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

Mehtab, Sidra; Sen, Jaydip; Dutta, Abhishek

doi:10.1007/978-981-16-0419-5_8

Cited by 85 publications

(50 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the challenging nature of the problems and their impact on real-world applications, several propositions exist in the literature for stock price prediction and robust portfolio design for optimizing returns and risk in a portfolio. The use of predictive models based on learning algorithms and deep neural net architectures for price stock price prediction is quite popular of late [3][4][5][6]. Hybrid models are also showcased that combine learning-based systems with the sentiments in the unstructured data on the web [7][8][9].…”

Section: Related Workmentioning

confidence: 99%

Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Model

Sen¹,

Mehtab²,

Dutta³

et al. 2021

2021 19th OITS International Conference on Information Technology (OCIT)

Self Cite

View full text Add to dashboard Cite

Accurate prediction of future prices of stocks is a difficult task to perform. Even more challenging is to design an optimized portfolio of stocks with the identification of proper weights of allocation to achieve the optimized values of return and risk. We present optimized portfolios based on the seven sectors of the Indian economy. The past prices of the stocks are extracted from the web from January 1, 2016, to December 31, 2020. Optimum portfolios are designed on the selected seven sectors. An LSTM regression model is also designed for predicting future stock prices. Five months after the construction of the portfolios, i.e., on June 1, 2021, the actual and predicted returns and risks of each portfolio are computed. The predicted and the actual returns indicate the very high accuracy of the LSTM model.

show abstract

Section: Related Workmentioning

confidence: 99%

Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Model

Sen¹,

Mehtab²,

Dutta³

et al. 2021

2021 19th OITS International Conference on Information Technology (OCIT)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Reinforcement learning-based models have made the automation of such models a reality. A vast gamut of applications, such as algo trading, capital asset pricing, stock price prediction, portfolio management can be very effectively designed and executed using deep learning and reinforcement learning frameworks [17][18][19][20][21][22][23][24][25][26].…”

Section: Emerging Trends In Modeling Techniquesmentioning

confidence: 99%

Introductory Chapter: Machine Learning in Finance-Emerging Trends and Challenges

Sen¹,

Sen²,

Dutta³

2021

Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

“…Many SPF approaches have been proposed in recent decades, such as traditional time-series analysis and forecasting [3][4][5][6], machine learning [7][8][9][10][11][12], and deep learning [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Designing an accurate SPF system requires considering fundamental issues such as feature selection, model fitting, and prediction.…”

Section: Introductionmentioning

confidence: 99%

“…Since deep learning SPF approaches depend only on the dataset and do not require stochasticity data or financial knowledge, we can build highperformance SPF systems without expert knowledge. Machine learning and deep learning models that have been proposed to improve SPF system performance include artificial neural networks (ANNs) [7,10], convolutional neural networks (CNNs) [13][14][15], and recurrent neural networks (RNNs), such as long short-term memory (LSTM) [21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

“…Another study [20] compared the performance of ARIMA and LSTM models for forecasting time-series data. Meanwhile, one study [21] used LSTM regression models to forecast a stock price dataset from India's NIFTY 50 index; the deep learning-based LSTM model performed better than traditional machine learning approaches. A study [22] that used ARIMA, LSTM, and bidirectional LSTM (BiLSTM) models to forecast financial time-series data found that the BiLSTM model obtained the best results.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stock-Price Forecasting Based on XGBoost and LSTM

Vuong¹,

Dat²,

Khoi³

et al. 2022

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Using time-series data analysis for stock-price forecasting (SPF) is complex and challenging because many factors can influence stock prices (e.g., inflation, seasonality, economic policy, societal behaviors). Such factors can be analyzed over time for SPF. Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches. This study, therefore, proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches. First, we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features. Then, we fed selected features into a deep long short-term memory (LSTM) network to forecast stock prices. The deep LSTM network was used to reflect the temporal nature of the input time series and fully exploit future contextual information. The complex structure enables this network to capture more stochasticity within the stock price. The method does not change when applied to stock data or Forex data. Experimental results based on a Forex dataset covering 2008-2018 showed that our approach outperformed the baseline autoregressive integrated moving average approach with regard to mean absolute error, mean squared error, and root-mean-square error.

show abstract

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

Cited by 85 publications

References 13 publications

Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Model

Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Model

Introductory Chapter: Machine Learning in Finance-Emerging Trends and Challenges

Stock-Price Forecasting Based on XGBoost and LSTM

Contact Info

Product

Resources

About