Cryptocurrency is a recent and popular topic that attracts the interest of investors and fund managers. Beyond the market discipline, researchers question the interaction between cryptocurrencies and macroeconomic variables. This study focuses on how the changes in gold and oil prices affect the daily price movements of various cryptocurrencies. The daily database used in this study includes the prices of the cryptocurrencies such as Bitcoin, Tether, Ethereum, Litecon and EOS for the period of August 1, 2017 and April 3, 2019. Initially, the stationarity of the time series is tested by Ng and Perron (2001) method. The existence of the cointegration relationship among the series is tested by Johansen (1988) technique. The presence of causality relationships among the series is investigated with the Dolado and Lütkepohl (1996) causality test. The empirical results support that there exists a cointegration relationship only in between Tether and gold and oil prices.
Purpose -This study conducts an analysis to reveal the interaction between Bitcoin and Exchange Rates to find out whether Bitcoin is becoming a substitution for the exchange rates. Methodology -To investigate the mutually interaction between the exchange rates and the Bitcoin, the interaction (relationship) between daily closing price of both exchange rates and Bitcoin was analyzed through the Var model. Thus, it was tried to show the sensitivity of the values of Bitcoin to the changes occured in the exchange rates. Findings -Based on Variance Decomposition analysis, BITCOIN and Euro can be considered as largely external variables and their prices are not significantly affected by USD. An interesting result in this study is that the USD exchange rate was found to be significantly sensitive to the Euro. Conclusion -Findings obtained from analysis show that Bitcoin and Excange Rates have not become an alternative tools for each other yet.
Risk management requires firms to mitigate the negative consequences of market dynamics on their performance outcomes. Traditional risk management solely addresses the threats and negative consequences of risk. However, total (effective) risk management is capable of regulating out-of-control market conditions to boost corporate performance by restraining market volatility and hence providing return sustainability considering the opportunities of risk as well. Based on a sample of 286 firm-year observations drawn from 26 firms listed on Borsa Istanbul, BIST-50 index, the empirical study examines the association between total risk management and firm performance and the moderating role of innovation, intellectual capital, and the pandemic period for the years 2011–2021. The analysis is performed by applying the hierarchical panel regression using ROE and ROI as proxies to measure firm performance. The results have shown that there is a positive association between total risk management and performance measures, especially among firms applying more innovation and intellectual capital investments. However, the effect of innovation on the performance relationship of total risk management (ROE) was found to be negative surprisingly. Moreover, results suggest that total risk management has a lower positive association with firm performance during the pandemic period for both performance measures.
The aim of this study is to comparatively analyze the backtest performances of trading disciplines applied in various portfolio baskets (Bist 30, 50 and 100) for different investment periods (short term – ytd and long term). According to the results of the analysis, it has been determined that in all trading disciplines, the investor has a higher return than the benchmark indicator in a 5-year term, that is, they can earn abnormal returns. Also, the return in the 5-year term is much higher than the 1-year and YTD returns. In the P / E & MA model, the Bist - 50 index in the 5-year period and the Bist - 100 index in the 1-year period provide the maximum return, while according to the P / E model, the Bist-30 and Bist -50 indices provide optimum returns in all maturity options. Based on these findings, it can be expected that if the trading disciplines used in this study are applied in a long term such as 5 years and on the portfolio basket consisting of Bist-30 and Bist-50 industrial stocks, it will maximize returns. In terms of risk and return, in YTD period, the sharpe and treynor ratios of the model portfolio formed in all trading disciplines except M /B trading discipline were lower than in 1 year in the 5-year investment period. This situation arose due to the increased risk of the portfolio as a result of the extended maturity and is in line with our expectations.
In this study, the relationship between the popularity of cryptocurrencies and their price, return and trading volumes are examined through time series analysis. The popularity variable is determined according the frequency of cryptocurrencies being searched on the internet. Stationarity of series is examined by Vogelsang and Perron (1998) structural breaks ADF unit root test. According to the test results, all series are found to be stationary at level values. VAR analyses and impulse-response functions are performed to reveal dynamic interaction between the series. According to impulse -response test results, returns of BITCOIN decreased against a decreasing shock in the number searches on the internet and its price and trading volume followed a fluctuating course. In order to see the causality relationship between variables the Granger causality test is conducted. Regression analyses are performed using ordinary least squares (OLS) method through three different equations. According to the result of the regression analysis, an increase in the number of internet searches for cryptocurrencies was found to positively affect prices, returns and trading volumes of all cryptocurrencies. The highest impact on prices and trading volume is observed in BITCOIN, while the highest effect on returns is observed in LITECOIN. According to the findings, popularity can be considered an important determinant for price, returns and trading volumes of cryptocurrencies.
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