This paper analyzes return enhancement patterns of Turkish REITs (T-REITs) from various perspectives over the period of July 2008 and March 2015. We find that T-REITs portfolio provides a slightly lower level of risk diversification benefit than investment trusts, but higher than the banks. The evidence suggests that portfolio managers and investors may not only be able to utilize knowledge deriving from the CAPM, but also utilize information retrieved from Fama-French model due to its relatively better performance on capturing the variation in T-REITs returns. Results also disclose that T-REITs show a degree of diversity in property focus, and reveal mainly defensive, small and financially distressed characteristics. Finally, based on the multiple observations, a case can be made for a possible linkage between property focus and yield improvement/risk taking structure of T-REITs. This study provides implications for the capacity of T-REITS and improve return enhancement capacity in an efficient portfolio management. Keywords: REIT, CAPM, Fama-French model, Turkish REITs, Borsa Istanbul, JEL codes: R30, G12, G1

The dynamic structure of electricity markets, where uncertainties abound due to, e.g., demand variations and renewable energy intermittency, poses challenges for market participants. We propose generative adversarial networks (GANs) to generate synthetic electricity price data. This approach aims to provide comprehensive data that accurately reflect the complexities of the actual electricity market by capturing its distribution. Consequently, we would like to equip market participants with a versatile tool for successfully dealing with strategy testing, risk model validation, and decision-making enhancement. Access to high-quality synthetic electricity price data is instrumental in cultivating a resilient and adaptive marketplace, ultimately contributing to a more knowledgeable and prepared electricity market community. In order to assess the performance of various types of GANs, we performed a numerical study on Turkey’s intraday electricity market weighted average price (IDM-WAP). As a key finding, we show that GANs can effectively generate realistic synthetic electricity prices. Furthermore, we reveal that the use of complex variants of GAN algorithms does not lead to a significant improvement in synthetic data quality. However, it requires a notable increase in computational costs.

Load modeling is crucial in improving energy efficiency and saving energy sources. In the last decade, machine learning has become favored and has demonstrated exceptional performance in load modeling. However, their implementation heavily relies on the quality and quantity of available data. Gathering sufficient high-quality data is time-consuming and extremely expensive. Therefore, generative adversarial networks (GANs) have shown their prospect of generating synthetic data, which can solve the data shortage problem. This study proposes GAN-based models (RCGAN, TimeGAN, CWGAN, and RCWGAN) to generate synthetic load data. It focuses on Türkiye's electricity load and generates realistic synthetic load data. The educated synthetic load data can reduce prediction errors in load when combined with recorded data and enhance risk management calculations.