Bibliografie tematiche / Online portfolio selection

Letteratura scientifica selezionata sul tema "Online portfolio selection"

Autore: Grafiati

Pubblicato: 8 giugno 2024

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Articoli di riviste sul tema "Online portfolio selection":

LEVINA, TATSIANA, e GLENN SHAFER. "PORTFOLIO SELECTION AND ONLINE LEARNING". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, n. 04 (agosto 2008): 437–73. http://dx.doi.org/10.1142/s0218488508005364.

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This paper studies a new strategy for selecting portfolios in the stock market. The strategy is inspired by two streams of previous work: (1) work on universalization of strategies for portfolio selection, which began with Thomas Cover's work on constant rebalanced portfolios, published in 1991,4 and (2) more general work on universalization of online algorithms,17,21,23,30 especially Vladimir Vovk's work on the aggregating algorithm and Markov switching strategies.32 The proposed investment strategy achieves asymptotically the same exponential rate of growth as the portfolio that turns out to be best expost in the long run and does not require any underlying statistical assumptions on the nature of the stock market.

Li, Bin, e Steven C. H. Hoi. "Online portfolio selection". ACM Computing Surveys 46, n. 3 (gennaio 2014): 1–36. http://dx.doi.org/10.1145/2512962.

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Stella, Fabio, e Alfonso Ventura. "Defensive online portfolio selection". International Journal of Financial Markets and Derivatives 2, n. 1/2 (2011): 88. http://dx.doi.org/10.1504/ijfmd.2011.038530.

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Xie, Kailin, Jianfei Yin, Hengyong Yu, Hong Fu e Ying Chu. "Passive Aggressive Ensemble for Online Portfolio Selection". Mathematics 12, n. 7 (23 marzo 2024): 956. http://dx.doi.org/10.3390/math12070956.

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Developing effective trend estimators is the main method to solve the online portfolio selection problem. Although the existing portfolio strategies have demonstrated good performance through the development of various trend estimators, it is still challenging to determine in advance which estimator will yield the maximum final cumulative wealth in online portfolio selection tasks. This paper studies an online ensemble approach for online portfolio selection by leveraging the strengths of multiple trend estimators. Specifically, a return-based loss function and a cross-entropy-based loss function are first designed to evaluate the adaptiveness of different trend estimators in a financial environment. On this basis, a passive aggressive ensemble model is proposed to weigh these trend estimators within a unit simplex according to their adaptiveness. Extensive experiments are conducted on benchmark datasets from various real-world stock markets to evaluate their performance. The results show that the proposed strategy achieves state-of-the-art performance, including efficiency and cumulative return.

Yamim, João Daniel Madureira, Carlos Cristiano Hasenclever Borges e Raul Fonseca Neto. "Online Portfolio Optimization with Risk Control". Trends in Computational and Applied Mathematics 22, n. 3 (2 settembre 2021): 475–93. http://dx.doi.org/10.5540/tcam.2021.022.03.00475.

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Portfolio selection is undoubtedly one of the most challenging topics in the area of finance. Since Markowitz's initial contribution in 1952, portfolio allocation strategies have been intensely discussed in the literature. With the development of online optimization techniques, dynamic learning algorithms have proven to be an effective approach to building portfolios, although they do not assess the risk related to each investment decision.In this work, we compared the performance of the Online Gradient Descent (OGD) algorithm and a modification of the method, that takes into account risk metrics controlling for the Beta of the portfolio. In order to control for the Beta, each asset was modeled using the CAPM model and a time-varying Beta that follows a random walk. We compared both the traditional OGD algorithm and the OGD with Beta constraints with the Uniform Constant Rebalanced Portfolio and two different indexes for the Brazilian market, composed of small caps and the assets that belong to the Ibovespa index. Controlling the Beta proved to be an efficient strategy when the investor chooses an appropriate interval for the beta during bull markets or bear markets. Moreover, the time-varying beta was an efficient metric to force the desired correlation with the market and also to reduce the volatility of the portfolio during bear markets.

Guo, Sini, Jia-Wen Gu e Wai-Ki Ching. "Adaptive online portfolio selection with transaction costs". European Journal of Operational Research 295, n. 3 (dicembre 2021): 1074–86. http://dx.doi.org/10.1016/j.ejor.2021.03.023.

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Li, Bin, Jialei Wang, Dingjiang Huang e Steven C. H. Hoi. "Transaction cost optimization for online portfolio selection". Quantitative Finance 18, n. 8 (24 agosto 2017): 1411–24. http://dx.doi.org/10.1080/14697688.2017.1357831.

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Das, Puja, Nicholas Johnson e Arindam Banerjee. "Online Lazy Updates for Portfolio Selection with Transaction Costs". Proceedings of the AAAI Conference on Artificial Intelligence 27, n. 1 (30 giugno 2013): 202–8. http://dx.doi.org/10.1609/aaai.v27i1.8693.

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A major challenge for stochastic optimization is the cost of updating model parameters especially when the number of parameters is large. Updating parameters frequently can prove to be computationally or monetarily expensive. In this paper, we introduce an efficient primal-dual based online algorithm that performs lazy updates to the parameter vector and show that its performance is competitive with reasonable strategies which have the benefit of hindsight. We demonstrate the effectiveness of our algorithm in the online portfolio selection domain where a trader has to pay proportional transaction costs every time his portfolio is updated. Our Online Lazy Updates (OLU) algorithm takes into account the transaction costs while computing an optimal portfolio which results in sparse updates to the portfolio vector. We successfully establish the robustness and scalability of our lazy portfolio selection algorithm with extensive theoretical and experimental results on two real-world datasets.

Yin, Jianfei, Ruili Wang, Yeqing Guo, Yizhe Bai, Shunda Ju, Weili Liu e Joshua Zhexue Huang. "Wealth Flow Model: Online Portfolio Selection Based on Learning Wealth Flow Matrices". ACM Transactions on Knowledge Discovery from Data 16, n. 2 (30 aprile 2022): 1–27. http://dx.doi.org/10.1145/3464308.

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This article proposes a deep learning solution to the online portfolio selection problem based on learning a latent structure directly from a price time series. It introduces a novel wealth flow matrix for representing a latent structure that has special regular conditions to encode the knowledge about the relative strengths of assets in portfolios. Therefore, a wealth flow model (WFM) is proposed to learn wealth flow matrices and maximize portfolio wealth simultaneously. Compared with existing approaches, our work has several distinctive benefits: (1) the learning of wealth flow matrices makes our model more generalizable than models that only predict wealth proportion vectors, and (2) the exploitation of wealth flow matrices and the exploration of wealth growth are integrated into our deep reinforcement algorithm for the WFM. These benefits, in combination, lead to a highly-effective approach for generating reasonable investment behavior, including short-term trend following, the following of a few losers, no self-investment, and sparse portfolios. Extensive experiments on five benchmark datasets from real-world stock markets confirm the theoretical advantage of the WFM, which achieves the Pareto improvements in terms of multiple performance indicators and the steady growth of wealth over the state-of-the-art algorithms.

Moon, Seung-Hyun, e Yourim Yoon. "Genetic Mean Reversion Strategy for Online Portfolio Selection with Transaction Costs". Mathematics 10, n. 7 (26 marzo 2022): 1073. http://dx.doi.org/10.3390/math10071073.

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Online portfolio selection (OLPS) is a procedure for allocating portfolio assets using only past information to maximize an expected return. There have been successful mean reversion strategies that have achieved large excess returns on the traditional OLPS benchmark datasets. We propose a genetic mean reversion strategy that evolves a population of portfolio vectors using a hybrid genetic algorithm. Each vector represents the proportion of the portfolio assets, and our strategy chooses the best vector in terms of the expected returns on every trading day. To test our strategy, we used the price information of the S&P 500 constituents from 2000 to 2017 and compared various strategies for online portfolio selection. Our hybrid genetic framework successfully evolved the portfolio vectors; therefore, our strategy outperformed the other strategies when explicit or implicit transaction costs were incurred.

Più fonti

Tesi sul tema "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.

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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.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
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.

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We use an adversarial expert based online learning algorithm to learn the optimal parameters required to maximise wealth trading zero-cost portfolio strategies. The learning algorithm is used to determine the relative population dynamics of technical trading strategies that can survive historical back-testing as well as form an overall aggregated portfolio trading strategy from the set of underlying trading strategies implemented on daily and intraday Johannesburg Stock Exchange data. The resulting population time-series are investigated using unsupervised learning for dimensionality reduction and visualisation. A key contribution is that the overall aggregated trading strategies are tested for statistical arbitrage using a novel hypothesis test proposed by Jarrow et al. [31] on both daily sampled and intraday time-scales. The (low frequency) daily sampled strategies fail the arbitrage tests after costs, while the (high frequency) intraday sampled strategies are not falsified as statistical arbitrages after costs. The estimates of trading strategy success, cost of trading and slippage are considered along with an offline benchmark portfolio algorithm for performance comparison. In addition, the algorithms generalisation error is analysed by recovering a probability of back-test overfitting estimate using a nonparametric procedure introduced by Bailey et al. [19]. The work aims to explore and better understand the interplay between different technical trading strategies from a data-informed perspective.

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.

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Cette thèse contient quelques problèmes de décision financière en ligne et des solutions. Les problèmes sont formulés comme des problèmes en ligne (OP) et des algorithmes en ligne (OA) sont créés pour résoudre. Comme il peut y avoir plusieurs OAs pour le même OP, il doit y avoir un critère afin de pouvoir faire des indications au sujet de la qualité d’un OA. Dans cette thèse ces critères sont le ratio compétitif (c), la différence compétitive (cd) et la performance numérique. Un OA qui a un c ou cd plus bas qu’un autre est à préférer. Un OA qui possède le c le plus petit est appelé optimal. Nous considérons les OPs suivants. Le problème de conversion en ligne (OCP), le problème de sélection de portefeuille en ligne (PSP) et le problème de gestion de trésorerie en ligne (CMP). Après le premier chapitre d’introduction, les OPs, la notation et l’état des arts dans le champ des OPs sont présentés. Dans le troisième chapitre on résoudre trois variantes des OCP avec des prix interconnectés. En Chapitre 4 on travaille encore sur le problème de recherche de série chronologie avec des prix interconnectés et on construit des nouveaux OAs. À la fin de ce chapitre l’OA k-DIV est créé pour le problème de recherche générale des k maximums. k-DIV est aussi optimal. En Chapitre 5 on résout le PSP avec des prix interconnectés. L’OA créé s’appelle OPIP et est optimal. En utilisant les idées de OPIP, on construit un OA pour le problème d’échange bidirectionnel qui s’appelle OCIP et qui est optimal. Avec OPIP, on construit un OA optimal pour le problème de recherche bidirectionnel (OA BUND) sachant les valeurs de θ_1 et θ_2. Pour des valeurs inconnues, on construit l’OA RUN qui est aussi optimal. Les chapitres 6 et 7 traitent sur le CMP. Dans les deux chapitres il y a des tests numériques afin de pouvoir comparer la performance des OAs nouveaux avec celle des OAs déjà établies. En Chapitre 6 on construit des OAs optimaux ; en chapitre 7 on construit des OA qui minimisent cd. L’OA BCSID résoudre le CMP avec des demandes interconnectées ; LOA aBBCSID résoudre le problème lorsqu’ on connaît les valeurs de θ_1,θ_2,m et M. L’OA n’est pas optimal. En Chapitre 7 on résout le CMP par rapport à m et M en minimisant cd (OA MRBD). Ensuite on construit l’OA HMRID et l’OA MRID pour des demandes interconnectées. MRID minimise cd et HMRID est un bon compromis entre la performance numérique et la minimisation de cd
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, e 黃靖雅. "Risk Measurements on Online Portfolio Selection". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/fhe978.

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碩士
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.

Libri sul tema "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.

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Li, Bin, e Steven Chu Hong Hoi. Online Portfolio Selection: Principles and Algorithms. Taylor & Francis Group, 2018.

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Li, Bin, e Steven Chu Hong Hoi. Online Portfolio Selection: Principles and Algorithms. Taylor & Francis Group, 2018.

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Li, Bin, e Steven Chu Hong Hoi. Online Portfolio Selection: Principles and Algorithms. Taylor & Francis Group, 2018.

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Li, Bin, e Steven Chu Hong Hoi. Online Portfolio Selection: Principles and Algorithms. Taylor & Francis Group, 1999.

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Li, Bin, e Steven Chu Hong Hoi. Online Portfolio Selection: Principles and Algorithms. Taylor & Francis Group, 2018.

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Dochow, Robert. Online Algorithms for the Portfolio Selection Problem. Springer Gabler, 2016.

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Dochow, Robert. Online Algorithms for the Portfolio Selection Problem. Springer Gabler. in Springer Fachmedien Wiesbaden GmbH, 2016.

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Capitoli di libri sul tema "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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Wu, Boqian, Benmeng Lyu e 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.

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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.

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Atti di convegni sul tema "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.

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Zhou, Haojie, Xiaoting Yao, Shuhui Cai e 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.

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Gao, Li, e 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.

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Balcar, Stepan, e 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.

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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.

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Abstract (sommario):

Aiming to improve the performance of existing reversion based online portfolio selection strategies, we propose a novel multi-period strategy named “Vector Autoregressive Weighting Reversion” (VAWR). Firstly, vector autoregressive moving-average algorithm used in time series prediction is transformed into exploring the dynamic relationships between different assets for more accurate price prediction. Secondly, we design the modified online passive aggressive technique and advance a scheme to weigh investment risk and cumulative experience to update the closed-form of portfolio. Theoretical analysis and experimental results confirm the effectiveness and robustness of our strategy. Compared with the state-of-the-art strategies, VAWR greatly increases cumulative wealth, and it obtains the highest annualized percentage yield and sharp ratio on various public datasets. These improvements and easy implementation support the practical applications of VAWR.

Yang, Fanfan, Xiangming Li, Jie Yang e 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.

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Yao, Xiaoting, e 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.

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Kumar, Abhishek, e 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.

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Gao, Li, e 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.

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Fakhri, Irkham, Deni Saepudin e 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.

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Rapporti di organizzazioni sul tema "Online portfolio selection":

Can agile be scaled? Association for Project Management, settembre 2017. http://dx.doi.org/10.61175/pyjd8197.

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Abstract (sommario):

The objective of the study was to understand the extent to which scaled agile tools, techniques and roles are practically in place in corporate portfolio, programme, project and development management methodologies, to determine the level of corporate commitment to exploiting scaled agile, e.g. pilot, full use, selective based on need, as well as drivers for selection or deselection of the framework based on the overheads. A qualitative approach was adopted – an online survey, with fi rst and second semistructured interviews of project managers who use scaled agile approaches have been held to establish the level of adoption, then explore elements adopted and their relative success. Following the data analysis, a Delphi review was undertaken to refl ect fi ndings and recommendations back to the target population for validation.