Volatility spillover in Indonesia, USA, and Japan capital market

Globalization and advanced information technology easing us for obtaining information from global stock markets. With that condition, volatility in domestic capital market could be affected by volatility from global stock markets. That concern will be answered in this research, about volatility spillover in Indonesia, USA, and Japan capital market. This research using daily return data from each country indices from January 2004 until December 2008 employing econometric model GARCH (1,1). The result showing us that there is one way volatility spillover between Indonesia and USA (USA effecting Indonesia). Meanwhile, there is bidirectional volatility spillover between Indonesia and Japan (Japan influnced Indonesia, and vice versa).Volatility, Volatility Spillover, GARCH

Recent Advances in Stock Market Prediction Using Text Mining: A Survey

Market prediction offers great profit avenues and is a fundamental stimulus for most researchers in this area. To predict the market, most researchers use either technical or fundamental analysis. Technical analysis focuses on analyzing the direction of prices to predict future prices, while fundamental analysis depends on analyzing unstructured textual information like financial news and earning reports. More and more valuable market information has now become publicly available online. This draws a picture of the significance of text mining strategies to extract significant information to analyze market behavior. While many papers reviewed the prediction techniques based on technical analysis methods, the papers that concentrate on the use of text mining methods were scarce. In contrast to the other current review articles that concentrate on discussing many methods used for forecasting the stock market, this study aims to compare many machine learning (ML) and deep learning (DL) methods used for sentiment analysis to find which method could be more effective in prediction and for which types and amount of data. The study also clarifies the recent research findings and its potential future directions by giving a detailed analysis of the textual data processing and future research opportunity for each reviewed study

THE EFFECT OF GOOGLE TREND AS DETERMINANT OF RETURN AND LIQUIDITY IN INDONESIA STOCK EXCHANGE

The impressive progress of information technology has substantially impacted economic development. Given this condition, the diffusion of information technology is related to the improvement of activities in the capital market, in which asymmetric information between investors can diminished to the lowest level. Thereby, we considered that information retrieval over the internet contributes to return and liquidity. We performed Google Trend (GT) as the surrogated indicator in attenuating the asymmetric information in Indonesia Stock Exchange. By utilizing 5976 observation data from 83 cross-sectional companies and 72 monthly time series ranging from January 2007 to December 2012, we noted that the information retrieval over the Internet has negative (p < 0.05) contribution to return (RET). On the other hand, we confirmed that the information retrieval over the internet (GT) is positively (p < 0.01) related to liquidity which is surrogated by trading volume (TV)

Economic Reforms and Constitutional Transition

This paper investigates the relationship between economic reforms and constitutional transition, which has been neglected by many transition economists. It is argued that assessment of reform performance might be very misleading if it is not recognized that economic reforms are just a small part of large scale of constitutional transition. Rivalry and competition between states and between political forces within each country are the driving forces for constitutional transition. We use Russia as an example of economic reforms associated with constitutional transition and China as an example of economic reforms in the absence of constitutional transition to examine features and problems in the two patterns of transition. It is concluded that under political monopoly of the ruling party, economic transition will be hijacked by state opportunism. Dual track approach to economic transition may generate very high long-term cost of constitutional transition that might well outweigh its short-term benefit of buying out the vested interests.constitutional transition, economic reform, division of labor, debate of shock therapy vs gradualism, debate of convergence vs institutional innovation

Korea\u27s Online Stock Trading Boom: Facts and Implications

The Asian financial crisis of 1997-98 was a major setback for Korea, one of the world’s most successful economies in the post-war era. Nevertheless, the financial crisis has led to some positive changes in Korea’s financial system. The Korean government, in conjunction with the IMF, is carrying out comprehensive structural reforms in the financial system. In the meantime, online stock trading has grown explosively since 1998 to the extent that Korea now has the world’s highest penetration rate in this area. In our paper, we analyze the fundamental factors driving the rapid growth of online stock trading in Korea. We then examine the implications of such growth on the competitive environment of Korea’s retail brokerage industry as well as the liquidity, volatility, cost structure and efficiency of the stock market. We also look at the relationship between financial reform and online trading. In addition to exploring possible changes in the value propositions of brokerage firms, we also examine new regulatory issues that have emerged in line with the rapid growth of online trading. In short, our main objective is to study the causes and effects of Korea’s online trading boom

Global Trade in the Emerging Business Environment

Global Trade in the Emerging Business Environment explores global trade dynamics in the emerging business environment. Globalization, technological advancements, Industry 4.0, China’s Belt and Road Initiative, and the COVID-19 pandemic are changing the global trade ecosystem. Companies and countries need to evaluate these rapid changes and adjust their respective business strategies and policy formulations. This book discusses such strategies and how firms and countries can reposition themselves within the current environment

An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

Machine learning techniques for predicting the stock market using daily market variables

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligencePredicting the stock market was never seen as an easy task. The complexity of the financial systems makes it extremely difficult for anything or anyone to predict what the future of prices holds, let it be a day, a week, a month or even a year. Many variables influence the market’s volatility and some of these may even be the gut feeling of an investor on a specific day. Several machine learning techniques were already applied to forecast multiple stock market indexes, some presenting good values of accuracy when it comes to predict whether the prices will go up or down, and low values of error when dealing with regression data. This work aims to apply some state-of-the-art algorithms and compare their performance with Long Short-term Memory (LSTM) as well as between each other. The variables used to this empirical work were the prices of the Dow Jones Industrial Average (DJIA) registered for every business day, from January 1st of 2006 to January 1st of 2018, for 29 companies. Some changes and adjustments were made to the original variables to present different data types to the algorithms. To ensure good quality and certainty when evaluating the flexibility and stability of each model, the error measure used was the Root Mean Squared Error and the Mann-Whitney U test was also applied to assess statistical significance of the results obtained.Prever a bolsa nunca foi considerado ser uma tarefa fácil. A complexidade dos sistemas financeiros torna extremamente difícil que um ser humano ou uma máquina consigam prever o que o futuro dos preços reserva, seja para um dia, uma semana, um mês ou um ano. Muitas variáveis influenciam a volatilidade do mercado e algumas podem até ser a confiança de um investidor em apostar em determinada empresa, naquele dia específico. Várias técnicas de aprendizagem automática foram aplicadas ao longo do tempo para prever vários índices de bolsas, algumas apresentando bons valores de precisão quando se tratou de prever se os preços subiam ou desciam e outras, baixos valores de erro ao lidar com dados de regressão. Este trabalho tem como objetivo aplicar alguns dos mais conhecidos algoritmos e comparar os seus desempenhos com o Long Short-Term Memory (LSTM), e entre si. As variáveis utilizadas para a elaboração deste trabalho empírico foram os preços da Dow Jones Industrial Average (DJIA) registados para todos os dias úteis, de 1 de Janeiro de 2006 a 1 de Janeiro de 2018, para 29 empresas. Algumas alterações e ajustes foram efetuados sobre as variáveis originais de forma a construír diferentes tipos de dados para posteriormente dar aos algoritmos. Para garantir boa qualidade e veracidade ao avaliar a flexibilidade e estabilidade de cada modelo, a medida de erro utilizada foi o erro médio quadrático da raíz e, de seguida, o teste U de Mann-Whitney foi aplicado para avaliar a significância estatística dos resultados obtidos