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PREDICITING BULL AND BEAR IN S&P500

Saidy, Dodou LU and Saleh Tabari, Arash LU (2017) NEKP01 20171
Department of Economics
Abstract
This paper seeks to investigate if factors extracted from macroeconomic and financial variables can improve the forecast accuracy of the bull and bear market in the S&P500 stock index. The study extended the models constructed by Nyberg(2012) and Chen (2009) by augmenting their model with factors. Very little, if any research has been done in modelling the bull and bear using this approach. After using the Bry-Boschan method to identify the two regimes in the stock market, eleven models were constructed using a static probit or dynamic probit model framework. The out of sample forecast results indicates that, probit models augmented with factors have a relatively lower Quadratic Probability Score (QPS) than the corresponding models without... (More)
This paper seeks to investigate if factors extracted from macroeconomic and financial variables can improve the forecast accuracy of the bull and bear market in the S&P500 stock index. The study extended the models constructed by Nyberg(2012) and Chen (2009) by augmenting their model with factors. Very little, if any research has been done in modelling the bull and bear using this approach. After using the Bry-Boschan method to identify the two regimes in the stock market, eleven models were constructed using a static probit or dynamic probit model framework. The out of sample forecast results indicates that, probit models augmented with factors have a relatively lower Quadratic Probability Score (QPS) than the corresponding models without factors. Among all the models employed, the dynamic probit model outperformed all the models, while the static probit model without factors is the least performing model. The results also showed that returns on assets and money stock are among the key leading indicators of the S&P500 stock index. Thus, there is evidence that factors can improve the forecast accuracy of the bull and bear market in the S&P500 stock index. (Less)
Please use this url to cite or link to this publication:
author
Saidy, Dodou LU and Saleh Tabari, Arash LU
supervisor
organization
course
NEKP01 20171
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Factors, Stock indices, Stock Markets, Probability, Dynamic & Static Probit
language
English
id
8913547
date added to LUP
2017-07-10 14:34:36
date last changed
2017-07-10 14:34:36
@misc{8913547,
  abstract     = {{This paper seeks to investigate if factors extracted from macroeconomic and financial variables can improve the forecast accuracy of the bull and bear market in the S&P500 stock index. The study extended the models constructed by Nyberg(2012) and Chen (2009) by augmenting their model with factors. Very little, if any research has been done in modelling the bull and bear using this approach. After using the Bry-Boschan method to identify the two regimes in the stock market, eleven models were constructed using a static probit or dynamic probit model framework. The out of sample forecast results indicates that, probit models augmented with factors have a relatively lower Quadratic Probability Score (QPS) than the corresponding models without factors. Among all the models employed, the dynamic probit model outperformed all the models, while the static probit model without factors is the least performing model. The results also showed that returns on assets and money stock are among the key leading indicators of the S&P500 stock index. Thus, there is evidence that factors can improve the forecast accuracy of the bull and bear market in the S&P500 stock index.}},
  author       = {{Saidy, Dodou and Saleh Tabari, Arash}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{PREDICITING BULL AND BEAR IN S&P500}},
  year         = {{2017}},
}