Measuring the Bid-Ask Spread: A Note on the Potential Downward Bias of the Thompson-Waller Estimator

2013 ◽  
Author(s):  
Yoichi Otsubo
Keyword(s):  
2016 ◽  
Vol 8 (2) ◽  
pp. 24-45
Author(s):  
Tania Hayu Safira ◽  
Febryanti Simon

This study is event study that was conduct to examine the differences of abnormal return, trading volume, trading frequency and bid-ask spread before and after the events of share split. The object of this research is the companies that did share split and listed in Indonesia Stock Exchange in 2008 - 2015. The samples are 30 companies chosen by purposive sampling method. The criteria are the company did not do corporate action right issue, pre-emptive rights, a share dividend and bonus shares in the same year with share split. Event window used in this study was 30 days consisting of 15 days before and 15 days after the share split. Data analysis technique begins with a test of normality using Kolmogorov – Smirnov and transform for unnormally distributed data. Then, test of hypothesis using Paired t – test to compare the differences before and after share split. The results of this study showed that volume trading activity and trading frequency had significant differences before and after the share split. While, variable abnormal return and bid-ask spread had not significant differences before and after the share split. Keywords: Abnormal return, bid-ask spread, share split, trading frequency, trading volume.


2007 ◽  
Author(s):  
Laurie Prather ◽  
David Michayluk ◽  
Li-Anne Elizabeth Woo ◽  
Henry Yip

2021 ◽  
pp. 1-21
Author(s):  
Malick Fall ◽  
Waël Louhichi ◽  
Jean Laurent Viviani

2020 ◽  
Vol 98 (Supplement_2) ◽  
pp. 58-58
Author(s):  
Megan A Gross ◽  
Claire Andresen ◽  
Amanda Holder ◽  
Alexi Moehlenpah ◽  
Carla Goad ◽  
...  

Abstract In 1996, the NASEM beef cattle committee developed and published an equation to estimate cow feed intake using results from studies conducted or published between 1979 and 1993 (Nutrient Requirements of Beef Cattle). The same equation was recommended for use in the most recent version of this publication (2016). The equation is sensitive to cow weight, diet digestibility and milk yield. Our objective was to validate the accuracy of this equation using more recent published and unpublished data. Criteria for inclusion in the validation data set included projects conducted or published within the last ten years, direct measurement of forage intake, adequate protein supply, and pen feeding (no tie stall or metabolism crate data). The validation data set included 29 treatment means for gestating cows and 26 treatment means for lactating cows. Means for the gestating cow data set was 11.4 ± 1.9 kg DMI, 599 ± 77 kg BW, 1.24 ± 0.14 Mcal/kg NEm per kg of feed and lactating cow data set was 14.5 ± 2.0 kg DMI, 532 ± 116.3 kg BW, and 1.26 ± 0.24 Mcal NEm per kg feed, respectively. Non intercept models were used to determine equation accuracy in predicting validation data set DMI. The slope for linear bias in the NASEM gestation equation did not differ from 1 (P = 0.07) with a 3.5% positive bias. However, when the NASEM equation was used to predict DMI in lactating cows, the slope for linear bias significantly differed from 1 (P < 0.001) with a downward bias of 13.7%. Therefore, a new multiple regression equation was developed from the validation data set: DMI= (-4.336 + (0.086427 (BW^.75) + 0.3 (Milk yield)+6.005785(NEm)), (R-squared=0.84). The NASEM equation for gestating beef cows was reasonably accurate while the lactation equation underestimated feed intake.


1998 ◽  
Vol 33 (3) ◽  
pp. 35-52
Author(s):  
Ravinder K. Bhardwaj ◽  
William T. Moore

2021 ◽  
pp. 1-27
Author(s):  
TOAN LUU DUC HUYNH ◽  
MEI WANG ◽  
VINH XUAN VO

This paper investigates the prediction power of economic policy uncertainty on Bitcoin trading (return, volume, and volatility) over the period from May 2013 to June 2019. We employ the Transfer Entropy model with the following two different regimes (i) stationary and (ii) nonstationary assumption. We construct different algorithm calculations for returns, volume and volatility to test how this proxy impacts. We find that the global Economic Policy Uncertainty negatively causes Bitcoin volumes and volatilities. Therefore, under uncertain regimes, investors are risk-averse to trade, which makes the market less volatile. Our findings confirm the existence of pessimistic risk premium, the theory of deteriorating liquidity and the widen bid-ask spread, which lead to a decline in trading volume under uncertainties in the Bitcoin market. By using different reliable data sources as well as expanding timeframe until May 2020 with COVID-19 pandemic, our results remain robust. Hence, the practical implications will be the useful tools for different parties in the Bitcoin market in the financial turbulence context.


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