doi: 10.3934/dcdss.2019061

Stock price fluctuation prediction method based on time series analysis

Information Academy of Renmin University of China, Beijing, China

* Corresponding author: Xiao-Qian Jiang

Received  June 2017 Revised  December 2017 Published  November 2018

With the rapid development of capital market and the great increment of people's income, more and more people want to invest money to the stock market and increase their wealth. Hence, the stock market has been an important part of the modern market economy. In this paper, we propose a novel stock price fluctuation prediction method based on the time series analysis technology. The main idea of this paper lies in that stock price of the future is predicted by mining and analyzing the historical data. Particularly, 16 technical indicators are chosen as input variables to the proposed model. Afterwards, we propose a hybrid ARIMA-ANN model to solve the stock price prediction problem, and the stock price is predicted by both the low volatility component and the high volatility component. Finally, experimental results demonstrate that the proposed can predict the stock price fluctuation with lower error rate.

Citation: Xiao-Qian Jiang, Lun-Chuan Zhang. Stock price fluctuation prediction method based on time series analysis. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2019061
References:
[1]

O. AcikgozA. CebiA. S. DalkilicA. KocaG. CetinZ. Gemici and S. Wongwises, A novel ANN-based approach to estimate heat transfer coefficients in radiant wall heating systems, Energy and Buildings, 144 (2017), 401-415.

[2]

M. BallingsD. Van den PoelN. Hespeels and R. Gryp, Evaluating multiple classifiers for stock price direction prediction, Expert Systems with Applications, 42 (2015), 7046-7056.

[3]

M. A. Boyacioglu and D. Avci, An Adaptive network-based fuzzy inference sys- tem (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange, Expert Systems with Applications, 37 (2010), 7908-7912.

[4]

R. Dash and P. Dash, Efficient stock price prediction using a Self Evolving Recurrent Neuro-Fuzzy Inference System optimized through a Modified technique, Expert Systems with Applications, 52 (2016), 75-90.

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I. Dawoud and S. Kaciranlar, An optimal k of kth MA-ARIMA models under a class of ARIMA model, Communications in Statistics-Theory and Methods, 46 (2017), 5754-5765. doi: 10.1080/03610926.2015.1112910.

[6]

I. Dawoud and S. Kaciranlar, An optimal k of kth MA-ARIMA models under AR(p) models, Communications in Statistics-Simulation and Computation, 46 (2017), 2842-2864. doi: 10.1080/03610918.2015.1065325.

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O. Eksi and B. K. O. Tas, Unconventional monetary policy and the stock market's reaction to Federal Reserve policy actions, North American Journal of Economics and Finance, 40 (2017), 136-147.

[9]

M. H. EsfeM. RejvaniR. Karimpour and A. A. A. Arani, Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT-Al2O3 nanoparticles by correlation and ANN methods using experimental data, Journal of Thermal Analysis and Calorimetry, 128 (2017), 1359-1371.

[10]

P. Ferreira, Portuguese and Brazilian stock market integration: A non-linear and detrended approach, Portuguese Economic Journal, 16 (2017), 49-63.

[11]

P. GharghoriE. D. Maberly and A. Nguyen, Informed trading around stock split announcements: Evidence from the option market, Journal of Financial and Quantitative Analysis, 52 (2017), 705-735.

[12]

M. GockenM. OzcaliciA. Boru and A. T. Dosdogru, Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction, Expert Systems with Applications, 44 (2016), 320-331.

[13]

R. HafeziJ. Shahrabi and E. Hadavandi, A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price, Applied Soft Computing, 29 (2015), 196-210.

[14]

Q. Han and J. F. Liang, Index futures trading restrictions and spot market quality: Evidence from the recent chinese stock market crash, Journal of Futures Markets, 37 (2017), 411-428.

[15]

S. E. HikichiE. G. Salgado and L. A. Beijo, Forecasting number of ISO 14001 certifications in the Americas using ARIMA models, Journal of Cleaner Production, 147 (2017), 242-253.

[16]

S. K. Hur and C. Y. Chung, Revisiting CAPM betas in an incomplete market: Evidence from the Korean stock market, Finance Research Letters, 21 (2017), 241-248.

[17]

C. F. JuangY. Y. Lin and C. C. Tu, A recurrent self-evolving fuzzy neural network with local feedbacks and its application to dynamic system processing, Fuzzy Sets and Systems, 161 (2010), 2552-2568.

[18]

T. LeirvikS. R. Fiskerstrand and A. B. Fjellvikas, Market liquidity and stock returns in the Norwegian stock market, Finance Research Letters, 21 (2017), 272-276.

[19]

X. Z. LengJ. H. WangH. B. JiQ. G. WangH. M. LiX. QianF. Y. Li and M. Yang, Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses, Chemosphere, 180 (2017), 513-522.

[20]

H. J. LiH. Z. AnW. FangY. WangW. Q. Zhong and L. L. Yan, Global energy investment structure from the energy stock market perspective based on a Heterogeneous Complex Network Model, Applied Energy, 194 (2017), 648-657.

[21]

M. S. Mahmud and P. Meesad, An innovative recurrent error-based neuro-fuzzy system with momentum for stock price prediction, Soft Computing, 20 (2016), 4173-4191.

[22]

W. MensiS. Hammoudeh and S. H. Kang, Dynamic linkages between developed and BRICS stock markets: Portfolio risk analysis, Finance Research Letters, 21 (2017), 26-33.

[23]

J. W. M. MwambaS. Hammoudeh and R. Gupta, Financial tail risks in conventional and Islamic stock markets: A comparative analysis, Pacific-Basin Finance Journal, 42 (2017), 60-82.

[24]

M. G. Novak and D. Veluscek, Prediction of stock price movement based on daily high prices, Quantitative Finance, 16 (2016), 793-826.

[25]

N. OliveiraP. Cortez and N. Areal, The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices, Expert Systems with Applications, 73 (2017), 125-144.

[26]

K. Park and H. Shin, Stock price prediction based on a complex interrelation network of economic factors, Engineering Applications of Artificial Intelligence, 26 (2013), 1550-1561.

[27]

J. PeltomakiM. Graham and P. Alagidede, Commodity-driven integration of stock markets in Africa, Applied Economics Letters, 24 (2017), 784-789.

[28]

D. Y. PengF. XinL. X. ZhangZ. P. GaoW. H. ZhangY. X. WangX. D. Chen and Y. Wang, Nonlinear time-series analysis of optical signals to identify multiphase flow behavior in microchannels, Aiche Journal, 63 (2017), 2378-2385.

[29]

A. Pohoata and E. Lungu, A complex analysis employing arima model and statistical methods on air pollutants recorded in ploiesti, romania, Revista De Chimie, 68 (2017), 818-823.

[30]

R. Ruby-FigueroaJ. SaavedraN. Bahamonde and A. Cassano, Permeate flux prediction in the ultrafiltration of fruit juices by ARIMA models, Journal of Membrane Science, 524 (2017), 108-116.

[31]

S. RyuC. L. Lau and B. C. Chun, The impact of Livestock Manure Control Policy on human leptospirosis in Republic of Korea using interrupted time series analysis, Epidemiology and Infection, 145 (2017), 1320-1325.

[32]

J. SchuermansL. DanneelsD. Van TiggelenT. Palmans and E. Witvrouw, Proximal Neuromuscular Control Protects Against Hamstring Injuries in Male Soccer Players: A Prospective Study With Electromyography Time-Series Analysis During Maximal Sprinting, American Journal of Sports Medicine, 45 (2017), 1315-1325.

[33]

G. Sheelapriya and R. Murugesan, Stock price trend prediction using Bayesian regularised radial basis function network model, Spanish Journal of Finance and Accounting-Revista Espanola De Financiacion Y Contabilida, 46 (2017), 189-211.

[34]

A. SiganosE. Vagenas-Nanos and P. Verwijmeren, Divergence of sentiment and stock market trading, Journal of Banking & Finance, 78 (2017), 130-141.

[35]

L. A. Smales, The importance of fear: Investor sentiment and stock market returns, Applied Economics, 49 (2017), 3395-3421.

[36]

B. Q. SunH. F. GuoH. R. KarimiY. J. Ge and S. Xiong, Prediction of stock index futures prices based on fuzzy sets and multivariate fuzzy time series, Neurocomputing, 151 (2015), 1528-1536.

[37]

M. Svoboda, stochastic model of short-term prediction of stock prices and its profitability in the czech stock market, E & M Ekonomie a Management, 19 (2016), 188-200.

[38]

P. Tufekci, Classification-based prediction models for stock price index movement, Intelligent Data Analysis, 20 (2016), 357-376.

[39]

H. G. A. ValeraM. J. Holmes and G. Hassan, Stock market uncertainty and interest rate behaviour: A panel GARCH approach, Applied Economics Letters, 24 (2017), 732-735.

[40]

J. Vanegas and F. Vasquez, Multivariate Adaptive Regression Splines (MARS), an alternative for the analysis of time series, Gaceta Sanitaria, 31 (2017), 235-237.

[41]

Y. WanX. L. Chen and Y. Shi, Adaptive cost dynamic time warping distance in time series analysis for classification, Journal of Computational and Applied Mathematics, 319 (2017), 514-520. doi: 10.1016/j.cam.2017.01.004.

[42]

D. WanX. H. Wei and X. G. Yang, Liquidity dynamics around intraday price jumps in chinese stock market, Journal of Systems Science & Complexity, 30 (2017), 434-463.

[43]

B. WengM. A. Ahmed and F. M. Megahed, Stock market one-day ahead movement prediction using disparate data sources, Expert Systems with Applications, 79 (2017), 153-163.

[44]

C. Z. Yao and Q. W. Lin, The mutual causality analysis between the stock and futures markets, Physica a-Statistical Mechanics and Its Applications, 478 (2017), 188-204. doi: 10.1016/j.physa.2017.02.071.

[45]

N. YokoyaX. X. Zhu and A. Plaza, Multisensor coupled spectral unmixing for time-series analysis, IEEE Transactions on Geoscience and Remote Sensing, 55 (2017), 2842-2857.

[46]

M. H. F. ZarandiM. ZarinbalN. Ghanbari and I. B. Turksen, A new fuzzy functions model tuned by hybridizing imperialist competitive algorithm and simulated annealing Application: Stock price prediction, Information Sciences, 222 (2013), 213-228. doi: 10.1016/j.ins.2012.08.002.

[47]

X. W. ZhangX. L. Zheng and D. D. Zeng, The dynamic interdependence of international financial markets: An empirical study on twenty-seven stock markets, Physica A-Statistical Mechanics and Its Applications, 472 (2017), 32-42.

[48]

J. D. ZhangV. HullZ. Y. OuyangL. HeT. ConnorH. B. YangJ. Y. HuangS. Q. ZhouZ. J. ZhangC. Q. ZhouH. M. Zhang and J. G. Liu, Modeling activity patterns of wildlife using time-series analysis, Ecology and Evolution, 7 (2017), 2575-2584.

[49]

Q. ZhaoY. M. ZhangW. Y. ZhangS. S. LiG. B. ChenY. B. WuC. QiuK. J. YingH. P. TangJ. A. HuangG. WilliamsR. Huxley and Y. M. Guo, Ambient temperature and emergency department visits: Time-series analysis in 12 Chinese cities, Environmental Pollution, 224 (2017), 310-316.

[50]

M. Zolfaghari and B. Sahabi, Impact of foreign exchange rate on oil companies risk in stock market: A Markov-switching approach, Journal of Computational and Applied Mathematics, 317 (2017), 274-289. doi: 10.1016/j.cam.2016.10.012.

[51]

L. ZolotoyJ. R. Frederickson and J. D. Lyon, Aggregate earnings and stock market returns: The good, the bad, and the state-dependent, Journal of Banking & Finance, 77 (2017), 157-175.

[52]

D. ZombreM. De Allegri and V. Ridde, Immediate and sustained effects of user fee exemption on healthcare utilization among children under five in Burkina Faso: A controlled interrupted time-series analysis, Social Science & Medicine, 179 (2017), 27-35.

show all references

References:
[1]

O. AcikgozA. CebiA. S. DalkilicA. KocaG. CetinZ. Gemici and S. Wongwises, A novel ANN-based approach to estimate heat transfer coefficients in radiant wall heating systems, Energy and Buildings, 144 (2017), 401-415.

[2]

M. BallingsD. Van den PoelN. Hespeels and R. Gryp, Evaluating multiple classifiers for stock price direction prediction, Expert Systems with Applications, 42 (2015), 7046-7056.

[3]

M. A. Boyacioglu and D. Avci, An Adaptive network-based fuzzy inference sys- tem (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange, Expert Systems with Applications, 37 (2010), 7908-7912.

[4]

R. Dash and P. Dash, Efficient stock price prediction using a Self Evolving Recurrent Neuro-Fuzzy Inference System optimized through a Modified technique, Expert Systems with Applications, 52 (2016), 75-90.

[5]

I. Dawoud and S. Kaciranlar, An optimal k of kth MA-ARIMA models under a class of ARIMA model, Communications in Statistics-Theory and Methods, 46 (2017), 5754-5765. doi: 10.1080/03610926.2015.1112910.

[6]

I. Dawoud and S. Kaciranlar, An optimal k of kth MA-ARIMA models under AR(p) models, Communications in Statistics-Simulation and Computation, 46 (2017), 2842-2864. doi: 10.1080/03610918.2015.1065325.

[7]

E. J. de FortunyT. De SmedtD. Martens and W. Daelemans, Evaluating and understanding text-based stock price prediction models, Information Processing & Management, 50 (2014), 426-441.

[8]

O. Eksi and B. K. O. Tas, Unconventional monetary policy and the stock market's reaction to Federal Reserve policy actions, North American Journal of Economics and Finance, 40 (2017), 136-147.

[9]

M. H. EsfeM. RejvaniR. Karimpour and A. A. A. Arani, Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT-Al2O3 nanoparticles by correlation and ANN methods using experimental data, Journal of Thermal Analysis and Calorimetry, 128 (2017), 1359-1371.

[10]

P. Ferreira, Portuguese and Brazilian stock market integration: A non-linear and detrended approach, Portuguese Economic Journal, 16 (2017), 49-63.

[11]

P. GharghoriE. D. Maberly and A. Nguyen, Informed trading around stock split announcements: Evidence from the option market, Journal of Financial and Quantitative Analysis, 52 (2017), 705-735.

[12]

M. GockenM. OzcaliciA. Boru and A. T. Dosdogru, Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction, Expert Systems with Applications, 44 (2016), 320-331.

[13]

R. HafeziJ. Shahrabi and E. Hadavandi, A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price, Applied Soft Computing, 29 (2015), 196-210.

[14]

Q. Han and J. F. Liang, Index futures trading restrictions and spot market quality: Evidence from the recent chinese stock market crash, Journal of Futures Markets, 37 (2017), 411-428.

[15]

S. E. HikichiE. G. Salgado and L. A. Beijo, Forecasting number of ISO 14001 certifications in the Americas using ARIMA models, Journal of Cleaner Production, 147 (2017), 242-253.

[16]

S. K. Hur and C. Y. Chung, Revisiting CAPM betas in an incomplete market: Evidence from the Korean stock market, Finance Research Letters, 21 (2017), 241-248.

[17]

C. F. JuangY. Y. Lin and C. C. Tu, A recurrent self-evolving fuzzy neural network with local feedbacks and its application to dynamic system processing, Fuzzy Sets and Systems, 161 (2010), 2552-2568.

[18]

T. LeirvikS. R. Fiskerstrand and A. B. Fjellvikas, Market liquidity and stock returns in the Norwegian stock market, Finance Research Letters, 21 (2017), 272-276.

[19]

X. Z. LengJ. H. WangH. B. JiQ. G. WangH. M. LiX. QianF. Y. Li and M. Yang, Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses, Chemosphere, 180 (2017), 513-522.

[20]

H. J. LiH. Z. AnW. FangY. WangW. Q. Zhong and L. L. Yan, Global energy investment structure from the energy stock market perspective based on a Heterogeneous Complex Network Model, Applied Energy, 194 (2017), 648-657.

[21]

M. S. Mahmud and P. Meesad, An innovative recurrent error-based neuro-fuzzy system with momentum for stock price prediction, Soft Computing, 20 (2016), 4173-4191.

[22]

W. MensiS. Hammoudeh and S. H. Kang, Dynamic linkages between developed and BRICS stock markets: Portfolio risk analysis, Finance Research Letters, 21 (2017), 26-33.

[23]

J. W. M. MwambaS. Hammoudeh and R. Gupta, Financial tail risks in conventional and Islamic stock markets: A comparative analysis, Pacific-Basin Finance Journal, 42 (2017), 60-82.

[24]

M. G. Novak and D. Veluscek, Prediction of stock price movement based on daily high prices, Quantitative Finance, 16 (2016), 793-826.

[25]

N. OliveiraP. Cortez and N. Areal, The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices, Expert Systems with Applications, 73 (2017), 125-144.

[26]

K. Park and H. Shin, Stock price prediction based on a complex interrelation network of economic factors, Engineering Applications of Artificial Intelligence, 26 (2013), 1550-1561.

[27]

J. PeltomakiM. Graham and P. Alagidede, Commodity-driven integration of stock markets in Africa, Applied Economics Letters, 24 (2017), 784-789.

[28]

D. Y. PengF. XinL. X. ZhangZ. P. GaoW. H. ZhangY. X. WangX. D. Chen and Y. Wang, Nonlinear time-series analysis of optical signals to identify multiphase flow behavior in microchannels, Aiche Journal, 63 (2017), 2378-2385.

[29]

A. Pohoata and E. Lungu, A complex analysis employing arima model and statistical methods on air pollutants recorded in ploiesti, romania, Revista De Chimie, 68 (2017), 818-823.

[30]

R. Ruby-FigueroaJ. SaavedraN. Bahamonde and A. Cassano, Permeate flux prediction in the ultrafiltration of fruit juices by ARIMA models, Journal of Membrane Science, 524 (2017), 108-116.

[31]

S. RyuC. L. Lau and B. C. Chun, The impact of Livestock Manure Control Policy on human leptospirosis in Republic of Korea using interrupted time series analysis, Epidemiology and Infection, 145 (2017), 1320-1325.

[32]

J. SchuermansL. DanneelsD. Van TiggelenT. Palmans and E. Witvrouw, Proximal Neuromuscular Control Protects Against Hamstring Injuries in Male Soccer Players: A Prospective Study With Electromyography Time-Series Analysis During Maximal Sprinting, American Journal of Sports Medicine, 45 (2017), 1315-1325.

[33]

G. Sheelapriya and R. Murugesan, Stock price trend prediction using Bayesian regularised radial basis function network model, Spanish Journal of Finance and Accounting-Revista Espanola De Financiacion Y Contabilida, 46 (2017), 189-211.

[34]

A. SiganosE. Vagenas-Nanos and P. Verwijmeren, Divergence of sentiment and stock market trading, Journal of Banking & Finance, 78 (2017), 130-141.

[35]

L. A. Smales, The importance of fear: Investor sentiment and stock market returns, Applied Economics, 49 (2017), 3395-3421.

[36]

B. Q. SunH. F. GuoH. R. KarimiY. J. Ge and S. Xiong, Prediction of stock index futures prices based on fuzzy sets and multivariate fuzzy time series, Neurocomputing, 151 (2015), 1528-1536.

[37]

M. Svoboda, stochastic model of short-term prediction of stock prices and its profitability in the czech stock market, E & M Ekonomie a Management, 19 (2016), 188-200.

[38]

P. Tufekci, Classification-based prediction models for stock price index movement, Intelligent Data Analysis, 20 (2016), 357-376.

[39]

H. G. A. ValeraM. J. Holmes and G. Hassan, Stock market uncertainty and interest rate behaviour: A panel GARCH approach, Applied Economics Letters, 24 (2017), 732-735.

[40]

J. Vanegas and F. Vasquez, Multivariate Adaptive Regression Splines (MARS), an alternative for the analysis of time series, Gaceta Sanitaria, 31 (2017), 235-237.

[41]

Y. WanX. L. Chen and Y. Shi, Adaptive cost dynamic time warping distance in time series analysis for classification, Journal of Computational and Applied Mathematics, 319 (2017), 514-520. doi: 10.1016/j.cam.2017.01.004.

[42]

D. WanX. H. Wei and X. G. Yang, Liquidity dynamics around intraday price jumps in chinese stock market, Journal of Systems Science & Complexity, 30 (2017), 434-463.

[43]

B. WengM. A. Ahmed and F. M. Megahed, Stock market one-day ahead movement prediction using disparate data sources, Expert Systems with Applications, 79 (2017), 153-163.

[44]

C. Z. Yao and Q. W. Lin, The mutual causality analysis between the stock and futures markets, Physica a-Statistical Mechanics and Its Applications, 478 (2017), 188-204. doi: 10.1016/j.physa.2017.02.071.

[45]

N. YokoyaX. X. Zhu and A. Plaza, Multisensor coupled spectral unmixing for time-series analysis, IEEE Transactions on Geoscience and Remote Sensing, 55 (2017), 2842-2857.

[46]

M. H. F. ZarandiM. ZarinbalN. Ghanbari and I. B. Turksen, A new fuzzy functions model tuned by hybridizing imperialist competitive algorithm and simulated annealing Application: Stock price prediction, Information Sciences, 222 (2013), 213-228. doi: 10.1016/j.ins.2012.08.002.

[47]

X. W. ZhangX. L. Zheng and D. D. Zeng, The dynamic interdependence of international financial markets: An empirical study on twenty-seven stock markets, Physica A-Statistical Mechanics and Its Applications, 472 (2017), 32-42.

[48]

J. D. ZhangV. HullZ. Y. OuyangL. HeT. ConnorH. B. YangJ. Y. HuangS. Q. ZhouZ. J. ZhangC. Q. ZhouH. M. Zhang and J. G. Liu, Modeling activity patterns of wildlife using time-series analysis, Ecology and Evolution, 7 (2017), 2575-2584.

[49]

Q. ZhaoY. M. ZhangW. Y. ZhangS. S. LiG. B. ChenY. B. WuC. QiuK. J. YingH. P. TangJ. A. HuangG. WilliamsR. Huxley and Y. M. Guo, Ambient temperature and emergency department visits: Time-series analysis in 12 Chinese cities, Environmental Pollution, 224 (2017), 310-316.

[50]

M. Zolfaghari and B. Sahabi, Impact of foreign exchange rate on oil companies risk in stock market: A Markov-switching approach, Journal of Computational and Applied Mathematics, 317 (2017), 274-289. doi: 10.1016/j.cam.2016.10.012.

[51]

L. ZolotoyJ. R. Frederickson and J. D. Lyon, Aggregate earnings and stock market returns: The good, the bad, and the state-dependent, Journal of Banking & Finance, 77 (2017), 157-175.

[52]

D. ZombreM. De Allegri and V. Ridde, Immediate and sustained effects of user fee exemption on healthcare utilization among children under five in Burkina Faso: A controlled interrupted time-series analysis, Social Science & Medicine, 179 (2017), 27-35.

Figure 1.  An example of the principle of stock price fluctuation prediction
Figure 2.  Internal structure of the ANN model
Figure 3.  Performance evaluation with RMSE
Figure 4.  Performance evaluation with MAPE
Figure 5.  Performance evaluation with MAE
Figure 6.  Predicted value for different stocks
Table 1.  Technical indicators used in stock price prediction.
ID Technical indicator
1 Previous close price
2 Previous highest price
3 Previous lowest price
4 Previous open5 price
5 Five day simple moving average of the close price
6 Ten day simple moving average of the close price
7 Five day exponential moving average of the close price
8 Ten day exponential moving average of the close price
9 Close price moving average convergence
10 Acceleration opening price
11 Acceleration lowest price
12 Acceleration highest price
13 Momentum open price
14 Momentum highest price
15 Momentum lowest price
16 Momentum close price
ID Technical indicator
1 Previous close price
2 Previous highest price
3 Previous lowest price
4 Previous open5 price
5 Five day simple moving average of the close price
6 Ten day simple moving average of the close price
7 Five day exponential moving average of the close price
8 Ten day exponential moving average of the close price
9 Close price moving average convergence
10 Acceleration opening price
11 Acceleration lowest price
12 Acceleration highest price
13 Momentum open price
14 Momentum highest price
15 Momentum lowest price
16 Momentum close price
Table 2.  Detailed information of various datasets
Dataset Duration Total samples Training samples Testing samples
BSE SENSEX 02.7.12-11.7.14 491 327 164
S & P500 02.7.12-06.8.14 512 341 171
Shanghai stock market 11.6.14-16.6.14 632 387 245
Dataset Duration Total samples Training samples Testing samples
BSE SENSEX 02.7.12-11.7.14 491 327 164
S & P500 02.7.12-06.8.14 512 341 171
Shanghai stock market 11.6.14-16.6.14 632 387 245
Table 3.  Performance comparison for all the three dataset
Method RMSE MAPE MAE
SERNFIS 0.0127 0.9365 0.0243
RSEFNN-LF 0.0129 1.5347 0.0396
ANFIS 0.0131 1.3588 0.0324
The proposed method 0.0124 0.8574 0.0219
Method RMSE MAPE MAE
SERNFIS 0.0127 0.9365 0.0243
RSEFNN-LF 0.0129 1.5347 0.0396
ANFIS 0.0131 1.3588 0.0324
The proposed method 0.0124 0.8574 0.0219
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