scholarly journals Sentiment Analysis of Current Reports Texts with Use of Cumulative Abnormal Return and Deep Neural Network

2021 ◽  
Author(s):  
Maciej A. Wujec
Keyword(s):  
Neural Network ◽  
Abnormal Return ◽  
Deep Neural Network ◽  
Subjective Assessment ◽  
Investment Strategy ◽  
Strategy Development ◽  
Cumulative Abnormal Return ◽  
Research Investment ◽  
Positive Class ◽  
Negative Class

The deep neural network - BERT model (Bidirectional Encoder Representations from Transformers) and the stocks cumulative abnormal return is used in this article to analyze the sentiment of financial texts. The proposed approach, unlike those used so far, does not require the creation of dictionaries, takes into account the broad context of words and their meaning in financial texts, eliminates the problem of ambiguity of words in various contexts, does not require manual labelling of data and is free from the subjective assessment of the researcher. The sentiment of financial texts in the meaning presented in this paper is directly related to the market reaction to the information contained in these texts. For texts belonging to one of the two classes (positive or negative) with the highest probability the BERT model gives the results of predictions with a precision level of 62.38% for the positive class and 55% for the negative class. The results at this level can be used in event study, market efficiency research, investment strategy development or support of investment analysts using fundamental analysis.

scholarly journals Analysis of the Financial Information Contained in the Texts of Current Reports: A Deep Learning Approach

2021 ◽  
Vol 14 (12) ◽  
pp. 582
Author(s):  
Maciej Wujec
Keyword(s):  
Deep Learning ◽  
Market Reaction ◽  
Subjective Assessment ◽  
Investment Strategy ◽  
Strategy Development ◽  
Fundamental Analysis ◽  
Text Documents ◽  
Significance Level ◽  
Study Results ◽  
Types Of Information

An important role in the fundamental analysis is played by the acquisition and analysis of various types of information about the company. Text documents are an increasingly important source of this information. Their accurate and quick analysis is an increasingly important challenge for financial analysts. Research in the area of financial text analysis is based on sentiment analysis. The deep neural networks and the stocks’ cumulative abnormal return are used in this article to analyze the sentiment of financial texts. The proposed approach, unlike those used so far, does not require manual labeling of data or the creation of dictionaries and is free from the subjective assessment of the researcher. Taking into account the broad context of words and their meaning in financial texts, it also eliminates the problem of ambiguity of words in various contexts. The sentiment of financial texts presented in this paper is directly related to the market reaction to the information contained in these texts. For texts belonging to one of the two classes (positive or negative) with the highest probability, the deep learning model gives predictions with a precision of 62% for the positive class and 55% for the negative class. The event study results show that the sentiment calculated under the proposed method can be successfully used to determine the probable direction of the market reaction to the information contained in current reports with a 1 percent significance level. The results can be used in market efficiency research, investment strategy development or support of investment analysts using fundamental analysis.

OVERREACTION ANOMALY DI PASAR MODAL INDONESIA (STUDI PADA SAHAM-SAHAM LQ-45 TAHUN 2014-2018)

2019 ◽  
Vol 24 (1) ◽  
pp. 88-99
Author(s):  
Herly Hadimas
Keyword(s):  
Abnormal Return ◽  
Sampling Method ◽  
Stock Exchange ◽  
Investment Strategy ◽  
Cumulative Abnormal Return ◽  
Purposive Sampling ◽  
Car Market ◽  
Loser Portfolio

This study aims to analyze whether market overreaction symptoms occur in Indonesia Stock Exchange, specifically on the LQ-45 Index from 2014 to 2018. This research was separated over 6 and 12 months. The sample was consistent stocks of LQ-45 index companies period 2014 to 2018, it is determined by purposive sampling method. Stocks were classified into two portfolios based on the value of Cumulative Abnormal Return (CAR). Winner portfolio was 3 stocks with the highest value of CAR, and loser portfolio was 3 stocks with the lowest value of CAR. Market overreaction is measured by Average Cumulative Abnormal Return (ACAR) loser portfolio outperformed of winner portfolio ACAR. As a result, the research found that overreaction indications were evidence, but no significance statistically. The result absence of market overreaction symptoms on the Indonesia Stock Exchange showed that the contrarian investment strategy was inappropriate to use, especially on LQ-45 index stocks. Keywords: Overreaction, winner-loser anomaly, LQ-45 Index.

The economic consequences of voluntary environmental reporting on shareholder wealth

2006 ◽  
Vol 3 (2) ◽  
pp. 1 ◽  
Author(s):  
Ruslaina Yusoff ◽  
Shariful Amran Abd Rahman ◽  
Wan Nazihah Wan Mohamed
Keyword(s):  
Abnormal Return ◽  
Listed Companies ◽  
Shareholder Wealth ◽  
Environmental Information ◽  
Economic Consequences ◽  
Share Price ◽  
Cumulative Abnormal Return ◽  
Environmental Reporting ◽  
Financial Report ◽  
Significant Difference

This study was carried out to examine the economic consequences ofvoluntary environmental reporting on shareholders' wealth among Malaysian Listed Companies that voluntarily disclosed environmental information in their financial report. One hundred andfifty two (152) companies of Bursa Malaysia (MSE) had been identified as a sample in the current study. Seventy six (76) companies were classified as environmental reporting companies while the remaining companies were classified as non-environmental reporting companies. The classification was done in order to determine the differences between share price, profitability and market equity for both types of companies. The study hypothesizes that voluntary environmental reporting leads to an improvement in the shareholders wealth. However, the results show that there is no significant difference between cumulative abnormal return for environmental and non-environmental reporting companies. Based on the results obtained, it can also be concluded that profitability and size of the companies do not have any significant roles in deciding whether or not to produce environmental reporting companies.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

Underwater Fish Images Classification by Deep Neural Network

2020 ◽  
Author(s):  
Ala Supriya ◽  
Chiluka Venkat ◽  
Aliketti Deepak ◽  
GV Hari Prasad
Keyword(s):  
Neural Network ◽  
Deep Neural Network
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