An Efficient Multi-Objective Optimization-Based Framework for Stock Market Prediction

An Efficient Multi-Objective Optimization-Based Framework for Stock Market Prediction

© 2022 by IJETT Journal
Volume-70 Issue-2
Year of Publication : 2022
Authors : Jyothi R, Dr Krishnamurthy G. N.
DOI :  10.14445/22315381/IJETT-V70I2P235

How to Cite?

Jyothi R, Dr Krishnamurthy G. N., "An Efficient Multi-Objective Optimization-Based Framework for Stock Market Prediction," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 310-318, 2022. Crossref,

Stock prediction is an important parameter for all business applications to improve business deals. To analyze the reason for the available stocks, the stocks should be estimated in the primary section. Several statistical and neural models were implemented to meet these issues, but the data`s complexity can still reduce the prediction outcomes. So, Long Short term memory (LSTM) has been introduced. However, the LSTM model has observed the worst results in some cases by consuming more time and less prediction accuracy. The current work has focused on designing a novel Artificial Bee and Buffalo-based Recurrent LSTM (AB-BRL) for the stock prediction framework to enhance the stock forecasting exactness score up to the desired level. In addition, to analyze the stability of the designed model, different error statistics were measured and compared with other models. Besides, the planned design is executed in the python environment. Finally, the novel AB-BRL has gained a good outcome by reducing the error percentage and maximizing the forecasting accuracy.

Stock Prediction, Mean Square Error, Big Data, Hybrid Optimization, Stock Prediction Accuracy.

[1] Haq, Anwar Ul, et al., Forecasting daily stock trend using multi-filter feature selection and deep learning, Expert Systems with Applications 168 (2021) 114444.
[2] Mondal, Saikat, A. Dutta, and P. Chatterjee. Application of Deep Learning Techniques for Precise Stock Market Prediction." National Conference on Machine Learning and Artificial Intelligence, (2020).
[3] Ye, Zhengxin Joseph, and Björn W. Schuller. Capturing dynamics of post-earnings-announcement drift using a genetic algorithm-optimized XGBoost. Expert Systems with Applications , 177 (2021) 114892.
[4] Samadi, Ali Hussein, SakineOwjimehr, and ZohoorNezhadHalafi., The cross-impact between financial markets, Covid-19 pandemic, and economic sanctions: The case of Iran., Journal of policy modelling 43(1) (2021): 34-55.
[5] Lin, Mei-Chen, and Yu-Ling Lin., Idiosyncratic skewness and cross-section of stock returns: Evidence from Taiwan, International Review of Financial Analysis (2021) 101816.
[6] Dai, Zhifeng, Huan Zhu, and Jie Kang. "New technical indicators and stock return predictability, International Review of Economics & Finance 71 (2021) 127-142.
[7] Dogan, Eyup, Mara Madaleno, and BuketAltinoz. "Revisiting the nexus of financialization and natural resource abundance in resource-rich countries: New empirical evidence from nine indices of financial development, Resources Policy 69 (2020): 101839.
[8] Mensi, Walid, et al., Oil, natural gas and BRICS stock markets: Evidence of systemic risks and co-movements in the time-frequency domain, Resources Policy ., 72 (2021) 102062.
[9] Liu, Hui, and Zhihao Long., An improved deep learning model for predicting stock market price time series., Digital Signal Processing 102 (2020) 102741.
[10] Thakkar, Ankit, and KinjalChaudhari. "Fusion in stock market prediction: a decade survey on the necessity, recent developments, and potential future directions., Information Fusion 65 (2021) 95-107.
[11] Moghar, Adil, and MhamedHamiche. "Stock market prediction using LSTM recurrent neural network., Procedia Computer Science 170 (2020) 1168-1173.
[12] de Pauli, Suellen Teixeira Zavadzki, Mariana Kleina, and Wagner Hugo Bonat.,Comparing artificial neural network architectures for Brazilian stock market prediction.., Annals of Data Science 7(4) (2020) 613-628.
[13] Kumar, Deepak, Pradeepta Kumar Sarangi, and RajitVerma., A systematic review of stock market prediction using machine learning and statistical techniques, Materials Today: Proceedings (2021).
[14] Saura, Jose Ramon., Using data sciences in digital marketing: Framework, methods, and performance metrics,. Journal of Innovation & Knowledge 6(2) (2021) 92-102.
[15] Ren, Qiubing, et al., A novel deep learning prediction model for concrete dam displacements using an interpretable mixed attention mechanism, Advanced Engineering Informatics 50 (2021) 101407.
[16] Salam, Roquia, and Abu Reza MdTowfiqul Islam. Potential of RT, Bagging and RS ensemble learning algorithms for reference evapotranspiration prediction using climatic data-limited humid region in Bangladesh., Journal of Hydrology ., 590 (2020): 125241.
[17] Seong, Byeongchan..,Smoothing and forecasting mixed-frequency time series with vector exponential smoothing models., Economic Modelling, 91 (2020) 463-468.
[18] Zareef, Muhammad, et al., An overview on the applications of typical non-linear algorithms coupled with NIR spectroscopy in food analysis., Food Engineering Reviews., (2020) 1-18.
[19] Alhnaity, Bashar, and MaysamAbbod.., A new hybrid financial time series prediction model., Engineering Applications of Artificial Intelligence., 95 (2020) 103873.
[20] Lee, Si Woon, and Ha Young Kim., Stock market forecasting with super-high dimensional time-series data using ConvLSTM, trend sampling, and specialized data augmentation, Expert Systems with Applications 161 (2020) 113704.
[21] Li, Xiaodong, Pangjing Wu, and WenpengWang, Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong., Information Processing & Management ., 57(5) (2020) 102212.
[22] Jiang, Weiwei., Applications of deep learning in stock market prediction: recent progress., , Expert Systems with Applications., (2021) 115537.
[23] Pang, Xiongwen, et al. An innovative neural network approach for stock market prediction. The Journal of Supercomputing 76(3) (2020) 2098-2118.
[24] Kumar, Krishna, and MdTanwirUddinHaider. Enhanced Prediction of Intra-day Stock Market Using Metaheuristic Optimization on RNN–LSTM Network, New Generation Computing., 39(1) (2021) 231-272.
[25] AbdelKawy, Rasha, Walid M. Abdelmoez, and Amin Shoukry., Asynchronous deep reinforcement learning model for automated multi-stock trading., Progress in Artificial Intelligence ., 10(1) (2021) 83-97.
[26] Rokhsatyazdi, Ehsan, et al. Optimizing LSTM Based Network For Forecasting Stock Market." 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, (2020).
[27] Liu, Hui, and Zhihao Long., An improved deep learning model for predicting stock market price time series., Digital Signal Processing ., 102 (2020) 102741.
[28] Das, SmrutiRekha, Debahuti Mishra, and Minakhi Rout., Stock market prediction using Firefly algorithm with evolutionary framework optimized feature reduction for OSELM method, Expert Systems with Applications: X 4 (2019) 100016.
[29] Wu, Jimmy Ming-Tai, et al., A graph-based CNN-LSTM stock price prediction algorithm with leading indicators, Multimedia Systems., (2021) 1-20.
[30] Chandar, S. Kumar., Grey Wolf Optimization-Elman neural network model for stock price prediction." Soft Computing., 25(1) (2021) 649-658.
[31] Leng, Na, and Jiang-Cheng Li., Forecasting the crude oil prices based on Econophysics and Bayesian approach, Physica A: Statistical Mechanics and its Applications ., 554 (2020) 124663.
[32] Veerasamy, Veerapandiyan, et al. , LSTM recurrent neural network classifier for high impedance fault detection in solar PV integrated power system., IEEE Access., 9 (2021) 32672-32687.
[33] Khan, RahatUllah, Biplab Bhattacharyya, and Gajendra Singh., A novel African buffalo optimization for the minimization of cogging torque in modified permanent magnet DC motor., Sustainable Energy Technologies and Assessments ., 46 (2021) 101240.
[34] Parsajoo, Maryam, DanialJahedArmaghani, and Panagiotis G. Asteris., A precise neuro-fuzzy model enhanced by artificial bee colony techniques for assessment of rock brittleness index., Neural Computing and Applications., (2021) 1-19.