Closing Price Prediction of Nifty Stock Using LSTM with Dense Network

2022 ◽  
pp. 382-392
Author(s):  
Dilip Singh ◽  
Bhupendra Kumar Gupta
Keyword(s):  
Dense Network ◽  
Price Prediction ◽  
Closing Price

TLBO-FLN: Teaching-Learning Based Optimization of Functional Link Neural Networks for Stock Closing Price Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 522-532 ◽  
Author(s):  
Sarat Chandra Nayak ◽  
Subhranginee Das ◽  
Mohammad Dilsad Ansari
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Fine Tuning ◽  
Functional Link ◽  
Price Prediction ◽  
Closing Price ◽  
Teaching Learning Based Optimization ◽  
Artificial Neural ◽  
Teaching Learning

Background and Objective: Stock closing price prediction is enormously complicated. Artificial Neural Networks (ANN) are excellent approximation algorithms applied to this area. Several nature-inspired evolutionary optimization techniques are proposed and used in the literature to search the optimum parameters of ANN based forecasting models. However, most of them need fine-tuning of several control parameters as well as algorithm specific parameters to achieve optimal performance. Improper tuning of such parameters either leads toward additional computational cost or local optima. Methods: Teaching Learning Based Optimization (TLBO) is a newly proposed algorithm which does not necessitate any parameters specific to it. The intrinsic capability of Functional Link Artificial Neural Network (FLANN) to recognize the multifaceted nonlinear relationship present in the historical stock data made it popular and got wide applications in the stock market prediction. This article presents a hybrid model termed as Teaching Learning Based Optimization of Functional Neural Networks (TLBO-FLN) by combining the advantages of both TLBO and FLANN. Results and Conclusion: The model is evaluated by predicting the short, medium, and long-term closing prices of four emerging stock markets. The performance of the TLBO-FLN model is measured through Mean Absolute Percentage of Error (MAPE), Average Relative Variance (ARV), and coefficient of determination (R2); compared with that of few other state-of-the-art models similarly trained and found superior.

Stock closing price prediction based on sentiment analysis and LSTM

2019 ◽  
Vol 32 (13) ◽  
pp. 9713-9729 ◽  
Author(s):  
Zhigang Jin ◽  
Yang Yang ◽  
Yuhong Liu
Keyword(s):  
Sentiment Analysis ◽  
Price Prediction ◽  
Closing Price

scholarly journals Stock Closing Price Prediction using Machine Learning Techniques

2020 ◽  
Vol 167 ◽  
pp. 599-606 ◽  
Author(s):  
Mehar Vijh ◽  
Deeksha Chandola ◽  
Vinay Anand Tikkiwal ◽  
Arun Kumar
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Price Prediction ◽  
Learning Techniques ◽  
Closing Price

scholarly journals Bitcoin Price Prediction using ARIMA Model

2020 ◽  
Author(s):  
Zeba Ayaz ◽  
Jinan Fiaidhi ◽  
Ahmer Sabah ◽  
Mahpara Anwer Ansari
Keyword(s):  
Web Services ◽  
Computer Science ◽  
Historical Data ◽  
Moving Average ◽  
Arima Model ◽  
Price Prediction ◽  
Digital Currency ◽  
The World ◽  
Closing Price ◽  
Primary Focus

Bitcoin is considered to be most valuable and expensive currency in the world. Besides being first decentralized digital currency, its value has also experienced a steep increase, from around 1 dollar in 2010 to around 18000 in 2017. In recent years, it has attracted considerable attention in a diverse set of fields, including economics, finance and computer science. In economics, the primary focus has always been on studying how it affects the market, determining reasons behinds its price fluctuations, and predicting its future prices. In computer science, the focus is on its vulnerabilities, scalability, and other techno-cryptoeconomic issues. Firstly, we are going to collect the historical data of Bitcoin prices over the years 2013 to 2019 and do prediction for the year 2020. We have aimed to justify the usefulness of traditional Autoregressive Integrative Moving Average (ARIMA) model for predicting bitcoin prices. We have predicted the closing price of bitcoin for first seven days of January 2020. Further, we have created web services using ASP.NET to make the predictions on bitcoin price online and lastly, we have plotted the results in a responsive chart using Highcharts.

scholarly journals Bitcoin Price Prediction using ARIMA Model

2020 ◽  
Author(s):  
Zeba Ayaz ◽  
Jinan Fiaidhi ◽  
Ahmer Sabah ◽  
Mahpara Anwer Ansari
Keyword(s):  
Web Services ◽  
Computer Science ◽  
Historical Data ◽  
Moving Average ◽  
Arima Model ◽  
Price Prediction ◽  
Digital Currency ◽  
The World ◽  
Closing Price ◽  
Primary Focus

Bitcoin is considered to be most valuable and expensive currency in the world. Besides being first decentralized digital currency, its value has also experienced a steep increase, from around 1 dollar in 2010 to around 18000 in 2017. In recent years, it has attracted considerable attention in a diverse set of fields, including economics, finance and computer science. In economics, the primary focus has always been on studying how it affects the market, determining reasons behinds its price fluctuations, and predicting its future prices. In computer science, the focus is on its vulnerabilities, scalability, and other techno-cryptoeconomic issues. Firstly, we are going to collect the historical data of Bitcoin prices over the years 2013 to 2019 and do prediction for the year 2020. We have aimed to justify the usefulness of traditional Autoregressive Integrative Moving Average (ARIMA) model for predicting bitcoin prices. We have predicted the closing price of bitcoin for first seven days of January 2020. Further, we have created web services using ASP.NET to make the predictions on bitcoin price online and lastly, we have plotted the results in a responsive chart using Highcharts.

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