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The major aim of this research is to determine the socio‐economic level of geographical investment regions through fuzzy multi‐criteria decision‐making (MCDM) method. The results obtained from this method are analyzed and compared with the current system and the differences are interpreted.

A user friendly MCDM method, the fuzzy TOPSIS, was selected and ten independent criteria out of 53 were used, that have been evaluated by reduction according to the correlations among them. Therefore, the rankings of the 26 geographical investment regions of Turkey were calculated based on their criteria.

The examinations of the rankings have shown that only four regions had similar rankings but the rankings of the remaining 22 regions differed according to the authority rankings. Furthermore, significant differences have been observed for eight regions.

In globalization process, certain issues are of particular importance in shaping the resource allocation policies of countries, through which they adjust their resources for manufacturing and service sectors to the changing competitive conditions and govern the effect of global economics on the human resources of their countries. The allowances taken from social and economic criteria have indicated the inter‐regional differences in terms of development.

From a policy perspective, this study highlighted that a large number of social and economic criteria failed in identifying homogenous groups of provinces and hence failed in producing realistic policies. However, the proposed method significantly contributed to obtaining more accurate rankings by using fuzzy decision‐making under multi‐criteria.

Investors who can transfer their savings to investments in a well-regulated market benefit not only themselves but also economic development. Hence, it is crucial for fund owners to evaluate their stock market investment decisions. The goal of the study is to understand which model determines the asset returns most efficiently. In this regard, the validity of single and multi-index asset pricing models (capital asset pricing model-CAPM and Fama–French models) has been examined in the Turkish Stock Exchange for 2009–2020, with the quantile regression (QR) approach.

On 18 portfolios comprised of quoted stocks in the Istanbul Stock Exchange 100 (ISE-100/BIST-100), we test the CAPM, the Fama and French three factor model (FF3) and the Fama and French five factor model (FF5). Empirical analyses have been carried out via QR approach regressing the portfolios' average weekly excess returns on risk premium/market factor (Rm-Rf), firm size, book value/market value (B/M), profitability and investments factors. QR estimation has been employed since QR is more effective and provides a better definition of the distribution’s tails.

Our empirical findings have revealed that the average excess weekly returns can be explained more strongly via CAPM. Moreover, Fama and French models are expected to give more reliable result with more data, whereas the market premium would give robust results for the Turkish Capital Market.

Individuals investing in financial assets must find the price model that best fits the market. The return can be approximated in the most appropriate manner using the right variables.

The study differs from other research by comparing the asset pricing models via examining the assets' weekly returns with QR in the Istanbul Stock Exchange 100 (ISE-100).