Data analytics in stock markets
| buir.advisor | Oğuz, Osman | |
| dc.contributor.author | Salari, Hajar Novin | |
| dc.date.accessioned | 2019-08-07T07:54:32Z | |
| dc.date.available | 2019-08-07T07:54:32Z | |
| dc.date.copyright | 2019-06 | |
| dc.date.issued | 2019-06 | |
| dc.date.submitted | 2019-07-12 | |
| dc.department | Department of Industrial Engineering | en_US |
| dc.description | Cataloged from PDF version of article. | en_US |
| dc.description | Thesis (M.S.): Bilkent University, Department of Industrial Engineering, İhsan Doğramacı Bilkent University, 2019. | en_US |
| dc.description | Includes bibliographical references (leaves 111-114). | en_US |
| dc.description.abstract | One of the important strategies that is employed in finance is data analytics. Data Analytics is the science of investigating raw data with intention of drawing meaningful information and useful conclusions. Recently, organizations started to consider data analytics as a way to improve business processes and, use the collected information in operational efficiencies for achieving revenue growth. In recent years, the usage of data analytics is rapidly growing for many other reasons, such as, optimizing business processes, increasing revenue, and improving customer interactions. In this research two kinds of data analytics, order imbalances and order flow imbalances are studied and two groups of models extended according them. These regression models are based on level regressions and percentage changes, and trying to answer whether data analytics can forecast one minute a head of price return for each stock or not. Moreover, the results are analyzed and interpreted for 27 stocks of Borsa Istanbul. In the next step, for understanding the power of prediction of data analytics, Fama-Macbeth regression is considered. In the first step, each portfolio’s return is regressed against one or more factor of time series. In the second step, the cross-section of portfolio returns is regressed against the factors, at each time step. Then, we discuss the Long-Short Portfolio approach which is widely used in finance literature. This method is an investing strategy that takes long positions in stocks that are expected to ascend and short positions in stocks that are expected to descend. In this part we show the number of days that are positive or negative and provide the t stats that adjusted by NW procedure for all data analytics in each day for this method. Finally, we discuss about the market efficiency and show whether according to our analysis Borsa Istanbul is an efficient market or not. | en_US |
| dc.description.degree | M.S. | en_US |
| dc.description.statementofresponsibility | by Hajar Novin Salari | en_US |
| dc.embargo.release | 2020-01-12 | |
| dc.format.extent | xvi, 114 leaves : illustrations ; 30 cm. | en_US |
| dc.identifier.itemid | B109745 | |
| dc.identifier.uri | http://hdl.handle.net/11693/52305 | |
| dc.language.iso | English | en_US |
| dc.publisher | Bilkent University | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Borsa İstanbul | en_US |
| dc.subject | Data analytics | en_US |
| dc.subject | High frequency trading | en_US |
| dc.subject | Order ımbalance | en_US |
| dc.subject | Order flowimbalance | en_US |
| dc.subject | Market efficiency | en_US |
| dc.subject | Fama-macbeth regression | en_US |
| dc.subject | Long-short portfolio | en_US |
| dc.subject | Newey-west test | en_US |
| dc.title | Data analytics in stock markets | en_US |
| dc.type | Thesis | en_US |