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Статті в журналах з теми "Online portfolio selection"

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Автор: Grafiati

Опубліковано: 8 червня 2024

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1

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.

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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.

2

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.

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3

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.

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4

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.

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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.

5

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.

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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.

6

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.

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7

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.

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8

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.

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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.

9

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.

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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.

10

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.

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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.

11

Huang, Dingjiang, Shunchang Yu, Bin Li, Steven C. H. Hoi, and Shuigeng Zhou. "Combination Forecasting Reversion Strategy for Online Portfolio Selection." ACM Transactions on Intelligent Systems and Technology 9, no. 5 (July 18, 2018): 1–22. http://dx.doi.org/10.1145/3200692.

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12

Tsagaris, Theodoros, Ajay Jasra, and Niall Adams. "Robust and adaptive algorithms for online portfolio selection." Quantitative Finance 12, no. 11 (November 2012): 1651–62. http://dx.doi.org/10.1080/14697688.2012.691175.

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13

Huang, Ding-jiang, Junlong Zhou, Bin Li, Steven C. H. Hoi, and Shuigeng Zhou. "Robust Median Reversion Strategy for Online Portfolio Selection." IEEE Transactions on Knowledge and Data Engineering 28, no. 9 (September 1, 2016): 2480–93. http://dx.doi.org/10.1109/tkde.2016.2563433.

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14

Yang, Xingyu, Huaping Li, Yong Zhang, N. A. Jin', and an He. "Reversion strategy for online portfolio selection with transaction costs." International Journal of Applied Decision Sciences 11, no. 1 (2018): 79. http://dx.doi.org/10.1504/ijads.2018.088632.

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15

Yang, Xingyu, Huaping Li, Yong Zhang, and Jin'an He. "Reversion Strategy for Online Portfolio Selection with Transaction Costs." International Journal of Applied Decision Sciences 11, no. 1 (2018): 1. http://dx.doi.org/10.1504/ijads.2018.10007603.

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16

Li, Bin, Steven C. H. Hoi, Peilin Zhao, and Vivekanand Gopalkrishnan. "Confidence Weighted Mean Reversion Strategy for Online Portfolio Selection." ACM Transactions on Knowledge Discovery from Data 7, no. 1 (March 2013): 1–38. http://dx.doi.org/10.1145/2435209.2435213.

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17

Guan, Hao, and Zhiyong An. "A local adaptive learning system for online portfolio selection." Knowledge-Based Systems 186 (December 2019): 104958. http://dx.doi.org/10.1016/j.knosys.2019.104958.

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18

Cai, Xia, and Zekun Ye. "Gaussian Weighting Reversion Strategy for Accurate Online Portfolio Selection." IEEE Transactions on Signal Processing 67, no. 21 (November 1, 2019): 5558–70. http://dx.doi.org/10.1109/tsp.2019.2941067.

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19

Zhang, Yong, and Xingyu Yang. "Online Portfolio Selection Strategy Based on Combining Experts’ Advice." Computational Economics 50, no. 1 (May 25, 2016): 141–59. http://dx.doi.org/10.1007/s10614-016-9585-0.

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20

Xu, L., F. Hutter, H. H. Hoos, and K. Leyton-Brown. "SATzilla: Portfolio-based Algorithm Selection for SAT." Journal of Artificial Intelligence Research 32 (July 1, 2008): 565–606. http://dx.doi.org/10.1613/jair.2490.

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It has been widely observed that there is no single "dominant" SAT solver; instead, different solvers perform best on different instances. Rather than following the traditional approach of choosing the best solver for a given class of instances, we advocate making this decision online on a per-instance basis. Building on previous work, we describe SATzilla, an automated approach for constructing per-instance algorithm portfolios for SAT that use so-called empirical hardness models to choose among their constituent solvers. This approach takes as input a distribution of problem instances and a set of component solvers, and constructs a portfolio optimizing a given objective function (such as mean runtime, percent of instances solved, or score in a competition). The excellent performance of SATzilla was independently verified in the 2007 SAT Competition, where our SATzilla07 solvers won three gold, one silver and one bronze medal. In this article, we go well beyond SATzilla07 by making the portfolio construction scalable and completely automated, and improving it by integrating local search solvers as candidate solvers, by predicting performance score instead of runtime, and by using hierarchical hardness models that take into account different types of SAT instances. We demonstrate the effectiveness of these new techniques in extensive experimental results on data sets including instances from the most recent SAT competition.

21

Ha, Youngmin, and Hai Zhang. "Algorithmic trading for online portfolio selection under limited market liquidity." European Journal of Operational Research 286, no. 3 (November 2020): 1033–51. http://dx.doi.org/10.1016/j.ejor.2020.03.050.

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22

Sirirut, Taksaporn, and Dawud Thongtha. "Online Portfolio Selection Based on Adaptive Kalman Filter through Fuzzy Approach." Journal of Mathematical Finance 12, no. 03 (2022): 480–96. http://dx.doi.org/10.4236/jmf.2022.123026.

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23

Peng, Zijin, Weijun Xu, and Hongyi Li. "A Novel Online Portfolio Selection Strategy with Multiperiodical Asymmetric Mean Reversion." Discrete Dynamics in Nature and Society 2020 (January 29, 2020): 1–13. http://dx.doi.org/10.1155/2020/5956146.

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Mean reversion is an important property when constructing efficient contrarian strategies. Researchers observe that mean reversion has multiperiodical and asymmetric nature simultaneously in real market. To better utilize mean reversion and improve the existing online portfolio selection strategies, we propose a new online strategy named multiperiodical asymmetric mean reversion (MAMR). The MAMR strategy incorporates a multipiecewise loss function with the moving average method and then imitates the passive-aggressive algorithm. We further provide a solution via convex optimization. This strategy runs in linear time and thus is suitable for large-scale trading applications. Our empirical results testing six real market datasets show that this strategy can achieve better results in bearing higher transaction cost.

24

Hazan, Elad, and Satyen Kale. "AN ONLINE PORTFOLIO SELECTION ALGORITHM WITH REGRET LOGARITHMIC IN PRICE VARIATION." Mathematical Finance 25, no. 2 (November 2, 2012): 288–310. http://dx.doi.org/10.1111/mafi.12006.

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25

Yang, Xingyu, Jin’an He, Jiayi Xian, Hong Lin, and Yong Zhang. "Aggregating expert advice strategy for online portfolio selection with side information." Soft Computing 24, no. 3 (May 21, 2019): 2067–81. http://dx.doi.org/10.1007/s00500-019-04039-7.

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26

Cindy Hadinata, Farah Margaretha Leon,. "The Influence Of Demography And Risk Tolerance Toward Portfolio Invesment Selection Of Post Graduate Students." Jurnal Manajemen 22, no. 3 (October 24, 2018): 360. http://dx.doi.org/10.24912/jm.v22i3.427.

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The purpose of this research is to give empiric evidence from the influence of demography variable toward portfolio investment selection and risk tolerance also the influence of risk tolerance toward portfolio investment selection. The methodology of this research was used primer data by questionnaire both online and offline to postgraduate students in Jakarta. Data succeeded to be collected for 258 respondents and data analyze method used descriptive statistic, coefficient concordance Kendall W and chi square analyze. The result of this research showed that demography variable significantly influence to investor risk tolerance for Postgraduate students in Jakarta. Gender and age significantly influence to risk tolerance and occupation is not influence to investor risk tolerance for Postgraduate students in West Jakarta. Meanwhile for gender significantly influence to investment portfolio selection. Risk tolerance significantly influence to investment portfolio selection for postgraduate students in West Jakarta. The implication for investor and investment advisor should understand the demography variable and investor risk tolerance level, therefore they make right investment.

27

Khedmati, Majid, and Pejman Azin. "An online portfolio selection algorithm using clustering approaches and considering transaction costs." Expert Systems with Applications 159 (November 2020): 113546. http://dx.doi.org/10.1016/j.eswa.2020.113546.

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28

Sievers, Silvan, Michael Katz, Shirin Sohrabi, Horst Samulowitz, and Patrick Ferber. "Deep Learning for Cost-Optimal Planning: Task-Dependent Planner Selection." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7715–23. http://dx.doi.org/10.1609/aaai.v33i01.33017715.

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As classical planning is known to be computationally hard, no single planner is expected to work well across many planning domains. One solution to this problem is to use online portfolio planners that select a planner for a given task. These portfolios perform a classification task, a well-known and wellresearched task in the field of machine learning. The classification is usually performed using a representation of planning tasks with a collection of hand-crafted statistical features. Recent techniques in machine learning that are based on automatic extraction of features have not been employed yet due to the lack of suitable representations of planning tasks.In this work, we alleviate this barrier. We suggest representing planning tasks by images, allowing to exploit arguably one of the most commonly used and best developed techniques in deep learning. We explore some of the questions that inevitably rise when applying such a technique, and present various ways of building practically useful online portfoliobased planners. An evidence of the usefulness of our proposed technique is a planner that won the cost-optimal track of the International Planning Competition 2018.

29

Zhang, Yong, Hong Lin, Xingyu Yang, and Wanrong Long. "Combining expert weights for online portfolio selection based on the gradient descent algorithm." Knowledge-Based Systems 234 (December 2021): 107533. http://dx.doi.org/10.1016/j.knosys.2021.107533.

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30

Chu, Gang, Wei Zhang, Guofeng Sun, and Xiaotao Zhang. "A new online portfolio selection algorithm based on Kalman Filter and anti-correlation." Physica A: Statistical Mechanics and its Applications 536 (December 2019): 120949. http://dx.doi.org/10.1016/j.physa.2019.04.185.

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31

Yang, Xingyu, Jin’an He, Hong Lin, and Yong Zhang. "Boosting Exponential Gradient Strategy for Online Portfolio Selection: An Aggregating Experts’ Advice Method." Computational Economics 55, no. 1 (April 10, 2019): 231–51. http://dx.doi.org/10.1007/s10614-019-09890-2.

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32

Schroeder, Pascal, Imed Kacem, and Günter Schmidt. "Optimal online algorithms for the portfolio selection problem, bi-directional trading and -search with interrelated prices." RAIRO - Operations Research 53, no. 2 (April 2019): 559–76. http://dx.doi.org/10.1051/ro/2018064.

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In this work we investigate the portfolio selection problem (P1) and bi-directional trading (P2) when prices are interrelated. Zhang et al. (J. Comb. Optim. 23 (2012) 159–166) provided the algorithm UND which solves one variant of P2. We are interested in solutions which are optimal from a worst-case perspective. For P1, we prove the worst-case input sequence and derive the algorithm optimal portfolio for interrelated prices (OPIP). We then prove the competitive ratio and optimality. We use the idea of OPIP to solve P2 and derive the algorithm called optimal conversion for interrelated prices (OCIP). Using OCIP, we also design optimal online algorithms for bi-directional search (P3) called bi-directional UND (BUND) and optimal online search for unknown relative price bounds (RUN). We run numerical experiments and conclude that OPIP and OCIP perform well compared to other algorithms even if prices do not behave adverse.

33

Wei, Pei. "Long-term General Asset Allocation for individual investors in Chinese securities market." BCP Business & Management 20 (June 28, 2022): 1207–16. http://dx.doi.org/10.54691/bcpbm.v20i.1120.

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Facing the boom of online transaction applications in Chinese securities market, individual investors in China vacillate between different assets while allocating assets. On account of the individual investors’ inferior ability to take risk, portfolio will be their best choices. However due to their unacquaintance to modern portfolio theory, individual investors need some instructions. As proved by Brinson in 1986, over 90% of the success of a portfolio owe to general asset selection. Hence, this paper primarily focused on providing suggestions to individual investors on general asset selection to help them to construct a portfolio in China securities market at present. Initially, this paper utilized the Mean-Variance model and CML approach to depict the market and analyzed the long-term asset allocation of multi-asset for individual investors. Based on the results, this paper suggested that individual investors should focus more on bond asset: with a portfolio of 97.37% of bond asset and 2.63% of stock asset, investors will be able to acquire a yield of 4.54% (annualized). And through this study, this paper found that Chinese securities market is not mature enough and teemed with regulations, making it to be competent than other mature market.

34

Ma, Tengfei, Patrick Ferber, Siyu Huo, Jie Chen, and Michael Katz. "Online Planner Selection with Graph Neural Networks and Adaptive Scheduling." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5077–84. http://dx.doi.org/10.1609/aaai.v34i04.5949.

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Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the convolutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference.Additionally, for cost-optimal planning, we propose a two-stage adaptive scheduling method to further improve the likelihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based.The code is available at https://github.com/matenure/GNN_planner.

35

Wang, Xin, Tao Sun, and Zhi Liu. "Kernel-Based Aggregating Learning System for Online Portfolio Optimization." Mathematical Problems in Engineering 2020 (January 28, 2020): 1–14. http://dx.doi.org/10.1155/2020/6595329.

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Recently, various machine learning techniques have been applied to solve online portfolio optimization (OLPO) problems. These approaches typically explore aggressive strategies to gain excess returns due to the existence of irrational phenomena in financial markets. However, existing aggressive OLPO strategies rarely consider the downside risk and lack effective trend representation, which leads to poor prediction performance and large investment losses in certain market environments. Besides, prediction with a single model is often unstable and sensitive to the noises and outliers, and the subsequent selection of optimal parameters also become obstacles to accurate estimation. To overcome these drawbacks, this paper proposes a novel kernel-based aggregating learning (KAL) system for OLPO. It includes a two-step price prediction scheme to improve the accuracy and robustness of the estimation. Specifically, a component price estimator is built by exploiting additional indicator information and the nonstationary nature of financial time series, and then an aggregating learning method is presented to combine multiple component estimators following different principles. Next, this paper conducts an enhanced tracking system by introducing a kernel-based increasing factor to maximize the future wealth of next period. At last, an online learning algorithm is designed to solve the system objective, which is suitable for large-scale and time-limited situations. Experimental results on several benchmark datasets from diverse real markets show that KAL outperforms other state-of-the-art systems in cumulative wealth and some risk-adjusted metrics. Meanwhile, it can withstand certain transaction costs.

36

Li, Bo, Qi Wang, Yuan Yu, Meng-Ze Sun, Liang-Xia Chen, Zhong-Liang Xiang, Feng Zhao, Qing-Cong Lv, and Zhi-Yong An. "A novel risk-control model for the online portfolio selection of high-frequency transactions." Knowledge-Based Systems 240 (March 2022): 108176. http://dx.doi.org/10.1016/j.knosys.2022.108176.

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37

Bowala, Sulalitha, and Japjeet Singh. "Optimizing Portfolio Risk of Cryptocurrencies Using Data-Driven Risk Measures." Journal of Risk and Financial Management 15, no. 10 (September 25, 2022): 427. http://dx.doi.org/10.3390/jrfm15100427.

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Portfolio risk management plays an important role in successful investments. Portfolio standard deviation, value-at-risk, expected shortfall, and maximum absolute deviation are widely used portfolio risk measures. However, the existing portfolio risk measures are vulnerable to larger skewness and kurtosis of the asset returns. Moreover, the traditional assumption of normality of the portfolio returns leads to the underestimation of portfolio risk. Cryptocurrencies are a decentralized digital medium of exchange. In contrast to physical money, cryptocurrency payments exist purely as digital entries on an online ledger called blockchain that describe specific transactions. Due to the high volume and high frequency of cryptocurrency transactions, risk forecasting using daily data is not enough, and a high-frequency analysis is required. High-frequency data reveal a very high excess kurtosis and skewness for returns of cryptocurrencies. In order to incorporate larger skewness and kurtosis of the cryptocurrencies, a data-driven portfolio risk measure is minimized to obtain the optimal portfolio weights. A recently proposed data-driven volatility forecasting approach with daily data are used to study risk forecasting for cryptocurrencies with high-frequency (hourly) big data. The paper emphasizes the superiority of portfolio selection of cryptocurrencies by minimizing the recently proposed risk measure over the traditional minimum variance portfolio.

38

Nuzzo, Iolanda, Nicola Caterino, Antonio Novellino, and Antonio Occhiuzzi. "Computer-Aided Decision Making for Regional Seismic Risk Mitigation Accounting for Limited Economic Resources." Applied Sciences 11, no. 12 (June 15, 2021): 5539. http://dx.doi.org/10.3390/app11125539.

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Seismic risk mitigation levels for an existing building are a balance between the reduction of risk and the cost of rehabilitation. Evidently, the more that is paid the more risk is reduced; however, due to limited public budgets a practical approach is needed to manage the risk reduction program when a portfolio of buildings is concerned. Basically, decision makers face a challenge when there are a large number of vulnerable buildings and there is no plan for how to allocate the appointed budget. This study develops a technological platform that implements a decision-making procedure to establish how to optimally distribute the budget in order to achieve the maximum possible portfolio risk reduction. Decisions are made based on various presumed intervention strategies dependent on building’s level of risk. The technological platform provides an interactive, user-friendly tool, available online, that supports stakeholders and decision makers in understanding what the best economic resource allocation will be after selecting the available budget for a specific portfolio of buildings. In addition, the ease of use enables the user to analyze the extent of risk reduction achievable for different budget levels. Therefore, the web platform represents a powerful tool to accomplish two challenging tasks, namely optimal budget selection and optimal budget allocation to gain territorial seismic risk mitigation.

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Padhi, Dushmanta Kumar, Neelamadhab Padhy, Akash Kumar Bhoi, Jana Shafi, and Seid Hassen Yesuf. "An Intelligent Fusion Model with Portfolio Selection and Machine Learning for Stock Market Prediction." Computational Intelligence and Neuroscience 2022 (June 23, 2022): 1–18. http://dx.doi.org/10.1155/2022/7588303.

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Developing reliable equity market models allows investors to make more informed decisions. A trading model can reduce the risks associated with investment and allow traders to choose the best-paying stocks. However, stock market analysis is complicated with batch processing techniques since stock prices are highly correlated. In recent years, advances in machine learning have given us a lot of chances to use forecasting theory and risk optimization together. The study postulates a unique two-stage framework. First, the mean-variance approach is utilized to select probable stocks (portfolio construction), thereby minimizing investment risk. Second, we present an online machine learning technique, a combination of “perceptron” and “passive-aggressive algorithm,” to predict future stock price movements for the upcoming period. We have calculated the classification reports, AUC score, accuracy, and Hamming loss for the proposed framework in the real-world datasets of 20 health sector indices for four different geographical reasons for the performance evaluation. Lastly, we conduct a numerical comparison of our method’s outcomes to those generated via conventional solutions by previous studies. Our aftermath reveals that learning-based ensemble strategies with portfolio selection are effective in comparison.

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Dombrovskii, Vladimir, and Tatiana Pashinskaya. "Design of model predictive control for constrained Markov jump linear systems with multiplicative noises and online portfolio selection." International Journal of Robust and Nonlinear Control 30, no. 3 (December 11, 2019): 1050–70. http://dx.doi.org/10.1002/rnc.4807.

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Shuliuk, Nadiya. "Experience of profile orientation on the basis of specialized online resources." SCIENTIFIC STUDIOS ON SOCIAL AND POLITICAL PSYCHOLOGY 50, no. 47 (July 3, 2021): 252–59. http://dx.doi.org/10.61727/sssppj/1.2021.252.

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The purpose of this publication is to analyze the foreign and Ukrainian experience in profile orientation and further career guidance work by means of online services. This must be helpful for education system to furtherly provide models of profile education, and solve the problems of students, parents and society. Based on a comparative analysis of information from various sources, it is shown the specifics of profile and career guidance work for high school students in France, the USA, Ukraine, Russia. It is made the attempt to generalize the organization principles in psychological-pedagogical assistance in the profile education, and in the implementation of selfdetermination’s professional models in high school students due to the online means. There are identified several modern trends. In the USA and France, children's acquaintance with the world of professions begins in primary school, and then there is a system of specialized guidance for senior school. This system claims the possibility to change the direction of education according to the chosen profile on various grounds. There is a typical organization of continuous career guidance in all foreign schemes of profile orientation which lasts throughout the school: monitoring the achievements, aptitudes, and hobbies of children; compiling their portfolio; and taking into account all this information in further career counseling, and selection of university applicants. In profile and professional orientation, the emphasis is on the selection of students capable of mastering complex knowledge-intensive technologies that have a clear potential for professional growth and personal development. Modern IT resources and specialized online platforms are presented as effective tools to provide such psychological services for students and their parents, as well as to provide methodological support to educators. The further research perspective is to find a solution to use profile orientation online resources in schools, based on the Ministry of Education and Science of Ukraine implementations; to develop new career guidance online platforms for Ukrainian schools

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Balcar, Štěpán, and Martin Pilát. "Heterogeneous Island Models and Their Application to Recommender Systems and Electric Vehicle Charging." International Journal on Artificial Intelligence Tools 29, no. 03n04 (June 2020): 2060010. http://dx.doi.org/10.1142/s0218213020600106.

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In this paper we describe a general framework for parallel optimization based on the island model of evolutionary algorithms. The framework runs a number of optimization methods in parallel with periodic communication. In this way, it essentially creates a parallel ensemble of optimization methods. At the same time, the system contains a planner that decides which of the available optimization methods should be used to solve the given optimization problem and changes the distribution of such methods during the run of the optimization. Thus, the system effectively solves the problem of online parallel portfolio selection. The proposed system is evaluated in a number of common benchmarks with various problem encodings as well as in two real-life problems — the optimization in recommender systems and the training of neural networks for the control of electric vehicle charging.

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Willmott, Taylor Jade, Erin Hurley, and Sharyn Rundle-Thiele. "Designing energy solutions: a comparison of two participatory design approaches for service innovation." Journal of Service Theory and Practice 32, no. 3 (March 17, 2022): 353–77. http://dx.doi.org/10.1108/jstp-03-2021-0040.

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PurposeParticipatory design involves users and other key stakeholders in processes that aim to ensure solutions generated meet their needs. This paper compares the processes and outcomes of two participatory design approaches (design thinking and co-design) to examine their utility in co-creating innovative service solutions for reducing household energy demand.Design/methodology/approachDesign thinking and co-design were implemented in two independent convenience samples of household energy users in Queensland, Australia. Workshops were conducted online using Zoom and Padlet technology. Informed by the capability-practice-ability (CPA) portfolio, a critical analysis based on the research team's experiences with implementing the two participatory design approaches is presented.FindingsThe key distinguishing features that set design thinking apart from co-design is extent of user involvement, solution diversity and resource requirements. With a shorter duration and less intensive user involvement, co-design offers a more resource efficient means of solution generation. In contrast, design thinking expands the solution space by allowing for human-centred problem framing and in so doing gives rise to greater diversity in solutions generated.Research limitations/implicationsMapping the six constellations of service design outlined in the CPA portfolio to the research team's experiences implementing two different participatory design approaches within the same context reconciles theoretical understanding of how capabilities, practices and abilities may differ or converge in an applied setting.Practical implicationsUnderstanding the benefits and expected outcomes across the two participatory design approaches will guide practitioners and funding agencies in the selection of an appropriate method to achieve desired outcomes.Originality/valueThis paper compares two forms of participatory design (design thinking and co-design) for service innovation in the context of household energy demand offering theoretical and practical insights into the utility of each as categorised within the CPA portfolio.

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Kim, Minyoung. "Cost-Sensitive Estimation of ARMA Models for Financial Asset Return Data." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/232184.

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The autoregressive moving average (ARMA) model is a simple but powerful model in financial engineering to represent time-series with long-range statistical dependency. However, the traditional maximum likelihood (ML) estimator aims to minimize a loss function that is inherently symmetric due to Gaussianity. The consequence is that when the data of interest are asset returns, and the main goal is to maximize profit by accurate forecasting, the ML objective may be less appropriate potentially leading to a suboptimal solution. Rather, it is more reasonable to adopt an asymmetric loss where the model's prediction, as long as it is in the same direction as the true return, is penalized less than the prediction in the opposite direction. We propose a quite sensible asymmetric cost-sensitive loss function and incorporate it into the ARMA model estimation. On the online portfolio selection problem with real stock return data, we demonstrate that the investment strategy based on predictions by the proposed estimator can be significantly more profitable than the traditional ML estimator.

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Nindya Amelia, Nindya Amelia. "IMPLEMENTASI BAURAN PROMOSI SEBAGAI STRATEGI KOMUNIKASI PEMASARAN MEMOPRO WEDDING ORGANIZER DALAM MENINGKATKAN KONSUMEN MEMOPRO." NIVEDANA : Jurnal Komunikasi dan Bahasa 4, no. 1 (August 10, 2023): 223–39. http://dx.doi.org/10.53565/nivedana.v4i1.864.

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In an effort to market products or services, companies need to develop products or services that are unique compared to their competitors. Therefore, the right promotion strategy is needed to increase consumers in using the services of a wedding organizer.This research was conducted to understand more about the implementation of the promotion mix as part of the marketing communication strategy carried out by Memopro Wedding Organizer to promote its services and increase consumers. This study uses Kotler's and Armstrong's promotional mix theory with a qualitative descriptive research method with data collection using interviews, observation, and documentation. The selection of informants was carried out through a purposive sampling technique.The results of this study show that Memopro's advertising promotion activities are carried out through websites, online billboards, and Instagram, and promotional activities through public relations are carried out through certification, collaboration with vendors, and holding seminars. The supporting factors for Memopro's promotional activities are the portfolio and also testimonials from previous customers and the inhibiting factor is the current lack of Memopro human resources.

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Frej, Eduarda Asfora, Lucia Reis Peixoto Roselli, Alexandre Ramalho Alberti, Murilo Amorim Britto, Evônio de Barros Campelo Júnior, Rodrigo José Pires Ferreira, and Adiel Teixeira de Almeida. "Collaborative Decision Model for Allocating Intensive Care Units Beds with Scarce Resources in Health Systems: A Portfolio Based Approach under Expected Utility Theory and Bayesian Decision Analysis." Mathematics 11, no. 3 (January 28, 2023): 659. http://dx.doi.org/10.3390/math11030659.

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The COVID-19 pandemic has brought health systems to the brink of collapse in several regions around the world, as the demand for health care has outstripped the capacity of their services, especially regarding intensive care. In this context, health system managers have faced a difficult question: who should be admitted to an intensive care unit (ICU), and who should not? This paper addresses this decision problem using Expected Utility Theory and Bayesian decision analysis. In order to estimate the chances of survival for patients, a structured protocol has been proposed conjointly with physicians, based on the Sequential Organ Failure Assessment (SOFA) score. A portfolio selection approach is proposed to support tackling the ICU allocation problem. A simulation study shows that the proposed approach is more advantageous than other approaches already presented in the literature, with respect to the number of lives saved. The patients’ probabilities of survival inside and outside the ICU are important parameters of the model. However, assessing such probabilities can be a difficult task for health professionals. In order to give due treatment to the imprecise information regarding these probabilities, a Monte Carlo simulation is used to estimate the probabilities of recommending a patient be admitted to the ICU is the most appropriate decision, given the conditions presented. The methodology was implemented in an Information and Decision System called SIDTriagem, which is available online for free. With regards to managerial implications, SIDTriagem has a great potential to help in the response to public health emergencies systems as it facilitates rational decision-making regarding allocating ICU beds when resources are scarce.

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Rácz, Attila, and Norbert Fogarasi. "Trading sparse, mean reverting portfolios using VAR(1) and LSTM prediction." Acta Universitatis Sapientiae, Informatica 13, no. 2 (December 1, 2021): 288–302. http://dx.doi.org/10.2478/ausi-2021-0013.

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Abstract We investigated the predictability of mean reverting portfolios and the VAR(1) model in several aspects. First, we checked the dependency of the accuracy of VAR(1) model on different data types including the original data itself, the return of prices, the natural logarithm of stock and on the log return. Then we compared the accuracy of predictions of mean reverting portfolios coming from VAR(1) with different generative models such as VAR(1) and LSTM for both online and o ine data. It was eventually shown that the LSTM predicts much better than the VAR(1) model. The conclusion is that the VAR(1) assumption works well in selecting the mean reverting portfolio, however, LSTM is a better choice for prediction. With the combined model a strategy with positive trading mean profit was successfully developed. We found that online LSTM outperforms all VAR(1) predictions and results in a positive expected profit when used in a simple trading algorithm.

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Howse, F., M. Ward, J. Horwood, B. Byrne, and A. Mirnezami. "Getting through the structured selection process." Bulletin of the Royal College of Surgeons of England 90, no. 2 (February 1, 2008): 56–58. http://dx.doi.org/10.1308/147363508x273768.

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In 2007 a new method of selection for specialist training in the UK was introduced. Traditional forms of application and interview were abandoned in favour of an online application system and a structured interview process. Due to widely reported shortcomings the online application system has since been abandoned. The interview process also underwent modification during the first and second round of applications, specifically to incorporate the consideration of CVs at interview in addition to trainee portfolios. Nevertheless, the basic structured interview format promises to become the norm for the selection of surgical trainees throughout the country, potentially for many years to come.

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Rambe, Sokhira Linda Vinde. "Assessment Ideas For Fostering Online Learning Autonomy." English Education : English Journal for Teaching and Learning 9, no. 01 (June 30, 2021): 25–34. http://dx.doi.org/10.24952/ee.v9i01.3561.

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Learning autonomy means learners have to take responsibility in handing their own learning with the help and supervision guided by teachers. Then, selecting types of assessment techniques can be a way to create students’ learning autonomy during the instructional process. Thus, the objective of this study is to determine some techniques of English learning assessment that can foster students’ learning autonomy in online learning environment. Qualitative research was applied as research design done at English Education Department of the Institute for Islamic Studies of Padangsidimpuan. Ten lecturers of English Education Department were chosen purposively as respondents and interview was used as data collection technique. Then, the data were analyzed through qualitative data analysis in which the data were described and elaborated in detail ways. Related to findings, this study found some techniques of English learning assessment that regarded effective to improve students’ independence in online learning i.e. portfolio assessment, discussion and problem solving, essays and summary writing, online presentation, multimedia presentation, mini research, and recording performance task. Finally, this study concluded that there are many assessment techniques to foster students’ online learning autonomy and the promotion of learning autonomy becomes an important aspect to be included in online learning environment.

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Gensler, Sonja, Peter Leeflang, and Bernd Skiera. "Impact of online channel use on customer revenues and costs to serve: Considering product portfolios and self-selection." International Journal of Research in Marketing 29, no. 2 (June 2012): 192–201. http://dx.doi.org/10.1016/j.ijresmar.2011.09.004.

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