Big data tools for Islamic financial analysis

Behavioural science states that emotions, principles and the manner of thinking can affect the behaviour of individuals and even investors in their decision making on financial markets. In this paper, we have tried to measure the investor sentiment by three means of big data. The first is based on a search query of a list of words related to Islamic context. The second is inferred from the engagement degree on social media. The last measure of sentiment is built, based on the Twitter API classified into positive and negative directions by a machine learning algorithm based on the naive Bayes method. Then, we investigate whether these sensations and emotions have an impact on the market sentiment and the price fluctuations by means of a vector autoregression model and Granger causality analysis. In the final step, we apply the agent‐based simulation by means of the sequential Monte Carlo method with the control of our Twitter measure on Islamic index returns. We show, then, that the three social media sentiment measures present a remarkable impact on the contemporaneous and lagged returns of the different Islamic assets studied. We also give an estimation of the parameters of the latent variables relative to the agent model studied.     

Promoting knowledge sharing in Tunisian KIFs through HRM Practices. The mediating role of human capital and learning climate

Abstract

Although human capital and learning climate have been considered in the SHRM Literature as two critical predictors of organizational success, little efforts have been made to examining their role in knowledge sharing. Our research aims at filling this gap. Using data from 203 knowledge workers in seven Tunisian KIFs, our study provides evidence, on a first level, for a direct link between the five HR practices and knowledge sharing. On a second level, we report a mediating role of human capital. In congruence with prior SHRM research, this result implies that the knowledge-based HR practices influence the value and uniqueness of the employees’ KSAOs, which in turn will improve their knowledge sharing behavior. On a third and final level, our results suggest that learning climate plays a mediation role as well. Such a result echoes recent research findings that confirmed the importance of learning climates in shaping employees’ behaviors toward achieving specific organizational objectives. Our findings provide important insights on the intermediary mechanisms between HRM practices and knowledge sharing behavior in KIFs and offer interesting research perspectives on the under-theorized role of human capital in a knowledge sharing context.     

Forecasting Inflation Using Constant Gain Least Squares

This paper assesses the usefulness of constant gain least squares when forecasting inflation. An out‐of‐sample forecast exercise is conducted, in which univariate autoregressive models for inflation in Australia, Sweden, the United Kingdom and the United States are used. The results suggest that it is possible to improve the forecast accuracy by employing constant gain least squares instead of ordinary least squares. In particular, when using a gain of 0.05, constant gain least squares generally outperforms the corresponding autoregressive model estimated with ordinary least squares. In fact, at longer forecast horizons, the root mean square forecast error is reliably lowered for all four countries and for all lag lengths considered in the study.     

Using design science to improve web search innovation in real estate

We analyze 14 leading real estate websites, seven residential and seven commercial. They exhibit limited demographic and geographic information systems (GIS) data display capability. They exhibit economic data display capability except one, and lack demographic, economic, and GIS data search capability. Real estate web searching is inefficient, time consuming, and inflexible. To overcome these limitations, we use design-science to create three search modes (Sc2, Sc3, and Sc4). Sc2 uses demographic data, Sc3 economic data, and Sc4 GIS data.