• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.185-192
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• 2022

Forecasting and time series modelling plays a vital role in the data analysis process. Time Series is widely used in analytics & data science. Forecasting stock prices is a popular and important topic in financial and academic studies. A stock market is an unregulated place for forecasting due to the absence of essential rules for estimating or predicting a stock price in the stock market. Therefore, predicting stock prices is a time-series problem and challenging. Machine learning has many methods and applications instrumental in implementing stock price forecasting, such as technical analysis, fundamental analysis, time series analysis, statistical analysis. This paper will discuss implementing the stock price, forecasting, and research using prophet and LSTM models. This process and task are very complex and involve uncertainty. Although the stock price never is predicted due to its ambiguous field, this paper aims to apply the concept of forecasting and data analysis to predict stocks.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.31-39
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• 2022

Stock prices are influenced by a number of external factors, such as laws and trends, as well as number-based internal factors such as trading volume and closing prices. Since many factors affect stock prices, it is very difficult to accurately predict stock prices using only fragmentary stock data. In particular, since the value of a company is greatly affected by the perception of people who actually trade stocks, emotional information about a specific company is considered an important factor. In this paper, we propose a deep learning-based stock price prediction model using sentiment analysis with news data considering temporal characteristics. Stock and news data, two heterogeneous data with different characteristics, are integrated according to time scale and used as input to the model, and the effect of time scale and sentiment index on stock price prediction is finally compared and analyzed. Also, we verify that the accuracy of the proposed model is improved through comparative experiments with existing models.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.31-36
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• 2021

As the global economy stagnated due to the Corona 19 virus from Wuhan, China, most countries, including the US Federal Reserve System, introduced policies to boost the economy by increasing the amount of money. Most of the stock investors tend to invest only by listening to the recommendations of famous YouTubers or acquaintances without analyzing the financial statements of the company, so there is a high possibility of the loss of stock investments. Therefore, in this research, I have used artificial intelligence deep learning techniques developed under the existing automatic trading conditions to analyze and predict macro-indicators that affect stock prices, giving weights on individual stock price predictions through correlations that affect stock prices. In addition, since stock prices react sensitively to real-time stock market news, a more accurate stock price prediction is made by reflecting the weight to the stock price predicted by artificial intelligence through stock market news text mining, providing stock investors with the basis for deciding to make a proper stock investment.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.67-74
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• 2021

This study aims to examine and analyze the effect of Risk Profile, Good Corporate Governance (GCG), Earnings, Capital (RGEC), and Earnings per Share (EPS) on stock prices with financial distress as an intervening variable. The sampling technique used purposive sampling based on certain criteria and data used was secondary data, that is, annual reports of commercial banks in Indonesia for the period of 2012-2018 with a sample of 23 banks from a total population of 81 banks. This type of research is explanative with a quantitative descriptive approach to describe or explain quantitative data. The data obtained was analyzed using SEM (Structural Equation Model) with the AMOS Program. The results showed that RGEC, EPS, and financial distress affect stock prices. This is based on testing the direct effect as indicated by a p-value that is smaller than 0.05. Based on the mediation test, the results show that financial distress cannot mediate the effect of RGEC and EPS on stock prices as indicated by a p-value greater than 0.05. The implication of this research is very important for investors to analyze stock price changes based on RGEC, EPS, and financial distress to gain profits. In addition, there are various warning signs indicating that a company is experiencing financial distress or it is heading towards such a state. Being aware of these signs can help prevent failure.

• Management Science and Financial Engineering
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• v.17 no.2
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• pp.85-97
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• 2011

In this study we examine the long term behavior of stock returns. The analysis reveals that negative autocorrelations of the returns exist for a super-long horizon as long as 10 years. This pattern, however, contrasts to predictions of previous stock price models which include random walks. We suggest the introduction of a fractionally integrated process into a nonstationary component of stock prices, and demonstrate empirically the existence of the process in NYSE stock returns. The predicted values of autocorrelation from our stock price model confirm the super-long term behavior of the returns observed in regression, indicating that inefficiency in the stock market could remain for a long time.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.103-114
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• 2011

Current series for testing stock market cointegrations tend to be restricted to analyzing the relations between stock market prices and may not be able to understand the whole picture of the variations in the stock market system. The nature of the variations in the stock prices, between the countries that experienced economic crisis and those did not, are different for a certain period of time, and accordingly excluding the potentially important variables in the stock market system causes statistical bias. This study considers domestic foreign exchange markets and financial markets in testing for the cointegrating relations of the stock prices in Korea and major investing countries. The results demonstrate the possibility of specification errors unless those markets are included in the statistical modeling process.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.277-283
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• 2005

In general, stock prices do not follow normal distributions and mean trend indexes, volatility indexes, and volume indicators relating to these non-normal stock price are widely used as buy-sell strategies. These general buy-sell strategies are rather intuitive than statistical reasoning. The non-normality problem can be solved by normalizing process and statistical buy-sell strategy can be obtained by using mean trend and volatility indexes together with normalized stock prices. In this paper, buy-sell strategy based on mean trend and volatility index with normalized stock prices are proposed and applied to KOSPI200 data to see the feasibility of the proposed buy-sell strategy.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.91-101
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• 2021

The paper examines the impact of the COVID-19 pandemic on the stock market prices. The vector autoregression model (VAR) has been used in this analysis to survey 341 stocks on the Ho Chi Minh City Stock Exchange (HOSE) for the period from January 23, 2020 to December 31, 2020. The empirical results obtained from the analysis of 11 economic sectors suggest that there is a statistically significant impact relationship between COVID-19 and the healthcare and utility industries. Additional findings show a statistically significant negative impact of COVID-19 on the utility share price at lag 1. Analysis of impulse response function (IRF) and forecast error variance decomposition (FEVD) show an inverse reaction of utility stock prices to the impact of COVID-19 and a gradual disappearing shock after two steps. Major findings show that there is a clear negative effect of the COVID-19 pandemic on share prices, and the daily increase in the number of confirmed cases, indicate that, in future disease outbreaks, early containment measures and positive responses are necessary conditions for governments and nations to protect stock markets from excessive depreciation. Utility stocks are among the most severely impacted shares on financial exchanges during a pandemic due to the high risk of immediate or irreversible closure of manufacturing lines and poor demand for basic amenities.

• Analyses & Alternatives
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• v.3 no.1
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• pp.135-157
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• 2019

The linkages between asset prices and macroeconomic outcomes are long-standing issue to both economists and monetary authorities. This paper explores the impact of asset prices on output and price in China. It focuses on the impacts of asset prices on the low quantiles of GDP gap and high quantiles of price gaprespectively. The main findings are the following: the influence of stock price gap, stock returns, and money growth on the different quantile of GDP gap and price gap are noticeable different, and there are significant impacts on the left tail of GDP gap distribution and on the right tail of price gap distribution. This implies that the results coming from simple regression will underestimate the economic risk imposed by asset price volatility. Moreover, these results also provide the caveat that one should cautiously distinguish the meaning of asset price gap and asset price growth rate and use them, through their contents are similar in some sense. One implication for monetarypolicy is that authority should interpret the relationship between asset prices and macro-economy in wider perspectives, and make the policy decision taking the impacts of asset prices on the tails of economy.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.18-30
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• 2023

We compared empirically the forecast accuracies of the LSTM model, and the ARIMA model. ARIMA model used auto.arima function. Data used in the model is 100 days. We compared with the forecast results for 50 days. We collected the stock closing prices of the top 4 companies by market capitalization in Korea such as "Samsung Electronics", and "LG Energy", "SK Hynix", "Samsung Bio". The collection period is from June 17, 2022, to January 20, 2023. The paired t-test is used to compare the accuracy of forecasts by the two methods because conditions are same. The null hypothesis that the accuracy of the two methods for the four stock closing prices were the same were rejected at the significance level of 5%. Graphs and boxplots confirmed the results of the hypothesis tests. The accuracies of ARIMA are higher than those of LSTM for four cases. For closing stock price of Samsung Electronics, the mean difference of error between ARIMA and LSTM is -370.11, which is 0.618% of the average of the closing stock price. For closing stock price of LG Energy, the mean difference is -4143.298 which is 0.809% of the average of the closing stock price. For closing stock price of SK Hynix, the mean difference is -830.7269 which is 1.00% of the average of the closing stock price. For closing stock price of Samsung Bio, the mean difference is -4143.298 which is 0.809% of the average of the closing stock price. The auto.arima function was used to find the ARIMA model, but other methods are worth considering in future studies. And more efforts are needed to find parameters that provide an optimal model in LSTM.