Using online textual data, principal component analysis and artificial neural networks to study business and innovation practices in technology-driven firms

Tollo, Giacomo di; Tanev, Stoyan; Liotta, Giacomo; March, Davide De

doi:10.1016/j.compind.2015.08.006

Cited by 13 publications

(3 citation statements)

References 41 publications

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“…Artificial neural networks (ANN), based on several studies, have emerged as popular tools for financial prediction [48][49][50]. Most of these studies have utilized historical market data as input features.…”

Section: Prediction Methodsmentioning

confidence: 99%

User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks

2023

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Predicting stock market trends is an intriguing and complex problem, which has drawn considerable attention from the research community. In recent years, researchers have employed machine learning techniques to develop prediction models by using numerical market data and textual messages on social networks as their primary sources of information. In this article, we propose User2Vec, a novel approach to improve stock market prediction accuracy, which contributes to more informed investment decision making. User2Vec is a unique method that recognizes the unequal impact of different user opinions on specific stocks, and it assigns weights to these opinions based on the accuracy of their associated social metrics. The User2Vec model begins by encoding each message as a vector. These vectors are then fed into a convolutional neural network (CNN) to generate an aggregated feature vector. Following this, a stacked bi-directional long short-term memory (LSTM) model provides the final representation of the input data over a period. LSTM-based models have shown promising results by effectively capturing the temporal patterns in time series market data. Finally, the output is fed into a classifier that predicts the trend of the target stock price for the next day. In contrast to previous attempts, User2Vec considers not only the sentiment of the messages, but also the social information associated with the users and the text content of the messages. It has been empirically proven that this inclusion provides valuable information for predicting stock direction, thereby significantly enhancing prediction accuracy. The proposed model was rigorously evaluated using various combinations of market data, encoded messages, and social features. The empirical studies conducted on the Dow Jones 30 stock market showed the model’s superiority over existing state-of-the-art models. The findings of these experiments reveal that including social information about users and their tweets, in addition to the sentiment and textual content of their messages, significantly improves the accuracy of stock market prediction.

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Section: Prediction Methodsmentioning

confidence: 99%

User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks

2023

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“…We based the research approach on using existing sustainable business models and innovation frameworks in order to develop a set of keywords related to the sustainability aspects companies usually deal with. We then used a web search and text analytics tools to measure the frequency of use (web counts) of these sets of keywords on the websites of a sample of firms (di Tollo et al, 2015).…”

Section: Research Approachmentioning

confidence: 99%

Sustainability-related Communication Patterns on the Websites of European Top R&D Spenders

Liotta¹,

Tanev²,

Gorra³

et al. 2020

TIM Review

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“…Artificial neural network (ANN) is a form of artificial intelligence based on the human neuronal system, which can be used to learn and compute functions for which the analytical relationships between inputs and outputs are unknown [14]. Multilayer perceptron (MLP), the best type of neural networks, consists of at least three layers: input layer, output layer, and intermediate or hidden layer(s) [33]. The number of hidden layers and neurons depends on complexity of the problem to be solved.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Prediction of Peak Velocity of Blasting Vibration Based on Artificial Neural Network Optimized by Dimensionality Reduction of FA-MIV

Zhang

Jin

2018

Mathematical Problems in Engineering

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Blasting vibration is harmful to the nearby habitants and dwellings in diverse geotechnical engineering. In this paper, a novel scheme based on Artificial Neural Network (ANN) method optimized by dimensionality reduction of Factor Analysis and Mean Impact Value (FA-MIV) is proposed to predict peak particle velocity (PPV) of blasting vibration. To construct the model, nine parameters of field measurement are taken as undetermined input parameters for research, while peak particle velocity (PPV) is considered as output parameter. With the application of FA, common factors are extracted from undetermined input parameters. Then, principal components are defined as a linear combination of common factors. The weight of each principal components effected on output parameter is ranked according to the calculation of MIV, and two principal components with minimum weight are eliminated. Ultimately, output parameter (PPV) is explained in a low-dimensional space with four input characteristic parameters. In the prepared database consisting of 108 datasets, 98 datasets are used for the training of the model, while the rest are used for testing performance. The performances of the ANN models are compared with regression analysis, in terms of coefficient of determination (R2) and mean absolute error (MAE). It is found that the performances of ANN models with using FA-MIV are superior to those of models without using FA-MIV in the prediction of PPV. In addition, the abilities of ANN models are all superior to regression analysis in the prediction of PPV. The result obtained from ELM is more accurate than BPNN and MVRA models.

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Using online textual data, principal component analysis and artificial neural networks to study business and innovation practices in technology-driven firms

Cited by 13 publications

References 41 publications

User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks

User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks

Sustainability-related Communication Patterns on the Websites of European Top R&D Spenders

Prediction of Peak Velocity of Blasting Vibration Based on Artificial Neural Network Optimized by Dimensionality Reduction of FA-MIV

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