• 제목/요약/키워드: Stock price analysis

검색결과 356건 처리시간 0.027초

Stock Forecasting Using Prophet vs. LSTM Model Applying Time-Series Prediction

  • Alshara, Mohammed Ali
    • International Journal of Computer Science & Network Security
    • /
    • 제22권2호
    • /
    • pp.185-192
    • /
    • 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.

뉴스 감성 앙상블 학습을 통한 주가 예측기의 성능 향상 (An Accurate Stock Price Forecasting with Ensemble Learning Based on Sentiment of News)

  • 김하은;박영욱;유시은;정성우;유준혁
    • 대한임베디드공학회논문지
    • /
    • 제17권1호
    • /
    • pp.51-58
    • /
    • 2022
  • Various studies have been conducted from the past to the present because stock price forecasts provide stability in the national economy and huge profits to investors. Recently, there have been many studies that suggest stock price prediction models using various input data such as macroeconomic indicators and emotional analysis. However, since each study was conducted individually, it is difficult to objectively compare each method, and studies on their impact on stock price prediction are still insufficient. In this paper, the effect of input data currently mainly used on the stock price is evaluated through the predicted value of the deep learning model and the error rate of the actual stock price. In addition, unlike most papers in emotional analysis, emotional analysis using the news body was conducted, and a method of supplementing the results of each emotional analysis is proposed through three emotional analysis models. Through experiments predicting Microsoft's revised closing price, the results of emotional analysis were found to be the most important factor in stock price prediction. Especially, when all of input data is used, error rate of ensembled sentiment analysis model is reduced by 58% compared to the baseline.

A Prediction of Stock Price Movements Using Support Vector Machines in Indonesia

  • ARDYANTA, Ervandio Irzky;SARI, Hasrini
    • The Journal of Asian Finance, Economics and Business
    • /
    • 제8권8호
    • /
    • pp.399-407
    • /
    • 2021
  • Stock movement is difficult to predict because it has dynamic characteristics and is influenced by many factors. Even so, there are some approaches to predict stock price movements, namely technical analysis, fundamental analysis, and sentiment analysis. Many researches have tried to predict stock price movement by utilizing these analysis techniques. However, the results obtained are varied and inconsistent depending on the variables and object used. This is because stock price movement is influenced by a variety of factors, and it is likely that those studies did not cover all of them. One of which is that no research considers the use of fundamental analysis in terms of currency exchange rates and the use of foreign stock price index movement related to the technical analysis. This research aims to predict stock price movements in Indonesia based on sentiment analysis, technical analysis, and fundamental analysis using Support Vector Machine. The result obtained has a prediction accuracy rate of 65,33% on an average. The inclusion of currency exchange rate and foreign stock price index movement as a predictor in this research which can increase average prediction accuracy rate by 11.78% compared to the prediction without using these two variables which only results in average prediction accuracy rate of 53.55%.

An Empirical Inquiry into Psychological Heuristics in the Context of the Korean Distribution Industry within the Stock Market

  • Jeong-Hwan LEE;Se-Jun LEE;Sam-Ho SON
    • 유통과학연구
    • /
    • 제21권9호
    • /
    • pp.103-114
    • /
    • 2023
  • Purpose: This paper aims to assess psychological heuristics' effectiveness on cumulative returns after significant stock price changes. Specifically, it compares availability and anchoring heuristics' empirical validity due to conflicting stock return predictions. Research Design, Data, and Methodology: This paper analyzes stock price changes of Korean distribution industry stocks in the KOSPI market from January 2004 to July 2022, where daily fluctuations exceed 10%. It evaluates availability heuristics using daily KOSPI index changes and tests anchoring heuristics using 52-week high and low stock prices as reference points. Results: As a result of the empirical analysis, stock price reversals did not consistently appear alongside changes in the daily KOSPI index. By contrast, stock price drifts consistently appeared around the 52-week highest stock price and 52-week lowest stock price. The result of the multiple regression analysis which controlled for both company-specific and event-specific variables supported the anchoring heuristics. Conclusions: For stocks related to the Korean distribution industry in the KOSPI market, the anchoring heuristics theory provides a consistent explanation for stock returns after large-scale stock price fluctuations that initially appear to be random movements.

주가수익률과 기업평가 (Price Earning Ratio And Firm Valuation)

  • 여동길
    • 산업경영시스템학회지
    • /
    • 제9권14호
    • /
    • pp.49-58
    • /
    • 1986
  • Those facts I have studied on the theoretical characteristics of stock price earning ratio related with firm evaluation are as followings. First, I have investigated stock valuation analysis under certainty in view of Miller's, Modigliani's and Linter's theories in Chapter Ⅱ, and it is found that stock valuation under uncertainty to which the basic model of MM theory and the concept of capitalization ratio are applied is the same output, as in the case under certainty. And I have examined the stock valuation of growth corporations in which net investment, total capitals and operating profits are expected. Second, I have reexamined the fact that stock price profits are the erotical indices of firm valuation and the firm valuation on the basis of stock price earning ratio in Chapter III. As a whole, I have surveyed the stock price earning ratio theory of the growth stocks and there have been found some problems as such scholars as Malkiel and others have suggested focusing on the stock price structure of growth stocks. To conclude, there must be incessant efforts for the study of security analysis to make it develop ideally.

  • PDF

VAR 모형을 이용한 주가, 금리, 물가, 주택가격의 관계에 대한 실증연구 (An Empirical Analysis on the Relationship between Stock Price, Interest Rate, Price Index and Housing Price using VAR Model)

  • 김재경
    • 유통과학연구
    • /
    • 제11권10호
    • /
    • pp.63-72
    • /
    • 2013
  • Purpose - This study analyzes the relationship and dynamic interactions between stock price index, interest rate, price index, and housing price indices using Korean monthly data from 2000 to 2013, based on a VAR model. This study also examines Granger causal relationships among these variables in order to determine whether the time series of one is useful in forecasting another, or to infer certain types of causal dependency between stochastic variables. Research design, data, and methodology - We used Korean monthly data for all variables from 2000: M1 to 2013: M3. First, we checked the correlations among different variables. Second, we conducted the Augmented Dickey-Fuller (ADF) test and the co-integration test using the VAR model. Third, we employed Granger Causality tests to quantify the causal effect from time series observations. Fourth, we used the impulse response function and variance decomposition based on the VAR model to examine the dynamic relationships among the variables. Results - First, stock price Granger affects interest rate and all housing price indices. Price index Granger, in turn, affects the stock price and six metropolitan housing price indices. However, none of the Granger variables affect the price index. Therefore, it is the stock markets (and not the housing market) that affects the housing prices. Second, the impulse response tests show that maximum influence on stock price is its own, and though it is influenced a little by interest rate, price index affects it negatively. One standard deviation (S.D.) shock to stock price increases the housing price by 0.08 units after two months, whereas an impulse shock to the interest rate negatively impacts the housing price. Third, the variance decomposition results report that the shock to the stock price accounts for 96% of the variation in the stock price, and the shock to the price index accounts for 2.8% after two periods. In contrast, the shock to the interest rate accounts for 80% of the variation in the interest rate after ten periods; the shock to the stock price accounts for 19% of the variation; however, shock to the price index does not affect the interest rate. The housing price index in 10 periods is explained up to 96.7% by itself, 2.62% by stock price, 0.68% by price index, and 0.04% by interest rate. Therefore, the housing market is explained most by its own variation, whereas the interest rate has little impact on housing price. Conclusions - The results of the study elucidate the relationship and dynamic interactions among stock price index, interest rate, price index, and housing price indices using VAR model. This study could help form the basis for more appropriate economic policies in the future. As the housing market is very important in Korean economy, any changes in house price affect the other markets, thereby resulting in a shock to the entire economy. Therefore, the analysis on the dynamic relationships between the housing market and economic variables will help with the decision making regarding the housing market policy.

The Effect of Management Disclosure and Analysis on the Stock Crash Risk: Evidence from Korea

  • Lee, A-Young;Chae, Soo-Joon
    • The Journal of Asian Finance, Economics and Business
    • /
    • 제5권4호
    • /
    • pp.67-72
    • /
    • 2018
  • The purpose of this study is to investigate the effect of quality of management discussion and analysis (MD&A) disclosure on stock price crash risk. The MD&A can be seen to reflect the management's intention on public announcement and reveals directly what the management says to communicate with outside investors. A firm's high-quality MD&A implies the management's commitment to communicating with the market, not allowing the managers to have incentives to hoard unfavorable news, which if revealed to the public, may lead to downward stock price corrections, damaging corporate values. The high-quality MD&A is, thus, likely to reduce the stock price crash risk. We use a logistic regression to test whether MD&A influences crash risk using listed companies in the Korean Stock Exchange (KSE) stock market between 2010 and 2013. Findings of the empirical test show that the higher the quality of MD&A, the less likely crash risk appears, implying that the MD&A disclosed adequately can be one of the factors mitigating firm's stock price crash risk. This study has implications as it presents the MD&A disclosure as a factor influencing stock price crash risk and suggests voluntary disclosure as well as mandatory disclosure acts as a variable that explains the risk of stock price crash.

The Stock Price Response of Palm Oil Companies to Industry and Economic Fundamentals

  • ARINTOKO, Arintoko
    • The Journal of Asian Finance, Economics and Business
    • /
    • 제8권3호
    • /
    • pp.99-110
    • /
    • 2021
  • This study aims to examine empirically the industry and economic fundamental factors that affect the stock prices of the leading palm oil company in Indonesia. The dynamics of stock price are analyzed using the autoregressive distribution lag (ARDL) model both for symmetric and asymmetric effects. The data used in this study are monthly data for the period from 2008:01 to 2020:03. In the long run, the company stock price moves in line with the competitor company stock price at the current time. The palm oil price has a positive effect on the stock price. Meanwhile, inflation negatively affects the stock price in the short run. The estimated equilibrium correction coefficient indicates a reasonably quick correction of the distortion of the stock price equilibrium in monthly dynamics. However, fundamental factors have asymmetric effects, especially the response of stock price when these factors decrease rather than increase in the short run. Stock prices that are responsive to declines in fundamental performance should be of particular concern to both investors and management in their strategic decision making. The results of this study will contribute to the enrichment of literature related to stock prices from the viewpoint of economic analysis on firm-level data.

The Effect of Managerial Ownership on Stock Price Crash Risk in Distribution and Service Industries

  • RYU, Haeyoung;CHAE, Soo-Joon
    • 유통과학연구
    • /
    • 제19권1호
    • /
    • pp.27-35
    • /
    • 2021
  • Purpose: This study is to investigate the effect of managerial ownership level in distribution and service companies on the stock price crash. The managerial ownership level affects the firm's information disclosure policy. If managers conceal or withholds business-related unfavorable factors over a long period, the firm's stock price is likely to plummet. In a similar vein, management's equity affects information opacity, and information asymmetry affects stock price collapse. Research design, data, and methodology: A regression analysis is conducted using the data on companies listed on the Korea Composite Stock Price Index (KOSPI) between 2012-2017 to examine the effect of the managerial ownership level on stock price crash risks. Results: Logistic and regression results indicate that the stock price crash risk was reduced as managerial ownership levels are increased. The managerial ownership level has a significant negative coefficient on stock price crash risk, negative conditional return skewness of firm-specific weekly return distribution, and asymmetric volatility between positive and negative price-to-earnings ratios. Conclusions: As the ownership and management align, the likeliness of withholding business-related information is reduced. This study's results imply that the stock price crash risk reduces as the managerial ownership level increases because shareholder and manager interests coincide, thereby reducing information asymmetry.

Revisiting the Effect of Financial Elements on Stock Performance Using Corporate Social Responsibility Cost Growth

  • JOUHA, Faraj;ALBAKAY, Khalleefah;GHOZALI, Imam;HARTO, Puji
    • The Journal of Asian Finance, Economics and Business
    • /
    • 제8권1호
    • /
    • pp.767-780
    • /
    • 2021
  • The purpose of this research is to analyze the effect of financial elements (asset growth, liability growth, equity growth, revenue growth, and profit growth) on stock price performance and to analyze the growth of Corporate Social Responsibility (CSR) costs as a moderating effect. The technique analysis used is regression analysis. Samples in this analysis are manufacturing firms listed on the Indonesian Stock Exchange (IDX) for the period 2014-2018. The use of regression models for hypothesis testing must fulfill several applicable assumptions such as Normality Test, Heteroscedasticity Test, Multicollinearity Test, Autocorrelation Test, Model Fit Test, Determination Coefficient Test, and Hypothesis Test. Data analysis used two research models, namely model 1 and model 2. Model 1 is without the moderating variable, and model 2 is with the moderating variable, that is, CSR cost growth. Based on the result of the regression analysis, it can be inferred that the asset, revenue, and profit growth have a positive impact on stock price results. Liabilities and equity growth do not affect stock price performance. Operating expense growth has a significant effect on price performance. CSR cost growth can moderate the effect of growth in financial statement elements on stock price performance but is not significant.