The determinants of main stock exchange index changes in emerging countries: evidence from Turkey in COVID-19 pandemic age

被引:25
作者
Kartal, Mustafa Tevfik [1 ]
Depren, Ozer [2 ]
Depren, Serpil Kilic [3 ]
机构
[1] Strateg Planning & Investor Relat Directorate Bor, Istanbul, Turkey
[2] Customer Experience Res Directorate Yapi Kredi Ba, Istanbul, Turkey
[3] Yildiz Tech Univ, Dept Stat, Istanbul, Turkey
来源
QUANTITATIVE FINANCE AND ECONOMICS | 2020年 / 4卷 / 04期
关键词
COVID-19; pandemic; determinants; main stock exchange index; machine learning; Turkey; DEFAULT SWAP SPREADS; FOREIGN OWNERSHIP; RETURN VOLATILITY; CDS SPREADS; EXPERIENCE; INVESTORS; BEHAVIOR; RISKS; LEVEL; FLOWS;
D O I
10.3934/QFE.2020025
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
With the emergence and spreading of COVID-19 pandemic all over the world, the uncertainty has been increasing for countries. Depending on this condition, especially emerging countries have been affected negatively by foreign portfolio investment outflows from stock exchanges, and main stock exchange indices have been collapsed. The study examines the causes of the main stock exchange index changes in Turkey in the COVID-19 period. In this context, 14 variables (3 global, 6 country-level, 5 market-level) are analyzed by employing random forest and support vector machine algorithms and using daily data between 01.02.2020 and 05.15.2020, which includes the pre-pandemic and the pandemic periods. The findings prove that (i) the most important variables are the retention amount of foreign investors in the equity market, credit default swap spreads, government bonds interest rates, Morgan Stanley Capital International (MSCI) emerging markets index, and volatility index in the pre-pandemic period; (ii) the importance of variables changes as MSCI emerging markets index, the volatility index, retention amount of foreign investors in the equity market, amount of securities held by the Central Bank of Republic of Turkey (CBRT), equity market traded value in the pandemic period; (iii) support vector machine has superior estimation accuracy concerning random forest algorithms in both pre-pandemic and pandemic period.
引用
收藏
页码:526 / 541
页数:16
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