Information content of web-based stock ratings: the case of Motley fool CAPS data

2018 ◽  
Vol 15 (3) ◽  
pp. 393-410
Author(s):  
Arvind Mahajan
Keyword(s):  
Information Content ◽  
Factor Model ◽  
Abnormal Returns ◽  
Predictive Ability ◽  
Data Set ◽  
Web Based ◽  
Stock Performance ◽  
Content Type ◽  
Unique Data ◽  
Asset Pricing Models

Purpose The purpose of this paper is to answer a fundamental question – are individual stock picks by a particular internet investment community informative enough to beat the market? The author observes that the stock picks by the CAPS community are reflective of existing information and portfolios based upon CAPS community stock rankings do not generate abnormal returns. The CAPS community is good at tracking existing performance but, it lacks predictive ability. Design/methodology/approach The study uses a unique data set of stock ratings from Motley Fools CAPS community to determine the information content embedded in these ratings. Observing predictive ability of this web-based stock ratings forum will raise questions about the efficiency of the financial markets. The author forms stock portfolios based on stocks’ star ratings, and star rating changes, and test if the long-short portfolio strategy generates significant α after controlling for single, and multi-factor asset pricing models, such as Fama-French three-factor model and Carhart four-factor model. Findings The paper finds no evidence that the CAPS community ratings contain “information content,” which can be exploited to generate abnormal returns. CAPS community ratings are good at tracking existing stock performance, but cannot be used to make superior forecasts to generate abnormal returns. The findings are consistent with the efficient market hypothesis. Furthermore, the author provides evidence that CAPS community ratings are themselves determined by stock performance rather than the other way around. Originality/value The study employs a unique data set capturing the stock ratings of a very popular web-based investment community to evaluate its ability to make better than random forecasts. Besides applying well-accepted asset pricing models to generate α, the study conducts causality tests to discern a causal relation between stock ratings and stock performance.

User password repetitive patterns analysis and visualization

2016 ◽  
Vol 24 (1) ◽  
pp. 93-115 ◽  
Author(s):  
Xiaoying Yu ◽  
Qi Liao
Keyword(s):  
Security Policy ◽  
Large Data ◽  
Efficient Algorithms ◽  
Privacy And Security ◽  
Data Set ◽  
Web Based ◽  
Content Type ◽  
Word Cloud ◽  
Individual Privacy ◽  
Password Security

Purpose – Passwords have been designed to protect individual privacy and security and widely used in almost every area of our life. The strength of passwords is therefore critical to the security of our systems. However, due to the explosion of user accounts and increasing complexity of password rules, users are struggling to find ways to make up sufficiently secure yet easy-to-remember passwords. This paper aims to investigate whether there are repetitive patterns when users choose passwords and how such behaviors may affect us to rethink password security policy. Design/methodology/approach – The authors develop a model to formalize the password repetitive problem and design efficient algorithms to analyze the repeat patterns. To help security practitioners to analyze patterns, the authors design and implement a lightweight, Web-based visualization tool for interactive exploration of password data. Findings – Through case studies on a real-world leaked password data set, the authors demonstrate how the tool can be used to identify various interesting patterns, e.g. shorter substrings of the same type used to make up longer strings, which are then repeated to make up the final passwords, suggesting that the length requirement of password policy does not necessarily increase security. Originality/value – The contributions of this study are two-fold. First, the authors formalize the problem of password repetitive patterns by considering both short and long substrings and in both directions, which have not yet been considered in past. Efficient algorithms are developed and implemented that can analyze various repeat patterns quickly even in large data set. Second, the authors design and implement four novel visualization views that are particularly useful for exploration of password repeat patterns, i.e. the character frequency charts view, the short repeat heatmap view, the long repeat parallel coordinates view and the repeat word cloud view.

Predictive view of the value relevance of earnings in India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hajam Abid Bashir ◽  
Manish Bansal ◽  
Dilip Kumar
Keyword(s):  
Capital Investment ◽  
Stock Return ◽  
Value Relevance ◽  
Stock Exchange ◽  
Predictive Ability ◽  
Cash Flows ◽  
Data Set ◽  
Content Type ◽  
Uniform Sample ◽  
Reported Earnings

Purpose This study aims to examine the value relevance of earnings in terms of predicting the value variables such as cash flow, capital investment (CI), dividend and stock return under the Indian institutional settings. Design/methodology/approach The study used panel Granger causality tests to examine causality relationships among variables and panel data regression models to check the statistical associations between earnings and value variables. Findings Based on a data set of 7,280 Bombay Stock Exchange-listed firm-years spanning over ten years from March 2009 to March 2018, the results show higher sensitivity of earnings toward cash flows, CI, divided and stock return and vice-versa. Further, the findings deduced from the empirical results demonstrate that earnings are positively related to value variables. Overall, the results established that earnings are value-relevant and have predictive ability to forecast the value variables that facilitate investors in portfolio valuation. The results are consistent with the predictive view of the value relevance of earnings. Several robustness checks confirm these results. Originality/value This study brings new empirical evidence from a distinct capital market, India, and provides a new facet to the value relevance debate in terms of its prediction view. The study is among earlier attempts that jointly measure the ability of earnings in forecasting different value variables by taking a uniform sample of firms at the same period. Hence, the study provides a comprehensive view of the predictive ability of reported earnings.

Artificial intelligence-based automatic visual inspection system for built heritage

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lukman E. Mansuri ◽  
D.A. Patel
Keyword(s):  
Artificial Intelligence ◽  
Visual Inspection ◽  
Image Data ◽  
Inspection System ◽  
Data Set ◽  
Web Based ◽  
Content Type ◽  
Built Heritage ◽  
Automatic Visual Inspection ◽  
Inspection Systems

PurposeHeritage is the latent part of a sustainable built environment. Conservation and preservation of heritage is one of the United Nations' (UN) sustainable development goals. Many social and natural factors seriously threaten heritage structures by deteriorating and damaging the original. Therefore, regular visual inspection of heritage structures is necessary for their conservation and preservation. Conventional inspection practice relies on manual inspection, which takes more time and human resources. The inspection system seeks an innovative approach that should be cheaper, faster, safer and less prone to human error than manual inspection. Therefore, this study aims to develop an automatic system of visual inspection for the built heritage.Design/methodology/approachThe artificial intelligence-based automatic defect detection system is developed using the faster R-CNN (faster region-based convolutional neural network) model of object detection to build an automatic visual inspection system. From the English and Dutch cemeteries of Surat (India), images of heritage structures were captured by digital camera to prepare the image data set. This image data set was used for training, validation and testing to develop the automatic defect detection model. While validating this model, its optimum detection accuracy is recorded as 91.58% to detect three types of defects: “spalling,” “exposed bricks” and “cracks.”FindingsThis study develops the model of automatic web-based visual inspection systems for the heritage structures using the faster R-CNN. Then it demonstrates detection of defects of spalling, exposed bricks and cracks existing in the heritage structures. Comparison of conventional (manual) and developed automatic inspection systems reveals that the developed automatic system requires less time and staff. Therefore, the routine inspection can be faster, cheaper, safer and more accurate than the conventional inspection method.Practical implicationsThe study presented here can improve inspecting the built heritages by reducing inspection time and cost, eliminating chances of human errors and accidents and having accurate and consistent information. This study attempts to ensure the sustainability of the built heritage.Originality/valueFor ensuring the sustainability of built heritage, this study presents the artificial intelligence-based methodology for the development of an automatic visual inspection system. The automatic web-based visual inspection system for the built heritage has not been reported in previous studies so far.

Does credit market inefficiency affect technology adoption? Evidence from Sub-Saharan Africa

2016 ◽  
Vol 76 (4) ◽  
pp. 494-511 ◽  
Author(s):  
Abdul-Hanan Abdallah
Keyword(s):  
Technology Adoption ◽  
Credit Market ◽  
Sub Saharan Africa ◽  
Food Crops ◽  
Agricultural Technologies ◽  
Data Set ◽  
Content Type ◽  
Unique Data ◽  
Market Inefficiency ◽  
Sub Saharan

Purpose The purpose of this paper is to investigate factors affecting the adoption of agricultural technologies in Sub-Saharan Africa, specifically the role of credit market inefficiency in adoption of agricultural technologies in the region. Design/methodology/approach Most importantly, the paper applies a 2SLS model on a unique data set on nine agrarian countries from Sub-Saharan Africa’s intensification of food crops agriculture (Afrint) to provide evidence on how credit market inefficiency affects adoption of technologies in the sub region. Findings The study finds that the relationship between credit and technology adoption is one-way causal relation (i.e. credit access leads to technology adoption) as opposed to a two-way relation (i.e. mutual dependent relation). Further, the results indicate that credit market inefficiency can be a major barrier to the adoption of yield enhancing technologies in Sub-Saharan Africa. Further, the study showed mixed results for household variables. The results give credence to studies that highlight the importance of infrastructure and risk control in the adoption of new technologies. Research limitations/implications The study is limited to only nine countries in Sub-Saharan Africa. Thus, the findings and interpretations should be considered as such. Further, there is the need for further research that considers all the region so as to establish whether or not there is a relationship between credit market inefficiencies and technology adoption in the region. Practical implications The policy implication is that microfinance institutions should consider scaling up their credit services to ensure that more households benefit from it, and in so doing technology adoption will be enhanced. Originality/value The main contribution of the study lies in its use of a unique data set from Sub-Saharan Africa’s intensification of food crops agriculture (Afrint) to investigation relationship between credit market inefficiency and technology adoption.

Economics of savings-based finance: an interest-free model of rotating savings and credit association in Turkey

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yusuf Dinç ◽  
Rashed Jahangir ◽  
Ruslan Nagayev ◽  
Fahrettin Çakır
Keyword(s):  
Financial Market ◽  
Design Methodology ◽  
Data Set ◽  
Content Type ◽  
Unique Data ◽  
Credit Associations ◽  
Free Model ◽  
Savings And Credit Associations ◽  
Financing And Investment ◽  
Credit Association

Purpose The emerging markets have been witnessing a remarkable revival of rotating savings and credit associations (ROSCAs) serving as alternative informal financing and investment platforms, also known as savings-based finance (SBF) in Turkey. The purpose of this study is to present the SBF model mathematically, analyse the performance of the SBF sector and propose a new Sharīʿah-compliant SBF model for the acquisition of durables. Design/methodology/approach The paper thoroughly reviews the concept and practice of ROSCA across the globe, mathematically models and empirically analyses the performance of Turkish SBF companies using a unique data set. Findings The study formulates a two-person SBF model and proposes a Mudarabah-Wakalah hybrid model with a new investment feature. It is found that the concept of ROSCA is being operationalized in 105 countries across the globe under different names with slight business model modifications. The research also reveals that the demand for financing of durables in Turkey significantly increased in recent years with the demand for housing is twice greater compared to vehicles. Most importantly, a strong significant inter- and intra-comovement is observed between these durables implying that the success of the sector in one segment has attracted the customers to other SBF products. It shows that the SBF institutions can effectively serve as the alternative financing houses for pooling savings and financing the durables, and they have strong potential to capture a larger financial market share in Turkey and even globally. Originality/value The study constructs mathematical models and proposes a new investment wing to an existing SBF wealth fund.

Early prediction of chronic disease using an efficient machine learning algorithm through adaptive probabilistic divergence based feature selection approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sandeepkumar Hegde ◽  
Monica R. Mundada
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Chronic Disease ◽  
Learning Algorithm ◽  
Predictive Ability ◽  
Experimental Result ◽  
Data Set ◽  
Content Type ◽  
Learning Classifier ◽  
Feature Selection Approach

Purpose According to the World Health Organization, by 2025, the contribution of chronic disease is expected to rise by 73% compared to all deaths and it is considered as global burden of disease with a rate of 60%. These diseases persist for a longer duration of time, which are almost incurable and can only be controlled. Cardiovascular disease, chronic kidney disease (CKD) and diabetes mellitus are considered as three major chronic diseases that will increase the risk among the adults, as they get older. CKD is considered a major disease among all these chronic diseases, which will increase the risk among the adults as they get older. Overall 10% of the population of the world is affected by CKD and it is likely to double in the year 2030. The paper aims to propose novel feature selection approach in combination with the machine-learning algorithm which can early predict the chronic disease with utmost accuracy. Hence, a novel feature selection adaptive probabilistic divergence-based feature selection (APDFS) algorithm is proposed in combination with the hyper-parameterized logistic regression model (HLRM) for the early prediction of chronic disease. Design/methodology/approach A novel feature selection APDFS algorithm is proposed which explicitly handles the feature associated with the class label by relevance and redundancy analysis. The algorithm applies the statistical divergence-based information theory to identify the relationship between the distant features of the chronic disease data set. The data set required to experiment is obtained from several medical labs and hospitals in India. The HLRM is used as a machine-learning classifier. The predictive ability of the framework is compared with the various algorithm and also with the various chronic disease data set. The experimental result illustrates that the proposed framework is efficient and achieved competitive results compared to the existing work in most of the cases. Findings The performance of the proposed framework is validated by using the metric such as recall, precision, F1 measure and ROC. The predictive performance of the proposed framework is analyzed by passing the data set belongs to various chronic disease such as CKD, diabetes and heart disease. The diagnostic ability of the proposed approach is demonstrated by comparing its result with existing algorithms. The experimental figures illustrated that the proposed framework performed exceptionally well in prior prediction of CKD disease with an accuracy of 91.6. Originality/value The capability of the machine learning algorithms depends on feature selection (FS) algorithms in identifying the relevant traits from the data set, which impact the predictive result. It is considered as a process of choosing the relevant features from the data set by removing redundant and irrelevant features. Although there are many approaches that have been already proposed toward this objective, they are computationally complex because of the strategy of following a one-step scheme in selecting the features. In this paper, a novel feature selection APDFS algorithm is proposed which explicitly handles the feature associated with the class label by relevance and redundancy analysis. The proposed algorithm handles the process of feature selection in two separate indices. Hence, the computational complexity of the algorithm is reduced to O(nk+1). The algorithm applies the statistical divergence-based information theory to identify the relationship between the distant features of the chronic disease data set. The data set required to experiment is obtained from several medical labs and hospitals of karkala taluk ,India. The HLRM is used as a machine learning classifier. The predictive ability of the framework is compared with the various algorithm and also with the various chronic disease data set. The experimental result illustrates that the proposed framework is efficient and achieved competitive results are compared to the existing work in most of the cases.

Even education has its limits: closing the wage gap

2015 ◽  
Vol 42 (5) ◽  
pp. 908-928 ◽  
Author(s):  
Gil S. Epstein ◽  
Dalit Gafni ◽  
Erez Siniver
Keyword(s):  
Ethnic Group ◽  
Work Experience ◽  
Wage Gap ◽  
Economic Outcomes ◽  
Data Set ◽  
Universities And Colleges ◽  
Content Type ◽  
Ethiopian Immigrants ◽  
Unique Data ◽  
Long Run

Purpose – Economic outcomes are compared for university graduates in Israel belonging to four different ethnic groups. A unique data set is used that includes all individuals who graduated with a first degree from universities and colleges in Israel between the years 1995 and 2008 and which tracks them for up to ten years from the year they graduated. The main finding is that education and experience appear to have a strong effect on earnings in the long run and that an ethnic group can improve its position relative to certain groups while there is no effect relative to other groups. The paper aims to discuss these issues. Design/methodology/approach – The authors consider three of the main factors determining the success of assimilation: size of the ethnic group; cultural differences between groups and skin color; and examine how these factors affect economic outcomes. The authors use a unique data set that includes all individuals who graduated with a first degree from universities and colleges in Israel between the years 1995 and 2008. Findings – The results obtained in this study show that on average native Jews attain the best economic outcomes, followed by FSU immigrants, Israeli Arabs and finally Ethiopian immigrants. Education and experience appear to have a strong effect on earnings in the long run. An ethnic group can improve its position relative to other groups as they accumulate work experience. Originality/value – This is the first time that the Ethiopian immigrants where taken into account.

Evaluating alternative performance benchmarks for Indian mutual fund industry

2017 ◽  
Vol 14 (2) ◽  
pp. 222-250 ◽  
Author(s):  
Sanjay Sehgal ◽  
Sonal Babbar
Keyword(s):  
Asset Pricing ◽  
Mutual Fund ◽  
Factor Model ◽  
Five Factor Model ◽  
Content Type ◽  
Asset Pricing Model ◽  
Fund Managers ◽  
Asset Pricing Models ◽  
Performance Benchmarks ◽  
Fama And French

Purpose The purpose of this paper is to perform a relative assessment of performance benchmarks based on alternative asset pricing models to evaluate performance of mutual funds and suggest the best approach in Indian context. Design/methodology/approach Sample of 237 open-ended Indian equity (growth) schemes from April 2003 to March 2013 is used. Both unconditional and conditional versions of eight performance models are employed, namely, Jensen (1968) measure, three-moment asset pricing model, four-moment asset pricing model, Fama and French (1993) three-factor model, Carhart (1997) four-factor model, Elton et al. (1999) five-index model, Fama and French (2015) five-factor model and firm quality five-factor model. Findings Conditional version of Carhart (1997) model is found to be the most appropriate performance benchmark in the Indian context. Success of conditional models over unconditional models highlights that fund managers dynamically manage their portfolios. Practical implications A significant α generated over and above the return estimated using Carhart’s (1997) model reflects true stock-picking skills of fund managers and it is, therefore, worth paying an active management fee. Stock exchanges and credit rating agencies in India should construct indices incorporating size, value and momentum factors to be used for purpose of benchmarking. Originality/value The study adds new evidence as to applicability of established asset pricing models as performance benchmarks in emerging market India. It examines role of higher order moments in explaining mutual fund returns which is an under researched area.

Systemic operational risk

2017 ◽  
Vol 18 (3) ◽  
pp. 252-267 ◽  
Author(s):  
Thomas Kaspereit ◽  
Kerstin Lopatta ◽  
Suren Pakhchanyan ◽  
Jörg Prokop
Keyword(s):  
Factor Model ◽  
Abnormal Returns ◽  
Operational Risk ◽  
Spillover Effects ◽  
Market Reactions ◽  
Content Type ◽  
Banking Market ◽  
Specific Factors ◽  
Stock Market Reactions ◽  
High Degree

Purpose The aim of this paper is to study the information content of operational loss events occurring at European financial institutions with respect to the announcing bank’s industry rivals from an equity investor’s perspective. Design/methodology/approach The authors conduct an event study to identify spillover effects of operational loss events using the Carhart (1997) four-factor model as a benchmark model. In addition, they conduct multiple regression analyses to investigate the extent to which firm-specific factors or the market environment affect abnormal returns. Findings They observe significant negative abnormal returns following operational loss announcements exceeding € 50 million for both the announcing firms and their competitors. In addition, they find that stock market reactions occur only within a very small event window around the announcement date, indicating a high degree of market efficiency. Finally, abnormal returns tend to be insignificant for smaller loss amounts. Originality/value While operational risk is often believed to be strictly firm-specific, the results show that large operational risk events are not purely idiosyncratic; rather, they are systemic in the sense that they have contagious effects on non-event banks. Thus, the authors shed new light on how operational risk affects equity investors’ investment behaviour in an opaque and highly interconnected banking market.

scholarly journals Tests of two optimal incentive models for executive stock options

2011 ◽  
Vol 9 (1) ◽  
pp. 136-155
Author(s):  
Jean M. Canil ◽  
Bruce A. Rosser
Keyword(s):  
Executive Compensation ◽  
Stock Options ◽  
Abnormal Returns ◽  
Outside Directors ◽  
Data Set ◽  
Stock Volatility ◽  
Unique Data ◽  
Exercise Price ◽  
Grant Size

Using a unique data set, we test theoretical propositions relating to grant size and exercise price in determination of optimal executive compensation. For Hall and Murphy, pay-performance sensitivity does not behave as predicted with respect to CEO risk aversion and diversification, but the latter supports observed grant size while ATM grants exhibit positive abnormal returns as predicted. Consistent with Choe, exercise price is found inversely related to leverage. The unexpected positive relation between grant size and stock volatility is conjectured driven by CEOs’ influencing large grants, which are found associated with weak corporate governance but ameliorated by outside directors.

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