An Efficient Hybrid Machine Learning Method for Time Series Stock Market Forecasting

Ebadati, E Omid Mahdi; Mortazavi, T Mohammad

doi:10.14311/nnw.2018.28.003

Cited by 30 publications

(9 citation statements)

References 4 publications

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“…Jothimani used regression methods to analyze the SSE Composite Index and predict stock prices [8]. Asghar used partial least squares to make a simple prediction of stock prices [9]. Cao et al used the least-squares trained regression model to select the price of the gold spot as an influencing factor to predict the trend of gold stocks [10].…”

Section: Determinants Of Stock Price Movementsmentioning

confidence: 99%

Predicting the Link between Stock Prices and Indices with Machine Learning in R Programming Language

Cao

2021

Journal of Mathematics

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This paper provides an in-depth analysis machine study of the relationship between stock prices and indices through machine learning algorithms. Stock prices are difficult to predict by a single financial formula because there are too many factors that can affect stock prices. With the development of computer science, the author now uses many computer science techniques to make more accurate predictions of stock prices. In this project, the author uses machine learning in R Studio to predict the prices of 35 stocks traded on the New York Stock Exchange and to study the interaction between the prices of four indices in different countries. Further, it is proposed to find the link between stocks and indices in different countries and then use the predictions to optimize the portfolio of these stocks. To complete this project, the author used Linear Regression, LASSO, Regression Trees, Bagging, Random Forest, and Boosted Trees to perform the analysis. The experimental results show that the MRDL deep multiple regression model proposed in this paper predicts the closing price trend of stocks with a mean square error interval [0.0043, 0.0821]. Additionally, 80% of the proposed DMISV, KDJSV, MACDV, and DKB stock buying and selling strategies have a return greater than 10%. The experimental results validate the effectiveness of the proposed buying and selling strategies and stock price trend prediction methods in this paper. Compared with other algorithms, the accuracy of the algorithm in this study is increased by 15%, and the efficiency of prediction is increased by 25%.

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Section: Determinants Of Stock Price Movementsmentioning

confidence: 99%

Predicting the Link between Stock Prices and Indices with Machine Learning in R Programming Language

Cao

2021

Journal of Mathematics

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“…They also compare the results with the LSTM and RF models, where the proposed model outperforms other methods. [39] WT-SAEs-LSTM Financial Time Series Stock Price Prediction Shekhar and Varshney [66] GA-SVM Financial Time Series Stock Price Prediction Ahmadi et al [67] ICA-SVM Financial Time Series Stock Price Prediction Ebadati and Mortazavi [68] GA-ANN Financial Time Series Stock Price Prediction Johari et al [69] GARCH-SVM Financial Time Series Stock Price Prediction…”

Section: Lstmmentioning

confidence: 99%

“…Ahmadi et al [67] compared the performance of two hybrid machine learning models in predicting the timing of the stock markets, using imperialist competition algorithm-SVM (ICA-SVM), and SVM-GA. Their results exposed that SVM-ICA has a higher performance compared with SVM-GA in the prediction of stock market trends for periods of 1-6 days. To predict stock prices using financial time series data, Ebadati and Mortazavi [68] applied a hybrid model by integrating GA-ANN, where GA was employed to select ANN features and optimize parameters. Their study suggests that this hybrid machine learning model has an improved sum square error (SSE) (i.e., performance accuracy) by 99.99% and improved time (i.e., speed accuracy) by 90.66%.…”

Section: Other Algorithmsmentioning

confidence: 99%

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

et al. 2020

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This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models.

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“…In this system, they used two machine learning methods, namely feed-forward neural network and the deep convolutional neural network. Ebadati and Mortazavi [15] also use the method of neural networks, applying the hybrid method of genetic algorithm (GA) and artificial neural network (ANN) to develop a method for predicting stock prices and time series. In the GA method, the output values are further converted into the developed ANN algorithm to correct errors at the exact point.…”

Section: Literature Researchmentioning

confidence: 99%

Evaluation of development of Apple Inc. stock price time series

Horák

Kaisler

2022

SHS Web Conf.

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The paper deals with the the development of a specific company’s stock price time series. The aim of the paper is to use the time series method for a detailed analysis and evaluation of the development of Apple Inc. stock prices. Daily data from 2000 to 2020, daily data from the period of the economic crisis between 2007 and 2009 and daily data from the Covid-19 pandemic period from March 2020 to the end of the same year are used. The data, from the period of 2000 - 2020 show a gradual increase in Apple’s stock prices. The most common factor leading to the increase in stock prices is the launch of a new product or service on the global market. On the contrary, the reason for the decline in stock prices is customer dissatisfaction, the excess of demand over supply, or the political situation. The analysis of time series for the period of the economic crisis points to the fact that thanks to the development, innovation and constant introduction of new products into the market, the company was not significantly affected by the crisis and neither were stock prices. Naturally, there were some fluctuations in prices, but at the end of 2009, the company even reached the highest stock prices in its history to date. The analysis of time series during the global pandemic of Covid-19 shows a steady rise in stock prices. Currently, the company sells more and more products and introduces new services that help us work, study or entertain ourselves in these difficult times, in the safety of our homes.

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An Efficient Hybrid Machine Learning Method for Time Series Stock Market Forecasting

Cited by 30 publications

References 4 publications

Predicting the Link between Stock Prices and Indices with Machine Learning in R Programming Language

Predicting the Link between Stock Prices and Indices with Machine Learning in R Programming Language

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

Evaluation of development of Apple Inc. stock price time series

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