Citation analysis for Algorithmic Finance / IOS Press

Algorithmic Finance / IOS Press

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Impact Factor

0.33

5-Years IF

5-Years H index

[Raw data] [50 most cited papers] [50 most relevant papers] [cites used to compute IF] [Recent citations ][Frequent citing series ]
[more data in EconPapers] [trace new citations] [Missing citations? Add them now] [Incorrect content? Let us know]

Main indicators

Raw data

	IF	AIF	IF5	DOC	CDO	CCU	CIF	CIT	D2Y	C2Y	D5Y	C5Y	%SC	CiY	II	AII
1990		0.11			0				0		0		(%)			0.06
1991		0.1			0				0		0		(%)			0.04
1992		0.1			0				0		0		(%)			0.05
1993		0.13			0				0		0		(%)			0.06
1994		0.14			0				0		0		(%)			0.06
1995		0.17			0				0		0		(%)			0.1
1996		0.22			0				0		0		(%)			0.09
1997		0.22			0				0		0		(%)			0.09
1998		0.24			0				0		0		(%)			0.12
1999		0.3			0				0		0		(%)			0.15
2000		0.36			0				0		0		(%)			0.14
2001		0.36			0				0		0		(%)			0.16
2002		0.37			0				0		0		(%)			0.18
2003		0.39			0				0		0		(%)			0.19
2004		0.4			0				0		0		(%)			0.18
2005		0.42			0				0		0		(%)			0.2
2006		0.45			0				0		0		(%)			0.19
2007		0.38			0				0		0		(%)			0.16
2008		0.39			0				0		0		(%)			0.17
2009		0.36			0				0		0		(%)			0.17
2010		0.34			0	1			0		0		(%)			0.15
2011		0.4		5	5	2	0.4	13	0		0		(%)	2	0.4	0.19
2012	0.2	0.44	0.2		5	1	0.2		5	1	5	1	(%)			0.2
2013	0.2	0.49	0.2	16	21	4	0.19	34	5	1	5	1	(%)	3	0.19	0.2
2014	0.56	0.52	0.43	5	26	9	0.35	2	16	9	21	9	(%)			0.23
2015	0.14	0.54	0.15		26	4	0.15		21	3	26	4	(%)			0.24
2016	0.4	0.6	0.5		26	13	0.5		5	2	26	13	(%)			0.27
2017		0.64	0.33		26	8	0.31		0		21	7	(%)			0.28

IF:	Impact Factor: C2Y / D2Y
AIF:	Average Impact Factor for series in RePEc in year y
IF5:	Impact Factor: C5Y / D5Y
DOC:	Number of documents published in year y
CDO:	Cumulative number of documents published until year y
CCU:	Cumulative number of citations to papers published until year y
CIF:	Cumulative impact factor
CIT:	Number of citations to papers published in year y
D2Y:	Number of articles published in y-1 plus y-2
C2Y:	Cites in y to articles published in y-1 plus y-2
D5Y:	Number of articles published in y-1 until y-5
C5Y:	Cites in y to articles published in y-1 until y-5
%SC:	Percentage of selft citations in y to articles published in y-1 plus y-2
CiY:	Cites in year y to documents published in year y
II:	Immediacy Index: CiY / Documents.
AII:	Average Immediacy Index for series in RePEc in year y

50 most cited documents in this series:

#	Year	Title	Cited
1	2013	Cluster formation and evolution in networks of financial market indices. (2013). Sandoval, Leonidas Junior . In: Algorithmic Finance. RePEc:ris:iosalg:0023. Full description at Econpapers \|\| Download paper	9
2	2013	A big data approach to analyzing market volatility. (2013). Leinweber, David ; Bethel, Wes E. ; Rube, Oliver ; Gu, Ming ; Wu, Kesheng. In: Algorithmic Finance. RePEc:ris:iosalg:0016. Full description at Econpapers \|\| Download paper	7
3	2011	Forecasting prices from level-I quotes in the presence of hidden liquidity. (2011). Reed, Josh ; Stoikov, Sasha ; Avellaneda, Marco. In: Algorithmic Finance. RePEc:ris:iosalg:0004. Full description at Econpapers \|\| Download paper	7
4	2013	The synchronized and long-lasting structural change on commodity markets: Evidence from high frequency data. (2013). Maystre, Nicolas ; Bicchetti, David. In: Algorithmic Finance. RePEc:ris:iosalg:0015. Full description at Econpapers \|\| Download paper	5
5	2013	Nonlinear support vector machines can systematically identify stocks with high and low future returns. (2013). Elkan, Charles ; Huerta, Ramon ; Corbacho, Fernando . In: Algorithmic Finance. RePEc:ris:iosalg:0024. Full description at Econpapers \|\| Download paper	4
6	2011	Efficient greek estimation in generic swap-rate market models. (2011). Joshi, Mark ; Yang, Chao. In: Algorithmic Finance. RePEc:ris:iosalg:0003. Full description at Econpapers \|\| Download paper	3
7	2013	Stock chatter: Using stock sentiment to predict price direction. (2013). Rechenthin, Michael ; Srinivasan, Padmini ; Street, Nick W.. In: Algorithmic Finance. RePEc:ris:iosalg:0012. Full description at Econpapers \|\| Download paper	3
8	2013	A multiscale model of high-frequency trading. (2013). Kirilenko, Andrei ; Meng, Xiangqian ; Sowers, Richard B.. In: Algorithmic Finance. RePEc:ris:iosalg:0025. Full description at Econpapers \|\| Download paper	3
9	2014	Linear-time accurate lattice algorithms for tail conditional expectation. (2014). Chen, Bryant ; Kao, Ming-Yang ; Ho, Jan-Ming ; Hsu, William W. Y., . In: Algorithmic Finance. RePEc:ris:iosalg:0010. Full description at Econpapers \|\| Download paper	2
10	2011	Markets are efficient if and only if P=NP. (2011). Maymin, Philip. In: Algorithmic Finance. RePEc:ris:iosalg:0001. Full description at Econpapers \|\| Download paper	1
11	2011	Behavioral biases and investor performance.. (2011). Feldman, Todd. In: Algorithmic Finance. RePEc:ris:iosalg:0005. Full description at Econpapers \|\| Download paper	1
12	2013	Discovering the ecosystem of an electronic financial market with a dynamic machine-learning method. (2013). Michailidis, George ; Mankad, Shawn. In: Algorithmic Finance. RePEc:ris:iosalg:0021. Full description at Econpapers \|\| Download paper	1
13	2013	Sparse, mean reverting portfolio selection using simulated annealing. (2013). Levendovszky, Janos ; Fogarasi, Norbert . In: Algorithmic Finance. RePEc:ris:iosalg:0013. Full description at Econpapers \|\| Download paper	1
14	2013	Optimizing sparse mean reverting portfolios. (2013). Levendovszky, Janos ; Sipos, Robert I.. In: Algorithmic Finance. RePEc:ris:iosalg:0019. Full description at Econpapers \|\| Download paper	1
15	2011	Binomial options pricing has no closed-form solution. (2011). Georgiadis, Evangelos . In: Algorithmic Finance. RePEc:ris:iosalg:0002. Full description at Econpapers \|\| Download paper	1
16	2013	Modeling market impact and timing risk in volume time. (2013). Mazur, Slava . In: Algorithmic Finance. RePEc:ris:iosalg:0018. Full description at Econpapers \|\| Download paper	1

50 most relevant documents in this series (papers most cited in the last two years)

#	Year	Title	Cited
1	2013	Cluster formation and evolution in networks of financial market indices. (2013). Sandoval, Leonidas Junior . In: Algorithmic Finance. RePEc:ris:iosalg:0023. Full description at Econpapers \|\| Download paper	6
2	2013	Nonlinear support vector machines can systematically identify stocks with high and low future returns. (2013). Elkan, Charles ; Huerta, Ramon ; Corbacho, Fernando . In: Algorithmic Finance. RePEc:ris:iosalg:0024. Full description at Econpapers \|\| Download paper	4
3	2011	Forecasting prices from level-I quotes in the presence of hidden liquidity. (2011). Reed, Josh ; Stoikov, Sasha ; Avellaneda, Marco. In: Algorithmic Finance. RePEc:ris:iosalg:0004. Full description at Econpapers \|\| Download paper	3
4	2013	The synchronized and long-lasting structural change on commodity markets: Evidence from high frequency data. (2013). Maystre, Nicolas ; Bicchetti, David. In: Algorithmic Finance. RePEc:ris:iosalg:0015. Full description at Econpapers \|\| Download paper	3
5	2014	Linear-time accurate lattice algorithms for tail conditional expectation. (2014). Chen, Bryant ; Kao, Ming-Yang ; Ho, Jan-Ming ; Hsu, William W. Y., . In: Algorithmic Finance. RePEc:ris:iosalg:0010. Full description at Econpapers \|\| Download paper	2
6	2013	A big data approach to analyzing market volatility. (2013). Leinweber, David ; Bethel, Wes E. ; Rube, Oliver ; Gu, Ming ; Wu, Kesheng. In: Algorithmic Finance. RePEc:ris:iosalg:0016. Full description at Econpapers \|\| Download paper	2

Citing documents used to compute impact factor 0:

Year	Title

Recent citations (cites in year: CiY)

Recent citations received in 2014

Year	Citing document

10 most frequent citing series

#	Series	Cites
1	Papers / arXiv.org	9
2	International Journal of Theoretical and Applied Finance (IJTAF) / World Scientific Publishing Co. Pte. Ltd.	1
3		1
4	Review of Behavioral Finance / Emerald Group Publishing	1
5	Working Papers / HAL	1

Warning!! This is still an experimental service. The results of this service should be interpreted with care, especially in research assessment exercises. The processing of documents is automatic. There still are errors and omissions in the identification of references. We are working to improve the software to increase the accuracy of the results.

Source data used to compute the impact factor of RePEc series.