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Your search keyword '"Neri, Filippo"' showing total 6 results
Start Over Author "Neri, Filippo" Topic financial markets
6 results on '"Neri, Filippo"'

1. Combining Machine Learning and Agent Based Modeling for Gold Price Prediction

Author

Neri, Filippo, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Cagnoni, Stefano, editor, Mordonini, Monica, editor, Pecori, Riccardo, editor, Roli, Andrea, editor, and Villani, Marco, editor

Published
2019
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2. Case Study on Modeling the Silver and Nasdaq Financial Time Series with Simulated Annealing

Author

Neri, Filippo, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Rocha, Álvaro, editor, Adeli, Hojjat, editor, Reis, Luís Paulo, editor, and Costanzo, Sandra, editor

Published
2018
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3. A Comparative Study of a Financial Agent Based Simulator Across Learning Scenarios

Author

Neri, Filippo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Cao, Longbing, editor, Bazzan, Ana L. C., editor, Symeonidis, Andreas L., editor, Gorodetsky, Vladimir I., editor, Weiss, Gerhard, editor, and Yu, Philip S., editor

Published
2012
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4. Learning and Predicting Financial Time Series by Combining Natural Computation and Agent Simulation

Author

Neri, Filippo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Di Chio, Cecilia, editor, Brabazon, Anthony, editor, Di Caro, Gianni A., editor, Drechsler, Rolf, editor, Farooq, Muddassar, editor, Grahl, Jörn, editor, Greenfield, Gary, editor, Prins, Christian, editor, Romero, Juan, editor, Squillero, Giovanni, editor, Tarantino, Ernesto, editor, Tettamanzi, Andrea G. B., editor, Urquhart, Neil, editor, and Uyar, A. Şima, editor

Published
2011
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5. Simulating and modelling the DAX index and the USO Etf financial time series by using a simple agent‐based learning architecture.

Author

Neri, Filippo and García‐Magariño, Iván

Subjects
*TIME series analysis, *SIMULATED annealing, *TIME management, *STOCK exchanges, *APPROXIMATION error, *FINANCIAL databases, *REINFORCEMENT learning
Abstract

This work presents an extensive case study on modelling the DAX (Deutscher Aktienindex) index and United States Oil Fund (USO) exchange‐traded fund (Etf) time series with the financial agent‐based system learning financial agent‐based simulator (L‐FABS) that exploits simulated annealing as a learning method. The USO Etf time series is highly correlated with oil price behaviour, and the DAX index is based on the weighted and accumulated behaviour of the share prices of some of the largest companies traded on the Frankfurt Stock Exchange. These two time series are driven by completely different economic factors and thus provide two diverse empirical settings to evaluate the effectiveness of our methodology. Our experimentation shows that a relatively simple computational representation of real financial markets is effective in capturing the overall behaviour of the time series with varying approximation levels while the prediction target is moved into the future. The reported experimental investigation of L‐FABS shows that it is robust notwithstanding the learning method used and the data sets exploited. L‐FABS indeed produced a relatively low approximation error in several settings even when evaluated with respect to other modelling approaches, for example, 0.88% and 1.61% errors on average for 1 day ahead experiments in, respectively, DAX index and USO Etf. [ABSTRACT FROM AUTHOR]

Published
2020
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6. Agent-based modeling under partial and full knowledge learning settings to simulate financial markets.

Author

Neri, Filippo

Subjects
*MULTIAGENT systems, *FINANCIAL markets, *COMPUTER simulation, *MACHINE learning, *TIME series analysis, *SIMULATED annealing, *TIME delay systems
Abstract

In the paper we show how L-FABS can be applied in a partial knowledge learning scenario or a full knowledge learning scenario to approximate financial time series. L-FABS combines agent-based simulation with machine learning to model the behavior of financial time series. We also discuss why Partial Knowledge and Full Knowledge learning scenario are relevant to the modeling of financial time series and how they can be used to assess the robustness of a modeling system for financial time series. In a Partial Knowledge learning setting usually only the initial conditions of the time series are provided, while in a Full Knowledge learning scenario any value of the financial time series is exploited as soon as it is available. An extensive experimental analysis of L-FABS is reported under a variety of financial time series and time frames. [ABSTRACT FROM AUTHOR]

Published
2012
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