Search results for: Korean stock market

Authors: Sohanur Rahman, Elisabeth Sinnewe, Larelle (Ellie) Chapple, Sarah Osborne

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Greater political attention to global climate change exposes firms to a higher level of political uncertainty, which can lead to adverse capital market consequences. However, a higher level of discourse on environment-specific political risk (EPR) between management and investors can mitigate information asymmetry, followed by less stock price crash risk. This study examines whether EPR discourse in discourse in the earnings conference calls (ECC) reduces firm-level stock price crash risk in the US market. This research also explores if adverse disclosures via media channels further moderates the association between EPR on crash risk. Employing a dataset of 28,933 firm-year observations from 2002 to 2020, the empirical analysis reveals that EPR discourse in ECC reduces future stock price crash risk. However, adverse disclosures via media channels can offset the favourable effect of EPR discourse on crash risk. The results are robust to the potential endogeneity concern in a quasi-natural experiment setting.

Keywords: earnings conference calls, environment, environment-specific political risk discourse, environmental disclosures, information asymmetry, reputation risk, stock price crash risk

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Authors: Mohammad Azam

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This work is an endeavor to empirically investigate the Gross Domestic Product-Growth as mediating variable between various factors and portfolio returns using a broad sample of 522 financial and non-financial firms enlisted on Pakistan Stock Exchange between January-1993 and June-2022. The study employs the Structural Equation modeling and Ordinary Least Square regression to determine the findings before and during the Covid-19 epidemiological situation, which has not received due attention by researchers. The analysis reveals that market and investment factors are redundant, whereas size and value show significant results, whereas Gross Domestic Product-Growth performs significant mediating impact for the whole time frame. Using before Covid-19 period, the results reveal that market, value, and investment are redundant, but size, profitability, and Gross Domestic Product-Growth are significant. During the Covid-19, the statistics indicate that market and investment are redundant, though size and Gross Domestic Product-Growth are highly significant, but value and profitability are moderately significant. The Ordinary Least Square regression shows that market and investment are statistically insignificant, whereas size is highly significant but value and profitability are marginally significant. Using the Gross Domestic Product-Growth augmented model, a slight growth in R-square is observed. The size, value and profitability factors are recommended to the investors for Pakistan Stock Exchange. Conclusively, in the Pakistani market, the Gross Domestic Product-Growth indicates a feeble moderating effect between risk-premia and portfolio returns.

Keywords: asset pricing puzzle, mediating role of GDP-growth, structural equation modeling, COVID-19 pandemic, Pakistan stock exchange

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Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: Taylan Kabbani, Ekrem Duman

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The design of adaptive systems that take advantage of financial markets while reducing the risk can bring more stagnant wealth into the global market. However, most efforts made to generate successful deals in trading financial assets rely on Supervised Learning (SL), which suffered from various limitations. Deep Reinforcement Learning (DRL) offers to solve these drawbacks of SL approaches by combining the financial assets price "prediction" step and the "allocation" step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with its environment to make optimal decisions through trial and error. In this paper, a continuous action space approach is adopted to give the trading agent the ability to gradually adjust the portfolio's positions with each time step (dynamically re-allocate investments), resulting in better agent-environment interaction and faster convergence of the learning process. In addition, the approach supports the managing of a portfolio with several assets instead of a single one. This work represents a novel DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem, or what is referred to as The Agent Environment as Partially observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. More specifically, we design an environment that simulates the real-world trading process by augmenting the state representation with ten different technical indicators and sentiment analysis of news articles for each stock. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which can learn policies in high-dimensional and continuous action spaces like those typically found in the stock market environment. From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of deep reinforcement learning in financial markets over other types of machine learning such as supervised learning and proves its credibility and advantages of strategic decision-making.

Keywords: the stock market, deep reinforcement learning, MDP, twin delayed deep deterministic policy gradient, sentiment analysis, technical indicators, autonomous agent

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Authors: Ethan Petersen

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In this paper, we analyze the volatility of Apple’s stock beginning January 3, 2005 up to October 9, 2014, then focus on a range from 30 days prior to each product announcement until 30 days after. Product announcements are filtered; announcements whose 60 day range is devoid of other events are separated. This filtration is chosen to isolate, and study, a potential cross-effect. Concerning Apple keynotes, there are two significant dates: the day the invitations to the event are received and the day of the event itself. As such, the statistical analysis is conducted for both invite-centered and event-centered time frames. A comparison to the VIX is made to determine if the trend is simply following the market or deviating. Regardless of the filtration, we find that there is a clear deviation from the market. Comparing these data sets, there are significantly different trends: isolated events have a constantly decreasing, erratic trend in volatility but an increasing, linear trend is observed for clustered events. According to the Efficient Market Hypothesis, we would expect a change when new information is publicly known and the results of this study support this claim.

Keywords: efficient market hypothesis, event study, volatility, VIX

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Authors: Azaz Zaman, Mirazur Rahman

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Capital market is a crucial financial market place where companies and the government can raise long-term funds and, at the same time, investors get the opportunity to invest in the listed companies. Capital markets play a vital role not only in shifting the funds from surplus entity to deficit for investment, but also in the overall economic development of any developing country like Bangladesh. Being the first and biggest capital market of Bangladesh, Dhaka Stock Exchange (DSE) is the prime bourse of the country. The differences in the investment preference— among three broad categories of investors in DSE including individual investors, institutional investors, and government— are easily observed. Authors of this article have used five categories of investors such as sponsors or directors of the company, institutional investors, foreign investors, government, and the general public in order to present a comparative analysis of their investment patterns. Obtaining data on the percentage of investment by these five types of investors in different sectors from the DSE website, this study aims to analyze the sector-wise investment preference of these investors using August 2018 data. The study has found that the sponsors or directors of the company have the highest percentage of investment in the textile industry which is close to 16%. The Bangladesh government, as an investor, has the highest percentage of investment in the fuel & power sector, approximately 32%. It has also found that the mutual funds' sector is mostly financed by institutional investors, nearly 28%. Foreign investors have their most investments in the banking sector, which is close to 22%. It has also revealed that the textile sector is mostly financed by the general public, close to 17%. Nevertheless, general public, surprisingly, has the lowest percentage of investment in the telecommunication sector, which is 0.10%.

Keywords: stock market investment, Dhaka stock exchange, capital market, Bangladesh

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Authors: Pratap Chandra Pati, Prabina Rajib, Parama Barai

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The traditional finance theory neglects the role of sentiment factor in asset pricing. However, the behavioral approach to asset-pricing based on noise trader model and limit to arbitrage includes investor sentiment as a priced risk factor in the assist pricing model. Investor sentiment affects stock more that are vulnerable to speculation, hard to value and risky to arbitrage. It includes small stocks, high volatility stocks, growth stocks, distressed stocks, young stocks and non-dividend-paying stocks. Since the introduction of Chicago Board Options Exchange (CBOE) volatility index (VIX) in 1993, it is used as a measure of future volatility in the stock market and also as a measure of investor sentiment. CBOE VIX index, in particular, is often referred to as the ‘investors’ fear gauge’ by public media and prior literature. The upward spikes in the volatility index are associated with bouts of market turmoil and uncertainty. High levels of the volatility index indicate fear, anxiety and pessimistic expectations of investors about the stock market. On the contrary, low levels of the volatility index reflect confident and optimistic attitude of investors. Based on the above discussions, we investigate whether market-wide fear levels measured volatility index is priced factor in the standard asset pricing model for the Indian stock market. First, we investigate the performance and validity of Fama and French three-factor model and Carhart four-factor model in the Indian stock market. Second, we explore whether India volatility index as a proxy for fearful market-based sentiment indicators affect the cross section of stock returns after controlling for well-established risk factors such as market excess return, size, book-to-market, and momentum. Asset pricing tests are performed using monthly data on CNX 500 index constituent stocks listed on the National stock exchange of India Limited (NSE) over the sample period that extends from January 2008 to March 2017. To examine whether India volatility index, as an indicator of fear sentiment, is a priced risk factor, changes in India VIX is included as an explanatory variable in the Fama-French three-factor model as well as Carhart four-factor model. For the empirical testing, we use three different sets of test portfolios used as the dependent variable in the in asset pricing regressions. The first portfolio set is the 4x4 sorts on the size and B/M ratio. The second portfolio set is the 4x4 sort on the size and sensitivity beta of change in IVIX. The third portfolio set is the 2x3x2 independent triple-sorting on size, B/M and sensitivity beta of change in IVIX. We find evidence that size, value and momentum factors continue to exist in Indian stock market. However, VIX index does not constitute a priced risk factor in the cross-section of returns. The inseparability of volatility and jump risk in the VIX is a possible explanation of the current findings in the study.

Keywords: India VIX, Fama-French model, Carhart four-factor model, asset pricing

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Authors: Talal Alsulaiman, Khaldoun Khashanah

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In this paper, we provided a literature survey on the artificial stock problem (ASM). The paper began by exploring the complexity of the stock market and the needs for ASM. ASM aims to investigate the link between individual behaviors (micro level) and financial market dynamics (macro level). The variety of patterns at the macro level is a function of the AFM complexity. The financial market system is a complex system where the relationship between the micro and macro level cannot be captured analytically. Computational approaches, such as simulation, are expected to comprehend this connection. Agent-based simulation is a simulation technique commonly used to build AFMs. The paper proceeds by discussing the components of the ASM. We consider the roles of behavioral finance (BF) alongside the traditionally risk-averse assumption in the construction of agent's attributes. Also, the influence of social networks in the developing of agents’ interactions is addressed. Network topologies such as a small world, distance-based, and scale-free networks may be utilized to outline economic collaborations. In addition, the primary methods for developing agents learning and adaptive abilities have been summarized. These incorporated approach such as Genetic Algorithm, Genetic Programming, Artificial neural network and Reinforcement Learning. In addition, the most common statistical properties (the stylized facts) of stock that are used for calibration and validation of ASM are discussed. Besides, we have reviewed the major related previous studies and categorize the utilized approaches as a part of these studies. Finally, research directions and potential research questions are argued. The research directions of ASM may focus on the macro level by analyzing the market dynamic or on the micro level by investigating the wealth distributions of the agents.

Keywords: artificial stock markets, market dynamics, bounded rationality, agent based simulation, learning, interaction, social networks

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Authors: Sofia Stoica

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In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices in the past five years of ten major tech companies – Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We experimented with a variety of models– a linear regressor model, K nearest Neighbors (KNN), a sequential neural network – and algorithms - Multiplicative Weight Update, and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement.

Keywords: finance, machine learning, opening price, stock market

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Authors: Tomasz L. Nawrocki, Danuta Szwajca

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The growing importance of reputation in building enterprise value and achieving long-term competitive advantage creates the need for its measurement and evaluation for the management purposes (effective reputation and its risk management). The paper presents practical application of self-developed corporate reputation assessment model from the viewpoint of stock market investors. The model has a pioneer character and example analysis performed for selected industry is a form of specific test for this tool. In the proposed solution, three aspects - informational, financial and development, as well as social ones - were considered. It was also assumed that the individual sub-criteria will be based on public sources of information, and as the calculation apparatus, capable of obtaining synthetic final assessment, fuzzy logic will be used. The main reason for developing this model was to fulfill the gap in the scope of synthetic measure of corporate reputation that would provide higher degree of objectivity by relying on "hard" (not from surveys) and publicly available data. It should be also noted that results obtained on the basis of proposed corporate reputation assessment method give possibilities of various internal as well as inter-branch comparisons and analysis of corporate reputation impact.

Keywords: corporate reputation, fuzzy logic, fuzzy model, stock market investors

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Authors: Fatma Ismailia, Malek Saihi

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This research provides an explanation of exchange incentives on the Tunis stock market from a behavioural point of view. The elucidation of the anomalies of excessive volume of transactions and that of excessive volatility cannot be done without the recourse to the psychological aspects of investors. The excessive confidence has been given the predominant role for the explanation of these phenomena. Indeed, when investors store increments, they become more confident about the precision of their private information and their exchange activities then become more aggressive on the subsequent periods. These overconfident investors carry out the intensive exchanges leading to an increase of securities volatility. The objective of this research is to identify whether the trading volume and the excessive volatility of securities observed on the Tunisian stock market come from the excessive exchange of overconfident investors. We use a sample of daily observations over the period January 1999 - October 2007 and we relied on various econometric tests including the VAR model. Our results provide evidence on the importance to consider the bias of overconfidence in the analysis of Tunis stock exchange specificities. The results reveal that the excess of confidence has a major impact on the trading volume while using daily temporal intervals.

Keywords: overconfidence, trading volume, efficiency, rationality, anomalies, behavioural finance, cognitive biases

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Authors: Sumit Sen, Sohan Khedekar, Umang Shinde, Shivam Bhargava

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This study has been undertaken to investigate whether the deep learning models are able to predict the future stock prices by training the model with the historical stock price data. Since this work required time series analysis, various models are present today to perform time series analysis such as Recurrent Neural Network LSTM, ARIMA and Facebook Prophet. Applying these models the movement of stock price of stocks are predicted and also tried to provide the future prediction of the stock price of a stock. Final product will be a stock price prediction web application that is developed for providing the user the ease of analysis of the stocks and will also provide the predicted stock price for the next seven days.

Keywords: Autoregressive Integrated Moving Average, Deep Learning, Long Short Term Memory, Time-series

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Authors: Kamel Malik Bensafta, Gervasio Semedo

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The purpose of this paper is to investigate the relationship between oil prices and socks markets. The empirical analysis in this paper is conducted within the context of Multivariate GARCH models, using a transform version of the so-called BEKK parameterization. We show that mean and uncertainty of US market are transmitted to oil market and European market. We also identify an important transmission from WTI prices to Brent Prices.

Keywords: oil volatility, stock markets, MGARCH, transmission, structural break

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Authors: Chyng Sun

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This brief report examines correlations between Korean men’s interest in gonzo pornography, perceptions of pornography’s functional value, and use of condoms. The report found that, neither a higher interest in gonzo or the perception that pornography is a source of sexual information was directly related to condom utilization. However, interest in gonzo pornography interacted with pornography perceptions to predict condomless sex. The findings suggest that Korean men who 1) had higher interest in viewing gonzo pornography, and 2) had a tendency to view pornography as a source of sexual information, are more likely to have sex without condoms. That is, when viewers consider pornography to be a form of sexual education, they are more likely to use the learned pornographic script to inform their sexual behavior.

Keywords: Korean, male, pornography, sexuality

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Authors: Massimiliano Frezza, Sergio Bianchi, Augusto Pianese

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The relation between stock price volatility and trading volume represents a controversial issue which has received a remarkable attention over the past decades. In fact, an extensive literature shows a positive relation between price volatility and trading volume in the financial markets, but the causal relationship which originates such association is an open question, from both a theoretical and empirical point of view. In this regard, various models, which can be considered as complementary rather than competitive, have been introduced to explain this relationship. They include the long debated Mixture of Distributions Hypothesis (MDH); the Sequential Arrival of Information Hypothesis (SAIH); the Dispersion of Beliefs Hypothesis (DBH); the Noise Trader Hypothesis (NTH). In this work, we analyze whether stock market efficiency can explain the diversity of results achieved during the years. For this purpose, we propose an alternative measure of market efficiency, based on the pointwise regularity of a stochastic process, which is the Hurst–H¨older dynamic exponent. In particular, we model the stock market by means of the multifractional Brownian motion (mBm) that displays the property of a time-changing regularity. Mostly, such models have in common the fact that they locally behave as a fractional Brownian motion, in the sense that their local regularity at time t0 (measured by the local Hurst–H¨older exponent in a neighborhood of t0 equals the exponent of a fractional Brownian motion of parameter H(t0)). Assuming that the stock price follows an mBm, we introduce and theoretically justify the Hurst–H¨older dynamical exponent as a measure of market efficiency. This allows to measure, at any time t, markets’ departures from the martingale property, i.e. from efficiency as stated by the Efficient Market Hypothesis. This approach is applied to financial markets; using data for the SP500 index from 1978 to 2017, on the one hand we find that when efficiency is not accounted for, a positive contemporaneous relationship emerges and is stable over time. Conversely, it disappears as soon as efficiency is taken into account. In particular, this association is more pronounced during time frames of high volatility and tends to disappear when market becomes fully efficient.

Keywords: volume–volatility relationship, efficient market hypothesis, martingale model, Hurst–Hölder exponent

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Authors: Sergio Andres Rojas, Julian Benavides Franco, Juan Tomas Sayago

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An increasing amount of research demonstrates how market mood affects financial markets, but their primary goal is to demonstrate how Trump's tweets impacted US interest rate volatility. Following that lead, this work evaluates the effect that Trump's tweets had during his presidency on local and international stock markets, considering not just volatility but the direction of the movement. Three indexes for Trump's tweets were created relating his activity with movements in the S&P500 using natural language analysis and machine learning algorithms. The indexes consider Trump's tweet activity and the positive or negative market sentiment they might inspire. The first explores the relationship between tweets generating negative movements in the S&P500; the second explores positive movements, while the third explores the difference between up and down movements. A pseudo-investment strategy using the indexes produced statistically significant above-average abnormal returns. The findings also showed that the pseudo strategy generated a higher return in the local market if applied to intraday data. However, only a negative market sentiment caused this effect on daily data. These results suggest that the market reacted primarily to a negative idea reflected in the negative index. In the international market, it is not possible to identify a pervasive effect. A rolling window regression model was also performed. The result shows that the impact on the local and international markets is heterogeneous, time-changing, and differentiated for the market sentiment. However, the negative sentiment was more prone to have a significant correlation most of the time.

Keywords: market sentiment, Twitter market sentiment, machine learning, natural dialect analysis

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Authors: Hamed Movahedizadeh, Annuar Md Nassir, Mehdi Karimimalayer, Navid Samimi Sedeh, Ehsan Bagherpour

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The aim of this paper is to evaluate Tehran’s Stock Exchange (TSE) performance regarding with impact of four macroeconomic factors including world crude Oil Price (OP), World Gold Price (GP), Consumer Price Index (CPI) and total Supplied Oil by Iran (SO) from January 2006 to December 2012 that Iran faced with economic and oil sanctions. Iran's exports of crude oil and lease condensate reduced to roughly 1.5 million barrels per day (bbl/d) in 2012, compared to 2.5 million bbl/d in 2011 due to hard sanctions. Monthly data are collected and subjected to a battery of tests through ordinary least square by EViews7. This study found that gold price and oil price are positively correlated with stock returns while total oil supplied and consumer price index have negative relationship with stock index, however, consumer price index tends to become insignificant in stock index. While gold price and consumer price index have short run relationship with TSE index at 10% of significance level this amount for oil price is significant at 5% and there is no significant short run relationship between supplied oil and Tehran stock returns. Moreover, this study found that all macroeconomic factors have long-run relationship with Tehran Stock Exchange Index.

Keywords: consumer price index, gold price, macroeconomic, oil price, sanction, stock market, supplied oil

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Authors: Vish Putcha, Chandrasekhar Putcha, Siva Hari

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In the current digital era of free trading and pandemic-driven remote work culture, markets worldwide gained momentum for retail investors to trade from anywhere easily. The number of retail traders rose to 24% of the market from 15% at the pre-pandemic level. Most of them are young retail traders with high-risk tolerance compared to the previous generation of retail traders. This trend boosted the growth of subscription-based market predictors and market data vendors. Young traders are betting on these predictors, assuming one of them is correct. However, 90% of retail traders are on the losing end. This paper presents multiple indicators and attempts to derive behavioral patterns from the underlying stocks. The two major indicators that traders and investors follow are technical and fundamental. The famous investor, Warren Buffett, adheres to the “Value Investing” method that is based on a stock’s fundamental Analysis. In this paper, we present multiple indicators from various methods to understand the behavior patterns of stocks. For this research, we picked five stocks with a market capitalization of more than $200M, listed on the exchange for more than 20 years, and from different industry sectors. To study the behavioral pattern over time for these five stocks, a total of 8 indicators are chosen from fundamental, technical, and financial indicators, such as Price to Earning (P/E), Price to Book Value (P/B), Debt to Equity (D/E), Beta, Volatility, Relative Strength Index (RSI), Moving Averages and Dividend yields, followed by detailed mathematical Analysis. This is an interdisciplinary paper between various disciplines of Engineering, Accounting, and Finance. The research takes a new approach to identify clear indicators affecting stocks. Statistical Analysis of the data will be performed in terms of the probabilistic distribution, then follow and then determine the probability of the stock price going over a specific target value. The Chi-square test will be used to determine the validity of the assumed distribution. Preliminary results indicate that this approach is working well. When the complete results are presented in the final paper, they will be beneficial to the community.

Keywords: stock pattern, stock market analysis, stock predictions, trading, investing, fundamental analysis, technical analysis, quantitative trading, financial analysis, behavioral analysis

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Authors: Saleem Abuleil, Khalid S. Alsamara

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Events represent a significant source of information on the web; they deliver information about events that occurred around the world in all kind of subjects and areas. These events can be collected and organized to provide valuable and useful information for decision makers, researchers, as well as any person seeking knowledge. In this paper, we discuss an ongoing research to target stock markets domain to observe and record changes (events) when they happen, collect them, understand the meaning of each one of them, and organize the information along with meaning in a well-structured format. By using Semantic Role Labeling (SRL) technique, we identified four factors for each event in this paper: verb of action and three roles associated with it, entity name, attribute, and attribute value. We have generated a set of rules and techniques to support our approach to analyze and understand the meaning of the events taking place in stock markets.

Keywords: natuaral language processing, Arabic language, event extraction and understanding, sematic role labeling, stock market

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Authors: Chih-Hsiang Chang

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This study provides insight into the effects of investor sentiment, excess optimism, overconfidence, the disposition effect, and herding formation on momentum profits. This study contributes to the field by providing a further examination of the relationship between psychological factors and momentum profits. The empirical results show that there is no evidence of significant momentum profits in Taiwan’s stock market. Additionally, investor sentiment in Taiwan’s stock market significantly influences its momentum profits.

Keywords: momentum profits, psychological factors, herding formation, investor sentiment

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Authors: Mussa Hussaini, Supasith Chonglerttham

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Stocks are usually classified according to their characteristics which are unique enough such that the performance of each category can be differentiated from another. The reasons behind such classifications in the financial market are sometimes financial innovation or it can also be because of finding a premium in a group of stocks with similar features. One of the major classifications in stocks market is called momentum strategy. Based on this strategy stocks are classified according to their past performances into past winners and past losers. Momentum in a stock market refers to the idea that stocks will keep moving in the same direction. In other word, stocks with rising prices (past winners stocks) will continue to rise and those stocks with falling prices (past losers stocks) will continue to fall. The performance of this classification has been well documented in numerous studies in different countries. These studies suggest that past winners tend to outperform past losers in the future. However, academic research in this direction has been limited in countries such as Thailand and to the best of our knowledge, there has been no such study in Thailand after the financial crisis of 1997. The significance of this study stems from the fact that Thailand is an open market and has been encouraging foreign investments as one of the means to enhance employment, promote economic development, and technology transfer and the main equity market in Thailand, the Stock Exchange of Thailand is a crucial channel for Foreign Investment inflow into the country. The equity market size in Thailand increased from $1.72 billion in 1984 to $133.66 billion in 1993, an increase of over 77 times within a decade. The main contribution of this paper is evidence for size category in the context of the equity market in Thailand. Almost all previous studies have focused solely on large stocks or indices. This paper extends the scope beyond large stocks and indices by including small and tiny stocks as well. Further, since there is a distinct absence of detailed academic research on momentum strategy in the Stock Exchange of Thailand after the crisis, this paper also contributes to the extension of existing literature of the study. This research is also of significance for those researchers who would like to compare the performance of this strategy in different countries and markets. In the Stock Exchange of Thailand, we examined the performance of momentum strategy from 2010 to 2014. Returns on portfolios are calculated on monthly basis. Our results on momentum strategy confirm that there is positive momentum profit in large size stocks whereas there is negative momentum profit in small size stocks during the period of 2010 to 2014. Furthermore, the equal weighted average of momentum profit of both small and large size category do not provide any indication of overall momentum profit.

Keywords: momentum strategy, past loser, past winner, stock exchange of Thailand

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Authors: Jinah Kwon

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Why are there North Korean women who prefer their life in China, living as an undocumented migrant, to legal residence in South Korea? What is the line between choice and coercion in trafficking and how does it relate to family, especially in Asian culture? Is family function as a haven in the unsecured world or a fetter against the better world? Are the current international mechanisms on trafficked victims fully reflecting the voices of the victims? This study is about the paradoxical conditions of North Korean women situated in China as the trafficked victim and as members of their Chinese family. In order to answer the questions above, this study explored the case of trafficked North Korean women in China. This mixed-methods study employed in-depth interviews of 18 trafficked women living in China and a survey of 98 North Korean origin women residing in South Korea. From the survey, 40 out of 98 women from the survey indicated an unexpected function of trafficking, which was used as a channel of supporting the subjectivity of women in the North Korean context. Such results supported the actual observation and narratives of North Korean women who experienced trafficking from the author’s two visits to the Northeastern area of China in 2012 and 2018, respectively. Based on the findings, the last part of the study makes policy implications on international trafficking mechanisms—theories by Gayatri Spivak and Herbert A. Simon was employed to approach the relatively less dealt aspect of trafficking.

Keywords: China, North Korean women, trafficking, victimhood

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Authors: Michiko Miyamoto

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This paper empirically investigates whether information technology (IT) strategies, business strategies, and divisions are aligned to meet overall business goals for Korean Small and medium-sized enterprises (SMEs), based on structure based Strategic Alignment Model, and make comparison with those of Japanese SMEs. Using 2,869 valid responses of Korean Human Capital Corporate Panel survey, a result of this study suggests that Korean human resources (HR) departments have a major influence over IT strategy, which is the same as Japanese SMEs, even though their management styles are quite different. As for IT strategy, it is not related to other departments at all for Korean SMEs. The Korean management seems to possess a great power over each division, such as Sales/Service, Research and Development/Technical Experts, HR, and Production.

Keywords: IT-business alignment, structured based strategic alignment model, structural equation model, human resources department

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Authors: Catherine Heyjung Sonu

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This paper examines the effect of accounting quality on the process in which stock price is determined by focusing on contribution-in-kind valuations using Korean setting. In Korea, a number of chaebol firms have transformed into holding company system starting in 2003. With an attempt to gain as much voting right, management sold shares of subsidiaries to purchase shares of the holding company. In so doing, management of these firms received share issues for the contribution in kind that has been made to obtain additional shares of the holding company. The price of these share issues against contribution in kind is allowed to be discounted up to 30%. Using this interesting setting in Korea, this paper examines whether accounting quality affects the extent of the discount applied to the share issues. If the accounting quality of the firm for which the management is receiving share issues is poor, the extent of discount is likely to be high. The extent of discount is likely lower for firms with superior accounting quality. Using 24 cases, we find that, on average, the extent of discount is larger for share issues in which the accounting quality, proxied by the absolute value of discretionary accruals, is poor. This paper provides insight by examining the effect of accounting quality on the stock market. It sheds light on the intersection between finance and accounting research and should be of interest to researchers and practitioners.

Keywords: Accounting quality, Contribution-in-kind, discount, holding company

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Authors: Hee-Jeong Ahn, Kee-Won Kim, Seung-Hoon Kim

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The knowledge of the relationship between characters can help readers to understand the overall story or plot of the literary fiction. In this paper, we present a method for extracting the specific relationship between characters from a Korean literary fiction. Generally, methods for extracting relationships between characters in text are statistical or computational methods based on the sentence distance between characters without considering Korean linguistic features. Furthermore, it is difficult to extract the relationship with direction from text, such as one-sided love, because they consider only the weight of relationship, without considering the direction of the relationship. Therefore, in order to identify specific relationships between characters, we propose a statistical method considering linguistic features, such as syntactic patterns and speech verbs in Korean. The result of our method is represented by a weighted directed graph of the relationship between the characters. Furthermore, we expect that proposed method could be applied to the relationship analysis between characters of other content like movie or TV drama.

Keywords: data mining, Korean linguistic feature, literary fiction, relationship extraction

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Authors: Yefrem Yefremov

Abstract:

In Kazakhstan, there are approximately 100,000 ethnic Koreans with the ethnonym "Koryo Saram". They are part of the global Korean diaspora around the world, deported to Kazakhstan by Stalin’s decree in 1937. Koryo Saram's diasporic identity is a composite of numerous identities based on a shared cultural heritage of the USSR and independent Kazakhstan and has mosaic character. The author has conducted a sociological survey to find out the main features of the identity of the Koryo Saram diaspora. The purpose of this paper is to depict the degree of ethnic, cultural, and diasporic identity of Koryo Saram and which effect on the preserving Korean diaspora in Kazakhstna do they have. The following elements impacting the above-mentioned identities were investigated in the survey: criteria by which Koryo Saram perceive themselves to be Korean, attitude of Koryo Saram to their ethnicity, degree of feeling of ethnocultural similarity between Koreans of Kazakhstan and Koreans of the Republic of Korea, degree of association of Koreans of Kazakhstan with other Koreans living in other CIS countries, degree of practicing Korean traditions Koryo Saram's attitudes towards interethnic marriages. The primary factor in defining the identity among the respondents is the factor of ethnic origin. Nationality is the second most significant component in establishing Koryo Saram’s identity. The maintenance of "Koreanness" of Koryo Sarams in the context of a multiethnic community, particularly in Kazakhstan, is based on genetic elements as well as the preservation of the culture. In conclusion, the high level of preserving Korean identity is being observed in the Korean Diaspora of Kazakhstan.

Keywords: diasporic identity, diaspora, ethnic identity, identity markers, korean diaspora, koreans of kazakhstan, koryo saram, multiethnicity

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Authors: Chang Soo Chon, Cheol Woong Ko, Han Sung Kim

Abstract:

The aim of this study is to develop an anterior lumbar interbody fusion (ALIF) PEEK cage suitable for Korean people. In this study, CT images were obtained from Korean male (173cm, 71kg) and 3D Korean lumbar models were reconstructed based on the CT images to investigate anatomical characteristics. Major design parameters of anterior lumbar interbody fusion (ALIF) PEEK Cage were selected using the morphological measurement information of the Korean Lumbar models. Through finite element analysis and mechanical tests, the developed ALIF PEEK Cage prototype was compared with the Fidji Cage (Zimmer.Inc, USA) and it was found that the ALIF prototype showed similar and/or superior mechanical performance compared to the FidJi Cage. Also, clinical validation for the ALIF PEEK Cage prototype was carried out to check predictable troubles in surgical operations. Finally, it is considered that the convenience and stability of the prototype was clinically verified.

Keywords: inter-body anterior fusion, ALIF cage, PEEK, Korean lumbar, CT image, animal test

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Authors: Sungjun Hong, Youah Lee, Jongwook Kim

Abstract:

In the last COP21 held in Paris on 2015, Korean government announced that Intended Nationally Determined Contributions (INDC) was 37% based on BAU by 2030. The GHG reduction rate of the transportation sector is the strongest among all sectors by 2020. In order to cope with Korean INDC, Korean government established that 3rd eco-friendly car deployment national plans at the end of 2015. In this study, we make the energy system model for estimating GHG emissions using LEAP model.

Keywords: INDC, greenhouse gas, LEAP, transportation

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Authors: Lethamaga Tladi, Ray Kekwaletswe

Abstract:

This study sought to conceptualise a multi-level framework for the effective use of stock ordering system in small enterprises in a rural area context. The interpretive research methodology has been used to enable the researcher to analyse, in-depth, and the subjective meanings of small enterprises’ employees in using the stock ordering system. The empirical data was collected from 13 small enterprises’ employees as participants through semi-structured interviews and observations. Interpretive Phenomenological Analysis (IPA) approach was used to analyse the small enterprises’ employee’s own account of lived experiences in relations to stock ordering system use in terms of their relatedness to, and cognitive engagement with. A case study of Kgautswane, a rural area in Limpopo Province, South Africa, served as a social context where the phenomenon manifested. Technology-Organisation-Environment Theory (TOE), Technology-to-Performance Chain Model (TPC), and Representation Theory (RT) underpinned this study. In this multi-level study, the findings revealed that; At the organisational level, the effective use of stock ordering system was found to be associated with the organisational performance gains such as efficiency, productivity, quality, competitiveness, and market share. Equally so, at the individual level, the effective use of stock ordering system minimised the end-user’s efforts and time to accomplish their tasks, which yields improved individual performance. The Multi-level framework for effective use of stock ordering system was presented.

Keywords: effective use, multi-dimensions of use, multi-level of use, multi-level research, small enterprises, stock ordering system

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Authors: Godwin Chigozie Okpara

Abstract:

This paper using standard GARCH, EGARCH, and TARCH models on day of the week return series (of 246 days) from the Nigerian Stock market estimated the model variants’ VaR. An asymmetric return distribution and fat-tail phenomenon in financial time series were considered by estimating the models with normal, student t and generalized error distributions. The analysis based on Akaike Information Criterion suggests that the EGARCH model with student t innovation distribution can furnish more accurate estimate of VaR. In the light of this, we apply the likelihood ratio tests of proportional failure rates to VaR derived from EGARCH model in order to determine the short and long positions VaR performances. The result shows that as alpha ranges from 0.05 to 0.005 for short positions, the failure rate significantly exceeds the prescribed quintiles while it however shows no significant difference between the failure rate and the prescribed quantiles for long positions. This suggests that investors and portfolio managers in the Nigeria stock market have long trading position or can buy assets with concern on when the asset prices will fall. Precisely, the VaR estimates for the long position range from -4.7% for 95 percent confidence level to -10.3% for 99.5 percent confidence level.

Keywords: downside risk, value-at-risk, failure rate, kupiec LR tests, GARCH models

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