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Akio Fukushima

Researcher at Seijo University

Publications -  10
Citations -  130

Akio Fukushima is an academic researcher from Seijo University. The author has contributed to research in topics: Monetary base & Currency. The author has an hindex of 4, co-authored 10 publications receiving 110 citations.

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The Market Efficiency of Bitcoin: A Weekly Anomaly Perspective

TL;DR: In this article, the authors examined empirically whether or not weekly price anomalies exist by checking the market efficiency of Bitcoin and showed that Bitcoin transactions are becoming and can become more efficient.
Journal Article

Impact of the Prevailing Internet on International Trade in Asia

TL;DR: In this article, the authors provided empirical evidence for the relationship between the prevailing Internet and international trade and economic growth in Asian countries, and the empirical results show that Internet promotes international trade both in developed countries and developing Asian countries; however, the effect is larger in Asia.
Journal ArticleDOI

How Does Price of Bitcoin Volatility Change

TL;DR: In this article, the authors examined how the volatile price of Bitcoin changes empirically and showed that there is a difference between short-term volatility and long-term stability. But they did not consider the effect of Bitcoin price fluctuations on the long term development of Bitcoin.
Journal ArticleDOI

Openness of the Economy, Diversification, Specialization, and Economic Growth

TL;DR: In this article, the authors examined whether openness of the economy promotes production diversification or production specialization and whether or not specialization/diversification spurs economic growth and found that greater openness does not always mean greater economic growth in emerging and developing countries.
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AR Model or Machine Learning for Forecasting GDP and Consumer Price for G7 Countries

TL;DR: A comparison of using an AR model and machine learning (LSTM) to forecast GDP and consumer price is conducted using recent cases from G7 countries and the empirical results show that the traditional forecasting AR model is a little more appropriate than the machine learning model, however, there is little difference to forecast consumer price between them.