An online portfolio strategy based on trend promote price tracing ensemble learning algorithm

Dai, H. L.; Liang, Chu-Xin; Dai, Hongming; Huang, Cui-Yin; Adnan, Rana Muhammad

doi:10.1016/j.knosys.2021.107957

Cited by 20 publications

(8 citation statements)

References 9 publications

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“…PP assumes the price continues to move at its maximum price in a recent time window but fails to capture the continuous depreciation of the asset price. Recently, there have been many works to handle the shortcomings of PP, for example, the trend peak price tracing [11] and the peak price tracking approach [18].…”

Section: Related Work and Problem Settingmentioning

confidence: 99%

“…Kumar and Segev [24] employed Bayesian methods to ensemble two strategies but not a trend estimator, improving future portfolio decisions by learning from past prediction errors. Dai et al [11] introduced trend peak price tracing (TPPT), a dynamic price forecasting method that adapts to market conditions by incorporating PP, EMA, or maintaining current values based on trend analysis, but it reaches a time complexity of O m 2 n .…”

Section: Ensemble Learningmentioning

confidence: 99%

“…In this paper, we use a standard and universal setting for portfolio selection in machine learning [7][8][9][10][11][12]. Consider a financial market with m assets for n periods.…”

Section: Problem Settingmentioning

confidence: 99%

“…Since each trend estimator is based on different prior assumptions, their effectiveness is contingent on the financial environment aligning with these assumptions. Furthermore, existing methods [11,12] attempt to utilize multiple trend estimators, but as the number of trends and assets increases, the computational cost becomes very high due to matrix calculations. It is still challenging to predetermine which trend estimator will maximize the final cumulative wealth efficiently for online portfolio selection tasks.…”

Section: Introductionmentioning

confidence: 99%

“…TPPT [11]: It uses adjustable historical windows and slope values for price prediction. The parameters are set as follows: w = 5, α = 0.5, and = 100.…”

mentioning

confidence: 99%

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Passive Aggressive Ensemble for Online Portfolio Selection

Xie,

Yin,

et al. 2024

Mathematics

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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.

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Section: Related Work and Problem Settingmentioning

confidence: 99%

Section: Ensemble Learningmentioning

confidence: 99%

“…In this paper, we use a standard and universal setting for portfolio selection in machine learning [7][8][9][10][11][12]. Consider a financial market with m assets for n periods.…”

Section: Problem Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…TPPT [11]: It uses adjustable historical windows and slope values for price prediction. The parameters are set as follows: w = 5, α = 0.5, and = 100.…”

mentioning

confidence: 99%

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