Measuring Financial Time Series Similarity with a View to Identifying Profitable Stock Market Opportunities

Dolphin, Rian; Smyth, Barry; Xu, Yang; Dong, Ruihai

doi:10.1007/978-3-030-86957-1_5

Cited by 8 publications

(6 citation statements)

References 33 publications

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“…Lin (2020) incorporated mixed metric scales such as interval and ratio scales into metric computation to retrieve design case similarity. Dolphin et al (2021) combined information about price deviations and trends into a measure in predicting stock trends and combined the adjusted correlation coefficient with the Euclidean measure to identify similarities from the two perspectives.…”

Section: Similarity Measurementioning

confidence: 99%

A review of the development and future challenges of case-based reasoning

Yan

Cheng

2023

Preprint

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Case-based reasoning (CBR), which is based on the cognitive assumption that similar problems have similar solutions, is an important problem-solving and learning method in the field of artificial intelligence. In this article, the development of CBR is mainly reviewed, and the major challenges of CBR are summarized. The paper is organized into four parts. First, the basic framework and concepts of CBR are introduced. Then, the developed technology and innovative work that were formed in solving problems by CBR are summarized. Moreover, the application fields of CBR are sorted. Finally, according to the idea of deep learning and interpretable artificial intelligence, the main challenges for the future development of CBR are proposed.

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Section: Similarity Measurementioning

confidence: 99%

A review of the development and future challenges of case-based reasoning

Yan

Cheng

2023

Preprint

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“…As pointed out in (Dolphin et al, 2021), two assets can be perfectly correlated, but their performances will be very different. It is because conventional correlation is a similarity m, correlation cannot be the only criterion to cluster assets with similar patterns assets, correlation cannot be the only criterion.…”

Section: Similarity Metric Of Financial Time Seriesmentioning

confidence: 99%

“…It is because conventional correlation is a similarity m, correlation cannot be the only criterion to cluster assets with similar patterns assets, correlation cannot be the only criterion. In this paper, we use the metric proposed in (Dolphin et al, 2021) to measure the similarity of two assets.…”

Section: Similarity Metric Of Financial Time Seriesmentioning

confidence: 99%

“…As pointed out in (Tang, Xu, & Zhou, 2022), one necessary criterion that wasn't in the previous literature is that correlations for two assets from the same cluster with any other asset should be similar. Following that paper's spirit, we use the similarity metric proposed in (Dolphin, Smyth, Xu, & Dong, 2021) instead of using correlation. It is because correlation sometimes can't capture similarities between two assets, as pointed out in (Dolphin et al, 2021) one asset can have negative returns, and the other asset can have high returns, but these two assets still are highly correlated.…”

Section: Introductionmentioning

confidence: 99%

“…Following that paper's spirit, we use the similarity metric proposed in (Dolphin, Smyth, Xu, & Dong, 2021) instead of using correlation. It is because correlation sometimes can't capture similarities between two assets, as pointed out in (Dolphin et al, 2021) one asset can have negative returns, and the other asset can have high returns, but these two assets still are highly correlated. The similarity metric we use here takes cumulative returns into consideration and therefore can capture the pattern similarities between assets.…”

Section: Introductionmentioning

confidence: 99%

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Continuous-Time Path-Dependent Exploratory Mean-Variance Portfolio Construction

Zhou¹

2023

Preprint

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In this paper, we present an extended exploratory continuous-time mean-variance framework for portfolio management. Our strategy involves a new clustering method based on simulated annealing, which allows for more practical asset selection. Additionally, we consider past wealth evolution when constructing the mean-variance portfolio. We found that our strategy effectively learns from the past and performs well in practice.

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