Ανάρτηση Ερευνητικoύ Δοκιμίου no 01/24

Ερευνητικό Δοκίμιο no 01/24 με τίτλο "Market Timing & Predictive Complexity" των Στυλιανού Αρβανίτη, Φωτεινής Κυριαζή και Δημητρίου Θωμάκου

Περίληψη

We propose a new method for selecting the local estimation window for forecasting and  trading financial returns. It is built around one particular definition of predictive complexity  and we apply it in the simplest of predictors, the sample mean. We derive the exact  conditions for the process of optimally selecting the local estimation window among a, theoretically found, grid of potential values of it. We use different loss functions, statistical and financial, which are considered individually and then pooled under two selection concepts, stochastic dominance and minimum description length, and find exact expressions  as to how the associated complexities and their combinations can be derived and applied.  Our results are based on a set of probabilistic assumptions for the time series under study  and, based on those, we offer an inferential procedure for testing the presence of excess  trading returns. Our empirical illustration on a set of diverse exchange trade funds (ETFs) across different asset classes suggests that the method works very well in practice and that  it can generate both statistical and financial performance enhancements. A number of  extensions to different predictors and different underlying assumptions is discussed.
 

Ο Στυλιανός Αρβανίτης είναι Καθηγητής στο τμήμα Οικονομικής Επιστήμης του Οικονομικού Πανεπιστημίου Αθηνών, η Φωτεινή Κυριαζή είναι Επίκουρη Καθηγήτρια στο τμήμα Διοίκησης Γεωργικών Επιχειρήσεων και Συστημάτων του Γεωπονικού Πανεπιστημίου Αθηνών και ο Δημήτριος Θωμάκος είναι Καθηγητής στο τμήμα Διοίκησης Επιχειρήσεων και Οργανισμών του Εθνικού και Καποδιστριακού Πανεπιστημίου Αθηνών.