Spatial analysis of Twitter sentiment and district-level housing prices

2019 ◽  
Vol 12 (2) ◽  
pp. 173-189
Author(s):  
Christopher Hannum ◽  
Kerem Yavuz Arslanli ◽  
Ali Furkan Kalay
Keyword(s):  
News Media ◽  
Housing Prices ◽  
District Level ◽  
Future Research ◽  
Scoring Method ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
Property Prices ◽  
Price Appreciation

Purpose Studies have shown a correlation and predictive impact of sentiment on asset prices, including Twitter sentiment on markets and individual stocks. This paper aims to determine whether there exists such a correlation between Twitter sentiment and property prices. Design/methodology/approach The authors construct district-level sentiment indices for every district of Istanbul using a dictionary-based polarity scoring method applied to a data set of 1.7 million original tweets that mention one or more of those districts. The authors apply a spatial lag model to estimate the relationship between Twitter sentiment regarding a district and housing prices or housing price appreciation in that district. Findings The findings indicate a significant but negative correlation between Twitter sentiment and property prices and price appreciation. However, the percentage of check-in tweets is found to be positively correlated with prices and price appreciation. Research limitations/implications The analysis is cross-sectional, and therefore, unable to answer the question of whether Twitter can Granger-cause changes in housing markets. Future research should focus on creation of a property-focused lexicon and panel analysis over a longer time horizon. Practical implications The findings suggest a role for Twitter-derived sentiment in predictive models for local variation in property prices as it can be observed in real time. Originality/value This is the first study to analyze the link between sentiment measures derived from Twitter, rather than surveys or news media, on property prices.

scholarly journals Effect of input credit on smallholder farmers’ output and income

2018 ◽  
Vol 78 (1) ◽  
pp. 98-115 ◽  
Author(s):  
Adam Iddrisu ◽  
Isaac Gershon Kodwo Ansah ◽  
Paul Kwame Nkegbe
Keyword(s):  
Smallholder Farmers ◽  
Northern Region ◽  
Northern Ghana ◽  
Primary Data ◽  
Future Research ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
Future Policy ◽  
On Farm

Purpose The purpose of this paper is to examine the effect of input credit on smallholder farmers’ output and income using Masara N’Arziki support project in Northern Ghana. Design/methodology/approach A cross-sectional primary data set was used to estimate the effect of project participation on farm output, yield and income using propensity score matching (PSM) methods. Findings The findings are that project participation is skewed towards experienced farmers with big-sized households and farms. The effect of project on outcomes is somewhat unsatisfactory in the sense that participation only raises output and yield, but not income. Research limitations/implications The paper only examined the project effect on farm outcomes among smallholder farmers participating in the programme in just one operational area in the Northern region. Future research should consider all the operational areas for an informed generalisation of findings. Practical implications Greater benefits to farmers from programme participation would require project management to review the contractual arrangement so that the high cost of input credit is significantly reduced. Originality/value The paper applied the PSM to estimate the effect of project participation on farm output, yield and income among smallholder farmers which is non-existent in the literature on the study area, at least as far as we know. This paper can inform future policy on the direction and nature of support for smallholder farmers in Northern Ghana.

Determinants of housing prices in Baku: empirical analyses

2019 ◽  
Vol 12 (2) ◽  
pp. 281-297 ◽  
Author(s):  
Khatai Aliyev ◽  
Mehin Amiraslanova ◽  
Nigar Bakirova ◽  
Narmin Eynizada
Keyword(s):  
Housing Market ◽  
Regression Models ◽  
Polynomial Regression ◽  
Housing Prices ◽  
Ordinary Least Squares ◽  
Real Estate Market ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
Factors Affecting

Purpose This paper aims to reveal major factors affecting housing prices (flats and houses) in Baku, the capital of Azerbaijan Republic. Design/methodology/approach Based on cross-sectional data set of 497 flats and 443 houses, polynomial regression models are estimated for flats and houses separately. Regression models are estimated by using ordinary least squares. Findings Location, largeness, repair level and existence of bill of sale are major price determinants for flats. For houses, number of rooms also matters. Findings reveals that houses are land intensive (more floors, less land area) toward city center, and vice versa. Price difference due to existence of bill of sale diminishes significantly toward the surrounding areas. Research limitations/implications The data set represents view of sellers and does not take into consideration price bargaining in time of sale; probability of information asymmetries exists which not could accounted for, and urgency of sale is not considered. Practical implications Estimation results can be used for housing valuation by real estate market participants and investors. Social implications Research findings reveal importance of bill of sale as a major price determinant and expected to attract policymakers’ attention to solve such a big social problem. Additionally, models can be based for price estimations in Baku housing market. Originality/value The study contributes to the literature by empirically analyzing housing market in Baku, Azerbaijan. Research produces new practically valuable findings.

The influence of board characteristics on corporate illegality

2017 ◽  
Vol 25 (2) ◽  
pp. 133-148 ◽  
Author(s):  
Gundeep Kaur Virk
Keyword(s):  
Board Of Directors ◽  
Large Fraction ◽  
Future Research ◽  
Manufacturing Companies ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Board Of Directors ◽  
The Impact ◽  
The Relationship

Purpose In light of frequent corporate scams and frauds, this paper aims to investigate the relationship of corporate illegality with the board of directors’ characteristics in Indian manufacturing companies. Design/methodology/approach The board of director characteristics of sample companies charged with violation of the Securities Exchange Board of India (SEBI) regulations from 2008 to 2013 are matched to an equivalent-sized control data set. A cross-sectional logistic regression model is applied to test the hypothesized association. Findings The findings suggest that the SEBI violations are less likely to occur when a large fraction of the board of directors consists of independent directors and when the individual directors have multiple appointments on the boards of other companies. However, it is observed that the size of the board and its meetings have no observable association with violation of the SEBI regulations. Research limitations/implications This work is likely to aid future research in exploring the impact of governance mechanisms on the occurrence of illegality. In future, studies may be conducted to investigate the probability of illegal corporate events using a larger sample size and corporate governance variables which have not been examined in the present study. Practical implications The analysis provides corporate policy makers and investors an insight to evaluate the vulnerability of a company being engaged in illegality based on its board features. Originality/value The present study is distinct from previous reports as it makes a novel attempt to gauge the relationship between the board of directors’ characteristics and the occurrence of illegality in the Indian corporate section.

The influence of movie's quality on its performance: evidence based on Oscar Awards

2014 ◽  
Vol 24 (2) ◽  
pp. 122-138 ◽  
Author(s):  
Weiling Zhuang ◽  
Barry Babin ◽  
Qian Xiao ◽  
Mihaela Paun
Keyword(s):  
Cross Sectional Study ◽  
Future Research ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
Quality Signal ◽  
Performance Quality ◽  
Quality Literature ◽  
Box Office ◽  
Quality Signals

Purpose – The purpose of this paper is to develop and empirically test a new framework that shows how different signals of movie quality along with key control variables affect consumers’ post-consumption evaluations, critics’ reviews (CR), and movie box office revenues. Design/methodology/approach – The data set consists of a sample of 332 movies released between 2000 and 2008. Regression was used to test the study hypotheses. Findings – The results suggest that the three signals of movie quality exhibit different effects on three movie performance measures. Of the three cues, the peripheral quality signal is positive related to movie box, moviegoers’ evaluations (ME), and CR. Furthermore, star performance quality is positive related to both ME and CR. Surprisingly, overall quality signal does not display any influence on movie performances. Research limitations/implications – The primary limitation is the use of cross-sectional study design and future research should apply for time-series technique to test the relationships between movie quality signals and movie performances. Practical implications – The findings suggest that consumers and critics evaluate movie qualities based on various movie quality signals. Furthermore, the characteristics of movies also have mixed impacts on movie performances. Movie studios may take these findings into account to produce better movies. Originality/value – This study proposes and empirically tests the impacts of three groups of movie signals – peripheral quality signal, star performance quality signal, and overall quality signal on motion picture performance. This study contributes to service quality literature and signal theory by categorizing different Academy Awards into three groups of quality signals and by empirically testing the proposed relationships.

Financial distress determinants among SMEs: empirical evidence from Sweden

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Large Scale ◽  
Global Financial Crisis ◽  
Binary Logistic Regression ◽  
Data Availability ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

Determinants of housing demand in the Erzurum province, Turkey

2014 ◽  
Vol 7 (4) ◽  
pp. 586-602 ◽  
Author(s):  
Erkan Oktay ◽  
Abdulkerim Karaaslan ◽  
Ömer Alkan ◽  
Ali Kemal Çelik
Keyword(s):  
Housing Prices ◽  
Binary Logistic Regression ◽  
Housing Demand ◽  
Outcome Variable ◽  
Binary Logistic Regression Analysis ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Content Type ◽  
Comprehensive Survey ◽  
Income Expectations

Purpose – The main aim of this study is to determine the factors that influence the housing demand of households in Erzurum, northeastern Turkey. Housing demand is generally affected by several factors including housing prices, individuals’ income, expectations and choices and so on, as a means of its demographic and socio-psychological contexts. Design/methodology/approach – A questionnaire-based cross-sectional survey was carried out, in which the outcome variable had binary responses such as whether to invest in housing or not. A binary logistic regression analysis was performed to estimate the underlying data. Findings – The questionnaire was conducted in 2,927 households living in Erzurum city center, and 47 per cent of the respondents claimed that they would consider investing in housing in the future. The estimation results reveal that demographic or socio-economic factors that may possibly influence housing demand of the respondents are as follows: household head’s and spouse’s occupation, monthly income, the number of individuals in the family and car ownership. Originality/value – This paper involves the most comprehensive survey addressing the housing demand in the East Anatolian Region, Turkey. Additionally, this paper aims to contribute to the existing housing literature through establishing the statistical analysis of housing demand in an unstudied territory of the world.

Regulation and information asymmetry

2008 ◽  
Vol 16 (1) ◽  
pp. 59-76 ◽  
Author(s):  
Robyn McLaughlin ◽  
Assem Safieddine
Keyword(s):  
Information Asymmetry ◽  
Stock Returns ◽  
Operating Performance ◽  
Cost Benefit ◽  
Future Research ◽  
Information Asymmetries ◽  
Cross Sectional ◽  
Industrial Firms ◽  
Content Type ◽  
Regulated Utilities

PurposeThis paper seeks to examine the potential for regulation to reduce information asymmetries between firm insiders and outside investors.Design/methodology/approachExtensive prior research has established that there are substantial effects of information asymmetry in seasoned equity offers (SEOs). The paper tests for a mitigating effect of regulation on such information asymmetries by examining differences in long‐run operating performance, changes in that performance, and announcement‐period stock returns between unregulated industrial firms and regulated utilities that issue seasoned equity. The authors also segment the samples by firm size, since smaller firms are likely to have greater asymmetries.FindingsConsistent with regulated utility firms having lower levels of information asymmetry, they have superior changes in abnormal operating performance than industrial firms pre‐ to post‐issue and their announcement period returns are significantly less negative. These findings are most pronounced for the smallest firms, firms likely to have the greatest information asymmetries and where regulation could have its greatest effect.Research limitations/implicationsThe paper does not examine costs of regulation. Thus, future research could seek to measure the cost/benefit trade‐off of regulation in reducing information asymmetry. Also, future research could examine cross‐sectional differences between different industries and regulated utilities.Practical implicationsRegulation reduces information asymmetry. Thus, regulation or mandated disclosure may be appropriate in industries/markets where information asymmetry is severe.Originality/valueThis paper is the first to compare the operating performance of regulated and unregulated SEO firms.

scholarly journals Unemployment and spell duration during the Great Recession in the EU

2015 ◽  
Vol 36 (2) ◽  
pp. 216-235 ◽  
Author(s):  
Carlos Gradín ◽  
Olga Cantó ◽  
Coral del Río
Keyword(s):  
Great Recession ◽  
Negative Impact ◽  
Labour Force ◽  
Future Research ◽  
Cross Sectional ◽  
Content Type ◽  
The Great Recession ◽  
Unemployment Spells ◽  
Practical Implications ◽  
The Eu

Purpose – The purpose of this paper is to analyze the different dynamic characteristics of unemployment in a selected group of European Union countries during the current Great Recession, which had unequal consequences on employment depending on the country considered. Design/methodology/approach – The paper follows Shorrocks’s proposal of a duration-sensitive measure of unemployment, and uses cross-sectional data reported by Eurostat coming from European Labour Force Surveys. Findings – The results add some evidence on the relevance of incorporating spells’ duration in measuring unemployment, finding remarkable differences in unemployment patterns in time among European countries. Research limitations/implications – In this paper unemployment is analyzed for all the labor force. Future research should investigate patterns across specific groups such as young people, women, immigrants or the low skilled. Practical implications – It is generally accepted that the negative impact of unemployment on individual welfare can be very different depending on its duration. However, conventional statistics on unemployment do not adequately capture to what extent the recession is not only increasing the incidence of unemployment but also its severity in terms of duration in time of ongoing unemployment spells. The paper shows an easy and practical way to do it in order to improve the understanding of the unemployment phenomenon, using information usually reported by statistical offices. Originality/value – First, the paper provides a tool for dynamic analysis of unemployment based on reported cross-sectional data. Second, the paper demonstrates the empirical relevance of considering spells’ duration when assessing differences in unemployment across countries or in unemployment trends. This is usually neglected or only partially addressed by most conventional measures of unemployment.

Comparing predictive performance of general regression neural network (GRNN) and hedonic regression model for factors affecting housing prices in “Pune-India”

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abhijat Arun Abhyankar ◽  
Harish Kumar Singla
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Multivariate Regression ◽  
Housing Prices ◽  
Predictive Performance ◽  
General Regression Neural Network ◽  
Multivariate Regression Model ◽  
Data Set ◽  
Content Type ◽  
General Regression

Purpose The purpose of this study is to compare the predictive performance of the hedonic multivariate regression model with the probabilistic neural network (PNN)-based general regression neural network (GRNN) model of housing prices in “Pune-India.” Design/methodology/approach Data on 211 properties across “Pune city-India” is collected. The price per square feet is considered as a dependent variable whereas distances from important landmarks such as railway station, fort, university, airport, hospital, temple, parks, solid waste site and stadium are considered as independent variables along with a dummy for amenities. The data is analyzed using a hedonic type multivariate regression model and GRNN. The GRNN divides the entire data set into two sets, namely, training set and testing set and establishes a functional relationship between the dependent and target variables based on the probability density function of the training data (Alomair and Garrouch, 2016). Findings While comparing the performance of the hedonic multivariate regression model and PNN-based GRNN, the study finds that the output variable (i.e. price) has been accurately predicted by the GRNN model. All the 42 observations of the testing set are correctly classified giving an accuracy rate of 100%. According to Cortez (2015), a value close to 100% indicates that the model can correctly classify the test data set. Further, the root mean square error (RMSE) value for the final testing for the GRNN model is 0.089 compared to 0.146 for the hedonic multivariate regression model. A lesser value of RMSE indicates that the model contains smaller errors and is a better fit. Therefore, it is concluded that GRNN is a better model to predict the housing price functions. The distance from the solid waste site has the highest degree of variable senstivity impact on the housing prices (22.59%) followed by distance from university (17.78%) and fort (17.73%). Research limitations/implications The study being a “case” is restricted to a particular geographic location hence, the findings of the study cannot be generalized. Further, as the objective of the study is restricted to just to compare the predictive performance of two models, it is felt appropriate to restrict the scope of work by focusing only on “location specific hedonic factors,” as determinants of housing prices. Practical implications The study opens up a new dimension for scholars working in the field of housing prices/valuation. Authors do not rule out the use of traditional statistical techniques such as ordinary least square regression but strongly recommend that it is high time scholars use advanced statistical methods to develop the domain. The application of GRNN, artificial intelligence or other techniques such as auto regressive integrated moving average and vector auto regression modeling helps analyze the data in a much more sophisticated manner and help come up with more robust and conclusive evidence. Originality/value To the best of the author’s knowledge, it is the first case study that compares the predictive performance of the hedonic multivariate regression model with the PNN-based GRNN model for housing prices in India.

The emergence of entrepreneurial team as a research field – way forward

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Willy Das ◽  
Satyasiba Das ◽  
Manojit Chattopadhyay
Keyword(s):  
Research Agenda ◽  
Research Field ◽  
Academic Disciplines ◽  
Future Research ◽  
Cross Sectional ◽  
Content Type ◽  
Future Research Agenda ◽  
Starting Point ◽  
Entrepreneurial Team ◽  
Research Stream

PurposeThe purpose of this paper is to review and critique the existing literature on entrepreneurial teams (ET) by taking a multi-disciplinary viewpoint and provide a future research agenda based on the identified themes and trends.Design/methodology/approachA systematic literature review (SLR) was undertaken using “business source complete”. Further scrutiny and application of exclusion criteria led to a final sample consisting of 139 papers from 27 different journals belonging to not just entrepreneurship and strategic management but also other disciplines like OB, finance, sociology, psychology, etc. Using qualitative thematic analysis, the authors identified 11 major themes.FindingsThe paper reviews both the eleven themes and the linkages between the themes. Thereby identifying areas that have been understudied and those that have received comparatively more attention. The review revealed that the research stream possesses certain conceptual and methodological concerns apart from its cross-sectional and primarily bivariate nature. Five such main concerns have been identified and discussed in detail. Other elements of the resulting research agenda include calls for more clinical process-oriented research, further attention to context, shifting the level of analysis, and a need to integrate across disciplines.Originality/valueThis paper incorporates a broad insight of ET across academic disciplines to show how future contributions could benefit by incorporating research from other fields. In doing so, provides a starting point for more nuanced discussions around the interrelationships between the different conversations that are taking place in the ET literature.

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