Bear market predictability in Singapore

This paper investigates a variety of macroeconomic variables in Singapore and United States (US) that can be used to predict the bear market in Singapore. We use the parametric Markov-Switching model to classify the state of the market and conduct in-sample and out-of-sample tests to find out the us...

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Main Authors: See, Yi Fong, Low, Priscilla Yi Xian, Chow, Sze Yan
Other Authors: Wang Wei Siang
Format: Final Year Project (FYP)
Language:English
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/10356/73858
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author See, Yi Fong
Low, Priscilla Yi Xian
Chow, Sze Yan
author2 Wang Wei Siang
author_facet Wang Wei Siang
See, Yi Fong
Low, Priscilla Yi Xian
Chow, Sze Yan
author_sort See, Yi Fong
collection NTU
description This paper investigates a variety of macroeconomic variables in Singapore and United States (US) that can be used to predict the bear market in Singapore. We use the parametric Markov-Switching model to classify the state of the market and conduct in-sample and out-of-sample tests to find out the useful macroeconomic variables. In addition, we propose a way to test the accuracy of our prediction as the testing of the accuracy of bear market predictions does not exist in prior research papers. We use the nonparametric Bry-Boschan algorithm and the moving average approach as the benchmark in calculating this accuracy. We find that TED spread and Singapore term spread are significant and have good predictive accuracy. A combination of these two variables in the multivariate model achieves on average an accuracy of about 67%. Our results also show that not all variables that are relevant and significant to the stock market index can predict the bear market accurately. Further, our results are in line with the extant literature that macroeconomic variables can predict the states of the stock market, generating satisfying performance.
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spelling ntu-10356/738582019-12-10T14:22:19Z Bear market predictability in Singapore See, Yi Fong Low, Priscilla Yi Xian Chow, Sze Yan Wang Wei Siang School of Humanities and Social Sciences DRNTU::Business::Finance::Equity This paper investigates a variety of macroeconomic variables in Singapore and United States (US) that can be used to predict the bear market in Singapore. We use the parametric Markov-Switching model to classify the state of the market and conduct in-sample and out-of-sample tests to find out the useful macroeconomic variables. In addition, we propose a way to test the accuracy of our prediction as the testing of the accuracy of bear market predictions does not exist in prior research papers. We use the nonparametric Bry-Boschan algorithm and the moving average approach as the benchmark in calculating this accuracy. We find that TED spread and Singapore term spread are significant and have good predictive accuracy. A combination of these two variables in the multivariate model achieves on average an accuracy of about 67%. Our results also show that not all variables that are relevant and significant to the stock market index can predict the bear market accurately. Further, our results are in line with the extant literature that macroeconomic variables can predict the states of the stock market, generating satisfying performance. Bachelor of Arts 2018-04-17T07:03:04Z 2018-04-17T07:03:04Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/73858 en Nanyang Technological University 42 p. application/pdf application/pdf
spellingShingle DRNTU::Business::Finance::Equity
See, Yi Fong
Low, Priscilla Yi Xian
Chow, Sze Yan
Bear market predictability in Singapore
title Bear market predictability in Singapore
title_full Bear market predictability in Singapore
title_fullStr Bear market predictability in Singapore
title_full_unstemmed Bear market predictability in Singapore
title_short Bear market predictability in Singapore
title_sort bear market predictability in singapore
topic DRNTU::Business::Finance::Equity
url http://hdl.handle.net/10356/73858
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AT lowpriscillayixian bearmarketpredictabilityinsingapore
AT chowszeyan bearmarketpredictabilityinsingapore