scholarly journals Anticipating Corporate Financial Performance from CEO Letters Utilizing Sentiment Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Siqi Che ◽  
Wenzhong Zhu ◽  
Xuepei Li
Keyword(s):  
Machine Learning ◽  
Financial Performance ◽  
Appraisal Theory ◽  
Computer Assisted ◽  
Learning Approaches ◽  
Sentiment Dictionary ◽  
Sentiment Orientation ◽  
Score Model ◽  
Qualitative Data Analysis Software ◽  
Corporate Social

With the emergence and tremendous growth of text mining, a computer-assisted approach for capturing sentiment viewpoints from textual data is gradually becoming a promising field, particularly when researchers are increasingly facing the problem of filtering bunches of useless information without capturing the essence in the big data era. This study aims at observing and classifying the sentiment orientation in CEO letters, digging the main corporate social responsibility (CSR) themes, and examining the effectiveness of CEO letters’ sentiment on forecasting financial performance. A specific sentiment dictionary has been proposed to identify and classify the sentiment orientation in CEO letters by utilizing the appraisal theory. Additionally, the qualitative data analysis software NVivo is applied to explore the CSR topics. Furthermore, a modified Altman’s Z-score model and machine-learning approach are employed to predict financial performance. The results of preliminary evaluations validate that approximately 62.14% of the texts represent positive polarity even when companies are not in a promising economic situation. The CSR themes mainly focus on business ethical responsibility, particularly ethical activities. Among various machine-learning approaches, the logistic regression approach is appropriate for predicting financial performance with the state-of-the-art accuracy of 70.46 %. The encouraging results indicate that the sentiment information inCEO letters is a vital factor for anticipating financial performance. This work not only offers a new analytic framework for associating linguistic theory with computer science and economic models but will also improve stakeholders’ decision-making.

Pengaruh Kinerja Keuangan Dan Corporate Social Responsibility (CSR) Terhadap Nilai Perusahaan Perbankan Di Bursa Efek Indonesia

2018 ◽  
Vol 26 (1) ◽  
pp. 95-111
Author(s):  
Sulastiningsih Sulastiningsih ◽  
Rizka Imanita Sholihati
Keyword(s):  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
Financial Performance ◽  
Firm Value ◽  
Research Method ◽  
Quantitative Research ◽  
Annual Report ◽  
Stock Exchange ◽  
Secondary Data ◽  
Corporate Social

This study aims to determine whether the financial performance measured by using CAR, ROA, LDR, BOPO, and CSR can affect the value of banking companies as measured by using PBV. This study uses secondary data taken from the annual report of banking companies during the year 2012-2016 listed on the Indonesia Stock Exchange. The number of samples of this study as many as 25 banking companies with a total of 125 data. This research method is quantitative research. The results of this study indicate the effect of CAR, ROA, LDR, BOPO, and CSR variables on firm value measured by using PBV in a banking company listed on the Indonesia Stock Exchange. Keywords: CAR, ROA, LDR, BOPO, CSR, PBV

IMPLEMENTASI KINERJA KEUANGAN TERHADAP NILAI SPIRITUALITAS PRIBADI

2012 ◽  
Vol 16 (3) ◽  
pp. 332
Author(s):  
Whedy Prasetyo
Keyword(s):  
Corporate Governance ◽  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
Financial Performance ◽  
Annual Report ◽  
Main Concern ◽  
Good Corporate Governance ◽  
Corporate Social ◽  
The Relationship ◽  
Personal Spirituality

Development of financial performance in the application of Good Corporate Governance and Corporate Social Responsibility which affects the values of honesty private individuals, in order to be able to run the accountability, value for money, fairness in financial management, transparency, control, and free of conflicts of interest (independence). The main concern in this study is focused on achieving value personal spirituality through the financial performance and capabilities of Good Corporate Governance (GCG) and Corporate Social Responsibility (CSR) in moderating the relationship with the financial performance of value personal spirituality. This study is a descriptive verifikatif. The unit of analysis in this study was 15 companies in Indonesia with a policy that has been applied through the concept since January of 2008 until now, with the support of the annual report of the company, the company's financial statements, company reports to the disclosure of Good Corporate Governance and Corporate Social Responsibility in the annual report. Overall reports published successively during the years 2008-2011. The results of this study indicate financial performance affects the value of personal spirituality, and for variable GCG obtained results that could moderate the relationship of financial performance to the value of personal spirituality. But for the disclosure of CSR variables obtained results can’t moderate the relationship with the financial performance of personal spirituality.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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