Browsing by Subject "Efficient market hypothesis"

Now showing 1 - 6 of 6

Open Access
The effectiveness of technical trading rules in cryptocurrency markets
(Elsevier, 2019) Corbet, S.; Eraslan, V.; Lucey, B.; Şensoy, Ahmet
We analyse various technical trading rules in the form of the moving average-oscillator and trading range break-out strategies to specifically test resistance and support levels and their trading performance using high-frequency Bitcoin returns. Overall, our results provide significant support for the moving average strategies. In particular, variable-length moving average rule performs the best with buy signals generating higher returns than sell signals.
Open Access
Financial economics of cryptocurrency markets
(2021-01) Aslan, Aylin
The financial sector is currently experiencing a gradual change, driven by near-term digital and technological innovations. Emerging distributed ledger technologies (DLT), such as Blockchain, open new avenues for investors and companies providing fast, secure, and low-cost peer-to-peer transactions. Bitcoin, the first application of Blockchain, has inspired other applications and products, and led to the creation of thousands of other cryptocurrencies and new wave of crowdfunding. The primary purpose of this study is to investigate both cryptocurrencies and cryptocurrency-based crowdfunding. This dissertation made up of three main parts. In the first part, the determinants of Initial Coin Offering (ICO) success and aftermarket performance of ICOs are analyzed. We find that higher ratings, shorter duration, smaller share for token sale, larger number of experts and more members in the developing team have a positive impact on ICO success. We also observe a significant relationship between offer price, market sentiment and longer term post-ICO performance. Yet, key to a successful ICO and post-ICO performance differ between boom vs bust periods in the cryptocurrency markets. The second part deals with the weak-form efficiency property of four largest cryptocurrencies by market capitalization, i.e. Bitcoin, Litecoin, Ripple and Ethereum. We use different Hurst exponent estimation techniques at different intraday frequencies. We reveal a U-shaped pattern for pricing efficiency with respect to the sampling frequency. The last part is about the hedge and safe-haven properties of Bitcoin, and its interlinkages to other precious metals (gold, silver, platinum, and palladium). Using high frequency data, we find evidence of spillover effects in volatility among Bitcoin and precious metals. Furthermore, the results suggest that the risk spillovers are time dependent and are sensitive to slowdowns in economic activity and political events. Overall, we contribute to the understanding of both market and corporate based approaches to the role of cryptocurrencies in capital markets.
Open Access
Forecasting high-frequency excess stock returns via data analytics and machine learning
(Wiley-Blackwell Publishing Ltd., 2021-11-23) Akyıldırım, E.; Nguyen, D. K.; Şensoy, Ahmet; Šikić, M.
Borsa Istanbul introduced data analytics to present additional information about its market conditions. We examine whether this product can be utilized via various machine learning methods to predict intraday excess returns. Accordingly, these analytics provide significant prediction ratios above 50% with ideal profit ratios that can reach up to 33%. Among all the methods considered, XGBoost (logistic regression) performs better in predicting excess returns in the long-term analysis (short-term analysis). Results provide evidence for the benefits of both the analytics and the machine learning methods and raise further discussion on the semistrong market efficiency.
Open Access
Forecasting high‐frequency excess stock returns via data analytics and machine learning
(John Wiley and Sons Inc., 2023-01) Akyildirim, E.; Nguyen, D.K.; Şensoy, Ahmet; Šikić, M.
Borsa Istanbul introduced data analytics to present additional information about its market conditions. We examine whether this product can be utilized via var ious machine learning methods to predict intraday excess returns. Accordingly, these analytics provide significant prediction ratios above 50% with ideal profit ratios that can reach up to 33%. Among all the methods considered, XGBoost (logistic regression) performs better in predicting excess returns in the long‐term analysis (short‐term analysis). Results provide evidence for the benefits of both the analytics and the machine learning methods and raise further discussion on the semistrong market efficiency.
Open Access
Intraday downward/upward multifractality and long memory in Bitcoin and Ethereum markets: an asymmetric multifractal detrended fluctuation analysis
(Elsevier, 2019) Mensi, W.; Lee, Y. -J.; Al-Yahyaee, K.; Şensoy, Ahmet; Yoon, S. -M.
This study examines high-frequency asymmetric multifractality, long memory, and weak-form efficiency for two major cryptocurrencies, namely, Bitcoin (BTC) and Ethereum (ETH), using the asymmetric multifractal detrended fluctuation analysis method to consider different market patterns. Our results show evidence of structural breaks and asymmetric multifractality. Moreover, the multifractality gap between the uptrend and downtrend is small when the time scale is small, and it increases as the time scale increases. The BTC market is more inefficient than ETH. The inefficiency is more (less) accentuated when the market follows a downward (upward) movement. The efficiency level varies based on each subperiod.
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.