Design and Research of Intelligent Quantitative Investment Model Based on PLR-IRF and DRNN Algorithm

Tang, Chunming; Zheng, Xinyue; Xiang, Yu; Chen, Chunkai; Zhu, Wenyan

doi:10.1109/itoec.2018.8740741

Cited by 5 publications

(6 citation statements)

References 16 publications

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“…The profitability of this method is higher than that of Chang et al (2009), Chen and He (2015) and BHS. Tang et al (2018) presented a dynamic threshold selection algorithm for PLR adapted from the relation between the percentages of peak and valley TPs in historical data. In continuation of studies devoted to selecting the PLR threshold, Tang et al (2019) suggested a fitness function.…”

Section: Turning Points (Tps) Detection Methodsmentioning

confidence: 99%

“…Although the PLR has intuitive results and high data compression properties, the selection of the PLR algorithm’s threshold value depends on the empirical value. Besides, the algorithm itself does not have the feature of selecting the threshold automatically (Tang et al , 2018). As the profitability of the detected TPs completely depends on the threshold, the PLR may not be a very good approach.…”

Section: Introductionmentioning

confidence: 99%

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A binary integer programming (BIP) model for optimal financial turning points detection

Yazdani

Khashei

Hejazi

2022

JM2

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Purpose This paper aims to detect the most profitable, i.e. optimal turning points (TPs), from the history of time series using a binary integer programming (BIP) model. TPs prediction problem is one of the most popular yet challenging topics in financial planning. Predicting profitable TPs results in earning profit by offering the opportunity to buy at low and selling at high. TPs detected from the history of time series will be used as the prediction model’s input. According to the literature, the predicted TPs’ profitability depends on the detected TPs’ profitability. Therefore, research for improving the profitability of detection methods has been never given up. Nevertheless, to the best of our knowledge, none of the existing methods can detect the optimal TPs. Design/methodology/approach The objective function of our model maximizes the profit of adopting all the trading strategies. The decision variables represent whether or not to detect the breakpoints as TPs. The assumptions of the model are as follows. Short-selling is possible. The time value for the money is not considered. Detection of consecutive buying (selling) TPs is not possible. Findings Empirical results with 20 data sets from Shanghai Stock Exchange indicate that the model detects the optimal TPs. Originality/value The proposed model, in contrast to the other methods, can detect the optimal TPs. Additionally, the proposed model, in contrast to the other methods, requires transaction cost as its only input parameter. This advantage reduces the process’ calculations.

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Section: Turning Points (Tps) Detection Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A binary integer programming (BIP) model for optimal financial turning points detection

Yazdani

Khashei

Hejazi

2022

JM2

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“…A larger PLR's sliding window will create long trend patterns; while the patterns will be sensitive when the sliding window is very small. Notably, the pro tability of the detected TPs by PLR depends on the sliding window, and PLR itself doesn't have the feature of selecting the sliding window automatically [19]. Hence, the corresponding literature has witnessed efforts towards enhancing the pro tability of the detected TPs, by setting the appropriate sliding window.…”

Section: Literature Reviewmentioning

confidence: 99%

“…There are some drawbacks regarding the best detection approach existing in the literature (i.e., PLR), which make this method unable to detect the most pro table TPs or the optimal TPs from the history of nancial time series. The pro tability of the detected TPs by PLR depends on the sliding window, and PLR itself doesn't have the feature of selecting the sliding window automatically [19]. Besides, Chang et al [21] believe that if the window size is not properly chosen, the sub-segments generated by the PLR may lead to the wrong trading decisions [21].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Although this method outperforms the previous TPs detection methods, determining the optimal percentage of the occurred TPs is still an open issue. Tang et al [19] proposed a new dynamic sliding window setting algorithm to improve the performance of PLR. This algorithm dynamically sets the sliding window for different nancial time series, different periods, and different price uctuations.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Mathematical Modeling Framework To Detect The Optimal Financial Turning Points

Yazdani

Khashei

Hejazi

2021

Preprint

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The financial markets have always witnessed the competition of their participants for gaining high and stable profits. The realization extent of this goal depends on the profitability of the trading points or turning points (TPs) ahead. TPs prediction problem is one of the most challenging yet important problems in the financial discipline. The first step towards predicting financial TPs is to detect TPs from the history of the corresponding financial time series. Literature indicates that the profitability of the predicted financial TPs depends on the profitability of the detected TPs. Given this, numerous efforts have been devoted to enhancing the profitability of the detected financial TPs. Nevertheless, to the best of our knowledge, none of the existing detection methods can detect the most profitable or the optimal TPs from the history of financial time series. The present study concerns this research gap and ensures detecting the optimal financial TPs by proposing a mathematical modeling framework. The proposed optimal TPs detection model in this paper will be structured concerning the three following assumptions. First, short-selling the financial asset is possible. Second, the time value for the investment money is not considered. Third, detecting consecutive buying TPs and consecutive selling TPs is not allowed. Empirical results with twenty real data sets indicate that the proposed model, in contrast to the existing TPs detection methods, detects the optimal TPs from the history of the financial time series.

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Stock turning points classification using a novel discrete learning-based methodology

2023

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Design and Research of Intelligent Quantitative Investment Model Based on PLR-IRF and DRNN Algorithm

Cited by 5 publications

References 16 publications

A binary integer programming (BIP) model for optimal financial turning points detection

A binary integer programming (BIP) model for optimal financial turning points detection

A Mathematical Modeling Framework To Detect The Optimal Financial Turning Points

Stock turning points classification using a novel discrete learning-based methodology

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