Forecasting index changes in the German DAX family
Friedrich‑Carl Franz1 Revised: 30 January 2020 / Published online: 18 February 2020 © The Author(s) 2020
Abstract
Combining market data with a publicly available monthly snapshot of Deutsche Börse’s index ranking list, I create a model that predicts index changes in the DAX, MDAX, SDAX, and TecDAX from 2010 to 2019 before they are ofcially announced. Even though I empirically show that index changes are predictable, they still earn sizeable post-announcement 1-day abnormal returns up to 1.42% and − 1.54% for promotions and demotions, respectively. While abnormal returns are larger in smaller stocks, I fnd no evidence that they are related to funding constraints or additional risk for trading on wrong predictions. A trading strategy that trades according to my model yields an annualized Sharpe ratio of 0.83 while being invested for just 4 days a year.
https://link.springer.com/content/pdf/10.1057/s41260-020-00153-6.pdf
Friedrich‑Carl Franz1 Revised: 30 January 2020 / Published online: 18 February 2020 © The Author(s) 2020
Abstract
Combining market data with a publicly available monthly snapshot of Deutsche Börse’s index ranking list, I create a model that predicts index changes in the DAX, MDAX, SDAX, and TecDAX from 2010 to 2019 before they are ofcially announced. Even though I empirically show that index changes are predictable, they still earn sizeable post-announcement 1-day abnormal returns up to 1.42% and − 1.54% for promotions and demotions, respectively. While abnormal returns are larger in smaller stocks, I fnd no evidence that they are related to funding constraints or additional risk for trading on wrong predictions. A trading strategy that trades according to my model yields an annualized Sharpe ratio of 0.83 while being invested for just 4 days a year.
https://link.springer.com/content/pdf/10.1057/s41260-020-00153-6.pdf
