TY - JOUR AU - Sipos, István Róbert AU - Ceffer, Attila AU - Horváth, Gábor AU - Levendovszky, János TI - Parallel MCMC sampling of AR-HMMs for prediction based option trading JF - ALGORITHMIC FINANCE J2 - ALGORITHM FINANCE VL - 8 PY - 2019 IS - 1-2 SP - 47 EP - 55 PG - 9 SN - 2158-5571 DO - 10.3233/AF-190243 UR - https://m2.mtmt.hu/api/publication/31144197 ID - 31144197 LA - English DB - MTMT ER - TY - JOUR AU - Levendovszky, János AU - Reguly, István Zoltán AU - Oláh, András AU - Ceffer, Attila TI - Low Complexity Algorithmic Trading by Feedforward Neural Networks JF - COMPUTATIONAL ECONOMICS J2 - COMPUT ECON VL - 54 PY - 2019 IS - 1 SP - 267 EP - 279 PG - 13 SN - 0927-7099 DO - 10.1007/s10614-017-9720-6 UR - https://m2.mtmt.hu/api/publication/3252486 ID - 3252486 LA - English DB - MTMT ER - TY - JOUR AU - Ceffer, Attila AU - Fogarasi, Norbert AU - Levendovszky, János TI - Trading by estimating the quantized forward distribution JF - APPLIED ECONOMICS J2 - APPL ECON VL - 50 PY - 2018 IS - 59 SP - 6397 EP - 6405 PG - 9 SN - 0003-6846 DO - 10.1080/00036846.2018.1486021 UR - https://m2.mtmt.hu/api/publication/3393080 ID - 3393080 LA - English DB - MTMT ER - TY - JOUR AU - Ceffer, Attila AU - Levendovszky, János AU - Fogarasi, Norbert TI - Applying Independent Component Analysis and Predictive Systems for Algorithmic Trading JF - COMPUTATIONAL ECONOMICS J2 - COMPUT ECON VL - 54 PY - 2018 IS - 1 SP - 281 EP - 303 PG - 23 SN - 0927-7099 DO - 10.1007/s10614-017-9719-z UR - https://m2.mtmt.hu/api/publication/3256024 ID - 3256024 N1 - WoS:hiba:000467688100013 2022-12-20 23:00 év nem egyezik LA - English DB - MTMT ER - TY - CONF AU - Ceffer, Attila AU - Levendovszky, János ED - Fredj, Jawadi TI - Trading by estimating the forward distribution using quantization and volatility information T2 - 3rd International Workshop on Financial Markets and Nonlinear Dynamics PY - 2017 SP - 1 EP - 14 PG - 14 UR - https://m2.mtmt.hu/api/publication/3342883 ID - 3342883 LA - English DB - MTMT ER - TY - JOUR AU - Sipos, István Róbert AU - Ceffer, Attila AU - Levendovszky, János TI - Parallel optimization of sparse portfolios with AR-HMMs JF - COMPUTATIONAL ECONOMICS J2 - COMPUT ECON VL - 49 PY - 2017 IS - 4 SP - 563 EP - 578 PG - 16 SN - 0927-7099 DO - 10.1007/s10614-016-9579-y UR - https://m2.mtmt.hu/api/publication/3043217 ID - 3043217 LA - English DB - MTMT ER - TY - CONF AU - Ceffer, Attila AU - Levendovszky, János AU - Fogarasi, Norbert TI - Applying ICA and NARX networks for algorithmic trading T2 - Fourth International Symposium in Computational Economics and Finance PY - 2016 SP - 1 EP - 16 UR - https://m2.mtmt.hu/api/publication/3183235 ID - 3183235 AB - 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. LA - English DB - MTMT ER - TY - CHAP AU - Ceffer, Attila AU - Levendovszky, János ED - Szakál, Anikó TI - Kolmogorov-Smirnov test for keystroke dynamics based user authentication T2 - 2016 17TH IEEE INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND INFORMATICS (CINTI 2016 PB - IEEE Hungary Section CY - Budapest SN - 1509039104 T3 - International Symposium on Computational Intelligence and Informatics, CINTI, ISSN 2380-8586 PY - 2016 SP - 105 EP - 110 PG - 6 DO - 10.1109/CINTI.2016.7846387 UR - https://m2.mtmt.hu/api/publication/3183215 ID - 3183215 AB - In this paper a novel user authentication method is proposed based on analyzing the keystroke patterns. Instead of using Nearest Neighbor classification with Dynamic Time Warping (DTW) distance measure, the typing dynamics are classified by the Kolmogorov-Smirnov test. First the typing pattern is translated into a sequence of holding times and latencies between consecutive keystrokes. In order to increase the classification performance, an adaptive preprocessing is used to get rid of the outliers. Then the Kolmogorov-Smirnov test is applied to classify the observed pattern. To further increase the correct classification ratio, semi-supervised self training methods are applied. One of the key objectives of the paper is to analyze the performance with respect to the length of typed characters, as in the case of authentication systems users cannot be asked to type long texts. The simulations have demonstrated that the proposed method performs better than the 1 Nearest Neighbor DTW classifier on all text lengths. It has also been shown that when the length of the typed text reduces to 10 characters, then the classification ratio sinks to 50% from 90.5% achieved in the case of longer texts in the range of 200 characters. LA - English DB - MTMT ER -