### Relate returns of Istanbul Stock Exchange with other international index
---
![](9.jpg)
---
This *intermediate* level data set has 536 rows and 9 columns.
The data sets includes returns of Istanbul Stock Exchange with seven other international index; SP, DAX, FTSE, NIKKEI, BOVESPA, MSCE_EU, MSCI_EM from Jun 5, 2009 to Feb 22, 2011 and is organized with regard to working days in Istanbul Stock Exchange.
This data set is recommended for learning and practicing your skills in **exploratory data analysis**, **data visualization**, **regression** and **classification** modelling techniques.
Feel free to explore the data set with multiple **supervised** and **unsupervised** learning techniques. The Following data dictionary gives more details on this data set:
---
### Data Dictionary
| Column Position | Atrribute Name | Definition | Data Type | Example | % Null Ratios |
| 3 | ISE.1 | Istanbul Stock Exchange - National 100 Index | Quantitative | 0.031813, -0.044349, 0.061708 | 0 |
| 4 | SP | Standard & Poor's 500 Return Index | Quantitative | 0.007787, -0.054262, 0.005538 | 0 |
| 5 | DAX | Stock Market Return Index of Germany | Quantitative | 0.008455, -0.01155, 0.034787 | 0 |
| 6 | FTSE | Stock Market Return Index Of UK | Quantitative | 0.012866, -0.009351, 0.037891 | 0 |
| 7 | NIKKEI | Stock Market Return Index Of Japan | Quantitative | 0.004162, 0.003239, -0.008182 | 0 |
| 8 | BOVESPA | Stock Market Return Index Of Brazil | Quantitative | 0.01892, -0.013151, 0.009838 | 0 |
| 9 | EU | MSCI European Index | Quantitative | 0.011341, -0.012045, 0.0328 | 0 |
| 10 | EM | MSCI Emerging Markets Index | Quantitative | 0.008773, -0.004029, 0.01032 | 0 |
---
### Acknowledgement
This data set has been sourced from the Machine Learning Repository of University of California, Irvine [Istanbul Stock Exchange Data Set (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/ISTANBUL+STOCK+EXCHANGE).
The UCI page mentions the following paper as the original source of the data set:
*Akbilgic, O., Bozdogan, H., Balaban, M.E., (2013) A novel Hybrid RBF Neural Networks model as a forecaster, Statistics and Computing. DOI 10.1007/s11222-013-9375-7 *