Corporate cash holdings always involve a trade-off between benefits and costs. This study investigates the determinants of corporate cash holdings. Cash flows, leverage, liquidity, cash flows volatility, profitability, growth opportunities, firm size, debt maturity, and dividend represent the independent variables in the research study. It is based on a panel data of 150 Pakistani non-financial listed firms on KSE during the period 2004-2012. Panel regression analysis has been conducted to determine the major factors affecting cash holdings. The results imply that growth opportunity, company size, cash flows, and profitability of the firms exert a positive effect while leverage and liquidity show a significant negative impact on corporate cash holding. The findings indicated that both pecking order theory and trade-off theory play an important role in explaining the determinants of corporate cash holdings. The findings are beneficial for managers, shareholders, investors, regulatory bodies, and researchers for developing appropriate policies.

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This is an explanatory study on the impact of financial literacy and parental socialization on the saving behavior of university level students. Using primary data collection method, 400 questionnaires were distributed to the students of universities across Pakistan. Pearson correlation and multiple regression analysis are employed by using SPSS. Our results demonstrate that financial literacy and parental socialization positively influence the saving behavior of students. Our result revealed that the students who have financial literacy exhibit more saving behavior as compared to others who do not have financial knowledge. It is also concluded that the student willingness to save increased due to receiving financial education from their parents.

This study evaluates the varying degree of predictability of commodities return through empirical analysis of AMH (Adaptive Market Hypothesis). We divide daily returns data (from 1996 to 2013) of commodities indices (Gold, Metal, Oil& Silver) into different crisis periods. We subject all the subsamples to linear/nonlinear tests to reveal how market efficiency (independency of returns) has behaved over time. All the linear (except variance ratio) and nonlinear tests are evident that commodity indices returns have been predictable (dependent) in some crisis periods while unpredictable (dependence) in the others thus consistent with the implication of AMH. Therefore, commodities markets are adaptive markets. The findings suggest the behavior of commodities’ markets is best explained by AMH than conventional/traditional EMH (Efficient Market Hypothesis).

PurposeBusiness Intelligence has gained a significant attraction in the recent past and facilitates managers for efficient business decision-making. Over the years, the attraction toward the cryptocurrency (CC) market has increased. Since the CC market is highly volatile, it is extremely sensitive to shocks and web data related to large events happening around the globe.Design/methodology/approachThis research study provides a business intelligence model to predict five top-performing CCs. In this study, deep learning, linear regression and support vector regression (SVR) are used to predict CC prices. The sentiment of some mega-events is also used to enhance the performance of these models.FindingsThe results show that models of business intelligence such as deep learning and SVR provide better results. Moreover, the results show that the incorporation of social media sentiment data significantly improves the performance of the proposed models. The overall accuracy of the model improves approximately twofold when multiple event sentiments were incorporated.Originality/valueThe use of social media sentiment of global and local events for different countries along with deep learning for CC forecasting.