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The purpose of this paper is to investigate the relationship between Islamic banking and industrial production by decomposing Islamic financing (IF) into profit and loss sharing (PLS) and non-profit and loss sharing (non-PLS) modes of financing.

This paper applies the autoregressive distributed lag (ARDL) approach and Toda and Yamamoto causality test on the monthly data set for Malaysia from 2010M1 to 2018M6.

The results reveal that IF plays an important role in boosting industrial production in the short run, as well as in the long run. Moreover, this positive effect mainly comes from non-PLS financing. In contrast, no significant relationship was found between PLS financing and industrial development neither in the short run nor in the long run.

The results have several policy implications. The existence of a time lag between the pooling of funds through PLS contracts and their channeling to industrial activities imply that Malaysian Islamic banks should maintain a long-term relationship with investment account holders. In addition, Islamic banks are called to increase the portion of PLS financing. The positive relationship between the industrial production index and IF (through non-PLS techniques) in the short and the long runs implies that policymakers in Malaysia should multiply their efforts to further expand the Islamic banking industry.

The originality of this study lies in decomposing Islamic banks’ financing into PLS financing (muḍārabah and mushārakah) and non-PLS financing to assess the contribution of each mode of financing in industrial development.

This study aims to examine the dynamic bidirectional causality between oil price (OIL) and stock market indexes in net oil-exporting (Russia) and net oil-importing (China) countries.

The authors use monthly data for the period starting from October 1995 to October 2021. In this study, the bootstrap rolling-window Granger causality approach introduced by Balcilar et al. (2010) and the probit regression model are performed in order to identify the bidirectional causality.

The results show that the causal periods mainly occur during economic, financial and health crises. For oil-exporting country, the results suggest that any increase (decrease) in the OIL leads to an appreciation (depreciation) in the stock market index. The effect of the stock market on OIL is more relevant for the oil-importing country than that for the oil-exporting one. The COVID-19 consequences are demonstrated in the impact of oil on the Russian stock market. The probit regression shows that the US financial instabilities increase the probability of causality between OIL and stock market indexes in Russia and China.

The dynamic relationship between the variables must be taken into account in investment decisions. As financial instabilities in the USA drive the relationship between oil and stocks, investors should consider geopolitical, economic and financial elements when constructing their portfolios. Shareholders are required to include other assets in their portfolios since oil–stock relationship is highly risky.

This study provides further evidence of the bidirectional oil–stock causal link. Additionally, it examines the impact of financial instabilities on the probability that the OIL and the stock market index cause each other through the Granger effect.

The purpose of this paper is to test the performance of momentum strategies and identify the sources of their profits.

To identify the main source of momentum profits, first, the bootstrap method with replacement was used. Then, to eliminate the existence of the small sample bias, the bootstrap method without replacement and the block bootstrap method were employed. In this case, when the authors draw the observations without replacement the random effect is reduced, whereas the resampling procedure is based on the random draw.

The empirical results show the existence of a small sample bias in the bootstrap method with replacement, and that the time‐series relations of stock returns are the main source of momentum profits.

To ensure the random effect of the draws, the authors develop a new resampling procedure called the mixed bootstrap method.