This paper attempts to investigate if adopting accurate forecasts from Neural Network (NN) models can lead to statistical and economically significant benefits in portfolio management decisions. In order to achieve that, three NNs, namely the Multi-Layer Perceptron (MLP), Recurrent Neural...

We investigate the performance of more than 21,000 technical trading rules on 12 categorical and country-specific markets over the 2004-2015 study period. For this purpose, we apply a discrete false discovery rate approach in more than 240,000 hypotheses and examine the profitability,...

In this study, the profitability of technical analysis and Bayesian Statistics in trading the EUR/USD, GBP/USD, and USD/JPY exchange rates are examined. For this purpose, seven thousand eight hundred forty-six technical rules are generated and their profitability is assessed through a novel data...

The financial technology revolution is a reality, as the financial world is gradually transforming into a digital domain of high-volume information and high-speed data transformation and processing. The more this transformation takes place, the more consumer and investor behaviour shifts towards...

This study introduces a Conditional Fuzzy inference (CF) approach in forecasting. The proposed approach is able to deduct Fuzzy Rules (FRs) conditional on a set of restrictions. This conditional rule selection discards weak rules and the generated forecasts are based only on the most powerful...

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...