Forecasting stock price movement direction by machine learning algorithm

Forecasting stock price movement direction (SPMD) is an essential issue for short-term investors and a hot topic for researchers. It is a real challenge concerning the efficient market hypothesis that historical data would not be helpful in forecasting because it is already reflected in prices. Some commonly-used classical methods are based on statistics and econometric models. However, forecasting becomes more complicated when the variables in the model are all nonstationary, and the relationships between the variables are sometimes very weak or simultaneous. The continuous development of powerful algorithms features in machine learning and artificial intelligence has opened a promising new direction. This study compares the predictive ability of three forecasting models, including support vector machine (SVM), artificial neural networks (ANN), and logistic regression. The data used is those of the stocks in the VN30 basket with a holding period of one day. With the rolling window method, this study got a highly predictive SVM with an average accuracy of 92.48%

Essays on the Vietnam Stock Market

This thesis consists of three substantive studies about the Vietnam stock market. In particular, I study the asymmetric information, corporate governance (CG) practices, and foreign investment of publicly listed companies in Vietnam, presented in Chapters 2, 3, and 4, respectively. In Chapter 2, I investigate the effectiveness of a market surveillance system (MSS) on improving the market quality of the Vietnam stock market, as measured by liquidity and informed trading level. I find that market liquidity decreased after the introduction of the MSS, and that the effect is more pronounced for small firms. Although informed trading, on average, does not change significantly after the MSS, subsample analysis indicates a significant decrease in informed trading among large and liquid firms. In Chapter 3, I investigate the relationship between firms’ CG practices and informed trading. I find a negative relationship between the two variables. Firms with better CG practices have a lower level of informed stock trading. Moreover, a natural experiment on a shock of firms’ CG practices generated by the CG policies shows that the negative relationship between CG practices and informed trading is a causal one, in which a change in the former causes a change in the latter. In another analysis around the implementation of the MSS, I find that the implementation of the surveillance system affects the relationship between the two variables, and this effect is driven by large and liquid firms. In Chapter 4, I investigate whether foreign investors in the Vietnam stock market are informed about firms’ performance. Using the residuals of foreign investor ownership as a measure of the abnormal foreign investor holding, I find that the abnormal foreign investor holding is positively correlated with firm performance in the following one year. I also find a positive correlation between abnormal foreign investor holding and the stock returns in the next three quarters. These findings indicate that foreign investors are informed about the firms up to a one-year period

Prediction of Stocks and Stock Price using Artificial Intelligence : A Bibliometric Study using Scopus Database

Prediction of stocks and the prices of the stock is one of the most crucial points of discussion amongst the researchers and analysts in the financial domain to date. Every stakeholder and most importantly the investor desires to earn higher profit for his investment in the market and try to use several different strategies to invest their money. There are numerous methods to predict and analyse the movement of the stock prices. They are broadly divided into – statistical and artificial intelligence-based methods. Artificial intelligence is used to predict the futuristic prices of stocks and use wide range of algorithms like – SVMs, CNNs, LSTMs, RNNs , etc. This bibliometric study focusses on the study based primarily on the Scopus database. We have considered important keywords, authors, citations along with the correlations between the co-appearing authors, source titles and keywords with the use of network diagrams for visualisation. On the basis of this paper, we conclude that there is ample opportunity for research in the domain of financial market

Data science in economics: Comprehensive review of advanced machine learning and deep learning methods

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models

Testing of the effect of investor attention of stock market return and volatility: Evidence in Vietnam stock market.

This study investigates the relationship between investors’ attention, which is measured by Google search volume index, and the index performance (index return and volatility) in Vietnamese stock market. I will test the role of attention in predicting market performance. Moreover, past return will be considered when measuring the impact of investors’ attention on future return and volatility. The data is obtained weekly from December, 2006 to November, 2014. Stock indices are Vnindex and Hastc. Google Search Volume Index (SVI) is used as a measure for investors’ attention. Granger causality test, VAR estimations and OLS method are applied in this study in order to test whether investors’ attention is useful in predicting future stock performance and the sign of this effect as well as how the effect of investor attention is affected by changes in the past return. Results show that both index return and volatility of Vnindex are fairly quickly influenced by search volume. This impact is not influenced by the sign of past return as well as the past return. In case of Hastc, there exists a delay in the impact of past search volume on the index return. Moreover, this impact will increase conditional on a unit change in the past return of Hastc. In the opposite direction, the results also suggest that search volume index is also affected by index return and volatility.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

How fearful are Commodities and US stocks in response to Global fear? Persistence and Cointegration analyses

This paper deals with the analysis of long-run relationships of fear indices for US stocks, commodities, and the energy sector with global fear indices for stocks and oil. Departing from the classical literature, fractional integration, and cointegration techniques are used to determine the degree of persistence in the long-run relationship of the indices. Our results are threefold. We first established a fractional cointegrating relationship between each of the global and oil fear indices and other fear indices. However, the long-run relationship tends to be weak for the technology stocks. In addition, the cointegrating framework reveals a nonstationary mean-reverting behaviour in the long-run relationship, implying that the effect of shocks from financial, economic, or other exogenous sources will be temporary though with long-lasting effects. These findings have crucial policy inferences for portfolio managers concerning investment decisions

Trend and Knowledge Structure of Cryptocurrency Reserch in the Scopus Database

Purpose: This study aims to utilize the bibliometric method to investigate the most important characteristics and key research topics in the literature on cryptocurrency research.   Theoretical framework: This study used a text mining framework based on domain-level and knowledge structure analysis.   Design/methodology/approach: Based on domain-level and knowledge structure analysis, this study used data from the Scopus database, which included 1,685 published articles from 2018 to 2023 on cryptocurrency research. Data analytics and visualization may be accomplished with the bibliometrix package in R software.   Findings: The result found that, there has been a fifty percent annual growth in cryptocurrency research since 2018. Studying the most frequently used terms and phrases in the research makes it possible to see which research areas have the greatest impact. According to the results, (1) cryptocurrency market, (2) market efficiency, (3) herding behavior, (4) COVID pandemic, (5) safe haven, (6) stock markets, (7) financial markets, and (8) volatility spillovers should be the emphasis of future research.   Research, Practical & Social implications: This article will be useful to scholars and practitioners looking for research directions. Based on the trending topics and knowledge structure of cryptocurrency research, this research also suggests potential new study topics for the future.   Originality/value: The value of these findings revealed an increase and a new aspect of cryptocurrency research in the business field related to the continued expansion of empirical research documents, researchers/authors, global collaboration, and co-citations

Exploring the Relationship between Tourism and Economic Growth in Small Island Economies: A Study of Fiji

This study examines the effect of tourism, measured by visitor arrivals) on the economic growth of Fiji, a small island economy, over the period 1975 to 2015. We use a neoclassical framework and regression analysis to examine the short-run and the long-run effects of tourism whilst accounting for structural breaks. We confirm the presence of a long-run association using the two-step procedure of Engle and Granger (1987) and the ARDL bounds test of Pesaran, Shin and Smith (2001). From the long-run results, we note that a 1% increase in visitor arrivals contribute about 0.22% to the GDP per capita. The short run elasticity is noted to be 0.19%. The study finds evidence of a unidirectional causality from economic growth to tourism, and mutually reinforcing effect between capital investment and tourism. Thus, we can expect greater impact of tourism on the economic growth through tourism related investment activities such as improvements in airports, roads, transportation, financial sector and telecommunications, and parks and beaches