Stock price prediction using machine learning on least-squares linear regression basis

Emioma, C. C.; Edeki, S.O.

doi:10.1088/1742-6596/1734/1/012058

Cited by 24 publications

(13 citation statements)

References 6 publications

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“…In the last few years, the tendency to use artificial intelligence-based stock forecasting models, specifically deep learning models, has increased [21,46]. However, there are also some other traditional artificial intelligence-based models, like support vector and linear regression analyses, which are used for this purpose [77,78]. Still, finding the most suitable model for stock forecasting is a vital area of research.…”

Section: Discussionmentioning

confidence: 99%

Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment

Maqsood

Yasmin

et al. 2022

Systems

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Stock exchange analysis is regarded as a stochastic and demanding real-world setting in which fluctuations in stock prices are influenced by a wide range of aspects and events. In recent years, there has been a great deal of interest in social media-based data analytics for analyzing stock exchange markets. This is due to the fact that the sentiments around major global events like Brexit or COVID-19 significantly affect business decisions and investor perceptions, as well as transactional trading statistics and index values. Hence, in this research, we examined a case study from the Brexit event to assess the influence that feelings on the subject have had on the stock markets of European Union (EU) nations. Brexit has implications for Britain and other countries under the umbrella of the European Union (EU). However, a common point of debate is the EU’s contribution preferences and benefit imbalance. For this reason, the Brexit event and its impact on stock markets for major contributors and countries with minimum donations need to be evaluated accurately. As a result, to achieve accurate analysis of the stock exchanges of different EU nations from two different viewpoints, i.e., the major contributors and countries contributing least, in response to the Brexit event, we suggest an optimal deep learning and machine learning model that incorporates social media sentiment analysis regarding Brexit to perform stock market prediction. More precisely, the machine learning-based models include support vector machines (SVM) and linear regression (LR), while convolutional neural networks (CNNs) are used as a deep learning model. In addition, this method incorporates around 1.82 million tweets regarding the major contributors and countries contributing least to the EU budget. The findings show that sentiment analysis of Brexit events using a deep learning model delivers better results in comparison with machine learning models, in terms of root mean square values (RMSE). The outcomes of stock exchange analysis for the least contributing nations in relation to the Brexit event can aid them in making stock market judgments that will eventually benefit their country and improve their poor economies. Likewise, the results of stock exchange analysis for major contributing nations can assist in lowering the possibility of loss in relation to investments, as well as helping them to make effective decisions.

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Section: Discussionmentioning

confidence: 99%

Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment

Maqsood

Yasmin

et al. 2022

Systems

View full text Add to dashboard Cite

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“…For time series analysis, window size as a key factor is number of prices of the past days for current price prediction. We experiment different window sizes (3,4,5,10,15,20,30) and window size is decided by the model with smallest error (MAPE, MAE, and RMSE). Secondly, we would perform all models from 6.1 to 6.5, and the one with smallest error is applied to further predict stock prices.…”

Section: Methodsmentioning

confidence: 99%

“…Over the past few decades, many researchers have used machine learning approaches to analyze financial information including financial time series data and textual data [5]. For time series data, there are various approaches such as artificial neural network (ANN), autoregressive integrated moving average (ARIMA), k nearest neighbor (KNN), recurrent neural network (RNN), support vector regression (SVR), and so on.…”

Section: Related Workmentioning

confidence: 99%

Combining Time Series and Sentiment Analysis for Stock Market Forecasting

Chou¹,

Ramachandran²

2021

Proceedings of the 3rd International Conference on Statistics: Theory and Applications

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Objective of this research is to build a model to predict stock price using sentimental information from news headlines and historical prices, and the model is able to not only conclude better results but also minimize the difference between predicted values and actual values. News headlines show impact on stock price. Unlike previous approaches where the textual information were usually calculated into sentiment score, we apply various approaches to extract information from news headlines. On the other hand, price data through time series are also useful to predict stock prices. Hence, improvement is made with combination of sentiment analysis of news headlines and time series analysis of historical prices, and the combination is able to complement nonavailability of sentiment lexicon and lack of news. Compared to time series models and word embedding models, our combined model shows smaller or similarly error measures MAPE, MAE, and RMSE with time series models, and reduces lag in graphs.

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“…Stock market predictions attempt to determine the value of stocks and to give individuals an accurate idea to understand the market. Stock forecasting can help companies improve economics, interest rates, and so on to influence the market [1]. However, predicting stocks is a very difficult task because almost all investors do not always correctly predict these hyper-parameters.…”

Section: Introductionmentioning

confidence: 99%

The Stock Price Prediction Based on Time Series Model, Multifactorial Regression, Machine Learnings

Jin¹,

Wei²,

Zhang³

2022

BCPBM

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In general, it is hard to forecast the prices the stock prices due to the stochastic fluctuations. This research aims to describe the process to use time series models, multifactorial regression, and machine learning to predict stock prices. ARIMA and EGARCH models are frequently used time series models to predict stock prices. Least-squares linear regression model, Lasso, and Polynomial Linear Regression model predict well in statistical regression methods. RNN and LSTM have higher prediction accuracy. Overall, time series models, statistical regression, and machine learning all can predict stock prices. Summarizing the different methods or models to forecast stock market trending can help investors to prepare relevant investing strategies. These results shed light on guiding further exploration of

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Stock price prediction using machine learning on least-squares linear regression basis

Cited by 24 publications

References 6 publications

Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment

Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment

Combining Time Series and Sentiment Analysis for Stock Market Forecasting

The Stock Price Prediction Based on Time Series Model, Multifactorial Regression, Machine Learnings

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