We introduce a novel multiple hypothesis testing framework for selecting outperforming mutual funds with control of luck, the functional False Discovery Rate “plus”. We show that our method, which incorporates informative covariates to estimate the false discovery rate, gains considerable...

This study explores the effectiveness of technical and fundamental analysis in predicting and trading the returns of twelve cryptocurrencies, namely Bitcoin, Ethereum, Ripple, Dash, Cardano, Avalanche, Binance Coin, Dogecoin, Polkadot, Litecoin, Terra and Solana. A universe of 7,846 technical...

This study investigates the debatable success of technical trading rules, through the years, on the trending energy market of crude oil. In particular, the large universe of 7846 trading rules proposed by Sullivan et al. (1999), divided into five families (filter rules, moving averages, support...

This paper utilizes a large universe of 18,410 technical trading rules (TTRs) and adopts a technique that controls for false discoveries to evaluate the performance of frequently traded spreads using daily data over 1990–2016. For the first time, the paper applies an excessive out-of-sample...

ABSTRACT In this paper a hybrid genetic algorithm–support vector regression (GA‐SVR) model in economic forecasting and macroeconomic variable selection is introduced. The proposed algorithm is applied to the task of forecasting US inflation and unemployment. GA‐SVR genetically optimizes...