Liquidity Provision of Limit Order Trading in the Futures Market Under Bull and Bear Markets

Chiang, Min Hsien; Lin, Tsai Yin; Yu, Chih Hsien Jerry

doi:10.1111/j.1468-5957.2009.02140.x

Cited by 6 publications

(10 citation statements)

References 38 publications

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“…See, among others, Biais, Hillion, and Spatt (1995), Griffiths, Smith, Turnbull, and White (2000), Ahn, Bae, and Chan (2001), Ranaldo (2004), Degryse, deJong, Ravenswaaij, and Wuyts (2005), and Linnainmaa and Roşu (2009). Relatively fewer studies examine the liquidity provision and the dynamics of the limit order book for futures markets, and they mainly focus on the interrelationship between the volatility and liquidity (e.g., Chiang, Lin, & Yu, 2009;Chiu, Chuang, & Wang, 2014;and Coppejans, Domowitz, & Madhavan, 2004). Following the motivation in Hagströmer and Nordén (2014), the index futures markets are chosen as the context to investigate the intertwined dynamics between trading patience, order flows, and liquidity suggested in liquidity-based trade models (e.g., Foucault et al, 2005 andRoşu, 2009), which assume symmetric information.…”

Section: Introductionmentioning

confidence: 99%

Trading Patience, Order Flows, and Liquidity in an Index Futures Market

2014

Journal of Futures Markets

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This study investigates the limit order book characteristics and the intertwined dynamics between trading patience, order flows, and liquidity in an index futures market. The limit order book displays a hump shape and the steepness of the hump is positively related to the number of large market orders. Bid and ask prices tend to move together in the same direction. Moreover, higher proportion of patient traders and higher order arrival rate lead to higher liquidity, after controlling for volatility and the intraday time effect. Liquidity is found to have a feedback effect on trading patience and the order arrival rate.

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Section: Introductionmentioning

confidence: 99%

Trading Patience, Order Flows, and Liquidity in an Index Futures Market

2014

Journal of Futures Markets

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“…Consequently, the number of potential traders in the market on a down-tick should be lower than in a market on zero-or up-ticks. Using data from the Taiwan Futures Exchange, Chiang et al (2009) find liquidity provision differs in the bull and bear markets. They find liquidity increases when institutional trading activity intensifies in a bull market but not so in a bear market.…”

Section: Introductionmentioning

confidence: 99%

Order size, order imbalance and the volatility–volume relation in a bull versus a bear market

Wee

Yang

2011

Accounting & Finance

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This paper revisits the volume-volatility relationship documented in Chan and Fong (2000) in a bull versus a bear market by using data from the period surrounding the subprime crisis in 2008. The relationship is predicted to be asymmetric because of the greater cost of short positions, the lack of liquidity and the differing trading strategies adopted in a bearish market. Our analysis shows the trading patterns in the bull and bear markets are different. Particularly, in a falling market characterised by higher volatility and lower liquidity, the frequency of order placement has doubled but orders are smaller in size. This provides evidence of investors' shift of trading strategy in relation to the status change of the market. Furthermore, we find evidence to support the proposition that volume is more informative in a bear market than in a bull market.

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“…Rule-based approaches require additional assumptions. As a disadvantage, specifying 1 See Edwards et al (2003); Candelon et al (2008); Chen (2009) and Kaminsky and Schmukler (2008) for applications of the approach proposed by Pagan and Sossounov (2003), and Chiang et al (2009) for the method proposed by Lunde and Timmermann (2004).…”

Section: Introductionmentioning

confidence: 99%

How to Identify and Forecast Bull and Bear Markets?

Kole

Dijk

2016

J of Applied Econometrics

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The state of the equity market, often referred to as a bull or a bear market, is of key importance for financial decisions and economic analyses. Its latent nature has led to several methods to identify past and current states of the market and forecast future states. These methods encompass semi-parametric rule-based methods and parametric regime-switching models. We compare these methods by new statistical and economic measures that take into account the latent nature of the market state. The statistical measure is based directly on the predictions, while the economic measure is based on the utility that results when a risk-averse agent uses the predictions in an investment decision. Our application of this framework to the S&P500 shows that rule-based methods are preferable for (in-sample) identification of the market state, but regime-switching models for (out-of-sample) forecasting. In-sample only the direction of the market matters, but for forecasting both means and volatilities of returns are important. Both the statistical and the economic measures indicate that these differences are significant.

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Liquidity Provision of Limit Order Trading in the Futures Market Under Bull and Bear Markets

Cited by 6 publications

References 38 publications

Trading Patience, Order Flows, and Liquidity in an Index Futures Market

Trading Patience, Order Flows, and Liquidity in an Index Futures Market

Order size, order imbalance and the volatility–volume relation in a bull versus a bear market

How to Identify and Forecast Bull and Bear Markets?

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