The connection between stock market prices and political support: evidence from Japan

Ohmura, Hanako

doi:10.1080/13504851.2020.1854660

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…D'Acunto et al [32] used VAR-LiNGAM, which incorporates time series into the Linear Non-Gaussian Acyclic Model (LiNGAM), a semiparametric causal inference algorithm, to reveal the causal structure of risk factors in stocks. Ohmura [33] analyzed the relationship between the stock market and political support using VAR-LiNGAM. Research has also examined the construction of networks that link causal relationships regarding performance among firms, known as causal chains [34][35][36].…”

Section: Causal Inference and Its Applicationsmentioning

confidence: 99%

Constructing Sentiment Signal-Based Asset Allocation Method with Causality Information

Taguchi,

Sakaji,

Izumi

et al. 2023

New Gener. Comput.

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This study demonstrates whether financial text is useful for the tactical asset allocation method using stocks. This can be achieved using natural language processing to create polarity indexes in financial news. We perform clustering of the created polarity indexes using the change point detection algorithm. In addition, we construct a stock portfolio and rebalanced it at each change point using an optimization algorithm. Consequently, the proposed asset allocation method outperforms the comparative approach. This result suggests that the polarity index is useful for constructing the equity asset allocation method.

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Section: Causal Inference and Its Applicationsmentioning

confidence: 99%

Constructing Sentiment Signal-Based Asset Allocation Method with Causality Information

Taguchi,

Sakaji,

Izumi

et al. 2023

New Gener. Comput.

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Testing volatility spillovers using GARCH models in the Japanese stock market during COVID-19

Spulbăr

Birău

Trivedi

et al. 2022

Investment Management and Financial Innovations

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This paper investigates volatility spillovers in the stock market in Japan during the COVID-19 pandemic by using GARCH family models. The empirical analysis is focused on the dynamics of the NIKKEI 225 stock market index during the sample period from July 30, 1998, to January 24, 2022. In other words, the sample period covers both the period of the global financial crisis (GFC) and the COVID-19 pandemic. The econometrics includes GARCH (1,1), GJR (1,1), and EGARCH (1,1) models. By applying GARCH family models, this empirical study also examines the long-term behavior of the Japanese stock market.The Japanese stock market is much more stable and efficient than emerging or frontier markets characterized by higher volatility and lower liquidity. The paper establishes that NIKKEI 225 index dynamics is different in intensity in the case of the two most recent extreme events analyzed, namely the global financial crisis (GFC)of 2007–2008 and the COVID-19 pandemic. The findings confirmed the presence of the leverage effect during the sample period. Moreover, the empirical results identified the presence of high volatility in the sample returns of the selected stock market. Nevertheless, the econometric framework showed that the negative implications of the GFC were much more severe and caused more significant contractions compared to the COVID-19 pandemic for the Japanese stock market. This study contributes to the existing literature by providing additional empirical evidence on the long-term behavior of the stock market in Japan, especially in the context of extreme events.

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Analyzing The Effect of Monetary Policies and Financial Stability Measures Across Global Markets amid Covid-19 Pandemics

Özkaya

2024

Haliç Üniversitesi Sosyal Bilimler Dergisi

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This study investigates causal relationships among financial stability measures and monetary policies implemented in US, EU and Turkey. Selected variables from stock markets, money markets, and bond markets are tested. The set of macrofinancial process covers S&P500 stock market, BIST-100 stock market; Volatility index; German, US and Turkey 10-year treasury bond interest data, European Union 27 average CPI, US CPI, Turkey CPI, ECB policy rate, FED policy rate, CBRT policy rate data. The Granger causality analysis is applied for first; each dataset of US, EU and the Turkish economy, separately. Second, mutual impact of stock markets, money markets and bond markets are examined. Finally CBOE Volatility index (VIX) is taken into account. It is aimed to observe spillover effects from developed economies to emerging markets amid pandemics. The dataset covers the monthly period of 2019M1 to 2023M6.

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The connection between stock market prices and political support: evidence from Japan

Cited by 4 publications

References 18 publications

Constructing Sentiment Signal-Based Asset Allocation Method with Causality Information

Constructing Sentiment Signal-Based Asset Allocation Method with Causality Information

Testing volatility spillovers using GARCH models in the Japanese stock market during COVID-19

Analyzing The Effect of Monetary Policies and Financial Stability Measures Across Global Markets amid Covid-19 Pandemics

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