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Year of publication
Subject
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Theorie 13 Theory 13 Portfolio selection 8 Portfolio-Management 8 Börsenkurs 6 Share price 6 Securities trading 5 Virtual currency 5 Virtuelle Währung 5 Wertpapierhandel 5 Anlageverhalten 4 Behavioural finance 4 Mathematical programming 4 Mathematische Optimierung 4 Stochastic process 4 Stochastischer Prozess 4 Volatility 4 graph theory 4 Aktienindex 3 Algorithm 3 Algorithmus 3 Forecasting model 3 Monte Carlo simulation 3 Option pricing theory 3 Optionspreistheorie 3 Prognoseverfahren 3 Risikomanagement 3 Risk management 3 Stock index 3 Volatilität 3 portfolio optimization 3 Anleihe 2 Blockchain 2 Bond 2 Clearing 2 Electronic trading 2 Elektronisches Handelssystem 2 Exchange rate 2 Financial analysis 2 Financial clearing 2
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Online availability
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Undetermined 38 Free 4
Type of publication
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Article 64 Book / Working Paper 4
Type of publication (narrower categories)
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Article in journal 14 Aufsatz in Zeitschrift 14 Nachruf 1
Language
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English 42 Undetermined 26
Author
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Chakravarty, Satya R. 21 Sarkar, Palash 21 Maymin, Philip 5 López de Prado, Marcos 3 Bicchetti, David 2 Gu, Ming 2 Leinweber, David 2 Mavungu, Masiala 2 Pennock, David M. 2 Wu, Kesheng 2 Avellaneda, Marco 1 Badshah, Ihsan Ullah 1 Bailey, David H. 1 Bethel, E. Wes 1 Bethel, Wes 1 Bishwal, Jaya Prakasah Narayan 1 Cadena, Carlos 1 Calkin, Neil J. 1 Calkin, Neil J. Calkin 1 Chakravary, Ranjan R. 1 Chen, Bryant 1 Chen, Yao-Tsung 1 Cheung, Albert C. 1 Chong, Terence Tai-Leung 1 Corbacho, Fernando 1 Daigler, Robert T. 1 Diamantaras, Konstantinos 1 Elkan, Charles 1 Feldman, Todd 1 Fogarasi, Norbert 1 Gavrila, Lucian-Ionut 1 Georgiadis, Evangelos 1 Gogas, Periklis 1 Ho, Jan-Ming 1 Hsu, William W.Y. 1 Huerta, Ramon 1 Hurwitz, Catalina I. 1 Ivaşcu, Codruę Florin 1 Joshi, Mark 1 Kao, Ming-Yang 1
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Published in...
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Algorithmic Finance 26 An introduction to algorithmic finance, algorithmic trading and blockchain 24 Algorithmic finance 14 Algorithmic Finance (2013), 2:3-4, 233-239 1 Algorithmic Finance (2013), 2:3-4, 241-267 1 Algorithmic Finance (2015), 4:1-2, 69-79 1 Algorithmic Finance 2014, 3:1-2, 3-20 1
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Source
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ECONIS (ZBW) 42 RePEc 26
Showing 61 - 68 of 68
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Discovering the ecosystem of an electronic financial market with a dynamic machine-learning method
Mankad, Shawn; Michailidis, George - In: Algorithmic Finance 2 (2013) 2, pp. 151-165
Not long ago securities were traded by human traders in face-to-face markets. The ecosystem of an open outcry market was well-known, visible to a human eye, and rigidly prescribed. Now trading is increasingly done in anonymous electronic markets where traders do not have designated functions or...
Persistent link: https://www.econbiz.de/10010933495
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A Minute with Giovanni Barone-Adesi
Maymin, Philip - In: Algorithmic Finance 2 (2013) 2, pp. 111-111
none
Persistent link: https://www.econbiz.de/10010933496
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A big data approach to analyzing market volatility
Wu, Kesheng; Bethel, E. Wes; Gu, Ming; Leinweber, David; … - In: Algorithmic Finance 2 (2013) 3-4, pp. 241-267
Understanding the microstructure of the financial market requires the processing of a vast amount of data related to individual trades, and sometimes even multiple levels of quotes. This requires computing resources that are not easily available to financial academics and regulators....
Persistent link: https://www.econbiz.de/10010991431
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Efficient greek estimation in generic swap-rate market models
Joshi, Mark; Yang, Chao - In: Algorithmic Finance 1 (2011) 1, pp. 17-33
We first develop an efficient algorithm to compute Deltas of interest rate derivatives for a number of standard market models. The computational complexity of the algorithms is shown to be proportional to the number of rates times the number of factors per step. We then show how to extend the...
Persistent link: https://www.econbiz.de/10010840410
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Behavioral biases and investor performance.
Feldman, Todd - In: Algorithmic Finance 1 (2011) 1, pp. 45-55
Research indicates that individual investors trade excessively and underperform the market indices, Barber and Odean 2000). The purpose of this paper is to help explain which behavioral biases, if any, can explain this result using a simulation approach. Results indicate that putting too much...
Persistent link: https://www.econbiz.de/10010840415
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Binomial options pricing has no closed-form solution
Georgiadis, Evangelos - In: Algorithmic Finance 1 (2011) 1, pp. 13-16
We set a lower bound on the complexity of options pricing formulae in the lattice metric by proving that no general explicit or closed form (hypergeometric) expression for pricing vanilla European call and put options exists when employing the binomial lattice approach. Our proof follows from...
Persistent link: https://www.econbiz.de/10010840416
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Markets are efficient if and only if P=NP
Maymin, Philip - In: Algorithmic Finance 1 (2011) 1, pp. 1-11
I prove that if markets are efficient, meaning current prices fully reflect all information available in past prices, then P=NP, meaning every computational problem whose solution can be verified in polynomial time can also be solved in polynomial time. I also prove the converse by showing how...
Persistent link: https://www.econbiz.de/10010991428
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Forecasting prices from level-I quotes in the presence of hidden liquidity
Avellaneda, Marco; Reed, Josh; Stoikov, Sasha - In: Algorithmic Finance 1 (2011) 1, pp. 35-43
Bid and ask sizes at the top of the order book provide information on short-term price moves. Drawing from classical descriptions of the order book in terms of queues and order-arrival rates (Smith et al., 2003), we consider a diffusion model for the evolution of the best bid/ask queues. We...
Persistent link: https://www.econbiz.de/10010991434
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