• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.807-814
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• 2021

As economic activities decrease, and the stock market decline due to COVID-19, many people are jumping into stock investment as an alternative source of income. As people's interest increases, many stock price analysis studies are underway to earn more profits. Due to the variance observed in the stock markets, it is necessary to analyze each stock independently and consistently. To solve this problem, we designed and implemented models and services that analyze stock prices using a reinforcement learning technique called Asynchronous Advantage Actor-Critic(A3C). Stock market data reflected external factors such as government bonds and KOSPI (Korea Composite Stock Price Index) as well as stock prices. Our proposed work provides a web service with a visual representation of predictions of stocks and stock information through which directions are given to investors to make safe investments without analyzing domestic and foreign stock market trends.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.67-74
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• 2023

Stock price prediction research in the financial sector aims to ensure trading stability and achieve profit realization. Conventional statistical prediction techniques are not reliable for actual trading decisions due to low prediction accuracy compared to randomly predicted results. Artificial intelligence models improve accuracy by learning data characteristics and fluctuation patterns to make predictions. However, predicting stock prices using long-term time series data remains a challenging problem. This paper proposes a stable and reliable stock price prediction method using K-means clustering-based data augmentation and normalization techniques and LSTM models specialized in time series learning. This enables obtaining more accurate and reliable prediction results and pursuing high profits, as well as contributing to market stability.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.10
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• pp.219-228
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• 2021

The study examines the behavior of stock prices after large price changes. It further examines the effect of firm size on stock returns, and the presence of the disposition effect. The study employs the event study methodology using daily price data from Pakistan Stock Exchange (PSX) for the period January 2001 to July 2012. Furthermore, to examine the factors that explain stock price behavior after large price movements, the study employs a two-way fixed-effect model that allows for the analysis of unobservable company and time fixed effects that explain market reversals or continuation. The findings suggest that winners perform better than losers after experiencing large price shocks thus showing a momentum behavior. In addition, the winners remain the winner, while the losers continue to lose more. This suggests that most of the investors in PSX behave rationally. Further, the study finds no evidence of disposition effect in PSX. The investors underreact to new information and the prices continue to move in the direction of initial change. The pooled regression estimates show that firm size is positively related to post-event abnormal returns while the fixed-effect model reveals the presence of unobservable firm-specific and time-specific effects that account for price continuation.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.513-522
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• 2014

Using the assumption that the price of a stock follows a geometric Brownian motion with constant volatility, Black and Scholes (BS) derived a formula that gives the price of a European call option on the stock as a function of the stock price, the strike price, the time to maturity, the risk-free interest rate, the dividend rate paid by the stock, and the volatility of the stock's return. However, implied volatilities of BS method tend to depend on the stock prices and the time to maturity in practice. To address this shortcoming, we estimate the implied volatility function as a function of the strike priceand the time to maturity for data consisting of the daily prices for KOSPI200 call options from January 2007 to May 2009 using support vector regression (SVR), the multiple additive regression trees (MART) algorithm, and ordinary least squaress (OLS) regression. In conclusion, use of MART or SVR in the BS pricing model reduced both RMSE and MAE, compared to the OLS-based BS pricing model.

• Journal of KIISE:Databases
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• v.34 no.3
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• pp.179-192
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• 2007

This paper addresses an approach that recommends investment types for stock investors by discovering useful rules from past changing patterns of stock prices in databases. First, we define a new rule model for recommending stock investment types. For a frequent pattern of stock prices, if its subsequent stock prices are matched to a condition of an investor, the model recommends a corresponding investment type for this stock. The frequent pattern is regarded as a rule head, and the subsequent part a rule body. We observed that the conditions on rule bodies are quite different depending on dispositions of investors while rule heads are independent of characteristics of investors in most cases. With this observation, we propose a new method that discovers and stores only the rule heads rather than the whole rules in a rule discovery process. This allows investors to define various conditions on rule bodies flexibly, and also improves the performance of a rule discovery process by reducing the number of rules. For efficient discovery and matching of rules, we propose methods for discovering frequent patterns, constructing a frequent pattern base, and indexing them. We also suggest a method that finds the rules matched to a query issued by an investor from a frequent pattern base, and a method that recommends an investment type using the rules. Finally, we verify the superiority of our approach via various experiments using real-life stock data.

• International Area Studies Review
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• v.20 no.3
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• pp.123-144
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• 2016

This paper investigates the linkages between returns both in foreign exchange and stock markets, and uncertainties in two markets using daily data for the period of 16 July 2004 to 30 June 2014 in Nepalese economy. Four hypotheses are tested about how uncertainty influences the stock index and exchange rates. From the empirical results, a bivariate EGARCH-M model is the best to explain the volatility in the two markets. There is a negative relationship from the exchange rates return to stock price return. Empirical results do provide strong empirical confirmation that negative effect of stock index uncertainty and positive effect of exchange rates uncertainty on average stock index. GARCH-in-mean variables in AR modeling are significant and shows that there is positive effect of exchange rates uncertainty and negative effect of stock index uncertainty on average exchange rates. Stock index shocks have longer lived effects on uncertainty in the stock market than exchange rates shock have on uncertainly in the foreign exchange market. The effect of the last period's shock, volatility is more sensitive to its own lagged values.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.777-782
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• 2010

We introduce some techniques to decompose the impulse (the unit sample) into several dilated pieces in the discrete time domain. From the decomposition of the impulse, we obtain localized moving averages. Thus we construct hybrid Bollinger bands that may give various strategies for stock traders. By simulations, we report that more than 94% of stock prices of companies in KOSPI 200 are inside this hybrid Bollinger band.

• East Asian Journal of Business Economics (EAJBE)
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• v.12 no.2
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• pp.1-7
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• 2024

Purpose: This study delves into the application of the Ohlson 1995 valuation model, particularly addressing the intricacies of the "Other information" variable. Our goal is to pinpoint the most suitable variables for substitution within this category, focusing specifically on the Mongolian Stock Exchange (MSE) context. Research design, data, and methodology: Employing data spanning from 2012 to 2022 from 60 MSE-listed companies, we conduct a comprehensive analysis encompassing both financial and non-financial indicators. Through meticulous examination, we aim to identify which variables effectively substitute for the "Other information" component of the Ohlson model. Results: Our findings reveal significant outcomes. While all financial variables within the model exhibit importance, certain non-financial indicators, notably the company's level and state ownership participation, emerge as particularly influential in determining stock prices on the MSE. Conclusions: This study not only contributes to a deeper understanding of valuation dynamics within the MSE but also provides actionable insights for future research endeavors. By refining key variables within the Ohlson model, this research enhances the accuracy and efficacy of financial analysis practices. Moreover, the implications extend to practitioners, offering valuable insights into the determinants of stock prices in the MSE and guiding strategic decision-making processes.

• Journal of Global Scholars of Marketing Science
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• v.16 no.2
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• pp.95-108
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• 2006

The major objective of this study is to test if an Olympic Games sponsorship program can influence investors' behavior: stock returns, stock volatility and transaction volumes. The paper deals with stock market reaction on Olympic sponsorship announcement for service organizations using event study method. Our research intention is to test 440 daily stock prices and transaction volumes, in order investigate the potent influence between the announcement of a grand sport sponsorship program and investors' behavior. For this study we examined the announcement data of three grand sponsors of Olympic Games of Athens 2004 (Alpha Bank. Delta and G.T.O) The main contribution of this study is to examine how stock investors' behavior is influenced by the sponsorship program of companies and to extend research scope of marketing field toward stock market. They authors suggest that organizations interested in influencing investors' behavior should invest in sponsorship activities at the sports' sector.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.131-138
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• 2020

In accordance with huge demand for capital to meet the expansion of steel production, there are more and more steel companies who have officially listed their stocks in HOSE and HNX. One of the key issues in successful initial public offerings and seasonal offerings for these companies is how to make stocks of steel companies become more attractive in the eyes of investors. The purpose of this research is to analyze the determinants of share prices of listed steel companies in Vietnam. This study utilized macro-economic variables, ratios and indicators representing characteristics of steel industry collected from Quarter 1/2006 to Quarter 4/2019 in association with the panel data and the feasible generalized least square (FGLS) model to evaluate the degree of these factors on the share prices. The results of the research show that ROE, Cons_rate, and CO2_rate are three main factors affecting the share prices of listed steel companies. Among which, ROE and Cons_rate have a positive effect, while CO2_rate has a negative effect on the share prices of listed steel companies. It also confirms the relationship between the environmental factor, construction industry factor and the stock prices. This lays foundations for recommendations for the future policies towards environmental protection and sustainable development.