@article{MTMT:33831953,
	title = {Quantum advantage of Monte Carlo option pricing},
	url = {https://m2.mtmt.hu/api/publication/33831953},
	author = {Udvarnoki, Zoltán András and Fáth, Gábor and Fogarasi, Norbert},
	doi = {10.1088/2399-6528/acd2a4},
	journal-iso = {J PHYSICS COMM},
	journal = {JOURNAL OF PHYSICS COMMUNICATIONS},
	volume = {7},
	unique-id = {33831953},
	abstract = {Quantum computers have the potential to provide quadratic speedup for Monte Carlo methods currently used in various classical applications. In this work, we examine the advantage of quantum computers for financial option pricing with the Monte Carlo method. Systematic and statistical errors are handled in a joint framework, and a relationship to quantum gate error is established. New metrics are introduced for the assessment of quantum advantage based on sample count and optimized error handling. We implement and analyze a Fourier series based approach and demonstrate its benefit over the more traditional rescaling method in function approximation. Our numerical calculations reveal the unpredictable nature of systematic errors, making consistent quantum advantage difficult with current quantum hardware. Our results indicate that very low noise levels, a two-qubit gate error rate below 10 −6 , are necessary for the quantum method to outperform the classical one, but a low number of logical qubits (ca. 20) may be sufficient to see quantum advantage already.},
	year = {2023},
	eissn = {2399-6528},
	pages = {055001}
}

@article{MTMT:32525405,
	title = {Improved sparse mean reverting portfolio selection using Simulated Annealing and Extreme Learning Machine},
	url = {https://m2.mtmt.hu/api/publication/32525405},
	author = {Rácz, Attila József and Fogarasi, Norbert},
	journal-iso = {ACTA UNIV SAP INFORM},
	journal = {ACTA UNIVERSITATIS SAPIENTIAE INFORMATICA},
	unique-id = {32525405},
	issn = {1844-6086},
	year = {2022},
	eissn = {2066-7760}
}

@article{MTMT:32808022,
	title = {Trading sparse, mean reverting portfolios using VAR(1) and LSTM prediction},
	url = {https://m2.mtmt.hu/api/publication/32808022},
	author = {Rácz, Attila József and Fogarasi, Norbert},
	doi = {10.2478/ausi-2021-0013},
	journal-iso = {ACTA UNIV SAP INFORM},
	journal = {ACTA UNIVERSITATIS SAPIENTIAE INFORMATICA},
	volume = {13},
	unique-id = {32808022},
	issn = {1844-6086},
	abstract = {We investigated the predictability of mean reverting portfolios and the VAR(1) model in several aspects. First, we checked the dependency of the accuracy of VAR(1) model on different data types including the original data itself, the return of prices, the natural logarithm of stock and on the log return. Then we compared the accuracy of predictions of mean reverting portfolios coming from VAR(1) with different generative models such as VAR(1) and LSTM for both online and offline data. It was eventually shown that the LSTM predicts much better than the VAR(1) model. The conclusion is that the VAR(1) assumption works well in selecting the mean reverting portfolio, however, LSTM is a better choice for prediction. With the combined model a strategy with positive trading mean profit was successfully developed. We found that online LSTM outperforms all VAR(1) predictions and results in a positive expected profit when used in a simple trading algorithm.},
	keywords = {Optimization; Portfolio selection; Mean reversion; Time-series prediction},
	year = {2021},
	eissn = {2066-7760},
	pages = {288-302}
}

@article{MTMT:3393080,
	title = {Trading by estimating the quantized forward distribution},
	url = {https://m2.mtmt.hu/api/publication/3393080},
	author = {Ceffer, Attila and Fogarasi, Norbert and Levendovszky, János},
	doi = {10.1080/00036846.2018.1486021},
	journal-iso = {APPL ECON},
	journal = {APPLIED ECONOMICS},
	volume = {50},
	unique-id = {3393080},
	issn = {0003-6846},
	year = {2018},
	eissn = {1466-4283},
	pages = {6397-6405},
	orcid-numbers = {Levendovszky, János/0000-0003-1406-442X}
}

@article{MTMT:3256024,
	title = {Applying Independent Component Analysis and Predictive Systems for Algorithmic Trading},
	url = {https://m2.mtmt.hu/api/publication/3256024},
	author = {Ceffer, Attila and Levendovszky, János and Fogarasi, Norbert},
	doi = {10.1007/s10614-017-9719-z},
	journal-iso = {COMPUT ECON},
	journal = {COMPUTATIONAL ECONOMICS},
	volume = {54},
	unique-id = {3256024},
	issn = {0927-7099},
	year = {2018},
	eissn = {1572-9974},
	pages = {281-303},
	orcid-numbers = {Levendovszky, János/0000-0003-1406-442X}
}

@article{MTMT:3251573,
	title = {On partial sorting in restricted rounds. Dedicated to the memory of Antal Ivanyi},
	url = {https://m2.mtmt.hu/api/publication/3251573},
	author = {Ivanyi, Antal and Fogarasi, Norbert},
	doi = {10.1515/ausi-2017-0002},
	journal-iso = {ACTA UNIV SAP INFORM},
	journal = {ACTA UNIVERSITATIS SAPIENTIAE INFORMATICA},
	volume = {9},
	unique-id = {3251573},
	issn = {1844-6086},
	year = {2017},
	eissn = {2066-7760},
	pages = {17-34}
}

@CONFERENCE{MTMT:3183235,
	title = {Applying ICA and NARX networks for algorithmic trading},
	url = {https://m2.mtmt.hu/api/publication/3183235},
	author = {Ceffer, Attila and Levendovszky, János and Fogarasi, Norbert},
	booktitle = {Fourth International Symposium in Computational Economics and Finance},
	unique-id = {3183235},
	abstract = {In this paper, a Nonlinear AutoRegressive network with 
		eXogenous inputs (NARX) is proposed for algorithmic trading by 
		predicting the future value of financial time series. This 
		network is highly capable of modeling vector autoregressive 
		VAR(p) time series. In order to avoid overfitting, the input 
		is pre-processed by Independent Component Analysis (ICA) to 
		filter out the most noise like component. In this way, the
		accuracy of the prediction is increased. The proposed 
		algorithm has a reduced number of free parameters which makes 
		fast learning and trading possible. The method is not only 
		tested on single asset price series, but also on predicting 
		the value of mean reverting portfolios obtained by maximizing 
		the predictability parameter of VAR(1) processes. The tests 
		were first performed on artificially generated data and then 
		on real data selected from Exchange Traded Fund (ETF) time 
		series including bid-ask spread. In both cases profit has been 
		achieved by the proposed method.},
	year = {2016},
	pages = {1-16}
}

@mastersthesis{MTMT:2944191,
	title = {Polynomial Time Heuristic Optimization Methods Applied to Problems in Computational Finance},
	url = {https://m2.mtmt.hu/api/publication/2944191},
	author = {Fogarasi, Norbert},
	publisher = {Budapest University of Technology and Economics},
	unique-id = {2944191},
	year = {2014}
}

@article{MTMT:2693507,
	title = {Sparse, mean reverting portfolio selection using simulated annealing},
	url = {https://m2.mtmt.hu/api/publication/2693507},
	author = {Fogarasi, Norbert and Levendovszky, János},
	doi = {10.3233/AF-13026},
	journal-iso = {ALGORITHM FINANCE},
	journal = {ALGORITHMIC FINANCE},
	volume = {2},
	unique-id = {2693507},
	issn = {2158-5571},
	year = {2013},
	eissn = {2157-6203},
	pages = {197-211},
	orcid-numbers = {Levendovszky, János/0000-0003-1406-442X}
}

@article{MTMT:2387414,
	title = {Improvements to the Hopfield Neural Network Solution of the Total Weighted Tardiness Scheduling Problem},
	url = {https://m2.mtmt.hu/api/publication/2387414},
	author = {Tornai, Kálmán and Fogarasi, Norbert and Levendovszky, János},
	doi = {10.3311/PPee.2090},
	journal-iso = {PERIOD POLYTECH ELECTR ENG COMP SCI},
	journal = {PERIODICA POLYTECHNICA-ELECTRICAL ENGINEERING AND COMPUTER SCIENCE},
	volume = {57},
	unique-id = {2387414},
	issn = {2064-5260},
	abstract = {This paper explores novel, polynomial time, heuristic, 
		approximate solutions to the NP-hard problem of finding the 
		optimal job schedule on identical machines which minimizes total 
		weighted tardiness (TWT). We map the TWT problem to
		quadratic optimization and demonstrate that the Hopfield Neural 
		Network (HNN) can successfully solve it. Furthermore, 
		the solution can be significantly sped up by choosing the 
		initial state of the HNN as the result of a known simple 
		heuristic, we call this Smart Hopfield Neural Network (SHNN). We 
		also demonstrate, through extensive simulations, that 
		by considering random perturbations to the Largest Weighted 
		Process First (LWPF) and SHNN methods, we can introduce 
		further improvements to the quality of the solution, we call the 
		latter Perturbed Smart Hopfield Neural Network (PSHNN). 
		Finally, we argue that due to parallelization techniques, such 
		as the use of GPGPU, the additional cost of these 
		improvements is small. Numerical simulations demonstrate that 
		PSHNN outperforms HNN in over 99% of all randomly 
		generated cases by an average of 3-7%, depending on the problem 
		size. On a specific, large scale scheduling problem
		arising in computational finance at Morgan Stanley, one of the 
		largest financial institutions in the world, PSHNN 
		produced a 5% improvement over the next best heuristic.},
	keywords = {NEURAL NETWORKS; Scheduling; Optimization; Quadratic programming},
	year = {2013},
	eissn = {2064-5279},
	pages = {57-64},
	orcid-numbers = {Tornai, Kálmán/0000-0003-1852-0816; Levendovszky, János/0000-0003-1406-442X}
}