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This paper aims to investigate the impact of leverage on the cost of capital and market value in the Indonesia Stock Exchange (IDX), where there are Sharīʿah and non-Sharīʿah compliant firms.

This study uses a mixed methods sequential exploratory design and is based on an empirical analysis undertaken with a sample of firms listed on the IDX. In particular, a qualitative analysis was conducted to identify the Sharīʿah-compliant firms and the qualitative study was designed to compare some financial elements in Sharīʿah and non-Sharīʿah compliant listed companies. The correlations among the main elements observed are considered and a principal component analysis describes the framework.

First, the results of the analysis show that for the Sharīʿah-compliant companies, identified as those that apply Islamic principles, the lower level of leverage that it is typical of these type of firms implies a higher cost of capital [cost of equity and weighted average cost of capital (WACC)] than non-Sharīʿah ones. Secondly, for the Sharīʿah-compliant companies, the lower level of leverage entails a higher market value measured by the multiples method (price/earning and enterprise value/operating profit) than for non-Sharīʿah ones.

This paper sheds new light on how leverage can affect the cost of capital and market value in the case of Sharīʿah and non-Sharīʿah compliant listed companies in the IDX. In particular, this research highlights the fact that Sharīʿah-compliant firms, despite having a higher WACC, create more market value compared to non-Sharīʿah compliant ones.

The aim of the paper is to test and demonstrate the potential benefits in applying reinforcement learning instead of traditional methods to optimize the content of a company's mobile application to best help travellers finding their ideal flights. To this end, two approaches were considered and compared via simulation: standard randomized experiments or A/B testing and multi-armed bandits.

The simulation of the two approaches to optimize the content of its mobile application and, consequently, increase flights conversions is illustrated as applied by Skyscanner, using R software.

The first results are about the comparison between the two approaches – A/B testing and multi-armed bandits – to identify the best one to achieve better results for the company. The second one is to gain experiences and suggestion in the application of the two approaches useful for other industries/companies.

The case study demonstrated, via simulation, the potential benefits to apply the reinforcement learning in a company. Finally, the multi-armed bandit was implemented in the company, but the period of the available data was limited, and due to its strategic relevance, the company cannot show all the findings.

The right algorithm can change according to the situation and industry but would bring great benefits to the company's ability to surface content that is more relevant to users and help improving the experience for travellers. The study shows how to manage complexity and data to achieve good results.

The paper describes the approach used by an European leading company operating in the travel sector in understanding how to adapt reinforcement learning to its strategic goals. It presents a real case study and the simulation of the application of A/B testing and multi-armed bandit in Skyscanner; moreover, it highlights practical suggestion useful to other companies.