Technological change in Agriculture: From urban to rural path for Agridrones
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Published
Oct 12, 2021
Pradeep Kumar Tiwari
Sai Santosh Malladi
Abstract
Humanity has always been interested in predicting what lies ahead. Also, when financial benefits are involved the quest becomes quite intense and interesting. One such area is the prediction of the stock market price movements and analysis. In this paper, we present a review of various prediction approaches ranging from Fundamental Analysis to modern Machine Learning and Hybrid models. As this is a very dynamic topic on which research activities are conducted around the globe, it is particularly challenging to classify a technique completely belonging to a certain paradigm. There exists some intersection in the techniques of various paradigms. We consider the broad spectrum of techniques under Traditional and Millennial groups to present the review.Fig.1. Initial Experiments and results: A. experimental setup B. Results.
How to Cite
Tiwari, P. K., & Malladi , S. S. . (2021). Technological change in Agriculture: From urban to rural path for Agridrones. SPAST Abstracts, 1(01). Retrieved from https://spast.org/techrep/article/view/2437
Article Details
Keywords
Stock Market, Machine Learning, Data Mining, Hybrid Models
References
References
[1] Abu-Mostafa, Yaser S., and Amir F. Atiya. “Introduction to financial forecasting.” Applied intelligence 6.3 (1996): 205-213.
[2] Zhong, Xiao, and David Enke. “Forecasting daily stock market return using dimensionality reduction.” Expert Systems with Applications 67 (2017): 126-139.
[3] Hu, Yong, et al. “Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review.” Applied Soft Computing 36 (2015): 534-551.
[4] Imam, Shahed, Richard Barker, and Colin Clubb. “The use of valuation models by UK investment analysts.” European Accounting Review 17.3 (2008): 503-535.
[5] Ben-Horim, Moshe, and Haim Levy. “Total risk, diversifiable risk and nondiversifiable risk: A pedagogic note.” Journal of Financial and Quantitative Analysis 15.2 (1980): 289-297.
[6] Huberman, Gur. “A simple approach to arbitrage pricing theory.” Journal of Economic Theory 28.1 (1982): 183-191.
[7] Fama, Eugene F., and Kenneth R. French. “The capital asset pricing model: Theory and evidence.” Journal of economic perspectives 18.3 (2004): 25-46.
[8] Womack, Kent L., and Ying Zhang. “Understanding risk and return, the CAPM, and the Fama-French three-factor model.” Tuck Case 03-111 (2003).
[9] Carhart, Mark M. “On persistence in mutual fund performance.” The Journal of finance 52.1 (1997): 57-82.
[10] Gordon, Myron J. “Dividends, earnings, and stock prices.” The review of economics and statistics 41.2 (1959): 99-105.
[11] Dutta, Avijan, Gautam Bandopadhyay, and Suchismita Sengupta. “Prediction of stock performance in indian stock market using logistic regression.” International Journal of Business and Information 7.1 (2012).
[12] Malkiel, Burton G. “Efficient market hypothesis.” Finance. Palgrave Macmillan, London, 1989. 127-134.
[13] Mlambo, Chipo, and Nicholas Biekpe. “The efficient market hypothesis: Evidence from ten African stock markets.” Investment Analysts Journal 36.66 (2007): 5-17.
[14] Degutis, Augustas, and Lina Novickytė. “The efficient market hypothesis: A critical review of literature and methodology.” Ekonomika 93 (2014): 7-23.
[15] Ying, Qianwei, et al. “Stock Investment and Excess Returns: A Critical Review in the Light of the Efficient Market Hypothesis.” Journal of Risk and Financial Management 12.2 (2019): 97.
[16] Agrawal, J. G., V. S. Chourasia, and A. K. Mittra. “State-of-the- art in stock prediction techniques.” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 2.4 (2013): 1360-1366.
[17] Boginski, Vladimir, Sergiy Butenko, and Panos M. Pardalos. “Statistical analysis of financial networks.” Computational statistics & data analysis 48.2 (2005): 431-443.
[18] Shah, Dev, Haruna Isah, and Farhana Zulkernine. “Stock Market Analysis: A Review and Taxonomy of Prediction Techniques.” International Journal of Financial Studies 7.2 (2019): 26.
[19] McMillan, David G. “Nonlinear predictability of stock market returns: Evidence from nonparametric and threshold models.” International Review of Economics & Finance 10.4 (2001): 353-368.
[20] Rojas, Ignacio, et al. “Soft-computing techniques and ARMA model for time series prediction.” Neurocomputing 71.4-6 (2008): 519-537.
[21] Ariyo, Adebiyi A., Adewumi O. Adewumi, and Charles K. Ayo. “Stock price prediction using the ARIMA model.” 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation. IEEE, 2014.
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[28] Bollerslev, Tim. “Generalized autoregressive conditional heteroskedasticity.” Journal of econometrics 31.3 (1986): 307-327.
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[31] Nelson, Daniel B. “Conditional heteroskedasticity in asset returns: A new approach.” Econometrica: Journal of the Econometric Society (1991): 347-370.
[32] Ding, Zhuanxin, Clive WJ Granger, and Robert F. Engle. “A long memory property of stock market returns and a new model.” Essays in econometrics: collected papers of Clive WJ Granger. 2001. 349-372.
[33] Alberg, Dima, Haim Shalit, and Rami Yosef. “Estimating stock market volatility using asymmetric GARCH models.” Applied Financial Economics 18.15 (2008): 1201-1208.
[34] Chong, Choo Wei, Muhammad Idrees Ahmad, and Mat Yusoff Abdullah. “Performance of GARCH models in forecasting stock market volatility.” Journal of forecasting 18.5 (1999): 333-343.
[35] González-Rivera, Gloria. “Smooth-transition GARCH models.” Studies in Nonlinear Dynamics & Econometrics 3.2 (1998).
[36] Ostertagová, Eva, and Oskar Ostertag. “The simple exponential smoothing model.” The 4th International Conference on Modelling of Mechanical and Mechatronic Systems, Technical University of Košice, Slovak Republic, Proceedings of conference. 2011.
[37] De Faria, E. L., et al. “Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods.” Expert Systems with Applications 36.10 (2009): 12506-12509.
[38] Lai, Kin Keung, et al. “Hybridizing exponential smoothing and neural network for financial time series predication.” International Conference on Computational Science. Springer, Berlin, Heidelberg, 2006.
[39] Maia, André Luis Santiago, and Francisco de AT de Carvalho. “Holt’s exponential smoothing and neural network models for forecasting interval-valued time series.” International Journal of Forecasting 27.3 (2011): 740-759.
[40] Bonga-Bonga, Lumengo, and Michael Makakabule. “Modeling stock returns in the South African stock exchange: A nonlinear approach.” European Journal of Economics, Finance and Administrative Sciences 19 (2010): 168-177.
[41] Bhuriya, Dinesh, et al. “Stock market predication using a linear regression.” 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA). Vol. 2. IEEE, 2017.
[42] Dutta, Avijan, Gautam Bandopadhyay, and Suchismita Sengupta. “Prediction of stock performance in indian stock market using logistic regression.” International Journal of Business and Information 7.1 (2012).
[43] Fu, Tak-chung, et al. “Pattern discovery from stock time series using self-organizing maps.” Workshop Notes of KDD2001 Workshop on Temporal Data Mining. 2001.
[44] Phetchanchai, Chawalsak, et al. “Index financial time series based on zigzag-perceptually important points.” Journal of Computer Science. 2010.
[45] Ge, Xianping. “Pattern matching in financial time series data.” final project report for ICS 278 (1998).
[46] Kim, Sang Hyuk, et al. “Pattern matching trading system based on the dynamic time warping algorithm.” Sustainability 10.12 (2018): 4641.
[47] Wang, Jar-Long, and Shu-Hui Chan. “Stock market trading rule discovery using pattern recognition and technical analysis.” Expert Systems with Applications 33.2 (2007): 304-315.
[48] Arévalo, Rubén, et al. “A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting.” Expert Systems with Applications 81 (2017): 177-192.
[49] Leigh, William, Russell Purvis, and James M. Ragusa. “Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support.” Decision support systems 32.4 (2002): 361-377.
[50] Guo, Xinyu, Xun Liang, and Xiang Li. “A stock pattern recognition algorithm based on neural networks.” Third International Conference on Natural Computation (ICNC 2007). Vol. 2. IEEE, 2007.
[51] Chen, Tai-liang, and Feng-yu Chen. “An intelligent pattern recognition model for supporting investment decisions in stock market.” Information Sciences 346 (2016): 261-274.
[52] Fu, Tak-chung, et al. “Stock time series pattern matching: Template-based vs. rule-based approaches.” Engineering Applications of Artificial Intelligence 20.3 (2007): 347-364.
[53] Huang, Wei, Yoshiteru Nakamori, and Shou-Yang Wang. “Forecasting stock market movement direction with support vector machine.” Computers & operations research 32.10 (2005): 2513-2522.
[54] Fenghua, W. E. N., et al. “Stock price prediction based on SSA and SVM.” Procedia Computer Science 31 (2014): 625-631.
[55] Chiu, Deng-Yiv, and Ping-Jie Chen. “Dynamically exploring internal mechanism of stock market by fuzzy-based support vector machines with high dimension input space and genetic algorithm.” Expert Systems with Applications 36.2 (2009): 1240-1248.
[56] Yu, Huanhuan, Rongda Chen, and Guoping Zhang. “A SVM stock selection model within PCA.” Procedia computer science 31 (2014): 406-412.
[57] Hegazy, Osman, Omar S. Soliman, and Mustafa Abdul Salam. “A machine learning model for stock market prediction.” arXiv preprint arXiv:1402.7351 (2014).
[58] Dey, Shubharthi, et al. “Forecasting to Classification: Predicting the direction of stock market price using Xtreme Gradient Boosting.” PESIT, Bengaluru, India, Working Paper (2016).
[59] Nobre, João, and Rui Ferreira Neves. “Combining principal component analysis, discrete wavelet transform and XGBoost to trade in the financial markets.” Expert Systems with Applications 125 (2019): 181-194.
[60] Zhang, Xiaoyun, and Wanyi Chen. “Stock Selection Based on Extreme Gradient Boosting.” 2019 Chinese Control Conference (CCC). IEEE, 2019.
[61] Roondiwala, Murtaza, Harshal Patel, and Shraddha Varma. “Predicting stock prices using LSTM.” International Journal of Science and Research (IJSR) 6.4 (2017): 1754-1756.
[62] Chen, Kai, Yi Zhou, and Fangyan Dai. “A LSTM-based method for stock returns prediction: A case study of China stock market.” 2015 IEEE international conference on big data (big data). IEEE, 2015.
[63] Samarawickrama, A. J. P., and T. G. I. Fernando. “A recurrent neural network approach in predicting daily stock prices an application to the Sri Lankan stock market.” 2017 IEEE International Conference on Industrial and Information Systems (ICIIS). IEEE, 2017.
[64] Soni, Sneha, and Shailendra Shrivastava. “Classification of Indian stock market data using machine learning algorithms.” Int J Comput Sci Eng 2.9 (2010): 2942-2946.
[65] Kumar, Indu, et al. “A comparative study of supervised machine learning algorithms for stock market trend prediction.” 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). IEEE, 2018.
[66] Nanda, S. R., Biswajit Mahanty, and M. K. Tiwari. “Clustering Indian stock market data for portfolio management.” Expert Systems with Applications 37.12 (2010): 8793-8798.
[67] Powell, Nicole, Simon Y. Foo, and Mark Weatherspoon. “Supervised and unsupervised methods for stock trend forecasting.” 2008 40th Southeastern Symposium on System Theory (SSST). IEEE, 2008.
[68] Wu, Kuo-Ping, Yung-Piao Wu, and Hahn-Ming Lee. “Stock Trend Prediction by Using K-Means and AprioriAll Algorithm for Sequential Chart Pattern Mining.” J. Inf. Sci. Eng. 30.3 (2014): 669-686.
[69] Suganthi, R., and P. Kamalakannan. “Analyzing stock market data using clustering algorithm.” International Journal of Future Computer and Communication 4.2 (2015): 108.
[70] Ma, Yibu. “The Research of Stock Predictive Model based on the Combination of CART and DBSCAN.” 2013 Ninth International Conference on Computational Intelligence and Security. IEEE, 2013.
[71] Lee, Anthony JT, et al. “An Effective Clustering Approach to Stock Market Prediction.” PACIS. 2010.
[72] Alessia, D., et al. “Approaches, tools and applications for sentiment analysis implementation.” International Journal of Computer Applications 125.3 (2015).
[73] Bollen, Johan, Huina Mao, and Xiaojun Zeng. “Twitter mood predicts the stock market.” Journal of computational science 2.1 (2011): 1-8.
[74] Mittal, Anshul, and Arpit Goel. “Stock prediction using twitter sentiment analysis.” Standford University, CS229 (2011 http:// cs229. stanford. edu/proj2011/Goel Mittal Stock Market Prediction Using Twitter Sentiment Analysis. pdf) 15 (2012).
[75] Kalyanaraman, Vaanchitha, et al. “Sentiment analysis on news articles for stocks.” 2014 8th Asia Modelling Symposium. IEEE, 2014.
[76] Im, Tan Li, et al. “Analysing market sentiment in financial news using lexical approach.” 2013 IEEE Conference on Open Systems (ICOS). IEEE, 2013.
[77] Lee, Heeyoung, et al. “On the Importance of Text Analysis for Stock Price Prediction.” LREC. Vol. 2014. 2014.
[1] Abu-Mostafa, Yaser S., and Amir F. Atiya. “Introduction to financial forecasting.” Applied intelligence 6.3 (1996): 205-213.
[2] Zhong, Xiao, and David Enke. “Forecasting daily stock market return using dimensionality reduction.” Expert Systems with Applications 67 (2017): 126-139.
[3] Hu, Yong, et al. “Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review.” Applied Soft Computing 36 (2015): 534-551.
[4] Imam, Shahed, Richard Barker, and Colin Clubb. “The use of valuation models by UK investment analysts.” European Accounting Review 17.3 (2008): 503-535.
[5] Ben-Horim, Moshe, and Haim Levy. “Total risk, diversifiable risk and nondiversifiable risk: A pedagogic note.” Journal of Financial and Quantitative Analysis 15.2 (1980): 289-297.
[6] Huberman, Gur. “A simple approach to arbitrage pricing theory.” Journal of Economic Theory 28.1 (1982): 183-191.
[7] Fama, Eugene F., and Kenneth R. French. “The capital asset pricing model: Theory and evidence.” Journal of economic perspectives 18.3 (2004): 25-46.
[8] Womack, Kent L., and Ying Zhang. “Understanding risk and return, the CAPM, and the Fama-French three-factor model.” Tuck Case 03-111 (2003).
[9] Carhart, Mark M. “On persistence in mutual fund performance.” The Journal of finance 52.1 (1997): 57-82.
[10] Gordon, Myron J. “Dividends, earnings, and stock prices.” The review of economics and statistics 41.2 (1959): 99-105.
[11] Dutta, Avijan, Gautam Bandopadhyay, and Suchismita Sengupta. “Prediction of stock performance in indian stock market using logistic regression.” International Journal of Business and Information 7.1 (2012).
[12] Malkiel, Burton G. “Efficient market hypothesis.” Finance. Palgrave Macmillan, London, 1989. 127-134.
[13] Mlambo, Chipo, and Nicholas Biekpe. “The efficient market hypothesis: Evidence from ten African stock markets.” Investment Analysts Journal 36.66 (2007): 5-17.
[14] Degutis, Augustas, and Lina Novickytė. “The efficient market hypothesis: A critical review of literature and methodology.” Ekonomika 93 (2014): 7-23.
[15] Ying, Qianwei, et al. “Stock Investment and Excess Returns: A Critical Review in the Light of the Efficient Market Hypothesis.” Journal of Risk and Financial Management 12.2 (2019): 97.
[16] Agrawal, J. G., V. S. Chourasia, and A. K. Mittra. “State-of-the- art in stock prediction techniques.” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 2.4 (2013): 1360-1366.
[17] Boginski, Vladimir, Sergiy Butenko, and Panos M. Pardalos. “Statistical analysis of financial networks.” Computational statistics & data analysis 48.2 (2005): 431-443.
[18] Shah, Dev, Haruna Isah, and Farhana Zulkernine. “Stock Market Analysis: A Review and Taxonomy of Prediction Techniques.” International Journal of Financial Studies 7.2 (2019): 26.
[19] McMillan, David G. “Nonlinear predictability of stock market returns: Evidence from nonparametric and threshold models.” International Review of Economics & Finance 10.4 (2001): 353-368.
[20] Rojas, Ignacio, et al. “Soft-computing techniques and ARMA model for time series prediction.” Neurocomputing 71.4-6 (2008): 519-537.
[21] Ariyo, Adebiyi A., Adewumi O. Adewumi, and Charles K. Ayo. “Stock price prediction using the ARIMA model.” 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation. IEEE, 2014.
[22] Banerjee, Debadrita. “Forecasting of Indian stock market using time-series ARIMA model.” 2014 2nd International Conference on Business and Information Management (ICBIM). IEEE, 2014.
[23] Adebiyi, Ayodele Ariyo, Aderemi Oluyinka Adewumi, and Charles Korede Ayo. “Comparison of ARIMA and artificial neural networks models for stock price prediction.” Journal of Applied Mathematics 2014 (2014).
[24] Merh, Nitin, Vinod P. Saxena, and Kamal Raj Pardasani. “A comparison between hybrid approaches of ANN and ARIMA for Indian stock trend forecasting.” Business Intelligence Journal 3.2 (2010): 23-43.
[25] Devi, B. Uma, D. Sundar, and P. Alli. “An effective time series analysis for stock trend prediction using ARIMA model for nifty midcap-50.” International Journal of Data Mining & Knowledge Management Process 3.1 (2013): 65.
[26] Mondal, Prapanna, Labani Shit, and Saptarsi Goswami. “Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices.” International Journal of Computer Science, Engineering and Applications 4.2 (2014): 13.
[27] Pai, Ping-Feng, and Chih-Sheng Lin. “A hybrid ARIMA and support vector machines model in stock price forecasting.” Omega 33.6 (2005): 497-505.
[28] Bollerslev, Tim. “Generalized autoregressive conditional heteroskedasticity.” Journal of econometrics 31.3 (1986): 307-327.
[29] Glosten, Lawrence R., Ravi Jagannathan, and David E. Runkle. “On the relation between the expected value and the volatility of the nominal excess return on stocks.” The journal of finance 48.5 (1993): 1779-1801.
[30] Franses, Philip Hans, and Dick Van Dijk. “Forecasting stock market volatility using (non‐linear) Garch models.” Journal of Forecasting 15.3 (1996): 229-235.
[31] Nelson, Daniel B. “Conditional heteroskedasticity in asset returns: A new approach.” Econometrica: Journal of the Econometric Society (1991): 347-370.
[32] Ding, Zhuanxin, Clive WJ Granger, and Robert F. Engle. “A long memory property of stock market returns and a new model.” Essays in econometrics: collected papers of Clive WJ Granger. 2001. 349-372.
[33] Alberg, Dima, Haim Shalit, and Rami Yosef. “Estimating stock market volatility using asymmetric GARCH models.” Applied Financial Economics 18.15 (2008): 1201-1208.
[34] Chong, Choo Wei, Muhammad Idrees Ahmad, and Mat Yusoff Abdullah. “Performance of GARCH models in forecasting stock market volatility.” Journal of forecasting 18.5 (1999): 333-343.
[35] González-Rivera, Gloria. “Smooth-transition GARCH models.” Studies in Nonlinear Dynamics & Econometrics 3.2 (1998).
[36] Ostertagová, Eva, and Oskar Ostertag. “The simple exponential smoothing model.” The 4th International Conference on Modelling of Mechanical and Mechatronic Systems, Technical University of Košice, Slovak Republic, Proceedings of conference. 2011.
[37] De Faria, E. L., et al. “Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods.” Expert Systems with Applications 36.10 (2009): 12506-12509.
[38] Lai, Kin Keung, et al. “Hybridizing exponential smoothing and neural network for financial time series predication.” International Conference on Computational Science. Springer, Berlin, Heidelberg, 2006.
[39] Maia, André Luis Santiago, and Francisco de AT de Carvalho. “Holt’s exponential smoothing and neural network models for forecasting interval-valued time series.” International Journal of Forecasting 27.3 (2011): 740-759.
[40] Bonga-Bonga, Lumengo, and Michael Makakabule. “Modeling stock returns in the South African stock exchange: A nonlinear approach.” European Journal of Economics, Finance and Administrative Sciences 19 (2010): 168-177.
[41] Bhuriya, Dinesh, et al. “Stock market predication using a linear regression.” 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA). Vol. 2. IEEE, 2017.
[42] Dutta, Avijan, Gautam Bandopadhyay, and Suchismita Sengupta. “Prediction of stock performance in indian stock market using logistic regression.” International Journal of Business and Information 7.1 (2012).
[43] Fu, Tak-chung, et al. “Pattern discovery from stock time series using self-organizing maps.” Workshop Notes of KDD2001 Workshop on Temporal Data Mining. 2001.
[44] Phetchanchai, Chawalsak, et al. “Index financial time series based on zigzag-perceptually important points.” Journal of Computer Science. 2010.
[45] Ge, Xianping. “Pattern matching in financial time series data.” final project report for ICS 278 (1998).
[46] Kim, Sang Hyuk, et al. “Pattern matching trading system based on the dynamic time warping algorithm.” Sustainability 10.12 (2018): 4641.
[47] Wang, Jar-Long, and Shu-Hui Chan. “Stock market trading rule discovery using pattern recognition and technical analysis.” Expert Systems with Applications 33.2 (2007): 304-315.
[48] Arévalo, Rubén, et al. “A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting.” Expert Systems with Applications 81 (2017): 177-192.
[49] Leigh, William, Russell Purvis, and James M. Ragusa. “Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support.” Decision support systems 32.4 (2002): 361-377.
[50] Guo, Xinyu, Xun Liang, and Xiang Li. “A stock pattern recognition algorithm based on neural networks.” Third International Conference on Natural Computation (ICNC 2007). Vol. 2. IEEE, 2007.
[51] Chen, Tai-liang, and Feng-yu Chen. “An intelligent pattern recognition model for supporting investment decisions in stock market.” Information Sciences 346 (2016): 261-274.
[52] Fu, Tak-chung, et al. “Stock time series pattern matching: Template-based vs. rule-based approaches.” Engineering Applications of Artificial Intelligence 20.3 (2007): 347-364.
[53] Huang, Wei, Yoshiteru Nakamori, and Shou-Yang Wang. “Forecasting stock market movement direction with support vector machine.” Computers & operations research 32.10 (2005): 2513-2522.
[54] Fenghua, W. E. N., et al. “Stock price prediction based on SSA and SVM.” Procedia Computer Science 31 (2014): 625-631.
[55] Chiu, Deng-Yiv, and Ping-Jie Chen. “Dynamically exploring internal mechanism of stock market by fuzzy-based support vector machines with high dimension input space and genetic algorithm.” Expert Systems with Applications 36.2 (2009): 1240-1248.
[56] Yu, Huanhuan, Rongda Chen, and Guoping Zhang. “A SVM stock selection model within PCA.” Procedia computer science 31 (2014): 406-412.
[57] Hegazy, Osman, Omar S. Soliman, and Mustafa Abdul Salam. “A machine learning model for stock market prediction.” arXiv preprint arXiv:1402.7351 (2014).
[58] Dey, Shubharthi, et al. “Forecasting to Classification: Predicting the direction of stock market price using Xtreme Gradient Boosting.” PESIT, Bengaluru, India, Working Paper (2016).
[59] Nobre, João, and Rui Ferreira Neves. “Combining principal component analysis, discrete wavelet transform and XGBoost to trade in the financial markets.” Expert Systems with Applications 125 (2019): 181-194.
[60] Zhang, Xiaoyun, and Wanyi Chen. “Stock Selection Based on Extreme Gradient Boosting.” 2019 Chinese Control Conference (CCC). IEEE, 2019.
[61] Roondiwala, Murtaza, Harshal Patel, and Shraddha Varma. “Predicting stock prices using LSTM.” International Journal of Science and Research (IJSR) 6.4 (2017): 1754-1756.
[62] Chen, Kai, Yi Zhou, and Fangyan Dai. “A LSTM-based method for stock returns prediction: A case study of China stock market.” 2015 IEEE international conference on big data (big data). IEEE, 2015.
[63] Samarawickrama, A. J. P., and T. G. I. Fernando. “A recurrent neural network approach in predicting daily stock prices an application to the Sri Lankan stock market.” 2017 IEEE International Conference on Industrial and Information Systems (ICIIS). IEEE, 2017.
[64] Soni, Sneha, and Shailendra Shrivastava. “Classification of Indian stock market data using machine learning algorithms.” Int J Comput Sci Eng 2.9 (2010): 2942-2946.
[65] Kumar, Indu, et al. “A comparative study of supervised machine learning algorithms for stock market trend prediction.” 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). IEEE, 2018.
[66] Nanda, S. R., Biswajit Mahanty, and M. K. Tiwari. “Clustering Indian stock market data for portfolio management.” Expert Systems with Applications 37.12 (2010): 8793-8798.
[67] Powell, Nicole, Simon Y. Foo, and Mark Weatherspoon. “Supervised and unsupervised methods for stock trend forecasting.” 2008 40th Southeastern Symposium on System Theory (SSST). IEEE, 2008.
[68] Wu, Kuo-Ping, Yung-Piao Wu, and Hahn-Ming Lee. “Stock Trend Prediction by Using K-Means and AprioriAll Algorithm for Sequential Chart Pattern Mining.” J. Inf. Sci. Eng. 30.3 (2014): 669-686.
[69] Suganthi, R., and P. Kamalakannan. “Analyzing stock market data using clustering algorithm.” International Journal of Future Computer and Communication 4.2 (2015): 108.
[70] Ma, Yibu. “The Research of Stock Predictive Model based on the Combination of CART and DBSCAN.” 2013 Ninth International Conference on Computational Intelligence and Security. IEEE, 2013.
[71] Lee, Anthony JT, et al. “An Effective Clustering Approach to Stock Market Prediction.” PACIS. 2010.
[72] Alessia, D., et al. “Approaches, tools and applications for sentiment analysis implementation.” International Journal of Computer Applications 125.3 (2015).
[73] Bollen, Johan, Huina Mao, and Xiaojun Zeng. “Twitter mood predicts the stock market.” Journal of computational science 2.1 (2011): 1-8.
[74] Mittal, Anshul, and Arpit Goel. “Stock prediction using twitter sentiment analysis.” Standford University, CS229 (2011 http:// cs229. stanford. edu/proj2011/Goel Mittal Stock Market Prediction Using Twitter Sentiment Analysis. pdf) 15 (2012).
[75] Kalyanaraman, Vaanchitha, et al. “Sentiment analysis on news articles for stocks.” 2014 8th Asia Modelling Symposium. IEEE, 2014.
[76] Im, Tan Li, et al. “Analysing market sentiment in financial news using lexical approach.” 2013 IEEE Conference on Open Systems (ICOS). IEEE, 2013.
[77] Lee, Heeyoung, et al. “On the Importance of Text Analysis for Stock Price Prediction.” LREC. Vol. 2014. 2014.
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GE3- Computers & Information Technology