PREDICTING THE CLOSING PRICE OF CRYPTOCURRENCY USING HYBRID ARIMA, REGRESSION AND MACHINE LEARNING

2021 ◽  
Author(s):  
Nguyen Dinh Thuan ◽  
Nguyen Minh Nhut ◽  
Hoang Tung ◽  
Vu Minh Sang
Keyword(s):  
Machine Learning ◽  
Closing Price

Machine Learning Models for Forecasting of Individual Stocks Price Patterns

2018 ◽  
pp. 111-129 ◽  
Author(s):  
Dilip Singh Sisodia ◽  
Sagar Jadhav
Keyword(s):  
Machine Learning ◽  
Time Window ◽  
Stock Exchange ◽  
Back Propagation ◽  
Future Price ◽  
Support Vector ◽  
Learning Models ◽  
Closing Price ◽  
Feature Values ◽  
Machine Learning Models

Stock investors always consider potential future prices before investing in any stock for making a profit. A large number of studies are found on the prediction of stock market indices. However, the focus on individual stock closing price predictions well ahead of time is limited. In this chapter, a comparative study of machine-learning-based models is used for the prediction of the closing price of a particular stock. The proposed models are designed using back propagation neural networks (BPNN), support vector regression (SVR) with SMOReg, and linear regression (LR) for the prediction of the closing price of individual stocks. A total of 37 technical indicators (features) derived from historical closing prices of stocks are considered for predicting the future price of stock in a time window of five days. The experiment is performed on stocks listed on Bombay Stock Exchange (BSS), India. The model is trained and tested using feature values extracted from the past five-year closing price of stocks of different sectors including aviation, pharma, banking, entertainment, and IT.

scholarly journals Development of a Stock Price Prediction Framework for Intelligent Media and Technical Analysis

2022 ◽  
Vol 12 (2) ◽  
pp. 719
Author(s):  
Sibusiso T. Mndawe ◽  
Babu Sena Paul ◽  
Wesley Doorsamy
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Technical Analysis ◽  
Memory Model ◽  
Short Term ◽  
Term Memory ◽  
Linear Discriminant ◽  
Stock Price Prediction ◽  
Closing Price ◽  
Long Short Term Memory

Equity traders are always looking for tools that will help them maximise returns and minimise risk, be it fundamental or technical analysis techniques. This research integrates tools used by equity traders and uses them together with machine learning and deep learning techniques. The presented work introduces a South African-based sentiment classifier to extract sentiment from new headlines and tweets. The experimental work uses four machine learning models for fundamental analysis and six long short-term memory model architectures, including a developed encoder-decoder long short-term memory model for technical analysis. Data used in the experiments is mined and collected from news sites, tweets from Twitter and Yahoo Finance. The results from 2 experiments show an accuracy of 96% in predicting one of the major telecommunication companies listed on the JSE closing price movement while using the linear discriminant analysis model and an RMSE of 0.023 in predicting a significant telecommunication company closing price using encoder-decoder long short-term memory. These findings reveal that the sentiment feature contains an essential fundamental value, and technical indicators also help move closer to predicting the 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 A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing

2021 ◽  
Author(s):  
Jaydip Sen ◽  
Sidra Mehtab
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Predictive Model ◽  
Language Processing ◽  
Stock Prices ◽  
Stock Price ◽  
Price Movement ◽  
Closing Price

Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, and collect its daily price movement over a period of three years (2015 – 2017). Based on the data of 2015 – 2017, we build various predictive models using machine learning, and then use those models to predict the closing value of NIFTY 50 for the period January 2018 till June 2019 with a prediction horizon of one week. For predicting the price movement patterns, we use a number of classification techniques, while for predicting the actual closing price of the stock, various regression models have been used. We also build a Long and Short-Term Memory (LSTM)-based deep learning network for predicting the closing price of the stocks and compare the prediction accuracies of the machine learning models with the LSTM model. We further augment the predictive model by integrating a sentiment analysis module on Twitter data to correlate the public sentiment of stock prices with the market sentiment. This has been done using Twitter sentiment and previous week closing values to predict stock price movement for the next week. We tested our proposed scheme using a cross validation method based on Self Organizing Fuzzy Neural Networks (SOFNN) and found extremely interesting results.

scholarly journals A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing

2021 ◽  
Author(s):  
Jaydip Sen ◽  
Sidra Mehtab
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Predictive Model ◽  
Language Processing ◽  
Stock Prices ◽  
Stock Price ◽  
Price Movement ◽  
Closing Price

Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, and collect its daily price movement over a period of three years (2015 – 2017). Based on the data of 2015 – 2017, we build various predictive models using machine learning, and then use those models to predict the closing value of NIFTY 50 for the period January 2018 till June 2019 with a prediction horizon of one week. For predicting the price movement patterns, we use a number of classification techniques, while for predicting the actual closing price of the stock, various regression models have been used. We also build a Long and Short-Term Memory (LSTM)-based deep learning network for predicting the closing price of the stocks and compare the prediction accuracies of the machine learning models with the LSTM model. We further augment the predictive model by integrating a sentiment analysis module on Twitter data to correlate the public sentiment of stock prices with the market sentiment. This has been done using Twitter sentiment and previous week closing values to predict stock price movement for the next week. We tested our proposed scheme using a cross validation method based on Self Organizing Fuzzy Neural Networks (SOFNN) and found extremely interesting results.

scholarly journals Machine Learning in Stock Price Forecast

2020 ◽  
Vol 214 ◽  
pp. 02050
Author(s):  
Zhen Sun ◽  
Shangmei Zhao
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Stock Price ◽  
Price Forecast ◽  
Closing Price ◽  
The Difference ◽  
Lstm Network ◽  
Better Than

This paper analyzed and compared the forecast effect of three machine learning algorithms (multiple linear regression, random forest and LSTM network) in stock price forecast using the closing price data of NASDAQ ETF and data of statistical factors. The test results show that the prediction effect of the closing price data is better than that of statistical factors, but the difference is not significant. Multiple linear regression is most suitable for stock price forecast. The second is random forest, which is prone to overfitting. The forecast effect of LSTM network is the worst and the values of RMSE and MAPE were the highest. The forecast effect of future stock price using closing price of NASDAQ ETF is better than that using statistical factors, but the difference is not significant.

scholarly journals A Machine Learning based approach to unleash the impact of COVID-19 on Indian Stock Market

2020 ◽  
Author(s):  
Nusrat Rouf ◽  
Majid Bashir Malik ◽  
Tasleem Arif
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Regression Model ◽  
Regression Models ◽  
Pearson Correlation ◽  
Mean Square ◽  
Random Forest Regression ◽  
The World ◽  
Closing Price ◽  
The Impact

Abstract Introduction: Advancement in information technology, be it hardware, software or communication technology, over few decades has rapidly impacted almost every field of study. Machine learning tools and techniques are nowadays applied to every field. It has opened the ways for interdisciplinary research by promising effective analyzation and decision-making strategies. COVID-19 has badly affected more than 200 countries within a short span of time. It has drastically affected both daily activities as well as economic activities. Herd behavior of investors has triggered panic selling. As a result, stock markets around the world have plunged down.Methods: In this paper, we analyze the impact of COVID-19 on NSE (National Stock Exchange) index Nifty50. We employ Pearson Correlation and investigate the impact of total confirmed cases and daily cases on Nifty50 closing price. We use various machine learning regression models for predictive analysis viz, linear regression with polynomial terms (quadratic, cubic), Decision Tree Regression and Random Forest Regression. Model performance is measured using MSE (Mean Square Error), RMSE (Root Mean Square Error) and R2 (R Squared) evaluators. Results: Correlation analysis reveals that total confirmed cases and daily cases in both India and the World have negative correlation with Nifty50 closing prices. Moreover, Nifty50 closing prices are more negatively correlated with total confirmed and daily cases in India. Predictive analysis shows that the Random Forest Regression model outperforms all other models. Conclusion: We analyze and predict the impact of COVID-19 on closing price of Nifty50 index. We employ Pearson Correlation and investigate the impact of COVID-19 on Nifty50 closing prices. We use various machine learning regression models to predict the closing price of Nifty50 index. Results reveal that the market volatility is directly proportional to increase in number of COVID-19 cases. Random Forest Regression model has comparatively shown better RMSE and R2 values.

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