Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models

Mehtab, Sidra; Sen, Jaydip; Dutta, Abhishek

doi:10.36227/techrxiv.15103602.v1

Cited by 7 publications

(10 citation statements)

References 12 publications

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“…Their comparison between the percentage of accuracy of both ARIMA and LSTM used on series data manifested that LSTM outperforms ARIMA. In [4], around ten classification models for predicting short-term stock price movement are set into place. Among these models including logistic regression, LSTM, Decision Tree, Random Forest, ANN, and Support Vector Machines (SVM) classifications, ANN generated the highest level of accuracy on average, and the most considerable prediction on short-term stock price movement is provided by LSTM.…”

Section: Related Researchmentioning

confidence: 99%

Stock Price Prediction Using Machine Learning Strategies

Chen¹

2023

BCPBM

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Being able to foresee the potential opportunities or crisis in stock market has always been desirable among investors. Especially during the Covid-19 global pandemic, the skill of risk management is of great importance to sustain in such an unstable environment. Apart from various kinds of strategies in traditional business analysis, a robust intelligent system that can correctly predict stock price is desired to determine investment strategies. At present, much related research involve with predicting the stock price trend, and most of them use deep learning methods. Although these research managed to achieve an ideal result of their tasks, seldom surveys focus on the summary of deep learning methods employed in stock price prediction. As a result, the aim of this paper is to summarize the machine learning methods used in forecasting stock price, the development context of the task, and, finally, analyze the development trend of the task based on previous published papers. These papers were classified by deep learning methods, which included Long Short-Term Memory (LSTM); Gated Recurrent Units (GRU); Recurrent Neural Network (RNN); and other hybrid deep learning methods. Furthermore, this paper identifies some of the dataset, variable, model, and results of each article. The survey adopted presents the results through the most used performance metrics: Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and Mean Square Error (MSE).

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Section: Related Researchmentioning

confidence: 99%

Stock Price Prediction Using Machine Learning Strategies

Chen¹

2023

BCPBM

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“…Numerous approaches have been proposed to solve this complex problem involving robust stock price prediction and the formation of the optimized combination of stocks to maximize the return on investment. Machine learning models have been extensively used by researchers in predicting future stock prices (Carta et al, 2021;Chatterjee et al, 2021;Mehtab & Sen, 2020a;Mehtab & Sen, 2019;Sarmento & Horta, 2020;Sen, J., 2018a;Sen & Datta Chaudhuri, 2017a). The prediction accuracies of the models are found to have been improved by the use of deep learning architectures and algorithms (Chatterjee et al, 2021;Chen et al, 2018;Chong et al, 2017;Mehtab & Sen, 2020a;Mehtab & Sen, 2020b;Mehtab & Sen, 2019;Sen, 2018a;Sen et al, 2021i;Sen et al, 2020;Sen & Mehtab, 2022b;Thormann et al, 2021;Tran et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

“…Machine learning models have been extensively used by researchers in predicting future stock prices (Carta et al, 2021;Chatterjee et al, 2021;Mehtab & Sen, 2020a;Mehtab & Sen, 2019;Sarmento & Horta, 2020;Sen, J., 2018a;Sen & Datta Chaudhuri, 2017a). The prediction accuracies of the models are found to have been improved by the use of deep learning architectures and algorithms (Chatterjee et al, 2021;Chen et al, 2018;Chong et al, 2017;Mehtab & Sen, 2020a;Mehtab & Sen, 2020b;Mehtab & Sen, 2019;Sen, 2018a;Sen et al, 2021i;Sen et al, 2020;Sen & Mehtab, 2022b;Thormann et al, 2021;Tran et al, 2019). Several approaches to text mining have been effectively applied on social media and the web to improve prediction accuracies even further (Li & Pan, 2022;Mehtab & Sen, 2019;Thormann et al, 2021;Zhang et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

“…Designing and optimizing portfolios poses a complex challenge, and researchers have put forth various solutions and methods to address it. Machine learning models have played a significant role in the efforts of researchers to forecast future stock prices Mehtab & Sen, 2020a;Mehtab & Sen, 2019;Sen, 2018a;Sen & Datta Chaudhuri, 2017a). The utilization of deep learning architectures and algorithms has led to enhancements in the predictive accuracy of these models Mehtab & Sen, 2020a;Mehtab & Sen, 2020b;Mehtab & Sen, 2019;Sen, 2018a;Sen et al, 2021i;Sen et al, 2020;Sen & Mehtab, 2022b;Mehtab & Sen, 2019).…”

Section: Related Workmentioning

confidence: 99%

“…Machine learning models have played a significant role in the efforts of researchers to forecast future stock prices Mehtab & Sen, 2020a;Mehtab & Sen, 2019;Sen, 2018a;Sen & Datta Chaudhuri, 2017a). The utilization of deep learning architectures and algorithms has led to enhancements in the predictive accuracy of these models Mehtab & Sen, 2020a;Mehtab & Sen, 2020b;Mehtab & Sen, 2019;Sen, 2018a;Sen et al, 2021i;Sen et al, 2020;Sen & Mehtab, 2022b;Mehtab & Sen, 2019). In the realm of stock price prediction and portfolio design, time series decompositionbased statistical and econometric methods enjoy significant popularity as well (Sen, 2022a;Sen, 2018b;Sen, 2017;Sen & Datta Chaudhuri, 2018;Sen & Datta Chaudhuri, 2017b;Sen & Datta Chaudhuri, 2016a;Sen & Datta Chaudhuri, 2016b;Sen & Datta Chaudhuri, 2016c;Sen & Datta Chaudhuri, 2016d;Sen & Datta Chaudhuri, 2015).…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Portfolio Optimization for the Indian Stock Market

Sen¹,

Dutta²

2022

Encyclopedia of Data Science and Machine Learning

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Portfolio optimization has been an area of research that has attracted a lot of attention from researchers and financial analysts. Designing an optimum portfolio is a complex task since it not only involves accurate forecasting of future stock returns and risks but also needs to optimize them. This article presents a systematic approach to portfolio design using two risk optimization approaches, the hierarchical risk parity algorithm and the hierarchical equal risk contribution algorithm on seven critical sectors and the NIFTY 50 stocks listed in the National Stock Exchange of India of the Indian stock market. The portfolios are built following the two approaches on historical stock prices from Jan 1, 2016 to Dec 31, 2020. The portfolio performances are evaluated on the test data from Jan 1, 2021 to Nov 1, 2021. The backtesting results of the portfolios indicate that the performance of the HRP portfolio is superior to that of the HERC portfolio on both training and the test data for the majority of the sectors studied in this work.

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Forecasting variance of NiftyIT index with RNN and DNN

Karthik

Raghunandan

Rao

et al. 2022

J. Phys.: Conf. Ser.

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A time series is an order of observations engaged serially in time. The prime objective of time series analysis is to build mathematical models that provide reasonable descriptions from training data. The goal of time series analysis is to forecast the forthcoming values of a series based on the history of the same series. Forecasting of stock markets is a thought-provoking problem because of the number of possible variables as well as volatile noise that may contribute to the prices of the stock. However, the capability to analyze stock market leanings could be vital to investors, traders and researchers, hence has been of continued interest. Plentiful arithmetical and machine learning practices have been discovered for stock analysis and forecasting/prediction. In this paper, we perform a comparative study on two very capable artificial neural network models i) Deep Neural Network (DNN) and ii) Long Short-Term Memory (LSTM) a type of recurrent neural network (RNN) in predicting the daily variance of NIFTYIT in BSE (Bombay Stock Exchange) and NSE (National Stock Exchange) markets. DNN was chosen due to its capability to handle complex data with substantial performance and better generalization without being saturated. LSTM model was decided, as it contains intermediary memory which can hold the historic patterns and occurrence of the next prediction depends on the values that preceded it. With both networks, measures were taken to reduce overfitting. Daily predictions of the NIFTYIT index were made to test the generalizability of the models. Both networks performed well at making daily predictions, and both generalized admirably to make daily predictions of the NiftyIT data. The LSTM-RNN outpaced the DNN in terms of forecasting and thus, grips more potential for making longer-term estimates.

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Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models

Cited by 7 publications

References 12 publications

Stock Price Prediction Using Machine Learning Strategies

Stock Price Prediction Using Machine Learning Strategies

Portfolio Optimization for the Indian Stock Market

Forecasting variance of NiftyIT index with RNN and DNN

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