Switching-regime regression for modeling and predicting a stock market return
MetadataShow full item record
It has been observed that certain economic and financial variables commonly exhibit switching behavior depending on their magnitude. This phenomenon in general cannot be naturally captured by the linear regression (LR), which assumes a linear relationship between the dependent and explanatory variables. To decipher investor behavior more appropriately by accounting for this observation, a switching-regime regression (SRR) is proposed and applied to the S&P 500 market return with respect to seven explanatory variables. It is shown that, compared with LR, the new regression results in a significantly improved adjusted R2, increasing from less than 4 % to over 50 %. In addition, SRR yields better out-of-sample forecasting performance, besides that the fitted values from the new regression even resemble the dip during the 2008 financial crisis, while those from LR do not. The study thus indicates that the switching-regime regression improves significantly the statistical properties including the goodness of fit as well as conforms more to investor behavior theory.
Showing items related by title, author, creator and subject.
Customer retention and cross-buying in premium banking services : the roles of switching costs and interaction qualityLaksamana, Patria (2012)The emergence of e-banking and intense competition in premium banking services have essentially evolved the way banks have conventionally conducted their business and the way customers interact with banks. Consequently, ...
Ismail, S.; Joher, Huson; Nassir, A.; Ali, M. (2008)The auditor switching phenomenon was found to have implications to the value credibility of financial reporting and the cost of monitoring management activities Therefore, it has been widely and extensively studied in ...
Pasha, S.; Vo, Ba-Ngu; Tuan, H.; Ma, W. (2009)The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown and time-varying number of targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. ...