Ersan, Oğuz
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Name Variants
Ersan, OĞUZ
ERSAN, Oğuz
Ersan, O.
O. Ersan
E., Oğuz
Oğuz ERSAN
Ersan, Oğuz
Ersan,Oguz
Ersan O.
E., Oguz
Oğuz Ersan
Oguz, Ersan
Ersan, Oguz
OĞUZ ERSAN
Ersan,O.
E.,Oguz
ERSAN, OĞUZ
Oğuz, Abdullah Ersan
Ersan, Oğuz
ERSAN, Oğuz
Ersan, O.
O. Ersan
E., Oğuz
Oğuz ERSAN
Ersan, Oğuz
Ersan,Oguz
Ersan O.
E., Oguz
Oğuz Ersan
Oguz, Ersan
Ersan, Oguz
OĞUZ ERSAN
Ersan,O.
E.,Oguz
ERSAN, OĞUZ
Oğuz, Abdullah Ersan
Ersan, Oğuz
Job Title
Doç. Dr.
Email Address
oguzersan@khas.edu.tr
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Scholarly Output
20
Articles
16
Citation Count
158
Supervised Theses
2
19 results
Scholarly Output Search Results
Now showing 1 - 10 of 19
Article Where do tourism tokens travel to and from?(Routledge Journals, Taylor & Francis Ltd, 2023) Aharon, David Y.; Demir, Ender; Ersan, OguzThis study aims to identify the sources of spillovers affecting tourism tokens and classify the type of assets to which they correspond. Using daily data for different asset classes from June 2018 through November 2022, we employ a TVP-VAR methodology to test the connectedness between two tourism tokens, two leading travel equity indices, and the two dominant cryptocurrencies, namely, Bitcoin and Ethereum. The findings show that tourism tokens are relatively independent of fluctuations in the traditional sources affecting the travel and leisure sector, such as the U.S. dollar, the price of oil, or travel equity indices. These results hint that tourism tokens are more closely related to cryptocurrencies rather than pure travel goods. The results may help decision-makers in the travel and hospitality industries considering the use of tourism tokens identify the potential forces impacting them.Article Pinstimation: an R Package for Estimating Probability of Informed Trading Models(Technische Universitaet Wien, 2023) Ghachem,M.; Ersan,O.The purpose of this paper is to introduce the R package PINstimation. The package is designed for fast and accurate estimation of the probability of informed trading models through the implementation of well-established estimation methods. The models covered are the original PIN model (Easley and O’Hara 1992; Easley et al. 1996), the multilayer PIN model (Ersan 2016), the adjusted PIN model (Duarte and Young 2009), and the volume-synchronized PIN (Easley, De Prado, and O’Hara 2011; Easley, López De Prado, and O’Hara 2012). These core functionalities of the package are supplemented with utilities for data simulation, aggregation and classification tools. In addition to a detailed overview of the package functions, we provide a brief theoretical review of the main methods implemented in the package. Further, we provide examples of use of the package on trade-level data for 58 Swedish stocks, and report straightforward, comparative and intriguing findings on informed trading. These examples aim to highlight the capabilities of the package in tackling relevant research questions and illustrate the wide usage possibilities of PINstimation for both academics and practitioners. © (2023), (Technische Universitaet Wien). All Rights Reserved.Article Daily and Intraday Herding Within Different Types of Investors in Borsa Istanbul(Routledge Journals, Taylor & Francis Ltd, 2019) Dalgıç, Nihan; Ekinci, Cumhur; Ersan, OğuzThis paper aims to explore the daily and intraday herd behavior of various investor groups trading in an emerging equity market, Borsa Istanbul (BIST). We analyze a one-year tick-by-tick order and trade data of BIST 100 Index stocks and document differences in herding behavior of investor groups considering market capitalization, market conditions, and announcements as well as daily and intraday periodicities. We find that nonprofessional investors (brokerage houses and domestic funds) tend to herd on large (small) stocks; their herding behavior mostly exhibits a U shape (an inverse U shape) during the day. All types of investors tend to herd in down markets on a daily basis while this behavior disappears, even inverts intraday.Article Detecting and date-stamping bubbles in fan tokens(Elsevier, 2024) Assaf, Ata; Demir, Ender; Ersan, OguzWe focus on the existence of bubbles in fan tokens, utilizing the Supremum Augmented DickeyFuller (SADF) and Generalized Supremum Augmented Dickey -Fuller (GSADF) tests. We use daily closing prices of the top 20 fan tokens according to their market capitalization, along with Bitcoin, Ethereum, and Chiliz. The evidence from the GSADF test results indicates that the prices of 13 out of 20 fan tokens and the three cryptocurrencies have explosive periods associated with bubbles. Our results also show that the percentage of bubble days is between 0 % and 5% for all fan tokens. Among the 13 fan tokens exhibiting bubble behavior in their prices, nine have multiple sub -periods associated with bubbles, while only four tokens have a single sub -period with explosive prices. Bubbles in token prices are short-lived bubbles; most last for a few days. As a robustness analysis, we also perform LPPLS (Log -Periodic Power Law Singularity), providing similar results. Further analysis shows that trading volume, fan token return, Economic Policy Uncertainty (EPU), Daily Infectious Disease Equity Market Volatility (EMVID) are positively associated with the presence of bubbles in fan token prices, while oil return is negatively associated with bubbles.Article Identifying Information Types in the Estimation of Informed Trading: an Improved Algorithm(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Ersan,O.; Ghachem,M.The growing frequency of news arrivals, partly fueled by the proliferation of data sources, has made the assumptions of the classical probability of informed trading (PIN) model outdated. In particular, the model’s assumption of a single type of information event no longer reflects the complexity of modern financial markets, making the accurate detection of information types (layers) crucial for estimating the probability of informed trading. We propose a layer detection algorithm to accurately find the number of distinct information types within a dataset. It identifies the number of information layers by clustering order imbalances and examining their homogeneity using properly constructed confidence intervals for the Skellam distribution. We show that our algorithm manages to find the number of information layers with very high accuracy both when uninformed buyer and seller intensities are equal and when they differ from each other (i.e., between 86% and 95% accuracy rates). We work with more than 500,000 simulations of quarterly datasets with various characteristics and make a large set of robustness checks. © 2024 by the authors.Article Economic Policy Uncertainty and Bank Credit Growth: Evidence From European Banks(Elsevier B.V., 2020) Danışman, Gamze Öztürk; Ersan, Oğuz; Demir, EnderUsing a sample of 2977 private and listed banks in the EU-5 countries (the United Kingdom, Germany, Spain, Italy, France) for the years 2009–2018, this paper explores the impact of Economic Policy Uncertainty (EPU) on credit growth. Using panel data fixed effects methodology and controlling for endogeneity using two-step difference GMM estimators, our findings indicate that uncertainty in economic policies hampers the credit growth of European banks. Our bank type-based analyses indicate that the effect is mainly valid for cooperative banks. Additional analyses imply that the negative impact of EPU on credit growth is more pronounced in civil law countries, increases with debt maturity, and weakens for banks with a larger number of employees and branches. Furthermore, the unfavorable effects are stronger in well-capitalized banks, banks with foreign subsidiaries, and banks with a higher share of wholesale funding. We also provide several policy implications for different economic actors.Article Impact of the Covid-19 Market Turmoil on Investor Behavior: a Panel Var Study of Bank Stocks in Borsa Istanbul(Mdpi, 2024) Ekinci, Cumhur; Ersan, OguzAssuming that investors can be foreign or local, do high-frequency trading (HFT) or not, and submit orders through a bank-owned or non-bank-owned broker, we associated trades to various investors. Then, building a panel vector autoregressive model, we analyzed the dynamic relation of these investors with returns and among each other before and during the COVID-19 market crash. Results show that investor groups have influence on each other. Their net purchases also interact with returns. Moreover, during the turmoil caused by the pandemic, except foreign investors not involved in HFT, the response of any investor group (retail/institutional, domestic investors doing HFT and those not doing HFT, and foreign investors doing HFT) significantly altered. This shows that the interrelation among investor groups is dynamic and sensitive to market conditions.Article The Speed of Stock Price Adjustment To Corporate Announcements: Insights From Turkey(Elsevier, 2020) Ersan, Oğuz; Şimşir, Serif Aziz; Şimsek, Koray D.; Afan, HasanThe market reaction speeds to the news flow are currently measured at the millisecond level in developed markets. We investigate, using a unique setting from Turkey, whether the market reaction speeds in less sophisticated markets are on par with those of developed markets. We find that market reaction times to corporate announcements are slower than documented in recent studies, although markets react to positive news more quickly than negative news. When high-frequency traders are more active in the market prior to announcements, the speed of price adjustment is slower. Finally, we find sizable profit opportunities for investors following event-driven strategies.Article High-Frequency Trading and Its Impact on Market Liquidity: a Review of Literature(2021) Bodur, Mehmet; Ersan, Oğuz; Ekinci, Cumhur; Dalgıç, NihanHigh-frequency trading (HFT) has been dominating the activity in developedfinancial markets in the last two decades. Despite its recent formation, theliterature on the impacts of HFT on financial markets and participants isbroad. However, there are ongoing debates and unanswered questionswithin many subtopics. We survey through the research towards HFT effectson liquidity in an attempt to explain the coexistence of evidence regardingboth the positive and the negative impacts of HFT. We name two mainfactors leading to mixed results. Former concerns the negative marketconditions such as intraday shocks, through which HFT trading patternsmay sharply change. Latter regards the certain characteristics of HFTliquidity provision with the potential to present externalities for the market.Article High-Frequency Trading and Market Quality: the Case of a Slightly Exposed Market(Elsevier Science Inc, 2022) Ekinci, Cumhur; Ersan, OguzImpacts of high-frequency trading (HFT) on market quality and various actors have been broadly studied. However, what happens when HFT is not a prominent figure in a market remains relatively unexplored. The paper seeks to answer this question focusing on 30 blue chip stocks in an emerging market, Borsa Istanbul, through Dec 2015 to Mar 2017. Despite a low share in the overall activity, HFT has observable effects, i.e. liquidity provision by non-HFT traders significantly reduces with HFT. Moreover, HFT generates profits on both positive and negative return days. Yet, HFT activity does not have an impact on volatility. These findings raise concerns regarding HFT and show potential externalities are not specific to the markets with HFT dominance.
