Comparison of stock price prediction using geometric Brownian motion and multilayer perceptron

Investor sentiment has been widely used in the research of the stock market, and how to accurately measure investor sentiment is still being explored. With the rise of social media, investor sentiment is no longer only influenced by macroeconomic data and news media, but also guided by We-Media and fragmented information. We take the data of China A-shares from January 2020 to December 2020 as the research object and propose a stock price prediction method that combines investor sentiment with multisource information. Firstly, the sentiment of macroeconomic data, brokerage research reports, news, and We-Media is calculated, respectively, and then the investor sentiment vector combining multisource information is obtained by the multilayer perceptron. Finally, the LSTM model is used to represent the stock time series characteristics. The results show that (1) the proposed algorithm is superior to the benchmark algorithm in terms of accuracy and F1-score, (2) investor sentiment vector can effectively measure the investment sentiment of stocks, and (3) compared with vector concatenation, multilayer perceptron can better represent investor sentiment.

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Comparative Analysis of Linear Regression, Multilayer Perceptron and Support Vector Machines for its utilization in Stock Price Prediction

International Journal of Modern Trends in Engineering & Research ◽

10.21884/ijmter.2017.4177.h97je ◽

2017 ◽

Vol 4 (6) ◽

pp. 7-12

Keyword(s):

Comparative Analysis ◽

Support Vector Machines ◽

Linear Regression ◽

Multilayer Perceptron ◽

Stock Price ◽

Support Vector ◽

Stock Price Prediction ◽

Price Prediction ◽

Vector Machines

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Improved AdaboostM1 for Stock Price Prediction Using Multi-layer Perceptron to Integrate Weak Learners

10.22541/au.162808405.52754851/v1 ◽

2021 ◽

Author(s):

Rebwar M. Nabi ◽

Soran AB.M. Saeed ◽

Rania Azad M. San Ahmed

Keyword(s):

Stock Market ◽

Multilayer Perceptron ◽

Stock Price ◽

Financial Time Series ◽

Time Series Prediction ◽

Data Availability ◽

Economic Gain ◽

Market Prices ◽

Stock Price Prediction ◽

Price Prediction

Investment in the stock market is currently very popular due to its economic gain. Therefore, numerous researchers and academicians work is focused on financial time series prediction due to its data availability and profitability. Based on the literature it can be seen that various versions of the AdaboostM1 algorithm have been applied in the stock market either by tuning the algorithm parameters or attempting various base learners but the accuracy has not yet reached to favorable and reliable level. Therefore, this study proposes an improved version of AdaboostM1(ADA), which is implemented in the Waikato Environment for Knowledge Analysis(WEKA) to predict stock market prices based on historical data. The improved AdaBoostM1 integrates the set of Multilayer Perceptron (MLP) predictors instead of using DecisionStumps, which is normally being applied. The enhanced AdaBoostM1 is named Adaboost with Multilayer Perceptron (ADA-MLP). As the result, the ADA-MLP was found to be outperforming the original ADA by 1.52%, in which the ADA-MLP achieved the CA of 100% on average while the ADA achieved 98.48%. Furthermore, the ADA-MLP was al

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A Historical Data based Ensemble System for Efficient Stock Price Prediction

Recent Patents on Computer Science ◽

10.2174/2213275912666190730161807 ◽

2019 ◽

Vol 12 ◽

Author(s):

Vijay Kumar Dwivedi ◽

Manoj Madhava Gore

Keyword(s):

Stock Price ◽

Prediction Models ◽

Historical Data ◽

Stock Exchange ◽

Primary Objective ◽

Lasso Regression ◽

Stock Price Prediction ◽

Price Prediction ◽

Consistent Manner ◽

Selection Operator

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems. Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction. Method: The proposed system combines various machine learning-based prediction models employing least absolute shrinkage and selection operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models. Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system. Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.

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