An exploration of the fuzzy inference system for the daily trading decision and its performance analysis based on fuzzy MCDM methods
Gespeichert in:
| Autor*in: |
Veeramani, C. [verfasserIn] Venugopal, R. [verfasserIn] Muruganandan, S. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Computational economics - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988, 62(2023), 3 vom: Okt., Seite 1313-1340 |
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| Übergeordnetes Werk: |
volume:62 ; year:2023 ; number:3 ; month:10 ; pages:1313-1340 |
| Links: |
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| DOI / URN: |
10.1007/s10614-022-10346-3 |
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| Katalog-ID: |
1866638866 |
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| 982 | |2 26 |1 00 |x DE-206 |b In the field of stock trading, forecasting the future stock price movement is an essential yet challenging area. Precise prediction of the stock market movement is warranted to have a deterministic return. In this regard, the current study attempts to develop a new indicator based on the Fuzzy Inference System (FIS). From the historical stock price, the technical indicators such as Moving Average Convergence and Divergence, Relative Strength Index, Stochastic Oscillator, and On-Balance-Volume values are calculated and fuzzified. FIS framework is developed through fuzzy rules that are based on the expert’s opinion on the fuzzified technical indicators. The FIS recommends daily trading decisions such as buy, hold, and sell signals. To validate the proposed FIS framework, the daily stock price of the top 25 companies listed in the NASDAQ for the period from 2015 to 2019 have been used. Using statistical methods and Fuzzy Multi-criteria Decision Making (FMCDM) methods, the performance of the proposed FIS model has been compared with the existing technical indicators as well as Buy and Hold strategy. Finally, the correlation of the FMCDM approaches is evaluated through Spearman’s and Kendall’s rank correlation. | ||
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10.1007/s10614-022-10346-3 doi (DE-627)1866638866 (DE-599)KXP1866638866 DE-627 ger DE-627 rda eng Veeramani, C. verfasserin aut An exploration of the fuzzy inference system for the daily trading decision and its performance analysis based on fuzzy MCDM methods C. Veeramani, R. Venugopal, S. Muruganandan 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fuzzy inference system (dpeaa)DE-206 Fuzzy multi criteria decision making analysis (dpeaa)DE-206 Ranking performance (dpeaa)DE-206 Technical indicator (dpeaa)DE-206 Venugopal, R. verfasserin aut Muruganandan, S. verfasserin aut Enthalten in Computational economics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 62(2023), 3 vom: Okt., Seite 1313-1340 Online-Ressource (DE-627)270427546 (DE-600)1477445-8 (DE-576)121190374 1572-9974 nnns volume:62 year:2023 number:3 month:10 pages:1313-1340 https://link.springer.com/content/pdf/10.1007/s10614-022-10346-3.pdf?pdf=button Verlag lizenzpflichtig https://doi.org/10.1007/s10614-022-10346-3 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2023 3 10 1313-1340 26 01 0206 4394302005 x1z 20-10-23 26 00 DE-206 In the field of stock trading, forecasting the future stock price movement is an essential yet challenging area. Precise prediction of the stock market movement is warranted to have a deterministic return. In this regard, the current study attempts to develop a new indicator based on the Fuzzy Inference System (FIS). From the historical stock price, the technical indicators such as Moving Average Convergence and Divergence, Relative Strength Index, Stochastic Oscillator, and On-Balance-Volume values are calculated and fuzzified. FIS framework is developed through fuzzy rules that are based on the expert’s opinion on the fuzzified technical indicators. The FIS recommends daily trading decisions such as buy, hold, and sell signals. To validate the proposed FIS framework, the daily stock price of the top 25 companies listed in the NASDAQ for the period from 2015 to 2019 have been used. Using statistical methods and Fuzzy Multi-criteria Decision Making (FMCDM) methods, the performance of the proposed FIS model has been compared with the existing technical indicators as well as Buy and Hold strategy. Finally, the correlation of the FMCDM approaches is evaluated through Spearman’s and Kendall’s rank correlation. |
| spelling |
10.1007/s10614-022-10346-3 doi (DE-627)1866638866 (DE-599)KXP1866638866 DE-627 ger DE-627 rda eng Veeramani, C. verfasserin aut An exploration of the fuzzy inference system for the daily trading decision and its performance analysis based on fuzzy MCDM methods C. Veeramani, R. Venugopal, S. Muruganandan 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fuzzy inference system (dpeaa)DE-206 Fuzzy multi criteria decision making analysis (dpeaa)DE-206 Ranking performance (dpeaa)DE-206 Technical indicator (dpeaa)DE-206 Venugopal, R. verfasserin aut Muruganandan, S. verfasserin aut Enthalten in Computational economics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 62(2023), 3 vom: Okt., Seite 1313-1340 Online-Ressource (DE-627)270427546 (DE-600)1477445-8 (DE-576)121190374 1572-9974 nnns volume:62 year:2023 number:3 month:10 pages:1313-1340 https://link.springer.com/content/pdf/10.1007/s10614-022-10346-3.pdf?pdf=button Verlag lizenzpflichtig https://doi.org/10.1007/s10614-022-10346-3 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2023 3 10 1313-1340 26 01 0206 4394302005 x1z 20-10-23 26 00 DE-206 In the field of stock trading, forecasting the future stock price movement is an essential yet challenging area. Precise prediction of the stock market movement is warranted to have a deterministic return. In this regard, the current study attempts to develop a new indicator based on the Fuzzy Inference System (FIS). From the historical stock price, the technical indicators such as Moving Average Convergence and Divergence, Relative Strength Index, Stochastic Oscillator, and On-Balance-Volume values are calculated and fuzzified. FIS framework is developed through fuzzy rules that are based on the expert’s opinion on the fuzzified technical indicators. The FIS recommends daily trading decisions such as buy, hold, and sell signals. To validate the proposed FIS framework, the daily stock price of the top 25 companies listed in the NASDAQ for the period from 2015 to 2019 have been used. Using statistical methods and Fuzzy Multi-criteria Decision Making (FMCDM) methods, the performance of the proposed FIS model has been compared with the existing technical indicators as well as Buy and Hold strategy. Finally, the correlation of the FMCDM approaches is evaluated through Spearman’s and Kendall’s rank correlation. |
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10.1007/s10614-022-10346-3 doi (DE-627)1866638866 (DE-599)KXP1866638866 DE-627 ger DE-627 rda eng Veeramani, C. verfasserin aut An exploration of the fuzzy inference system for the daily trading decision and its performance analysis based on fuzzy MCDM methods C. Veeramani, R. Venugopal, S. Muruganandan 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fuzzy inference system (dpeaa)DE-206 Fuzzy multi criteria decision making analysis (dpeaa)DE-206 Ranking performance (dpeaa)DE-206 Technical indicator (dpeaa)DE-206 Venugopal, R. verfasserin aut Muruganandan, S. verfasserin aut Enthalten in Computational economics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 62(2023), 3 vom: Okt., Seite 1313-1340 Online-Ressource (DE-627)270427546 (DE-600)1477445-8 (DE-576)121190374 1572-9974 nnns volume:62 year:2023 number:3 month:10 pages:1313-1340 https://link.springer.com/content/pdf/10.1007/s10614-022-10346-3.pdf?pdf=button Verlag lizenzpflichtig https://doi.org/10.1007/s10614-022-10346-3 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2023 3 10 1313-1340 26 01 0206 4394302005 x1z 20-10-23 26 00 DE-206 In the field of stock trading, forecasting the future stock price movement is an essential yet challenging area. Precise prediction of the stock market movement is warranted to have a deterministic return. In this regard, the current study attempts to develop a new indicator based on the Fuzzy Inference System (FIS). From the historical stock price, the technical indicators such as Moving Average Convergence and Divergence, Relative Strength Index, Stochastic Oscillator, and On-Balance-Volume values are calculated and fuzzified. FIS framework is developed through fuzzy rules that are based on the expert’s opinion on the fuzzified technical indicators. The FIS recommends daily trading decisions such as buy, hold, and sell signals. To validate the proposed FIS framework, the daily stock price of the top 25 companies listed in the NASDAQ for the period from 2015 to 2019 have been used. Using statistical methods and Fuzzy Multi-criteria Decision Making (FMCDM) methods, the performance of the proposed FIS model has been compared with the existing technical indicators as well as Buy and Hold strategy. Finally, the correlation of the FMCDM approaches is evaluated through Spearman’s and Kendall’s rank correlation. |
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10.1007/s10614-022-10346-3 doi (DE-627)1866638866 (DE-599)KXP1866638866 DE-627 ger DE-627 rda eng Veeramani, C. verfasserin aut An exploration of the fuzzy inference system for the daily trading decision and its performance analysis based on fuzzy MCDM methods C. Veeramani, R. Venugopal, S. Muruganandan 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fuzzy inference system (dpeaa)DE-206 Fuzzy multi criteria decision making analysis (dpeaa)DE-206 Ranking performance (dpeaa)DE-206 Technical indicator (dpeaa)DE-206 Venugopal, R. verfasserin aut Muruganandan, S. verfasserin aut Enthalten in Computational economics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 62(2023), 3 vom: Okt., Seite 1313-1340 Online-Ressource (DE-627)270427546 (DE-600)1477445-8 (DE-576)121190374 1572-9974 nnns volume:62 year:2023 number:3 month:10 pages:1313-1340 https://link.springer.com/content/pdf/10.1007/s10614-022-10346-3.pdf?pdf=button Verlag lizenzpflichtig https://doi.org/10.1007/s10614-022-10346-3 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2023 3 10 1313-1340 26 01 0206 4394302005 x1z 20-10-23 26 00 DE-206 In the field of stock trading, forecasting the future stock price movement is an essential yet challenging area. Precise prediction of the stock market movement is warranted to have a deterministic return. In this regard, the current study attempts to develop a new indicator based on the Fuzzy Inference System (FIS). From the historical stock price, the technical indicators such as Moving Average Convergence and Divergence, Relative Strength Index, Stochastic Oscillator, and On-Balance-Volume values are calculated and fuzzified. FIS framework is developed through fuzzy rules that are based on the expert’s opinion on the fuzzified technical indicators. The FIS recommends daily trading decisions such as buy, hold, and sell signals. To validate the proposed FIS framework, the daily stock price of the top 25 companies listed in the NASDAQ for the period from 2015 to 2019 have been used. Using statistical methods and Fuzzy Multi-criteria Decision Making (FMCDM) methods, the performance of the proposed FIS model has been compared with the existing technical indicators as well as Buy and Hold strategy. Finally, the correlation of the FMCDM approaches is evaluated through Spearman’s and Kendall’s rank correlation. |
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10.1007/s10614-022-10346-3 doi (DE-627)1866638866 (DE-599)KXP1866638866 DE-627 ger DE-627 rda eng Veeramani, C. verfasserin aut An exploration of the fuzzy inference system for the daily trading decision and its performance analysis based on fuzzy MCDM methods C. Veeramani, R. Venugopal, S. Muruganandan 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fuzzy inference system (dpeaa)DE-206 Fuzzy multi criteria decision making analysis (dpeaa)DE-206 Ranking performance (dpeaa)DE-206 Technical indicator (dpeaa)DE-206 Venugopal, R. verfasserin aut Muruganandan, S. verfasserin aut Enthalten in Computational economics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 62(2023), 3 vom: Okt., Seite 1313-1340 Online-Ressource (DE-627)270427546 (DE-600)1477445-8 (DE-576)121190374 1572-9974 nnns volume:62 year:2023 number:3 month:10 pages:1313-1340 https://link.springer.com/content/pdf/10.1007/s10614-022-10346-3.pdf?pdf=button Verlag lizenzpflichtig https://doi.org/10.1007/s10614-022-10346-3 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2023 3 10 1313-1340 26 01 0206 4394302005 x1z 20-10-23 26 00 DE-206 In the field of stock trading, forecasting the future stock price movement is an essential yet challenging area. Precise prediction of the stock market movement is warranted to have a deterministic return. In this regard, the current study attempts to develop a new indicator based on the Fuzzy Inference System (FIS). From the historical stock price, the technical indicators such as Moving Average Convergence and Divergence, Relative Strength Index, Stochastic Oscillator, and On-Balance-Volume values are calculated and fuzzified. FIS framework is developed through fuzzy rules that are based on the expert’s opinion on the fuzzified technical indicators. The FIS recommends daily trading decisions such as buy, hold, and sell signals. To validate the proposed FIS framework, the daily stock price of the top 25 companies listed in the NASDAQ for the period from 2015 to 2019 have been used. Using statistical methods and Fuzzy Multi-criteria Decision Making (FMCDM) methods, the performance of the proposed FIS model has been compared with the existing technical indicators as well as Buy and Hold strategy. Finally, the correlation of the FMCDM approaches is evaluated through Spearman’s and Kendall’s rank correlation. |
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Precise prediction of the stock market movement is warranted to have a deterministic return. In this regard, the current study attempts to develop a new indicator based on the Fuzzy Inference System (FIS). From the historical stock price, the technical indicators such as Moving Average Convergence and Divergence, Relative Strength Index, Stochastic Oscillator, and On-Balance-Volume values are calculated and fuzzified. FIS framework is developed through fuzzy rules that are based on the expert’s opinion on the fuzzified technical indicators. The FIS recommends daily trading decisions such as buy, hold, and sell signals. To validate the proposed FIS framework, the daily stock price of the top 25 companies listed in the NASDAQ for the period from 2015 to 2019 have been used. Using statistical methods and Fuzzy Multi-criteria Decision Making (FMCDM) methods, the performance of the proposed FIS model has been compared with the existing technical indicators as well as Buy and Hold strategy. 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26 00 DE-206 In the field of stock trading, forecasting the future stock price movement is an essential yet challenging area. Precise prediction of the stock market movement is warranted to have a deterministic return. In this regard, the current study attempts to develop a new indicator based on the Fuzzy Inference System (FIS). From the historical stock price, the technical indicators such as Moving Average Convergence and Divergence, Relative Strength Index, Stochastic Oscillator, and On-Balance-Volume values are calculated and fuzzified. FIS framework is developed through fuzzy rules that are based on the expert’s opinion on the fuzzified technical indicators. The FIS recommends daily trading decisions such as buy, hold, and sell signals. To validate the proposed FIS framework, the daily stock price of the top 25 companies listed in the NASDAQ for the period from 2015 to 2019 have been used. Using statistical methods and Fuzzy Multi-criteria Decision Making (FMCDM) methods, the performance of the proposed FIS model has been compared with the existing technical indicators as well as Buy and Hold strategy. Finally, the correlation of the FMCDM approaches is evaluated through Spearman’s and Kendall’s rank correlation An exploration of the fuzzy inference system for the daily trading decision and its performance analysis based on fuzzy MCDM methods C. Veeramani, R. Venugopal, S. Muruganandan Fuzzy inference system (dpeaa)DE-206 Fuzzy multi criteria decision making analysis (dpeaa)DE-206 Ranking performance (dpeaa)DE-206 Technical indicator (dpeaa)DE-206 |
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exploration of the fuzzy inference system for the daily trading decision and its performance analysis based on fuzzy mcdm methods |
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An exploration of the fuzzy inference system for the daily trading decision and its performance analysis based on fuzzy MCDM methods |
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https://link.springer.com/content/pdf/10.1007/s10614-022-10346-3.pdf?pdf=button https://doi.org/10.1007/s10614-022-10346-3 |
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Venugopal, R. Muruganandan, S. |
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10.1007/s10614-022-10346-3 |
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2024-07-04T13:23:21.406Z |
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code="h">1313-1340</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4394302005</subfield><subfield code="y">x1z</subfield><subfield code="z">20-10-23</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">In the field of stock trading, forecasting the future stock price movement is an essential yet challenging area. Precise prediction of the stock market movement is warranted to have a deterministic return. In this regard, the current study attempts to develop a new indicator based on the Fuzzy Inference System (FIS). From the historical stock price, the technical indicators such as Moving Average Convergence and Divergence, Relative Strength Index, Stochastic Oscillator, and On-Balance-Volume values are calculated and fuzzified. FIS framework is developed through fuzzy rules that are based on the expert’s opinion on the fuzzified technical indicators. The FIS recommends daily trading decisions such as buy, hold, and sell signals. To validate the proposed FIS framework, the daily stock price of the top 25 companies listed in the NASDAQ for the period from 2015 to 2019 have been used. Using statistical methods and Fuzzy Multi-criteria Decision Making (FMCDM) methods, the performance of the proposed FIS model has been compared with the existing technical indicators as well as Buy and Hold strategy. Finally, the correlation of the FMCDM approaches is evaluated through Spearman’s and Kendall’s rank correlation.</subfield></datafield></record></collection>
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| score |
7.399208 |