Titolo:  Alternative neural network approaches for enhancing stock picking using earnings forecasts
Data di pubblicazione:  2013
ALIANO, MAURO  (Corresponding)
Autori:  Aliano, M.; Galloppo, G.
Numero degli autori:  2
Lingua:  Inglese
Pagina iniziale:  77
Pagina finale:  96
Numero di pagine:  20
Digital Object Identifier (DOI):  10.1057/9781137293770_6
Titolo del libro:  Asset pricing, real estate and public finance over the crisis
Editore:  Palgrave Macmillan
ISBN:  9781349451333
Abstract:  Interest in financial markets has increased in the last couple of decades, among fund managers, policy makers, investors, borrowers, corporate treasurers and specialized traders. Forecasting the future returns has always been a major concern for the players in stock markets and one of the most challenging applications studied by researchers and practitioners extensively. Predicting the financial market is a very complex task, because the financial time series are inherently noisy and non-stationary and more it is often argued that the financial market is very efficient. Fama (1970) defined efficient market hypothesis (EMH) where the idea is a market in which security prices at any time ‘fully reflect’ all available information both for firms’ production—investment decisions, and investors’ securities selection. Furthermore, in EMH context no investor is in a position to make unexploited profit opportunities by forecasting futures prices on the basis of past prices. On the other hand, a large number of researchers, investors, analysts, practitioners etc. use different techniques to forecast the stock index and prices. In the last decade, applications associated with artificial neural network (ANN) have drawn noticeable attention in both academic and corporate research.
Parole chiave:  Neural network; volatility; ANN; efficient market hypothesis; root mean square error; stock market; neural network model; stock return; back propagation neural network
Revisione (peer review):  Comitato scientifico
Caratterizzazione prevalente:  scientifica
Rilevanza:  internazionale
Tipologia: 2.1 Contributo in volume (Capitolo o Saggio)

File in questo prodotto:
File Descrizione Tipologia Licenza  
neural_nets_paper_scan.pdf  versione editoriale Administrator   Richiedi una copia

Questionario e social

Condividi su: