maturity. This supports the negotiation hypothesis, but not the price resolution hypothesis. Price clustering also tends to be inversely related to moneyness. This effect is linked to the intrinsic value component of option price. Both the maturity and moneyness effects act in an opposite direction to what would be anticipated on the basis of price level alone; hence, these two effects are identified as additional influences on option price clustering. It is also found that the designated market maker scheme at NYSE Euronext London International Financial Futures Exchange (LIFFE) has little influence on trade price clustering. © 2012 Wiley Periodicals, Inc. Jrl Fut Mark 33:55–76, 2013