数据驱动的股价视角-宏观经济变量:2016-2020年印度尼西亚经济

Agung Nusantara, Sri Nawatmi, A. Santosa
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引用次数: 0

摘要

理论驱动视角的使用非常普遍,尤其是在经济学研究中,甚至成为一种不可避免的方法。当数据作为一种现实形式与理论不协同时,就会出现问题。由此得出的结论很可能与理论陈述不同。一种涉及数据驱动的方法是向量自回归(VAR)模型,它将所有涉及的变量作为内生变量。本研究旨在确定变量、股价、消费者价格指数、雅加达银行间超额汇率、汇率和净余额贸易之间的关系在统计上更准确。观测时间为2016年1月至2020年12月。本研究发现,股价变量与宏观经济变量之间存在递归关系。VAR模型将净平衡贸易变量确定为3种类型的部门股票中的内生变量,并且只有与之相似的制造业部门股票。这些结果具有两个理论后果:首先,在不区分部门的情况下设定股价会带来泛化错误的风险。第二,将股价设定为内生变量意味着假设市场是完美的,有效的和市场参与者有理性的行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Driven Perspective on Stock Price - Macroeconomic Variables: Indonesia Economy 2016-2020
AbstractThe use of a theory-driven perspective is very common, especially in economics research, and even becomes an inevitable approach. Problems arise when data, as a form of reality, does not synergize with theory. The resulting conclusion is very likely to be different from the theoretical statement. One method that refers to data-driven is the Vector Auto-Regressive (VAR) model, which puts all the variables involved in a position as endogenous variables. This study seeks to identify a statistically more accurate relationship in the relationship between variables, stock prices, consumer price index, Jakarta Inter-Bank Over rate, exchange rate, and Net Balance Trade. Observations were made from January 2016 to December 2020. This study found evidence that there is a recursive relationship between stock price variables and macroeconomic variables. The VAR model identifies the Net Balance Trade variable as an endogenous variable in 3 types of sectoral stocks and only manufacturing sector stocks that resemble it. These results have two theoretical consequences: first, setting stock prices without differentiating sectors carries the risk of generalization errors. Second, setting stock prices as the endogenous variable means assuming that the market is perfect, and efficient and market participants have rational behavior.
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