JOURNAL OF APPLIED MICROECONOMETRICS

Examining the relationship between financial ratios and stock returns: An application on BIST 30 index

2024-05-19T01:04:32+03:00

Investors trading in capital markets aim to maximize the returns they will obtain from this market. For this reason, determining the factors affecting stock returns is important for investors. The aim of this study is to examine the relationship between financial ratios and stock returns of companies that are listed on the BIST 30 Index as of 2024 and traded on the stock exchange uninterruptedly between the 2016Q2-2023Q4 periods. The financial ratios used in the research include the current ratio, return on equity ratio, asset turnover ratio, inventory turnover ratio, debt/equity ratio, and debt/asset ratio. Stock returns are measured by the rate of return. The relationship between the return rates of stocks of companies listed on the BIST 30 index and the financial ratios of these companies will be examined through the panel data analysis method. In the analysis results; According to the analysis results, the relationship between the current ratio and inventory turnover ratios and the return rate of stocks is significant and negative. The relationship between return on equity ratio, asset turnover ratio and debt/equity ratio and stock returns is significant and positive. The relationship between debt/asset ratio and return rate is meaningless.

Volatility spillovers of global economic policy uncertainty and fear index among cryptocurrencies: A wavelet-based DCC-GARCH approach

2024-05-19T00:34:41+03:00

This research analyzes the dynamic relationships between the economic and political uncertainty index and the fear index in global markets and cryptocurrencies using the wavelet-based DCC-GARCH method, considering different time scales. Monthly data sets for the periods 2012–April 2024 for GEPU,VIX, and Bitcoin and April 2016–April 2024 for Ethereum are used in the study. Findings are obtained in terms of the volatility interaction between cryptocurrencies (Bitcoin and Ethereum) and GEPU and VIX, as well as four different time scales representing the short, medium, and long term. As a result of the analysis based on raw data, it was found that there is no volatility interaction between cryptocurrencies and GEPU and VIX returns. However, there is a volatility interaction between
past volatility shocks and current period volatility shocks in the 4-8 and 16-32 month investment cycle periods of VIX, Bitcoin, GEPU, and Ethereum and time scales. These results, which show that volatility shocks persist in both 4-month and 16-month investment cycles, have significant implications for investors and policymakers. They highlight the need for comprehensive information about changes in the global economy and politics, and they are expected to provide insights for both investors and policymakers.

A Solution to Errors-in-variables Bias in Multivariate Linear Regression using Compact Genetic Algorithms

2024-01-19T15:18:30+03:00

We address the classical errors-in-variables (EIV) problem in multivariate linear regression with N dependent variables where each left-hand-side variable is a function of a common predictor X subject to measurement error. Our contribution consists in employing the remaining N −1 regressions as extra information to obtain a filtered version of the mismeasured series X. We test the performance of our approach using simulations whereby we control for different cases like low vs. high R2 models, small vs. large sample or small vs. large measurement error variances. The results suggest that the multivariate-Compact Genetic Algorithm (mCGA) approach yields
estimates with lower mean-square-errors (MSEs). The MSEs are decreasing as the number of dependent variables increases. When there is no measurement error, our method gives results similar to those that would have been obtained by ordinary least-squares.