ROBUST TRADING RULE SELECTION AND FORECASTING ACCURACY
Küçük Resim Yok
Tarih
2014
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Heidelberg
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Trading rules performing well on a given data set seldom lead to promising out-of-sample results, a problem which is a consequence of the in-sample data snooping bias. Efforts to justify the selection of trading rules by assessing the out-of-sample performance will not really remedy this predicament either, because they are prone to be trapped in what is known as the out-of-sample data-snooping bias. Our approach to curb the data-snooping bias consists of constructing a framework for trading rule selection using a-priori robustness strategies, where robustness is gauged on the basis of time-series bootstrap and multi-objective criteria. This approach focuses thus on building robustness into the process of trading rule selection at an early stage, rather than on an ex-post assessment of trading rule fitness. Intra-day FX market data constitute the empirical basis of the proposed investigations. Trading rules are selected from a wide universe created by evolutionary computation tools. The authors show evidence of the benefit of this approach in terms of indirect forecasting accuracy when investing in FX markets.
Açıklama
1st International Conference on Forecasting Economic and Financial Systems (FEFS) / 5th International Workshop on Singular Spectrum Analysis and its Applications (SSA) -- MAY 17-20, 2012 -- Beijing, PEOPLES R CHINA
Anahtar Kelimeler
A-Priori Robustness, Data-Snooping Bias, Efficient Market Hypothesis, Evolutionary Computation, İntra-Day Fx Markets, Time-Series Bootstrap, Trading Rule Selection
Kaynak
Journal of Systems Science & Complexity
WoS Q Değeri
Q4
Scopus Q Değeri
Q2
Cilt
27
Sayı
1
