Attila Ceffer- Budapest University of Technology and Economics
Attila Ceffer
- Budapest University of Technology and Economics
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8
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Publications (8)
In this article, a novel algorithm is developed for electronic trading on financial time series. The new method uses quantization and volatility information together with feedforward neural networks for achieving high-frequency trading (HFT). The proposed procedures are based on estimating the Forward Conditional Probability Distribution (FCPD) of...
In this paper, a novel algorithm is developed for electronic trading on financial time series. The new method uses quantization and volatility information together with FeedForward Neural Networks (FFNN) for achieving High Frequency Trading (HFT). The proposed procedures are based on estimating the Forward Conditional Probability Distribution (FCPD...
In this paper, novel neural based algorithms are developed for electronic trading on financial time series. The proposed method is estimation based and trading actions are carried out after estimating the forward conditional probability distribution. The main idea is to introduce special encoding schemes on the observed prices in order to obtain an...
In this paper, a Nonlinear AutoRegressive network with eXogenous inputs and a support vector machine are proposed for algorithmic trading by predicting the future value of financial time series. These architectures are capable of modeling and predicting vector autoregressive VAR(p) time series. In order to avoid overfitting, the input is pre-proces...
In this paper, a Nonlinear AutoRegressive network with eX-ogenous 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...
In this paper we optimize mean reverting portfolios subject to cardinality constraints. First, the parameters of the corresponding Ornstein–Uhlenbeck (OU) process are estimated by auto-regressive Hidden Markov Models (AR-HMM), in order to capture the underlying characteristics of the financial time series. Portfolio optimization is then performed b...
