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This study aims to empirically investigate the connection between Islamic finance and economic growth in Turkey using the endogenous growth model.

It applies quantile regression with the Markov chain marginal bootstrap resampling technique by adopting total Islamic financing as the main exogenous explanatory factor in the endogenous growth model, while the gross domestic product (GDP) is employed as a measure of economic growth. The sample consists of all full-fledged participation (Islamic) banks operating in Turkey spanning from 2013Q4 until 2019Q4. The study uses academic literature, official financial reports from the Participation Banks Association of Turkey, REDmoney Group, Islamic Financial Services Board (IFSB) and the International Monetary Fund (IMF) database.

The results show that Islamic finance is promoting economic growth in Turkey, which mirrors the success of the New Turkish Economy Program (2019–2021) which aims at boosting economic growth by enhancing the Islamic finance share in the Turkish banking sector and the global market.

Turkey has a dual banking system (conventional and participation (Islamic)) and both can influence the country's real economy. This study is limited to the influence of Islamic banking on Turkish economic growth. The study also restricts its size and coverage from 2013Q4 to 2019Q4, to cover the years over which data for all variables included in the research are available.

This paper suggests the adoption of the Turkish successful experiment as a path to reach economic growth by increasing the Islamic finance share in the banking industry for countries that seek to promote economic growth by Islamic finance, as the findings of this paper support.

This study is the first that examines the influence of Islamic finance on economic growth under a new theoretical framework of the endogenous growth model in Turkey using a robust non-parametric approach.

This paper aims to investigate empirically whether Islamic securities enhance economic growth in the Southeast Asian region based on the endogenous growth theory using the non-parametric analysis.

This paper applies panel quantile regression with Markov chain Monte Carlo optimization as an optimal non-parametric approach to investigate the effect of Islamic securities on economic growth starting from 2013Q4 to 2019Q4 in Southeast Asia. Total issued Islamic securities holdings are employed as a measure for Islamic securities, while the gross domestic product is employed as a proxy for economic growth. The sample includes all working Islamic financial foundations in the top progressive Islamic securities markets' countries of Southeast Asia (Malaysia, Indonesia and Brunei Darussalam).

The findings confirm that the increase of issuing Islamic securities in Islamic capital markets of Southeast Asia is increasing the levels of economic growth, reflecting the weighty role of the Islamic capital market development as an active contributor to economic growth.

This research would fill the literature gap by exploring Islamic securities–economic growth nexus in Southeast Asia using a robust non-parametric approach based on the endogenous growth theory for better estimation results. The findings of this review serve as a roadmap for financial analysts, policymakers and decision makers to stimulate the Islamic securities markets as another source of finance which can promote the economic growth.

This research is the first that investigates empirically the Islamic securities–economic growth nexus in Southeast Asia using a new empirical investigation built on the non-parametric analysis and outlined within the theoretical context of the endogenous growth model to gain robust evidence about this nexus.

This paper aims to empirically explore the nexus between Islamic finance and economic growth across Southeast Asia based on the perception of the endogenous growth model.

This paper applied the dynamic panel one-step system GMM as an optimum estimation approach to study the influence of Islamic finance on economic growth in Southeast Asia from 2013Q4 to 2019Q4. This paper used total Islamic financing as the major exogenous explanatory factor inside the endogenous growth model, whereas the gross domestic product was used as the measurement of economic growth. The sample consisted of all complete Islamic banks operating in Southeast Asia (Malaysia, Brunei Darussalam and Indonesia).

The findings demonstrated that Islamic finance is promoting economic growth in Southeast Asia, which reflects the weighty role of Islamic finance as an energetic contributor to economic growth.

This paper would enrich the literature by studying the nexus between Islamic finance and economic growth in Southeast Asia based on the perception of endogenous growth model, as the results of this paper assist as an attendant for financial scholars, decision-makers and policymakers to expand Islamic finance globally as an alternative funding source for the best involvement to economic growth.

Despite the existing studies on the nexus between Islamic finance and economic growth, this paper is the first that explores empirically the nexus between Islamic finance and economic growth in Southeast Asia based on the theoretical background of the endogenous growth model to obtain solid information on this nexus.

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

The purpose of this paper is to investigate the link between the financial performance of Islamic finance and economic growth in all of Malaysia, Indonesia, Brunei, Turkey and Saudi Arabia within the endogenous growth model framework.

This study applied dynamic panel system GMM to estimate the impact of the financial performance of Islamic finance on economic growth using quarterly data (2014:1-2018:4). CAMELS system parameters were employed as variables of the financial performance of Islamic finance and gross domestic product (GDP) as a proxy of economic growth. The sample contained all Islamic banks working in the five countries.

The findings demonstrated that the only significant factor of the financial performance of Islamic finance, which affects the endogenous economic growth, is profitability through return on equity (ROE). The experimental findings also indicated the necessity of stimulating other financial performance factors of Islamic finance to achieve a significant contribution to economic growth.

The analysis in this paper would fill the literature gap by investigating the link between financial performance of Islamic finance and economic growth, as this study serves as a guide for the academians, researchers and decision-makers who want to achieve economic growth through stimulating Islamic finance in the banking sector. However, this study may well be extended to investigate the link between the financial performance of Islamic finance and economic growth over the Z-score model as another measure for the financial performance of Islamic finance.

This paper is the first that investigates the link between financial performance of Islamic finance and economic growth empirically using CAMELS parameters within the endogenous growth model to provide robust information about this link based on a sample of the top pioneer Islamic finance countries.

The purpose of this paper is to predict the daily accuracy improvement for the Jakarta Islamic Index (JKII) prices using deep learning (DL) with small and big data of symmetric volatility information.

This paper uses the nonlinear autoregressive exogenous (NARX) neural network as the optimal DL approach for predicting daily accuracy improvement through small and big data of symmetric volatility information of the JKII based on the criteria of the highest accuracy score of testing and training. To train the neural network, this paper employs the three DL techniques, namely Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG).

The experimental results show that the optimal DL technique for predicting daily accuracy improvement of the JKII prices is the LM training algorithm based on using small data which provide superior prediction accuracy to big data of symmetric volatility information. The LM technique develops the optimal network solution for the prediction process with 24 neurons in the hidden layer across a delay parameter equal to 20, which affords the best predicting accuracy based on the criteria of mean squared error (MSE) and correlation coefficient.

This research would fill a literature gap by offering new operative techniques of DL to predict daily accuracy improvement and reduce the trading risk for the JKII prices based on symmetric volatility information.

This research is the first that predicts the daily accuracy improvement for JKII prices using DL with symmetric volatility information.

The purpose of this paper is to investigate the link between Islamic finance for entrepreneurship activities and economic growth in Malaysia within the model of endogenous growth.

This study applied a parametric analysis represented by vector autoregression (VAR) Granger causality and a non-parametric analysis represented in the bootstrapped quantile regression to examine the effect of Islamic finance for entrepreneurship activities on economic growth within the model of endogenous growth. This paper used a sample of all Islamic banks working in Malaysia covering a period from 2014 first quarter until 2019 third quarter (2014Q1–2019Q3).

The findings demonstrated that Islamic finance for entrepreneurship activities are promoting economic growth in Malaysia which indicates that Islamic finance is a vital contributor to economic growth through financing entrepreneurial domains small and medium-sized enterprises.

The analysis in this paper would fill the literature gap by investigating the link between Islamic finance for entrepreneurship activities and economic growth within the model of endogenous growth in Malaysia as this study serves as a guide for the researchers and decision-makers to the necessity of merging Islamic finance as a major player in the economy to finance the entrepreneurial domain which contributes to economic growth.

This study is the first that investigates the relationship between Islamic finance for entrepreneurship activities and economic growth empirically using the causality and quantile regression within a new theoretical approach over the model of endogenous growth to provide a proven valuable experiment from Malaysia concerning Islamic finance for the entrepreneurial domain which promotes economic growth.

This paper aims to investigate empirically whether Sukuk financing is boosting the economic growth in Southeast Asia within the framework of the endogenous growth model.

This paper applied dynamic panel one-step system generalized method of moments as an optimal estimation approach to investigate the impact of Sukuk financing on economic growth in Southeast Asia spanning from 2013Q4–2019Q3. Sukuk financing was proxied by the total issued Sukuk holdings, while economic growth was proxied by gross domestic product. The sample covered all full-fledged Islamic financial institutions in the most developed Sukuk financial markets countries in Southeast Asia (Malaysia, Indonesia and Brunei).

The findings demonstrated that Sukuk financing is boosting economic growth in Southeast Asia, which reflects the significant role of the Islamic financial markets of Sukuk as a vital contributor to economic growth.

This paper would fill the literature by investigating the link between Sukuk financing and economic growth in Southeast Asia within the framework of the endogenous growth model, as the outcome of this paper serves as a guide for financial researchers, decision-makers and policymakers to improve the Sukuk market globally as an alternative financing source for the best contribution to economic growth.

This paper is the first that investigates empirically the link between Sukuk financing and economic growth in Southeast Asia with a new theoretical context of the endogenous growth model to gain robust information about this link.

The purpose of this paper is to apply various data mining techniques for predicting the financial performance of Islamic banking in Indonesia through the main exogenous determinants of profitability by choosing the best data mining technique based on the criteria of the highest accuracy score of testing and training.

This paper used data mining techniques to predict the financial performance of Islamic banking by applying all of LASSO regression, random forest (RF), artificial neural networks and k-nearest neighbor (KNN) over monthly data sets of all the full-fledged Islamic banks working in Indonesia from January 2011 until March 2020. This study used return on assets as a real measurement of financial performance, whereas the capital adequacy ratio, asset quality and liquidity management were used as exogenous determinants of financial performance.

The experimental results showed that the optimal task for predicting the financial performance of Islamic banking in Indonesia is the KNN technique, which affords the best-predicting accuracy, and gives the optimal knowledge from the financial performance of Islamic banking determinants in Indonesia. As well, the RF provides closer values to the optimal accuracy of the KNN, which makes it another robust technique in predicting the financial performance of Islamic banking.

This paper restricted modeling the financial performance of Islamic banking to profitability through the main determinants of return of assets in Indonesia. Future research could consider enlarging the modeling of financial performance using other models such as CAMELS and Z-Score to predict the financial performance of Islamic banking under data mining techniques.

Owing to the lack of using data mining techniques in the Islamic banking sector, this paper would fill the literature gap by providing new effective techniques for predicting financial performance in the Islamic banking sector using data mining approaches, which can be efficient tools in business and management modeling for financial researchers and decision-makers in the Islamic banking sector.

According to the author’s knowledge, this paper is the first that provides data mining techniques for predicting the financial performance of the Islamic banking sector in Indonesia.