Browsing by Author "Sensoy, Ahmet"

Now showing 1 - 7 of 7

Open Access
Green cryptocurrencies and portfolio diversification in the era of greener paths
(Elsevier Ltd, 2024-03) Ali, Fahad; Khurram, M. U.; Sensoy, Ahmet; Vo, X. V.
The shift towards cleaner fuels from hydrocarbons has influenced nearly all market types and asset classes, and cryptocurrencies are no exception. The complex mechanism of blockchain and mining consumes high levels of electricity and surges environmental footprints in electronic waste generation. Existing studies that examine green and sustainable investments are limited to sustainable equities or green bonds; therefore, this study opens up a new research direction by considering green (energy-efficient) cryptocurrencies. First, this study develops a four-step screening process to systematically select cryptocurrencies that are greener than others. A comprehensive set of green and non-green assets and a battery of empirical tests are then employed to examine the diversification benefits of selected green cryptocurrencies against several well-diversified equity portfolios at the global, regional, and country levels. The diversification benefits of green cryptocurrencies are compared with non-green cryptocurrencies using (i) the four-moment modified value at risk and conditional value at risk, (ii) four different portfolio optimization strategies, and (iii) dynamic correlation-based hedge and safe-haven regression analyses. The results show that green cryptocurrencies provide diversification benefits that are at least comparable to, and in some cases, superior to, non-green (energy-intensive) cryptocurrencies. Cardano and Tezos are identified as green cryptocurrencies offering the most diversification benefits to investors, followed by EOS, Steller, and IOTA. This study provides valuable insights to investors and policymakers, specifically those concerned with achieving sustainability and ESG-compliance (environmental-social-governance) goals and seeking green assets to hedge and diversify various traditional investments.
Open Access
Interest rate uncertainty and the predictability of bank revenues
(John Wiley & Sons, Ltd, 2022-06-24) Cepni, O.; Demirer, R.; Gupta, R.; Sensoy, Ahmet
This paper examines the predictive power of interest rate uncertainty over pre-provision net revenues (PPNR) in a large panel of bank holding companies (BHC). Utilizing a linear dynamic panel model based on Bayes predictor, we show that supplementing forecasting models with interest rate uncertainty improves the forecasting performance with the augmented model yielding lower forecast errors in comparison to a baseline model which includes unemployment rate, federal funds rate, and spread variables. Further separating PPNRs into two components that reflect net interest and non-interest income, we show that the predictive power of interest rate uncertainty is concentrated on the non-interest component of bank revenues. Finally, examining the point predictions under a severely stressed scenario, we show that the model can successfully predict the negative effect on overall bank revenues with a rise in the non-interest component of income during 2009:Q1. Overall, the findings suggest that stress testing exercises that involve bank revenue models can benefit from the inclusion of interest rate uncertainty and the cross-sectional information embedded in the panel of BHCs.
Open Access
Nonlinear nexus between cryptocurrency returns and COVID-19 news sentiment
(Elsevier, 2022-12) Banerjee, Ameet Kumar; Akhtaruzzaman, Md; Dionisio, Andreia; Almeida, Dora; Sensoy, Ahmet
The paper examines how various COVID-19 news sentiments differentially impact the behaviour of cryptocurrency returns. We used a nonlinear technique of transfer entropy to investigate the relationship between the top 30 cryptocurrencies by market capitalisation and COVID-19 news sentiment. Results show that COVID-19 news sentiment influences cryptocurrency returns. The nexus is unidirectional from news sentiment to cryptocurrency returns, in contrast to past findings. These results have practical implications for policymakers and market participants in understanding cryptocurrency market dynamics under extremely stressful market conditions.
Open Access
Other people's money: A comparison of institutional investors
(Elsevier, 2022-12) Eraslan, V.; Omole, John; Sensoy, Ahmet; Ozdamar, Melisa
Using unique equity ownership data, we investigate the stock picking preferences and return forecasting performances of institutional investors that manage their own money against those that manage others’. We reveal that these investors’ preferences significantly differ in historical patterns, liquidity and prudence when picking stocks. In particular, ‘own money managers’ display a risk-seeking behaviour whereas “others’ money managers” exhibit risk-averse characteristics. However, our results indicate that both types of investors are well informed, albeit own money managers excel in the short-term while others’ money managers are successful in the long-term.
Open Access
Prediction of cryptocurrency returns using machine learning
(Springer, 2021-02) Akyildirim, E.; Goncu, A.; Sensoy, Ahmet
In this study, the predictability of the most liquid twelve cryptocurrencies are analyzed at the daily and minute level frequencies using the machine learning classification algorithms including the support vector machines, logistic regression, artificial neural networks, and random forests with the past price information and technical indicators as model features. The average classification accuracy of four algorithms are consistently all above the 50% threshold for all cryptocurrencies and for all the timescales showing that there exists predictability of trends in prices to a certain degree in the cryptocurrency markets. Machine learning classification algorithms reach about 55–65% predictive accuracy on average at the daily or minute level frequencies, while the support vector machines demonstrate the best and consistent results in terms of predictive accuracy compared to the logistic regression, artificial neural networks and random forest classification algorithms.
Open Access
Pricing efficiency and asymmetric multifractality of major asset classes before and during COVID-19 crisis
(Elsevier, 2022-11-10) Mensi, Walid; Sensoy, Ahmet; Vo, Xuan Vinh; Kang, Sang Hoon
We examine the impact of COVID-19 pandemic crisis on the pricing efficiency and asymmetric multifractality of major asset classes (S&P500, US Treasury bond, US dollar index, Bitcoin, Brent oil, and gold) within a dynamic framework. Applying permutation entropy on intraday data that covers between April 30, 2019 and May 13, 2020, we show that efficiency of all sample asset classes is deteriorated with the outbreak, and in most cases this deterioration is significant. Results are found to be robust under different analysis schemes. Brent oil is the highest efficient market before and during crisis. The degree of efficiency is heterogeneous among all markets. The analysis by an asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) approach shows evidence of asymmetric multifractality in all markets which rise with the scales. The inefficiency is higher during downward trends before the pandemic crisis as well as during COVID-19 except for gold and Bitcoin. Moreover, the pandemic intensifies the inefficiency of all markets except Bitcoin. Findings reveal increased opportunities for price predictions and abnormal returns gains during the COVID-19 outbreak.
Open Access
Statistical arbitrage in jump-diffusion models with compound poisson processes
(Springer Nature, 2021-02-26) Akyildirim, E.; Fabozzi, J.F.; Goncu, A.; Sensoy, Ahmet
We prove the existence of statistical arbitrage opportunities for jump-diffusion models of stock prices when the jump-size distribution is assumed to have finite moments. We show that to obtain statistical arbitrage, the risky asset holding must go to zero in time. Existence of statistical arbitrage is demonstrated via ‘buy-and-hold until barrier’ and ‘short until barrier’ strategies with both single and double barrier. In order to exploit statistical arbitrage opportunities, the investor needs to have a good approximation of the physical probability measure and the drift of the stochastic process for a given asset.