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Date-stamping US housing market explosivity

Balcilar, Mehmet; Katzke, Nico; Gupta, Rangan - 2018

Generalized supADF (GSADF) test procedure developed by Phillips et al. (Testing for multiple bubbles: Historical episodes of …

Persistent link: https://www.econbiz.de/10011812671

Do extreme market value ratios mean that the market is informationally inefficient? : a study of the Warsaw Stock Exchange

Karasiński, Jacek; Zduńczak, Patryk - In: Journal of economics & management 43 (2021) 1, pp. 206-224

Aim/purpose - The aim of this paper is to verify whether extremely high values of market value ratios are the symptoms of informational inefficiency of the market in a weak form. The authors intend to examine whether these phenomena co-occur with each other. Design/methodology/approach -...

Persistent link: https://www.econbiz.de/10013166614

Random Walks in Dividend Yields and Bubbles

Bidian, Florin - 2014

A nonstationary dividend yield, having a unit root, is seen as proof of bubbles (Craine 1993). This inference is not … valid. A sufficient condition for the absence, respectively presence of bubbles is the uniform divergence, respectively … equilibrium dividend yield is a random walk without a deterministic trend or drift, but bubbles are still absent …

Persistent link: https://www.econbiz.de/10013058778

Date-stamping US housing market explosivity

Balcilar, Mehmet; Katzke, Nico; Gupta, Rangan - 2017

Generalized sup ADF (GSADF) test procedure developed by Phillips, Shi, and Yu (Testing for Multiple Bubbles: Historical Episodes …

Persistent link: https://www.econbiz.de/10011674010

Forecasting Nigerian stock market returns using ARIMA and artificial neural network models

Isenahd, Godknows M.; Olubusoye, Olusanya E. - In: CBN journal of applied statistics 5 (2014) 2, pp. 25-48

The study reports empirical evidence that artificial neural network based models are applicable to forecasting of stock market returns. The Nigerian stock market logarithmic returns time series was tested for the presence of memory using the Hurst coefficient before the models were trained. The...

Persistent link: https://www.econbiz.de/10011488820