Takuji Kinkyo

Takuji Kinkyo

orcid

Research Focus & Metrics

Academic Career Length

20Years (2007-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 November 2016 in Economics Letters
Verified
Journal Article
Closed access
KTKatsuyuki Tanaka
Katsuyuki Tanaka
TKTakuji Kinkyo
Takuji Kinkyo
Shigeyuki HamoriShigeyuki Hamori
Shigeyuki Hamori

1 March 2014 in Journal of International Financial Markets, Institutions and Money
Verified
Journal Article
Open access
WCWang Chen
Wang Chen
Shigeyuki HamoriShigeyuki Hamori
Shigeyuki Hamori
TKTakuji Kinkyo
Takuji Kinkyo

31 March 2023 in Journal of International Financial Markets, Institutions and Money
Verified
Journal Article
Closed access
LXLei Xu
Lei Xu
TKTakuji Kinkyo
Takuji Kinkyo

14 June 2015 in Emerging Markets Finance and Trade
Verified
Journal Article
Closed access
WCWang Chen
Wang Chen
TKTakuji Kinkyo
Takuji Kinkyo
This article examines the dynamic relationship between financial development and income inequality using the PMG. We find that financial development will reduce inequality in the long run, while it can increase inequality in the short run. Using the estimates of country-specific short-run coefficients, we also find that adverse short-run effects of financial development are associated with the vulnerabilities of countries in terms of their greater susceptibility to crises and poor quality of governance. Good governance seems to be important for achieving inclusive growth though financial development.

20 June 2019 in Economic Inquiry
Verified
Journal Article
Closed access
PDF file
KTKatsuyuki Tanaka
Katsuyuki Tanaka
THTakuo Higashide
Takuo Higashide
TKTakuji Kinkyo
Takuji Kinkyo
Shigeyuki HamoriShigeyuki Hamori
Shigeyuki Hamori

Verified
Journal Article
Open access
PDF file
Zhaojie LuoZhaojie Luo
Zhaojie Luo
XCXiaojing Cai
Xiaojing Cai
KTKatsuyuki Tanaka
Katsuyuki Tanaka
TTTetsuya Takiguchi
Tetsuya Takiguchi
TKTakuji Kinkyo
Takuji Kinkyo
Shigeyuki HamoriShigeyuki Hamori
Shigeyuki Hamori
This paper proposes a novel approach, based on convolutional neural network (CNN) models, that forecasts the short-term crude oil futures prices with good performance. In our study, we confirm that artificial intelligence (AI)-based deep-learning approaches can provide more accurate forecasts of short-term oil prices than those of the benchmark Naive Forecast (NF) model. We also provide strong evidence that CNN models with matrix inputs are better at short-term prediction than neural network (NN) models with single-vector input, which indicates that strengthening the dependence of inputs and providing more useful information can improve short-term forecasting performance.