Switching-regime regression for modeling and predicting a stock market return
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 variab...
| Main Authors: | , |
|---|---|
| Format: | Journal Article |
| Published: |
2019
|
| Online Access: | http://hdl.handle.net/20.500.11937/78261 |
| _version_ | 1848763948722028544 |
|---|---|
| author | Szulczyk, Kenneth Zhang, Changyong |
| author_facet | Szulczyk, Kenneth Zhang, Changyong |
| author_sort | Szulczyk, Kenneth |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:11:34Z |
| format | Journal Article |
| id | curtin-20.500.11937-78261 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:11:34Z |
| publishDate | 2019 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-782612020-06-08T08:30:09Z Switching-regime regression for modeling and predicting a stock market return Szulczyk, Kenneth Zhang, Changyong 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. 2019 Journal Article http://hdl.handle.net/20.500.11937/78261 10.1007/s00181-019-01763-9 restricted |
| spellingShingle | Szulczyk, Kenneth Zhang, Changyong Switching-regime regression for modeling and predicting a stock market return |
| title | Switching-regime regression for modeling and predicting a stock market return |
| title_full | Switching-regime regression for modeling and predicting a stock market return |
| title_fullStr | Switching-regime regression for modeling and predicting a stock market return |
| title_full_unstemmed | Switching-regime regression for modeling and predicting a stock market return |
| title_short | Switching-regime regression for modeling and predicting a stock market return |
| title_sort | switching-regime regression for modeling and predicting a stock market return |
| url | http://hdl.handle.net/20.500.11937/78261 |