Academic literature on the topic 'Online portfolio selection'
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Journal articles on the topic "Online portfolio selection":
LEVINA, TATSIANA, and GLENN SHAFER. "PORTFOLIO SELECTION AND ONLINE LEARNING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 04 (August 2008): 437–73. http://dx.doi.org/10.1142/s0218488508005364.
Li, Bin, and Steven C. H. Hoi. "Online portfolio selection." ACM Computing Surveys 46, no. 3 (January 2014): 1–36. http://dx.doi.org/10.1145/2512962.
Stella, Fabio, and Alfonso Ventura. "Defensive online portfolio selection." International Journal of Financial Markets and Derivatives 2, no. 1/2 (2011): 88. http://dx.doi.org/10.1504/ijfmd.2011.038530.
Xie, Kailin, Jianfei Yin, Hengyong Yu, Hong Fu, and Ying Chu. "Passive Aggressive Ensemble for Online Portfolio Selection." Mathematics 12, no. 7 (March 23, 2024): 956. http://dx.doi.org/10.3390/math12070956.
Yamim, João Daniel Madureira, Carlos Cristiano Hasenclever Borges, and Raul Fonseca Neto. "Online Portfolio Optimization with Risk Control." Trends in Computational and Applied Mathematics 22, no. 3 (September 2, 2021): 475–93. http://dx.doi.org/10.5540/tcam.2021.022.03.00475.
Guo, Sini, Jia-Wen Gu, and Wai-Ki Ching. "Adaptive online portfolio selection with transaction costs." European Journal of Operational Research 295, no. 3 (December 2021): 1074–86. http://dx.doi.org/10.1016/j.ejor.2021.03.023.
Li, Bin, Jialei Wang, Dingjiang Huang, and Steven C. H. Hoi. "Transaction cost optimization for online portfolio selection." Quantitative Finance 18, no. 8 (August 24, 2017): 1411–24. http://dx.doi.org/10.1080/14697688.2017.1357831.
Das, Puja, Nicholas Johnson, and Arindam Banerjee. "Online Lazy Updates for Portfolio Selection with Transaction Costs." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 30, 2013): 202–8. http://dx.doi.org/10.1609/aaai.v27i1.8693.
Yin, Jianfei, Ruili Wang, Yeqing Guo, Yizhe Bai, Shunda Ju, Weili Liu, and Joshua Zhexue Huang. "Wealth Flow Model: Online Portfolio Selection Based on Learning Wealth Flow Matrices." ACM Transactions on Knowledge Discovery from Data 16, no. 2 (April 30, 2022): 1–27. http://dx.doi.org/10.1145/3464308.
Moon, Seung-Hyun, and Yourim Yoon. "Genetic Mean Reversion Strategy for Online Portfolio Selection with Transaction Costs." Mathematics 10, no. 7 (March 26, 2022): 1073. http://dx.doi.org/10.3390/math10071073.
Dissertations / Theses on the topic "Online portfolio selection":
Lorenz, Julian Michael. "Optimal trading algorithms : portfolio transactions, multiperiod portfolio selection, and competitive online search /." Zürich : ETH, 2008. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=17746.
SCHUTZ, GUILHERME AUGUSTO. "A NEURAL NETWORK FOR ONLINE PORTFOLIO SELECTION WITH SIDE INFORMATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=36111@1.
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
O mercado financeiro é essencial na economia, trazendo estabilidade, acesso a novos tipos de investimentos, e aumentando a capacidade das empresas no acesso ao crédito. A constante busca por reduzir o papel de especialistas humanos na tomada de decisão, visa reduzir o risco inerente as emoções intrínsecas do ser humano, do qual a máquina não compartilha. Como consequência, reduzindo efeitos especulativos no mercado, e aumentando a precisão nas decisões tomadas. Neste trabalho é discutido o problema de seleção de portfólios online, onde um vetor de alocações de ativos é requerido em cada passo. O algoritmo proposto é o multilayer perceptron with side information - MLPi. Este algoritmo utiliza redes neurais para a solução do problema quando o investidor tem acesso a informações futuras sobre o preço dos ativos. Para avaliar o uso da informação lateral na seleção de portfolio, testamos empiricamente o MLPi em contraste com dois algoritmos, um baseline e o estado-da-arte. Como baseline é utilizado o buy-and-hold. O estado-da-arte é o algoritmo online moving average mean reversion proposto por Li e Hoi (2012). Para avaliar a utilização de informação lateral no algoritmo MLPi é definido um benchmark baseado numa solução ótima simples utilizando a informação lateral, mas sem considerar a acurácia da informação futura. Para os experimentos, utilizamos informações a nível de minuto do mercado de ações brasileiro, operados na bolsa de valores B3. É simulado um preditor de preço com 7 níveis de acurácia diferentes para 200 portfólios. Os resultados apontam que tanto o benchmark quanto o MLPi superam os dois algoritmos selecionados, para níveis de acurácia de um ativo maiores que 50 por cento, e na média, o MLPi supera o benchmark em todos os níveis de acurácia simulados.
The financial market is essential in the economy, bringing stability, access to new types of investments, and increasing the ability of companies to access credit. The constant search for reducing the role of human specialists in decision making aims to reduce the risk inherent in the intrinsic emotions of the human being, which the machine does not share. As a consequence, reducing speculative effects in the market, and increasing the precision in the decisions taken. In this paper, we discuss the problem of selecting portfolios online, where a vector of asset allocations is required in each step. The proposed algorithm is the multilayer perceptron with side information - MLPi. This algorithm uses neural networks to solve the problem when the investor has access to future information on the price of the assets. To evaluate the use of side information in portfolio selection, we empirically tested MLPi in contrast to two algorithms, a baseline and the state-of-the-art. As a baseline, buy-andhold is used. The state-of-the-art is the online moving average mean reversion algorithm proposed by Li and Hoi (2012). To evaluate the use of side information in the algorithm MLPi a benchmark based on a simple optimal solution using the side information is defined, but without considering the accuracy of the future information. For the experiments, we use minute-level information from the Brazilian stock market, traded on the B3 stock exchange. A price predictor is simulated with 7 different accuracy levels for 200 portfolios. The results show that both the benchmark and MLPi outperform the two algorithms selected, for asset accuracy levels greater than 50 percent, and on average, MLPi outperforms the benchmark at all levels of simulated accuracy.
Murphy, Nicholas John. "An online learning algorithm for technical trading." Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31048.
Yamim, João Daniel Madureira. "Um modelo de seleção de carteiras de ações baseado em otimização convexa online." Universidade Federal de Juiz de Fora (UFJF), 2018. https://repositorio.ufjf.br/jspui/handle/ufjf/6816.
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CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Desde o trabalho seminal de Harry Markowitz, em 1952, que iniciou a moderna te-oria de carteiras, as estratégias de alocação de portfólio foram intensamente discutidas na literatura. Com o desenvolvimento de técnicas de otimização online, os algoritmos de aprendizado dinâmico se mostraram uma abordagem efetiva para construir portfólios (COVER, 1991; ARGAWAL et al., 2006). No entanto, poucos trabalhos conectam a lite-ratura tradicional, evoluída a partir do trabalho de Markowitz (1952) com a literatura de otimização online, que evoluiu a partir do trabalho de Cover (1991). O principal objetivo deste trabalho é implementar técnicas de otimização convexa online para: (i) executar estratégias de alocação de portfólio; (ii) conectar esses algoritmos com fatores risco usados em metodologias tradicionais. Dois métodos de algoritmos online foram implementados e adaptados, o Online Gradient Descendent (OGD) e o Online Newton Step (ONS). Além disso, duas novas versões para o algoritmo OGD são propostas para controlar o risco em carteiras. O primeiro, busca limitar o investimento máximo para ações e, o segundo, visa controlar o /3 das carteiras. Ambas as estratégias foram comparadas com o Uniform Constant Rebalanced Portfolio (UCRP) e o Dow Jones Industrial Index (DJIA). Foram utilizados dados do DJIA de março de 1987 até fevereiro de 2009 com observações se-manais. O algoritmo OGD apresentou o maior retorno acumulado entre as estratégias testadas. Ambos os algoritmos (OGD e ONS) apresentaram melhor desempenho do que o UCRP e DJIA ao longo do período. Além disso, o mecanismo de controle de risco pro-posto provou ser uma ferramenta útil para melhorar os resultados relacionados ao valor em risco (VaR) e ao valor condicional em risco (CVaR) das carteiras.
Since the seminal work of Harry Markowitz (1952), which initiated the modern theory of portfolios, the strategies of portfolio allocation were extensively discussed in economic literature. With the development of online optimization techniques, dynamic learning algorithms emerged as an effective approach to develop investment portfolios (COVER, 1991; ARGAWAL et al., 2006). However, there are few attempts aiming to connect the traditional literature of portfolio investment, which evolved based on Markowitz (1952) work, with the recent online methods, developed from Cover (1991). The main objec-tive of this work is to implement online convex optimization techniques to: (i) perform strategies of portfolio allocation; (ii) couple these algorithms with risk factors used in traditional models. Two methods of online algorithms were implemented and adapted, the Online Gradient Descendent (OGD) and the Online Newton Step (ONS). Besides, two new versions for the OGD algorithm are proposed in order to control risk in portfolios. The first one, seeks to limit maximum investment for stocks and, the second, aims to keep control of the /3 of portfolios. Both strategies were compared with the Uniform Constant Re-Balanced Portfolio (UCRP) and the Dow Jones Industrial Index (DJIA). Data from weekly observations of DJIA from March 1987 until February 2009 are used. The OGD algorithm presented the best accumulated return among all strategies. Both algorithms (OGD and ONS) performed better than the UCRP and DJIA index. Furthermore, the risk control mechanism proposed proved to be an useful tool in order to improve results related to the Value at Risk (VaR) and Conditional Value at Risk (CVaR) of the portfolios.
Schroeder, Pascal. "Performance guaranteeing algorithms for solving online decision problems in financial systems." Electronic Thesis or Diss., Université de Lorraine, 2019. http://www.theses.fr/2019LORR0143.
This thesis contains several online financial decision problems and their solutions. The problems are formulated as online problems (OP) and online algorithms (OA) are created to solve them. Due to the fact that there can be various OA for the same OP, there must be some criteria with which one can make statements about the quality of an OA. In this thesis these criteria are the competitive ratio (c), the competitive difference (cd) and the numerical performance. An OA with a lower c is preferable to another one with a higher value. An OA that has the lowest c is called optimal. We consider the following OPS. The online conversion problem (OCP), the online portfolio selection problem (PSP) and the cash management problem (CMP). After the introductory chapter, the OPs, the notation and the state of the art in the field of OPs is presented. In the third chapter, three variants of the OCP with interrelated prices are solved. In the fourth chapter the time series search with interrelated prices is revisited and new algorithms are created. At the end of the chapter, the optimal OA k-DIV for the general k-max search with interrelated prices is developed. In Chapter 5 the PSP with interrelated prices is solved. The created OA OPIP is optimal. Using the idea of OPIP, an optimal OA for the two-way trading is created (OCIP). Having OCIP, an optimal OA for the bi-directional search knowing the values of θ_1 and θ_2 is created (BUND). For unknown θ_1 and θ_2, the optimal OA RUNis created. The chapter ends with an empirical (for OPIP) and experimental (for OCIP, BUND and RUN) testing. Chapters 6 and 7 deal with the CMP. In both of them, a numerical testing is done in order to compare the numerical performance of the new OAs to the one of the already established ones. In Chapter 6 an optimal OA is constructed; in Chapter 7, OAs are designed which minimize cd. The OA BCSID solves the CMP with interrelated demands to optimality. The OA aBBCSID solves the CMP when the values of de θ_1, θ_2,m and M are known; however, this OA is not optimal. In Chapter 7 the CMP is solved, knowing m and M and minimizing cd (OA MRBD). For the interrelated demands, a heuristic OA (HMRID) and a cd-minimizing OA (MRID) is presented. HMRID is good compromise between the numerical performance and the minimization of cd. The thesis concludes with a short discussion about shortcomings of the considered OPs and the created OAs. Then some remarks about future research possibilities in this field are given
HUANG, JING-YA, and 黃靖雅. "Risk Measurements on Online Portfolio Selection." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/fhe978.
靜宜大學
財務工程學系
107
This paper attempts to the Value at Risk (VaR) of online portfolios. The online portfolio includes Buy and Hold, Best Stock, Constant Rebalance Portfolio, Follow the Winner and Follow the Loser, the investment sample is fifty Exchange Traded Funds (ETF), using the variance-covariance method to calculate the Value at Risk, and comparing the risk of the wealth of different investment methods. Verify that the portfolio is effective in controlling the risk loss of the Exchange Traded Funds. The empirical results show that the return rate of the Exchange Traded Funds portfolio is less than the Value at Risk of the portfolio, and the loss representing the portfolio is controlled by the maximum loss calculated by the Value at Risk. It is proved that the method of the portfolio risk value is quite reliable. In the measured results, it is found that in the Best stock strategy and Follower Loser strategy, the portfolio approach is not completely less than the maximum loss of the ETF's Value at Risk, which means that using these three portfolios has the opportunity to increase the risk, but Buy and Hold, Constant Rebalance Portfolio, Follow the winner's UP strategy and the EG strategy, these four portfolio approaches can completely reduce and control the maximum loss of the Exchange Traded Funds.
Books on the topic "Online portfolio selection":
Dochow, Robert. Online Algorithms for the Portfolio Selection Problem. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13528-7.
Li, Bin, and Steven Chu Hong Hoi. Online Portfolio Selection: Principles and Algorithms. Taylor & Francis Group, 2018.
Li, Bin, and Steven Chu Hong Hoi. Online Portfolio Selection: Principles and Algorithms. Taylor & Francis Group, 2018.
Li, Bin, and Steven Chu Hong Hoi. Online Portfolio Selection: Principles and Algorithms. Taylor & Francis Group, 2018.
Li, Bin, and Steven Chu Hong Hoi. Online Portfolio Selection: Principles and Algorithms. Taylor & Francis Group, 1999.
Li, Bin, and Steven Chu Hong Hoi. Online Portfolio Selection: Principles and Algorithms. Taylor & Francis Group, 2018.
Dochow, Robert. Online Algorithms for the Portfolio Selection Problem. Springer Gabler, 2016.
Dochow, Robert. Online Algorithms for the Portfolio Selection Problem. Springer Gabler. in Springer Fachmedien Wiesbaden GmbH, 2016.
Book chapters on the topic "Online portfolio selection":
Dochow, Robert. "Portfolio Selection Problems." In Online Algorithms for the Portfolio Selection Problem, 9–43. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13528-7_2.
Dochow, Robert. "Introduction." In Online Algorithms for the Portfolio Selection Problem, 1–7. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13528-7_1.
Dochow, Robert. "Performance Evaluation." In Online Algorithms for the Portfolio Selection Problem, 45–77. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13528-7_3.
Dochow, Robert. "Selected Algorithms from the Literature." In Online Algorithms for the Portfolio Selection Problem, 79–108. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13528-7_4.
Dochow, Robert. "Proposed Algorithms with Risk Management." In Online Algorithms for the Portfolio Selection Problem, 109–26. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13528-7_5.
Dochow, Robert. "Empirical Testing of Algorithms." In Online Algorithms for the Portfolio Selection Problem, 127–52. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13528-7_6.
Dochow, Robert. "A Software Tool for Testing Algorithms." In Online Algorithms for the Portfolio Selection Problem, 153–61. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13528-7_7.
Dochow, Robert. "Conclusions and Future Work." In Online Algorithms for the Portfolio Selection Problem, 163–67. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13528-7_8.
Wu, Boqian, Benmeng Lyu, and Jiawen Gu. "Weighted Multivariate Mean Reversion for Online Portfolio Selection." In Machine Learning and Knowledge Discovery in Databases: Research Track, 255–70. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43424-2_16.
Lee, Chia-Jung. "Two Algorithms with Logarithmic Regret for Online Portfolio Selection." In Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications, 397–402. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03766-6_45.
Conference papers on the topic "Online portfolio selection":
Li, Yen-Huan. "Online Positron Emission Tomography By Online Portfolio Selection." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053230.
Zhou, Haojie, Xiaoting Yao, Shuhui Cai, and Na Zhang. "Transaction cost regularization for online portfolio selection." In 2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA). IEEE, 2022. http://dx.doi.org/10.1109/icdsca56264.2022.9987825.
Gao, Li, and Ruohao Yang. "Sparse Online Portfolio Selection Based on Proximal Gradient." In 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI). IEEE, 2019. http://dx.doi.org/10.1109/mlbdbi48998.2019.00078.
Balcar, Stepan, and Martin Pilat. "Online Parallel Portfolio Selection with Heterogeneous Island Model." In 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2018. http://dx.doi.org/10.1109/ictai.2018.00119.
Cai, Xia. "Vector Autoregressive Weighting Reversion Strategy for Online Portfolio Selection." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/616.
Yang, Fanfan, Xiangming Li, Jie Yang, and Neng Ye. "Online Newton Step for Portfolio Selection with Side Information." In 2018 5th International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2018. http://dx.doi.org/10.1109/icisce.2018.00182.
Yao, Xiaoting, and Na Zhang. "Elastic-Net Regularized Online Portfolio Selection with Transaction Costs." In 2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE, 2023. http://dx.doi.org/10.1109/eebda56825.2023.10090727.
Kumar, Abhishek, and Aviv Segev. "Bayesian Ensembled Knowledge Extraction Strategy for Online Portfolio Selection." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020708.
Gao, Li, and Weiguo Zhang. "Weighted Moving Average Passive Aggressive Algorithm for Online Portfolio Selection." In 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). IEEE, 2013. http://dx.doi.org/10.1109/ihmsc.2013.84.
Fakhri, Irkham, Deni Saepudin, and Aniq Rohmawati. "Online Portfolio Selection of LQ45 Stocks Index with the Adaptive Online Moving Average Method." In International Conference on Advanced Information Scientific Development. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0012639200003848.
Reports on the topic "Online portfolio selection":
Can agile be scaled? Association for Project Management, September 2017. http://dx.doi.org/10.61175/pyjd8197.