Stock daily return prediction using expanded features and feature selection
نویسندگان
چکیده
منابع مشابه
Return Prediction and Stock Selection from Unidentified Historical Data
The experimental approach is applied to explore the value of unidentified historical information in stock-return prediction. Return sequences were randomly drawn cross section and time from historical S&P500 data. Subjects were requested to predict returns or select stocks from 12 preceding realizations. The hypothesis that predictions are randomly assigned to historical sequences is rejected i...
متن کاملDaily Stock Prediction Using Neuro-genetic Hybrids
We propose a neuro-genetic daily stock prediction model. Traditional indicators of stock prediction are utilized to produce useful input features of neural networks. The genetic algorithm optimizes the neural networks under a 2D encoding and crossover. To reduce the time in processing mass data, a parallel genetic algorithm was used on a Linux cluster system. It showed notable improvement on th...
متن کاملportfolio selection using return mean, return standard deviation and liquidity in tehran stock exchange
markowitz, in his portfolio selection theory, stated that investors select their portfolios according to two criteria of risk and return. accordingly, he presented his mathematical model. one of the criticisms of this model is that while investors, practically, consider different criteria in forming their portfolios, it only considers the return mean and return standard deviation. liquidity is ...
متن کاملOn stock return prediction with LSTM networks
Artificial neural networks are, again, on the rise. The decreasing costs of computing power and the availability of big data together with advancements of neural network theory have made this possible. In this thesis, LSTM (long short-term memory) recurrent neural networks are used in order to perform financial time series forecasting on return data of three stock indices. The indices are S&P 5...
متن کاملEpileptic Seizure Prediction Using Hybrid Feature Selection
A comprehensive research of Electroencephalography (EEG) is carried out on Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) domains. In this scenario, the hybrid feature extraction is performed by utilizing entropy features like Shannon entropy, log-energy entropy and Renyi entropy. Generally, the entropy measures are effective in evaluation of non-linear interrelation an...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES
سال: 2017
ISSN: 1300-0632,1303-6203
DOI: 10.3906/elk-1704-256