Search results
1 – 10 of over 5000Prajwal Eachempati and Praveen Ranjan Srivastava
This study aims to develop two sentiment indices sourced from news stories and corporate disclosures of the firms in the National Stock Exchange NIFTY 50 Index by extracting…
Abstract
Purpose
This study aims to develop two sentiment indices sourced from news stories and corporate disclosures of the firms in the National Stock Exchange NIFTY 50 Index by extracting sentiment polarity. Subsequently, the two indices would be compared for the predictive accuracy of the stock market and stock returns during the post-digitization period 2011–2018. Based on the findings this paper suggests various options for financial strategy.
Design/methodology/approach
The news- and disclosure-based sentiment indices are developed using sentiment polarity extracted from qualitative content from news and corporate disclosures, respectively, using qualitative analysis tool “N-Vivo.” The indices developed are compared for stock market predictability using quantitative regression techniques. Thus, the study is conducted using both qualitative data and tools and quantitative techniques.
Findings
This study shows that the investor is more magnetized to news than towards corporate disclosures though disclosures contain both qualitative as well as quantitative information on the fundamentals of a firm. This study is extended to sectoral indices, and the results show that specific sectoral news impacts sectoral indices intensely over market news. It is found that the market discounts information in disclosures prior to its release. As disclosures in quarterly statements are delayed information input, firms can use voluntary disclosures to reduce the communication gap with investors by using the internet. Managers would do so only when the stock price is undervalued and tend to ignore the market and the shareholder in other cases. Otherwise, disclosure sentiment attracts only long horizon traders.
Practical implications
Finance managers need to improve disclosure dependence on investors by innovative disclosure methodologies irrespective of the ruling market price. In this context, future studies on investor sentiment would be interesting as they need to capture man–machine interactions reflected in market sentiment showing the interplay of human biases with machine-driven decisions. The findings would be useful in developing the financial strategy for protecting firm value.
Originality/value
This study is unique in providing a comparative analysis of sentiment extracted from news and corporate disclosures for explaining the stock market direction and stock returns and contributes to the behavioral finance literature.
Details
Keywords
Giuseppe Bruno, Emilio Esposito, Michele Mastroianni and Daniela Vellutino
A vast amount of literature has highlighted that accessibility is becoming crucial in evaluating e-procurement web site effectiveness. In this context, this paper shows some…
Abstract
A vast amount of literature has highlighted that accessibility is becoming crucial in evaluating e-procurement web site effectiveness. In this context, this paper shows some results of multidisciplinary research whose aim is to identify a model to evaluate e-procurement web site accessibility. The specific goal is to identify a group of web site attributes and characteristics that can be measured using quantitative indicators. For this purpose, a model based on a three-level hierarchical system has been introduced. The proposed model has been used to evaluate three Italian public e-procurement web sites. Finally, the conclusions and some indications on future developments of research are illustrated.
The purpose of this article is to discuss advantages and disadvantages of various means to manage morphological variation of keywords in monolingual information retrieval.
Abstract
Purpose
The purpose of this article is to discuss advantages and disadvantages of various means to manage morphological variation of keywords in monolingual information retrieval.
Design/methodology/approach
The authors present a compilation of query results from 11 mostly European languages and a new general classification of the language dependent techniques for management of morphological variation. Variants of the different techniques are compared in some detail in terms of retrieval effectiveness and other criteria. The paper consists mainly of an overview of different management methods for keyword variation in information retrieval. Typical IR retrieval results of 11 languages and a new classification for keyword management methods are also presented.
Findings
The main results of the paper are an overall comparison of reductive and generative keyword management methods in terms of retrieval effectiveness and other broader criteria.
Originality/value
The paper is of value to anyone who wants to get an overall picture of keyword management techniques used in IR.
Details
Keywords
The article describes a project undertaken at the National Library of Wales to compare automated systems for the storage and retrieval of historic cartographic records. The…
Abstract
The article describes a project undertaken at the National Library of Wales to compare automated systems for the storage and retrieval of historic cartographic records. The selection and purchase of software, cataloguing of a representative sample of historic cartographic materials, system customisation and data input is outlined. Following the evaluation of systems, conclusions are drawn for future automated map catalogue development.
The purpose of this column is to explore methods in which information professionals can contribute to the intelligence of open discovery systems.
Abstract
Purpose
The purpose of this column is to explore methods in which information professionals can contribute to the intelligence of open discovery systems.
Design/methodology/approach
This is a conceptual column based on experience in this field and a review of the relevant literature.
Findings
There are several ways in which information professionals can contribute to make their discovery systems more robust through careful semantic analysis.
Originality/value
This column explores ways in which the fields of linguistics, semantic analysis, semiotics, and web ontologies can assist information professionals in building responsive and robust back end indexing systems that can be coupled to front end open discovery frameworks.
Details
Keywords
F.W. Matthews and A.D. Shillingford
KWIC—Key Word In Context—is a form of automatic indexing using computers. It is automatic in the sense that the computer system determines the indexing from free text input. Luhn…
Abstract
KWIC—Key Word In Context—is a form of automatic indexing using computers. It is automatic in the sense that the computer system determines the indexing from free text input. Luhn first described the method in which text of a length which can be accommodated on a single line of print is indexed at the centre of the page, the words that precede and follow the indexing keyword being displayed on the same line. As a control device a list of words for which no indexing entry should be made is stored in the computer program. This list, often referred to as a ‘stop list’, includes the articles, conjunctions and commonly occurring trivial words which do not form useful indexing entries. Less frequently occurring ‘useless’ entries are carried, but at little cost and with little damage to the usefulness of the index. The system is often referred to as quick and dirty: quick in that it is cheap to run, easy to prepare and not demanding of intellectual decisions at input; dirty in that useless entries will appear in the index, and, since the indexing vocabulary is uncontrolled, the user must consider alternative words that may express the concept for which he is searching. When the input is prepared in‐house a number of control features can be used to supplement the stop list to make the index more effective but requiring more thought at input. This paper concerns a number of such added control features.
Hamid Hassani, Azadeh Mohebi, M.J. Ershadi and Ammar Jalalimanesh
The purpose of this research is to provide a framework in which new data quality dimensions are defined. The new dimensions provide new metrics for the assessment of lecture video…
Abstract
Purpose
The purpose of this research is to provide a framework in which new data quality dimensions are defined. The new dimensions provide new metrics for the assessment of lecture video indexing. As lecture video indexing involves various steps, the proposed framework containing new dimensions, introduces new integrated approach for evaluating an indexing method or algorithm from the beginning to the end.
Design/methodology/approach
The emphasis in this study is on the fifth step of design science research methodology (DSRM), known as evaluation. That is, the methods that are developed in the field of lecture video indexing as an artifact, should be evaluated from different aspects. In this research, nine dimensions of data quality including accuracy, value-added, relevancy, completeness, appropriate amount of data, concise, consistency, interpretability and accessibility have been redefined based on previous studies and nominal group technique (NGT).
Findings
The proposed dimensions are implemented as new metrics to evaluate a newly developed lecture video indexing algorithm, LVTIA and numerical values have been obtained based on the proposed definitions for each dimension. In addition, the new dimensions are compared with each other in terms of various aspects. The comparison shows that each dimension that is used for assessing lecture video indexing, is able to reflect a different weakness or strength of an indexing method or algorithm.
Originality/value
Despite development of different methods for indexing lecture videos, the issue of data quality and its various dimensions have not been studied. Since data with low quality can affect the process of scientific lecture video indexing, the issue of data quality in this process requires special attention.
Details
Keywords
Suzan Abed, Basil Al-Najjar and Clare Roberts
This paper aims to investigate empirically the common alternative methods of measuring annual report narratives. Five alternative methods are employed, a weighted and un-weighted…
Abstract
Purpose
This paper aims to investigate empirically the common alternative methods of measuring annual report narratives. Five alternative methods are employed, a weighted and un-weighted disclosure index and three textual coding systems, measuring the amount of space devoted to relevant disclosures.
Design/methodology/approach
The authors investigate the forward-looking voluntary disclosures of 30 UK non-financial companies. They employ descriptive analysis, correlation matrix, mean comparison t-test, rankings and multiple regression analysis of disclosure measures against determinants of corporate voluntary reporting.
Findings
The results reveal that while the alternative methods of forward-looking voluntary disclosure are highly correlated, important significant differences do nevertheless emerge. In particular, it appears important to measure volume rather than simply the existence or non-existence of each type of disclosure. Overall, we detect that the optimal method is content analysis by text-unit rather than by sentence.
Originality/value
This paper contributes to the extant literature in forward-looking disclosure by reporting important differences among alternative content analyses. However, the decision regarding whether this should be a computerised or a manual content analysis appears not to be driven by differences in the resulting measures. Rather, the choice is the outcome of a trade-off between the time involved in setting up coding rules for computerised analysis versus the time saved undertaking the analysis itself.
Details
Keywords
Valentin Penca, Siniša Nikolić, Dragan Ivanović, Zora Konjović and Dušan Surla
The main aim of this paper is to develop a CRIS systems search profile that would enable CRIS users to perform unified and semantically rich search for the records stored in the…
Abstract
Purpose
The main aim of this paper is to develop a CRIS systems search profile that would enable CRIS users to perform unified and semantically rich search for the records stored in the CRIS systems.
Design/methodology/approach
Prior to the search profile construction, diverse representative types of the scientific research data store systems (CRISs, digital libraries, institutional repositories, and search portals) were analyzed versus available search modes, indexes and query types.
Findings
The new SRU/W standard based search profile (CRIS profile) for the purpose of searching scientific research data was proposed, that supports search for all types of data identified through an exhaustive analysis covering all major scientific and research data store systems.
Research limitations/implications
Constraints of the proposed profile could appear from the fact that data identified in analyzed systems do not comprise all scientific research data recognized by CERIF standard which, in turn, could call for the profile extension.
Practical implications
Search profile has been verified on the data in the existing CRIS systems at the University of Novi Sad. The CRIS search profile enables unified and semantically rich search for the data stored in heterogeneous distributed scientific research data store systems.
Originality/value
The new SRU/W-based search profile extensively supports the search domain of scientific research data in CRIS systems. Commitments to SRU/W and CQL standards enable interoperability among heterogeneous, distributed scientific research data sources.
Details
Keywords
Elena Fedorova and Polina Iasakova
This paper aims to investigate the impact of climate change news on the dynamics of US stock indices.
Abstract
Purpose
This paper aims to investigate the impact of climate change news on the dynamics of US stock indices.
Design/methodology/approach
The empirical basis of the study was 3,209 news articles. Sentiment analysis was performed by a pre-trained bidirectional FinBERT neural network. Thematic modeling is based on the neural network, BERTopic.
Findings
The results show that news sentiment can influence the dynamics of stock indices. In addition, five main news topics (finance and politics natural disasters and consequences industrial sector and Innovations activism and culture coronavirus pandemic) were identified, which showed a significant impact on the financial market.
Originality/value
First, we extend the theoretical concepts. This study applies signaling theory and overreaction theory to the US stock market in the context of climate change. Second, in addition to the news sentiment, the impact of major news topics on US stock market returns is examined. Third, we examine the impact of sentimental and thematic news variables on US stock market indicators of economic sectors. Previous works reveal the impact of climate change news on specific sectors of the economy. This paper includes stock indices of the economic sectors most related to the topic of climate change. Fourth, the research methodology consists of modern algorithms. An advanced textual analysis method for sentiment classification is applied: a pre-trained bidirectional FinBERT neural network. Modern thematic modeling is carried out using a model based on the neural network, BERTopic. The most extensive topics are “finance and politics of climate change” and “natural disasters and consequences.”
Details