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1
Akbulaev, Nurkhodzha, Basti Aliyeva, and Shehla Rzayeva. "Analysis of the Influence of the Price of Raw Oil and Natural Gas on the Prices of Indices and Shares of the Turkish Stock Exchange." Pénzügyi Szemle = Public Finance Quarterly 66, no. 1 (2021): 151–66. http://dx.doi.org/10.35551/pfq_2021_1_8.
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This article is a review on the impact of prices and their dependence on the cost of oil and natural gas on the world stock markets. The main studies and results achieved in the field of the impact of prices on both the stock index and industrial stocks and the dependence on the level of oil prices are presented. The paper presents an econometric study on the choice of offers on the securities market that allows us to identify the main specifics of changes in prices for the stock index and industrial shares in the daily period from 13. 05. 2012 to 01. 12. 2019. The article uses methods for estimating the impact of the price of natural gas and WTI crude oil using the Gretl statistical program, taking into account the selection of the main correlation features of the price matrix. Of the 13 proposed research models, only one model showed its statistical insignificance. A paired linear model of the CocaCola share price dependence and its dependence on NGFO prices was presented and analyzed in detail. Based on the results of econometric modeling, linear regression models were constructed for the dependence of stock prices on the NGFO and WTISPOT prices. The Gretl environment allows you to evaluate the situation in the econometric environment and make a forecast based on the obtained models of the dependence of stock prices and make appropriate conclusions.
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Zhu, Rong, Zuo Quan Zhang, Xiao Yue Li, Xuan Wu, and Su Zhang. "The Study on the Plasticity Theoretical Models of the Volatility of Stock Prices." Advanced Materials Research 518-523 (May 2012): 5963–67. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.5963.
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This paper analyzes the characteristics of the stock price fluctuation compared with elastic-plastic theory in mechanics and introduces the concept of stock equilibrium price, plasticity of stock price analogically. A basic model of the stock plasticity under the relationship between stock price fluctuation and trading volume changes is also built. Tested by 20 kinds of stocks from Shanghai and Shenzhen stock markets in China by using the econometric analysis software Eviews3.0 afterwards, the basic model is improved, and three developed models are built from it. Finally, this paper obtains more scientific and reasonable stock price plasticity model after the comparative analysis of the four previous models.
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Nautiyal, Neeraj, and P. C. Kavidayal. "Analysis of Institutional Factors Affecting Share Prices: The Case of National Stock Exchange." Global Business Review 19, no. 3 (March 14, 2018): 707–21. http://dx.doi.org/10.1177/0972150917713865.
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This study offers empirical findings on the impact of institutional variables on firm’s stock market price performance. In order to identify the influence of companies financial on NIFTY 50 Index, our sample consists of balanced panel of 30 actively traded companies (that becomes the study’s index representative) over a massive transition period, 1995–2014. Attempts have been made with a wide range of econometric models and estimators, from the relatively straightforward to (static) more complex (dynamic panel analyses) to deal with the relevant econometric issues. Results indicate that increasing debt in capital structure does not establish any significant relation with the stock prices. Earnings per share (EPS) shows a poor explanation of price variation. Economic value added (EVA) indicates a positive relation with current as well as previous year’s stock price performances. However, dividend payout (DIVP) and dividend per share (DPS) achieve negative relationship at moderately significant level. The present study confirms that performance of companies fundamental ratios will be essential and immensely helpful to investors and analysts in assessing the better stocks that belong to different industry groups.
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Shi, Chao, and Xiaosheng Zhuang. "A Study Concerning Soft Computing Approaches for Stock Price Forecasting." Axioms 8, no. 4 (October 18, 2019): 116. http://dx.doi.org/10.3390/axioms8040116.
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Financial time-series are well known for their non-linearity and non-stationarity nature. The application of conventional econometric models in prediction can incur significant errors. The fast advancement of soft computing techniques provides an alternative approach for estimating and forecasting volatile stock prices. Soft computing approaches exploit tolerance for imprecision, uncertainty, and partial truth to progressively and adaptively solve practical problems. In this study, a comprehensive review of latest soft computing tools is given. Then, examples incorporating a series of machine learning models, including both single and hybrid models, to predict prices of two representative indexes and one stock in Hong Kong’s market are undertaken. The prediction performances of different models are evaluated and compared. The effects of the training sample size and stock patterns (viz. momentum and mean reversion) on model prediction are also investigated. Results indicate that artificial neural network (ANN)-based models yield the highest prediction accuracy. It was also found that the determination of optimal training sample size should take the pattern and volatility of stocks into consideration. Large prediction errors could be incurred when stocks exhibit a transition between mean reversion and momentum trend.
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Olena Nikolaieva, Anzhela Petrova, and Rostyslav Lutsenko. "FORECASTING OF THE STOCK RATE OF LEADING WORLD COMPANIES USING ECONOMETRIC METHODS AND DCF ANALYSIS." International Journal of Innovative Technologies in Economy, no. 2(29) (May 31, 2020): 33–41. http://dx.doi.org/10.31435/rsglobal_ijite/31052020/7067.
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In this article, we will cover various models for forecasting the stock price of global companies, namely the DCF model, with well-reasoned financial analysis and the ARIMA model, an integrated model of autoregression − moving average, as an econometric mechanism for point and interval forecasting. The main goal is to compare the obtained forecasting results and evaluate their real accuracy. The article is based on forecasting stock prices of two companies: Coca-Cola HBC AG (CCHGY) and Nestle S.A. (NSRGF). At the moment, it is not determined which approach is better for predicting the stock price − the analysis of financial indicators or the use of econometric data analysis methods.
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Peñalvo, Francisco José García, Tamanna Maan, Sunil K. Singh, Sudhakar Kumar, Varsha Arya, Kwok Tai Chui, and Gaurav Pratap Singh. "Sustainable Stock Market Prediction Framework Using Machine Learning Models." International Journal of Software Science and Computational Intelligence 14, no. 1 (January 1, 2022): 1–15. http://dx.doi.org/10.4018/ijssci.313593.
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Prediction of stock prices is a challenging task owing to its volatile and constantly fluctuating nature. Stock price prediction has sparked the interest of various investors, data analysists, and researchers because of high returns on their investments. A sustainable framework for stock price prediction is proposed to quantify the factors affecting the stock price and impact of technology on the ever-changing business world. The proposed framework also helps to understand how technology can be used to predict the future price of stocks by using some historical dataset to produce desirable results using machine learning algorithms. The aim of this research paper is to learn about stock price prediction by using different machine learning algorithms and comparing their performance. The results reveal that Fb-prophet should be preferred for more precise prediction among different ML algorithms.
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MISSAOUI, Sahbi, and Nizar RAISSI. "Underpricing Process of IPOs in Tunis Stock Exchange: An Agent-Based Modelling Approach." Accounting and Finance Research 10, no. 2 (April 7, 2021): 1. http://dx.doi.org/10.5430/afr.v10n2p1.
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The fundamental problematic treated in our study was an attempt to explain an anomaly in the issuance of new stocks in IPOs process. The objective of this research is to analyze the effect of certain variables on the level of undervaluation by presenting certain econometric models issued from Agent-based modelling approach. Certain variables can be predictive of the phenomenon of undervaluation such as: the Stock equity distributed to institutional investors, liquidity in the secondary market measured by the price range and the type of investor who can be insiders or outsiders, in addition to these variables we have introduced some control variables which in turn help explain the level of underpricing and which are the age of the company, its size and dimension, the volume of trade and the volatility. Empirically and based on a sample of 16 companies, we were able to respond to our problematic. In fact, according to the hypotheses tests, the prices of the newly introduced stocks on the stock exchange are mostly undervalued which were aligned with our study. Thereby, the methodology adopted based to Dynamic linear models (DLM) that allows offering a very generic framework to analyse time series data. The results of this research were, in part, consistent with work done in developed countries (especially in USA and Europe). Indeed, the undervaluation is in a positive relationship with certain explanatory variables such as the Institutional ownership (INST), Insiders ownership (INSID), Price range (FOUR), etc. On the other hand, we were able to identify significant negative relationships between the initial undervaluation and the basic variable Outsiders ownership (OUTSID), the size of companies listed on the Tunis Stock exchange (BVMT) and the volume of issued stocks.
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Majewski, Sebastian, Waldemar Tarczynski, and Malgorzata Tarczynska-Luniewska. "Measuring investors’ emotions using econometric models of trading volume of stock exchange indexes." Investment Management and Financial Innovations 17, no. 3 (September 30, 2020): 281–91. http://dx.doi.org/10.21511/imfi.17(3).2020.21.
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Traditional finance explains all human activity on the ground of rationality and suggests all decisions are rational because all current information is reflected in the prices of goods. Unfortunately, the development of information technology and a growth of demand for new, attractive possibilities of investment caused the process of searching new, unique signals supporting investment decisions. Such a situation is similar to risk-taking, so it must elicit the emotional reactions of individual traders.The paper aims to verify the question that the market risk may be the determinant of traders’ emotions, and if volatility is a useful tool during the investment process as the measure of traders’ optimism, similarly to Majewski’s work (2019). Likewise, various econometric types of models of estimation of the risk parameter were used in the research: classical linear using OLS, general linear using FGLS, and GARCH(p, q) models using maximum likelihood method. Hypotheses were verified using the data collected from the most popular world stock exchanges: New York, Frankfurt, Tokyo, and London. Data concerned stock exchange indexes such as SP500, DAX, Nikkei, and UK100.
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EKSTRÖM, ERIK, and JOHAN TYSK. "OPTIONS WRITTEN ON STOCKS WITH KNOWN DIVIDENDS." International Journal of Theoretical and Applied Finance 07, no. 07 (November 2004): 901–7. http://dx.doi.org/10.1142/s0219024904002694.
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There are two common methods for pricing European call options on a stock with known dividends. The market practice is to use the Black–Scholes formula with the stock price reduced by the present value of the dividends. An alternative approach is to increase the strike price with the dividends compounded to expiry at the risk-free rate. These methods correspond to different stock price models and thus in general give different option prices. In the present paper we generalize these methods to time- and level-dependent volatilities and to arbitrary contract functions. We show, for convex contract functions and under very general conditions on the volatility, that the method which is market practice gives the lower option price. For call options and some other common contracts we find bounds for the difference between the two prices in the case of constant volatility.
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Khoa, Bui Thanh, and Tran Trong Huynh. "Forecasting stock price movement direction by machine learning algorithm." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (December 1, 2022): 6625. http://dx.doi.org/10.11591/ijece.v12i6.pp6625-6634.
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<p><span lang="EN-US">Forecasting stock price movement direction (SPMD) is an essential issue for short-term investors and a hot topic for researchers. It is a real challenge concerning the efficient market hypothesis that historical data would not be helpful in forecasting because it is already reflected in prices. Some commonly-used classical methods are based on statistics and econometric models. However, forecasting becomes more complicated when the variables in the model are all nonstationary, and the relationships between the variables are sometimes very weak or simultaneous. The continuous development of powerful algorithms features in machine learning and artificial intelligence has opened a promising new direction. This study compares the predictive ability of three forecasting models, including <a name="_Hlk106797328"></a>support vector machine (SVM), artificial neural networks (ANN), and logistic regression. The data used is those of the stocks in the VN30 basket with a holding period of one day. With the rolling window method, this study got a highly predictive SVM with an average accuracy of 92.48%.</span></p>
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Hamad, Dr Abed Ali, and Dr Ahmad Hussein Battal. "Use GARCH Models to Build a Econometric Model to Predict Average Daily Closing Prices of the Iraqi Stock Exchange for the Period 2013-2016." Webology 18, Special Issue 04 (September 30, 2021): 385–400. http://dx.doi.org/10.14704/web/v18si04/web18136.
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This research aims to build a standard model for the analysis and prediction of the average daily closing price fluctuations for companies registered in the Iraq Stock Exchange for the period 07/01/2013 to 30/06/2016, using the conditional generalized Heteroscedasticity Generalized Autoregressive (GARCH) models. As these models deal with the fluctuations that occur in the financial time series. The results of the analysis showed that the best model for predicting the volatility of average closing prices in the Iraq Stock Exchange is the EGARCH model (3,1), depending on the statistical criteria used in the preference between the models (Akaike Information Criterion, Schwarz Criterion), and these models can provide information for investors in order to reduce the risk resulting from fluctuations in stock prices in the Iraqi financial market.
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Fang, Hao, Yen-Hsien Lee, and William Chang. "Nonlinear short-run adjustments between house and stock prices in emerging Asian regions." Panoeconomicus 65, no. 1 (2018): 37–63. http://dx.doi.org/10.2298/pan140125018f.
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This study uses the powerful nonparametric cointegration test to examine whether nonlinear cointegration exists between prices of used houses and corresponding stock markets in China and the four Asian Tigers. Then, it uses the smooth transition vector error-correction model (STVECM) to explore the adjustment efficiencies of the short-run house and corresponding stockreturn dynamics when there is disequilibrium between house and stock prices. The empirical results indicate that there is a nonlinear cointegration between the house prices and corresponding stock prices in China, South Korea, Singapore, and Taiwan, and that the speed of price adjustment to equilibrium is always greater for houses than stocks when there are large positive and negative deviations. Moreover, the short-run speed of adjustment of the large negative and positive deviations is equal in China, South Korea, and Taiwan, but unequal in Singapore. With the exception of South Korea, the results of the Granger causality test indicate that stock prices clearly lead used house prices, which means a wealth effect exists in most Asian countries. Our study confirms that the STVECM can be used to analyze the short-run adjustment efficiency of house and stock return dynamics in China, South Korea, Singapore, and Taiwan; thus, supporting models of interaction between noise and arbitrage traders.
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Hong, Harrison, and Jeremy C. Stein. "Disagreement and the Stock Market." Journal of Economic Perspectives 21, no. 2 (April 1, 2007): 109–28. http://dx.doi.org/10.1257/jep.21.2.109.
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A large catalog of variables with no apparent connection to risk has been shown to forecast stock returns, both in the time series and the cross-section. For instance, we see medium-term momentum and post-earnings drift in returns—the tendency for stocks that have had unusually high past returns or good earnings news to continue to deliver relatively strong returns over the subsequent six to twelve months (and vice-versa for stocks with low past returns or bad earnings news); we also see longer-run fundamental reversion—the tendency for “glamour” stocks with high ratios of market value to earnings, cashflows, or book value to deliver weak returns over the subsequent several years (and vice-versa for “value” stocks with low ratios of market value to fundamentals). To explain these patterns of predictability in stock returns, we advocate a particular class of heterogeneous-agent models that we call “disagreement models.” Disagreement models may incorporate work on gradual information flow, limited attention, and heterogeneous priors, but all highlight the importance of differences in the beliefs of investors. Disagreement models hold the promise of delivering a comprehensive joint account of stock prices and trading volume—and some of the most interesting empirical patterns in the stock market are linked to volume.
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DeJong, David N., and Charles H. Whiteman. "Modeling Stock Prices without Knowing How to Induce Stationarity." Econometric Theory 10, no. 3-4 (August 1994): 701–19. http://dx.doi.org/10.1017/s0266466600008732.
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Bayesian procedures for evaluating linear restrictions imposed by economic theory on dynamic econometric models are applied to a simple class of presentvalue models of stock prices. The procedures generate inferences that are not conditional on ancillary assumptions regarding the nature of the nonstationarity that characterizes the data. Inferences are influenced by prior views concerning nonstationarity, but these views are formally incorporated into the analysis, and alternative views are easily adopted. Viewed in light of relatively tight prior distributions that have proved useful in forecasting, the present-value model seems at odds with the data. Researchers less certain of the interaction between dividends and prices would find little reason to look beyond the present-value model.
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Ghosh, Papiya, and Brishti Guha. "THE STUDY OF RELATIONSHIP BETWEEN TOBIN’S Q AND US STOCK PERFORMANCE OF SELECTED FIRMS." International Journal of Advanced Economics 1, no. 2 (June 22, 2020): 85–94. http://dx.doi.org/10.51594/ijae.v1i2.56.
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The objective of study was to test the dynamic effects of changes in Tobin’Q on stock prices of selected 249 US public companies of different industry categories. Panel unit roots tests and cointegration tests are implemented. Next, DOLS and GMM models are estimated. Annual data for the 2004-2012 period are used for the above selected US companies. Panel unit root tests provide somewhat mixed evidence of non-stationarity of both variables. There is clear evidence of cointegration between the above variables. The negative coefficient of the error-correction term shows convergence toward long-run equilibrium, though at slow pace. The estimates also reveal shortrun net positive interactive feedback effects between the variables. Both DOLS and GMM estimates display similar picture of overvaluation of stocks in terms of upward movement in Tobin’s Q beyond 0-to-1 range. For most parts of the sample period, the US stock market was in declining mode due to heightening of economic uncertainties during the Great Recession and several years beyond. Tobin’s Q should be improved to boost stock prices. This is more of a long-run phenomenon. In the short run, both reinforce each other. The topic is unique and the existing literature on this topic is scant. Relatively new econometric techniques have been applied for estimation using panel data. The results are quite insightful, in our view.
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Rahman, Matiur, and Muhammad Mustafa. "Dynamics of Tobin’s Q and US Stock Performance." International Review of Business and Economics 2, no. 2 (2018): 52–68. http://dx.doi.org/10.56902/irbe.2018.2.2.3.
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To study the dynamic effects of changes in Tobin’s Q on stock prices of selected 249 US public companies of different industry categories. Panel unit roots tests and cointegration tests are implemented. Next, DOLS and GMM models are estimated. Annual data for the 2004-2012 period are used for the above selected US companies. Panel unit root tests provide somewhat mixed evidence of non-stationarity of both variables. There is clear evidence of cointegration between the above variables. The negative coefficient of the error-correction term shows convergence toward long-run equilibrium, though at slow pace. The estimates also reveal shortrun net positive interactive feedback effects between the variables. Both DOLS and GMM estimates display similar picture of overvaluation of stocks in terms of upward movement in Tobin’s Q beyond 0-to-1 range. For most parts of the sample period, the US stock market was in declining mode due to heightening of economic uncertainties during the Great Recession and several years beyond. Tobin’s Q should be improved to boost stock prices. This is more of a long-run phenomenon. In the short run, both reinforce each other. The topic is unique and the existing literature on this topic is scant. Relatively new econometric techniques have been applied for estimation using panel data. The results are quite insightful, in our view.
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Bundala, Ntogwa N. "Homo-Hetero Pairing Regression Model: An Econometric Predictive Model of Homo Paired Data." International Journal of Finance Research 3, no. 2 (July 31, 2022): 147–86. http://dx.doi.org/10.47747/ijfr.v3i2.792.
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The study aimed to examine the technical and fundamental hypotheses in NYSE, NASDAQ and S&P 500 stock exchange markets. The main determinants (variables) that were examined were stock trading volumes, closing stock prices and stock information available in the stock exchange market. The 240 days, 197 days and 253 days data of closing stock prices and trading volumes at NYSE, S&P500 and NASDAQ stock exchange markets were systematically collected from June 2021 to June 2022. The data was analysed by using the Homo-Hetero Pairing (HHP) Regression Model. This model was developed to detect the linear and non-linear behaviour of data. The study evidenced that both the technical and fundamental hypotheses in NYSE, S&P500 and NASDAQ stock exchange markets are defined by the inverse and S-curved models in two distinctive pairing classes called the positive-positive pairing (PPP) class and the negative-positive pairing (NPP) class. The study concluded that the optimal prediction of the stock price or return is achieved by the fundamentalists in the stock exchange markets. The study recommends that stock investors should priorities the use of the fundamental hypothesis to make their portfolio investment decision. Moreover, the study recommends the application of the HHP regression model in financial markets, economics, psychology, sociology, and medicine studies. In addition, the HHP regression model is recommended for the prediction of water waves in the investigation of hydrodynamic and erosion-accretion processes
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FRAME, SAMUEL J., and CYRUS A. RAMEZANI. "BAYESIAN ESTIMATION OF ASYMMETRIC JUMP-DIFFUSION PROCESSES." Annals of Financial Economics 09, no. 03 (December 2014): 1450008. http://dx.doi.org/10.1142/s2010495214500080.
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The hypothesis that asset returns are normally distributed has been widely rejected. The literature has shown that empirical asset returns are highly skewed and leptokurtic. The affine jump-diffusion (AJD) model improves upon the normal specification by adding a jump component to the price process. Two important extensions proposed by Ramezani and Zeng (1998) and Kou (2002) further improve the AJD specification by having two jump components in the price process, resulting in the asymmetric affine jump-diffusion (AAJD) specification. The AAJD specification allows the probability distribution of the returns to be asymmetrical. That is, the tails of the distribution are allowed to have different shapes and densities. The empirical literature on the "leverage effect" shows that the impact of innovations in prices on volatility is asymmetric: declines in stock prices are accompanied by larger increases in volatility than the reverse. The asymmetry in AAJD specification indirectly accounts for the leverage effect and is therefore more consistent with the empirical distributions of asset returns. As a result, the AAJD specification has been widely adopted in the portfolio choice, option pricing, and other branches of the literature. However, because of their complexity, empirical estimation of the AAJD models has received little attention to date. The primary objective of this paper is to contribute to the econometric methods for estimating the parameters of the AAJD models. Specifically, we develop a Bayesian estimation technique. We provide a comparison of the estimated parameters under the Bayesian and maximum likelihood estimation (MLE) methodologies using the S&P 500, the NASDAQ, and selected individual stocks. Focusing on the most recent spectacular market bust (2007–2009) and boom (2009–2010) periods, we examine how the parameter estimates differ under distinctly different economic conditions.
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Madhavan, Vinodh, and Partha Ray. "Price and Volatility Linkages Between Indian Stocks and Their European GDRs." Journal of Emerging Market Finance 18, no. 2_suppl (June 21, 2019): S213—S237. http://dx.doi.org/10.1177/0972652719846353.
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This article tests for price and volatility linkages between Indian global depositary receipts (GDRs) traded in Luxembourg/London and their underlying shares traded in Mumbai. The relationship is studied between the GDR price and the domestic share price along with the appropriate exchange rates, the foreign stock index and the domestic stock index using the vector autoregression (VAR) and dynamic conditional correlation (DCC) specification of multivariate generalised autoregressive conditional heteroscedasticity (GARCH) models. VAR results indicate a similarity between the two prices of scrips: one trading in Mumbai and the other trading in Luxembourg (London). Further, DCC-GARCH model outcomes point to, by and large, a high-dynamic correlation between Indian GDRs traded in Luxembourg/London and their underlying stocks listed in Mumbai. Thus, the price and volatility linkages between the Indian stock and its European counterpart are invariant with respect to the choice of the foreign stock exchange. Such a similarity in findings, notwithstanding the difference in degree of information disclosure as well as listing requirements at London and Luxembourg, is perhaps indicative of the stock-exchange-invariant nature of law of one price. JEL Classification: G15, C22
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Srivastava, H., P. Solomon, and S. P. Singh. "Do Exogenous Shocks in Macroeconomic Variables Respond to Changes in Stock Prices?" Finance: Theory and Practice 26, no. 6 (December 30, 2022): 104–14. http://dx.doi.org/10.26794/2587-5671-2022-26-6-104-114.
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The research aims to examine the unexpected changes in stock prices due to external shocks given to the macroeconomic variables to forecast future stock market returns. The study applies two econometric models such as «Variance Decomposition» (VDC) and «Impulse Response Function» (IRF) for examining the exogenous shocks in macroeconomic variables respond to changes in stock prices. Monthly time series data of five significant macroeconomic variables Real Exchange Rate, Interest Rate, Consumer Price Index (CPI), Crude Oil Prices, and Trade Openness, taken as independent variables and BSE SENSEX as a dependent variable. The research period is from Jan 2009 to Dec 2019. The study has taken the responsibility to reveal a few strong evidences for changes in stock prices due to exogenous shocks in Exchange Rate, Trade Openness, Inflation, and Interest rate along with crude oil prices. According to the results, changes in the stock market are due to external factors like changes in dividend policy or capital loss, and some changes in the stock market are due to its own innovative shocks. This study suggests to reduce unexpected changes in stock prices frequently, companies should control capital loss and focus on stable return/dividend policies. There are divergent views in the literature review in the context of measures of these variables, however no research has been done on exogenous shocks in macroeconomic variables to BSE SENSEX for the Indian stock market with this particular data set and duration.
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Zhang, Junhao, and Yifei Lei. "Deep Reinforcement Learning for Stock Prediction." Scientific Programming 2022 (April 30, 2022): 1–9. http://dx.doi.org/10.1155/2022/5812546.
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Investors are frequently concerned with the potential return from changes in a company’s stock price. However, stock price fluctuations are frequently highly nonlinear and nonstationary, rendering them to be uncontrollable and the primary reason why the majority of investors earn low long-term returns. Historically, people have always simulated and predicted using classic econometric models and simple machine learning models. In recent years, an increasing amount of research has been conducted using more complex machine learning and deep learning methods to forecast stock prices, and their research reports also indicate that their prediction accuracy is gradually improving. While the prediction results and accuracy of these models improve over time, their adaptability in a volatile market environment is questioned. Highly optimized machine learning algorithms include the following: FNN and the RNN are incapable of predicting the stock price of random walks and their results are frequently not consistent with stock price movements. The purpose of this article is to increase the accuracy and speed of stock price volatility prediction by incorporating the PG method’s deep reinforcement learning model. Finally, our tests demonstrate that the new algorithm’s prediction accuracy and reward convergence speed are significantly higher than those of the traditional DRL algorithm. As a result, the new algorithm is more adaptable to fluctuating market conditions.
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Callado, Antônio André Cunha, and Carla Renata Silva Leitão. "Dynamics of Stock Prices and Market Efficiency." International Business Research 11, no. 6 (May 9, 2018): 29. http://dx.doi.org/10.5539/ibr.v11n6p29.
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Over the last few decades, academic research on market efficiency has taken a leading position in the field of financial theories. The objective of this paper is to present contradictions within the evidence about market efficiency and discuss efficiency measurement as an emerging approach. The paper presents the evolution of research and also the lack of convergence between evidence provided by the literature and the lack of consistent arguments for explaining them. The paper also presents a framework that illustrates intermediate levels of efficiency and the first approach designed to measuring market efficiency. Finally the paper points out that divergences amongst the empirical evidence found in the literature should be considered as a key issue and further efforts should focus on specific conceptual elements inherent to its operationalization. Therefore, econometric models should not be given the exclusive responsibility of explaining market efficiency, nor possibility of incorporating alternative epistemological perspectives into the efficient / inefficient duality should be kept outside.
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Baranovskyi, O., M. Kuzheliev, D. Zherlitsyn, and K. Serdyukov. "CRYPTOCURRENCY MARKET TRENDS AND FUNDAMENTAL ECONOMIC INDICATORS: CORRELATION AND REGRESSION ANALYSIS." Financial and credit activity: problems of theory and practice 3, no. 38 (June 30, 2021): 249–61. http://dx.doi.org/10.18371/fcaptp.v3i38.237454.
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Abstract. The first cryptocurrency was born in 2008. Already today, virtual financial assets and tokens are a significant part of trading in global financial markets. The cryptocurrency market capitalization currently exceeds 600 billion U.S. dollars. However, there is a lot of discussion about cryptocurrency functions and the correlation between Bitcoin prices and the basic economic indices.
Therefore, the purpose of the paper is to define the statistical substantiation of the influence of fundamental economic indicators on the market of virtual financial assets and the possibility of using cryptocurrency as the investment assets.
This article is based on the theoretical principles and methods of econometric analysis; the system approach methods to define the main vehicles and trends of the international financial market. The study presents correlation analysis, regression models with paired and multiple variables. For these models, R-Studio instruments are the main tools of quality estimation and results interpretation.
The article shows the results of the correlation analysis of Bitcoin’s U.S. dollar price dynamics and changes in the main stock, monetary market indicators, cryptocurrencies market tendency, levels of the United States fundamental economic indicators for the period from 2014 to 2021. Traditional multifactorial regression models are used to determine the level and the impact of individual indicators of the world stock market at the U.S. dollar price of Bitcoin. A comparison of the level of volatility of key investment financial assets in the market of cryptocurrencies and stock markets is carried out.
The authors determine the level of correlation dependence and make a regression model of the impact of fundamental economic indicators and stock market trends on the dynamics of U.S. dollar prices for key cryptocurrencies. The article presents conclusions on trends and problems of using cryptocurrencies as an investment asset, considering volatility and profitability.
Implementation of the results allows to clarify the economic essence of cryptocurrencies as a specific financial vehicle, as well as improving the existing models of investment management, considering the statistical characteristics of the virtual financial assets.
The main direction of further research is to build models of medium-term prediction of prices for the main cryptocurrencies as an investment asset in conditions of changes in global financial markets, which must consider the fundamental economic indicators of the world economy and trends on key stock and commodity markets.
Keywords: virtual financial asset, cryptocurrency, bitcoin, econometric model, financial market, economic indicator, investment asset.
JEL Classification D53, E44, G15, C58
Formulas: 3; fig.: 3; tabl.: 3; bibl.: 31.
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Ji, Xuan, Jiachen Wang, and Zhijun Yan. "A stock price prediction method based on deep learning technology." International Journal of Crowd Science 5, no. 1 (March 5, 2021): 55–72. http://dx.doi.org/10.1108/ijcs-05-2020-0012.
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Purpose Stock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with nonstationary time series data. With the rapid development of the internet and the increasing popularity of social media, online news and comments often reflect investors’ emotions and attitudes toward stocks, which contains a lot of important information for predicting stock price. This paper aims to develop a stock price prediction method by taking full advantage of social media data. Design/methodology/approach This study proposes a new prediction method based on deep learning technology, which integrates traditional stock financial index variables and social media text features as inputs of the prediction model. This study uses Doc2Vec to build long text feature vectors from social media and then reduce the dimensions of the text feature vectors by stacked auto-encoder to balance the dimensions between text feature variables and stock financial index variables. Meanwhile, based on wavelet transform, the time series data of stock price is decomposed to eliminate the random noise caused by stock market fluctuation. Finally, this study uses long short-term memory model to predict the stock price. Findings The experiment results show that the method performs better than all three benchmark models in all kinds of evaluation indicators and can effectively predict stock price. Originality/value In this paper, this study proposes a new stock price prediction model that incorporates traditional financial features and social media text features which are derived from social media based on deep learning technology.
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Tufail, Saira, and Sadia Batool. "An Analysis of the Relationship between Inflation and Gold Prices: Evidence from Pakistan." LAHORE JOURNAL OF ECONOMICS 18, no. 2 (July 1, 2013): 1–35. http://dx.doi.org/10.35536/lje.2013.v18.i2.a1.
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In this study, we formulate a new inflation equation to capture the potential effects of gold and stock prices on inflation in Pakistan. We aim to assess the inflation-hedging properties of gold compared to other assets such as real estate, stock exchange securities, and foreign currency holdings. Applying time-series econometric techniques (cointegration and vector error correction models) to data for 1960–2010, we find that gold is a potential determinant of inflation in Pakistan. On the other hand, it also provides a complete hedge against unexpected inflation. Real estate assets are more than a complete hedge against expected inflation, although stock exchange securities outperform gold and real estate as a hedge against unexpected inflation. Foreign currency proves to be an insignificant hedge against inflation. Given the dual nature of the relationship between gold and inflation, it is increasingly important for the government to monitor and regulate the gold market in Pakistan. Moreover, stock market investment should be encouraged by the government given that asset price inflation does not pose a critical problem for Pakistan as yet.
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Gregoriou, Andros, and Mark Rhodes. "The accuracy of spread decomposition models in capturing informed trades." Review of Behavioral Finance 9, no. 1 (April 10, 2017): 2–13. http://dx.doi.org/10.1108/rbf-02-2017-0016.
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Purpose The purpose of this paper is to examine the empirical relationship between trades undertaken by informed agents (managers) and the proxies for informed trades computed by bid-ask spread decomposition models. Design/methodology/approach An econometric application of spread decomposition models to data from the London Stock Exchange, with an examination of whether the model predictions are co-integrated with actual outcomes. Findings The authors find overwhelming evidence of non-stationary behaviour between the actual and predicted informed trade prices. The findings suggest that there is a clear need for an alternative to extant spread decomposition models perhaps incorporating findings from behavioural finance. Originality/value Given the importance of stock market liquidity and the extensive use of spread decomposition models in predicting informed trades, the authors believe that the research conducted in the paper is an important contribution to the market microstructure literature.
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Tarczyński, Waldemar, Urszula Mentel, Grzegorz Mentel, and Umer Shahzad. "The Influence of Investors’ Mood on the Stock Prices: Evidence from Energy Firms in Warsaw Stock Exchange, Poland." Energies 14, no. 21 (November 5, 2021): 7396. http://dx.doi.org/10.3390/en14217396.
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The subject of this publication is an analysis of the sentiment of stock exchange investors in terms of making investment decisions in the energy sector of the Polish stock exchange. The investment mood is considered in the context of the possible impact of weather factors on investment decisions. Possible effects are verified in relation to the rates of return and the volume of trading of energy sector entities. The analysis is carried out both in terms of co-integration analyses as well as in econometric terms, in the cross-section of classic OLS models or causality analysis using VAR vector autoregression models. The main purpose of the issues discussed is the problem of indicating (illustrating) the presence or absence of mutual relations between weather factors and the stock market in terms of the methods considered.
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Abbahaddou, Kaoutar, Mohammed Salah Chiadmi, and Rajae Aboulaich. "An Enhanced Adaptative System based on Machine Learning for Predicting the Evolution of Islamic Stock Prices." WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 19 (October 11, 2022): 1661–68. http://dx.doi.org/10.37394/23207.2022.19.150.
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This paper suggests an enhanced machine-learning-based system to guide future stock price decisions. In reality, most existing machine learning systems, such as SEA (Stream Ensemble Algorithm), VFDT (Very Fast Decision Tree ), and online bagging and boosting, keep models updated with only new data and reduce training timeframes to allow working rapidly with the most recent model. However, limited learning times and the exclusion of essential information from previous data may result in a bad performance. When it comes to learning models, our system takes a different approach. It builds several models based on random selections of historical data from the main stock as well as related stocks. The best models are then combined to generate a final, performant model. We performed an empirical study on five Islamic stock market indices. We can say from the results that our system outperforms the existing published algorithms. This framework can contribute then to having an enhanced system that will enable different stakeholders to make rapid decisions based on the forecasted trend of indices.
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Hannum, Christopher, Kerem Yavuz Arslanli, and Ali Furkan Kalay. "Spatial analysis of Twitter sentiment and district-level housing prices." Journal of European Real Estate Research 12, no. 2 (August 8, 2019): 173–89. http://dx.doi.org/10.1108/jerer-08-2018-0036.
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Purpose Studies have shown a correlation and predictive impact of sentiment on asset prices, including Twitter sentiment on markets and individual stocks. This paper aims to determine whether there exists such a correlation between Twitter sentiment and property prices. Design/methodology/approach The authors construct district-level sentiment indices for every district of Istanbul using a dictionary-based polarity scoring method applied to a data set of 1.7 million original tweets that mention one or more of those districts. The authors apply a spatial lag model to estimate the relationship between Twitter sentiment regarding a district and housing prices or housing price appreciation in that district. Findings The findings indicate a significant but negative correlation between Twitter sentiment and property prices and price appreciation. However, the percentage of check-in tweets is found to be positively correlated with prices and price appreciation. Research limitations/implications The analysis is cross-sectional, and therefore, unable to answer the question of whether Twitter can Granger-cause changes in housing markets. Future research should focus on creation of a property-focused lexicon and panel analysis over a longer time horizon. Practical implications The findings suggest a role for Twitter-derived sentiment in predictive models for local variation in property prices as it can be observed in real time. Originality/value This is the first study to analyze the link between sentiment measures derived from Twitter, rather than surveys or news media, on property prices.
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Jiang, Xiaoquan, and Qiang Kang. "Cross-Sectional PEG Ratios, Market Equity Premium, and Macroeconomic Activity." Journal of Accounting, Auditing & Finance 35, no. 3 (January 8, 2018): 471–500. http://dx.doi.org/10.1177/0148558x17748277.
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This article explores the information content of PEG ratios (price/earnings to growth ratios) for future aggregate returns and economic fundamentals. We first establish an analytic link between PEG ratios and time-varying expected returns of stocks. We then combine the link with empirical asset pricing models to extract market-wide information from cross-sectional PEG ratios. The resultant cross-section estimates of the risk premiums on stock betas serve as proxies for market-wide information. The proxies contain salient information about future market equity premiums and macroeconomic activity both in-sample and out-of-sample. Moreover, the proxies outperform aggregate PEG ratios and the cross-section beta-premium estimate based on conventional valuation ratios and retain incremental power in forecasting future market equity premiums. The results are robust to using various econometric methods for standard error adjustments.
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Milon, J. Walter. "Travel Cost Methods for Estimating the Recreational Use Benefits of Artificial Marine Habitat." Journal of Agricultural and Applied Economics 20, no. 1 (July 1988): 87–101. http://dx.doi.org/10.1017/s0081305200025681.
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AbstractThe growing popularity of marine recreational fishing has created considerable interest in artificial marine habitat development to maintain and enhance coastal fishery stocks. This paper provides a comparative evaluation of travel cost methods to estimate recreational use benefits for new habitat site planning. Theoretical concerns about price and quality effects of substitute sites, corner solutions in site choice, and econometric estimation are considered. Results from a case study indicate that benefit estimates are influenced by the way these concerns are addressed, but relatively simple single site models can provide defensible estimates. Practical limitations on data collection and model estimation are also considered.
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Hami, Mustapha El, and Ahmed Hefnaoui. "Analysis of Herding Behavior in Moroccan Stock Market." Journal of Economics and Behavioral Studies 11, no. 1(J) (March 10, 2019): 181–90. http://dx.doi.org/10.22610/jebs.v11i1(j).2758.
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Frontier markets, particularly the Moroccan financial market, are characterized by a narrowness of market, inability to absorb erratic price fluctuations and the low liquidity of securities that encourage investors to herd and imitate those who have all the information about the market. A quantitative research approach was used to analyze the existence of herding n Moroccan stock market. The daily data used in this study concerns the period from 04/01/2010 to 29/12/2017 and contains the daily returns of the MASI and a total of 43 traded stocks. Statistical and econometric methods such as multidimensional scaling and Cross-sectional absolute deviation were used. Subsequently, after the regression models were examined, findings indicated that the first stocks with the highest similarity to the index return are BMCE, BCP, IAM, ATW and CMSR, and the first stocks with the highest dissimilarity are PAP, IBC and SNP, This will have to allow investors to choose profitable alternatives and avoid those that present a possible risk. The results did also show the existence of herding in the Moroccan stock market both upward and downward. This finding was supported by the clear existence of a non-linearity between market performance and CSAD measurement, which confirms the prediction of a non-linear inversion relationship between CSAD and ð‘…ð‘š. This could be due to the low level of transparency that prevails in frontier stock exchanges and reduces the quality of their information environment, which leads investors not to react rationally and to draw information from the transactions of their peers.
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Rudzkis, Rimantas, Roma Valkavičienė, and Virmantas Kvedaras. "Prediction of Baltic Sectorial Share Price Indices." Lietuvos statistikos darbai 53, no. 1 (December 20, 2014): 53–59. http://dx.doi.org/10.15388/ljs.2014.13894.
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Extending the research started in [31], the paper uses econometric methods for the short-term forecasting of quarterly values of sector indexes of stock prices from the OMX Baltic stock exchange. The ARMA models and modelling methodology that was used to build the statistical models in the previous paper are now augmented with the algorithms of time series aggregation and identification of special features of the series. Here, the search for informative factors relies on the study of related literature. The specification of models is further tailored using the traditional significance (p-value) analysis of regressors and a cross-validation analysis. The latter is implemented in this paper using the Jack-knife approach. The data period analysed covers the years 2000–2013. The results of the analysis indicate that the inclusion not only of recent autoregressive terms but also of some aggregated characteristics (as certain special features of indexes) improves the precision of forecasting substantially. The calculations were performed using the statistical analysis software SAS.
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Manikandan, Narayanan, and Srinivasan Subha. "Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks." Scientific World Journal 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/6709352.
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Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.
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Bayram, Mehmet, and Muzaffer Akat. "Market-neutral trading with fuzzy inference, a new method for the pairs trading strategy." Engineering Economics 30, no. 4 (October 30, 2019): 411–21. http://dx.doi.org/10.5755/j01.ee.30.4.14350.
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Financial pricing and prediction of stock markets is a specific and relatively narrow field, which have been mainly explored by mathematicians, economists and financial engineers. Prediction with the purpose of making profits in a martingale domain is a hard task. Pairs trading, a market neutral arbitrage strategy, attempts to resolve the drawback of unpredictability and yield market independent returns using relative pricing idea. If two securities have similar characteristics, so should their prices. Deviation from the acceptable similarity range in prices is considered an anomaly, and whenever noticed, trading is executed assuming the anomaly will correct itself.This work proposes a fuzzy inference model for the market-neutral pairs trading strategy. Fuzzy logic lets mimicking human decision-making in a complex trading environment and taking advantage of arbitrage opportunities that the crisp models may miss to acquire for the trade decision-making. Spread between two co-integrated stocks and volatility of the spread is used as decision-making inputs. Spread is a measure of the distance between two stocks and volatility is an indicator of how soon the spread would disappear. We conclude that fuzzy engine contributes to the profitability and efficiency of pairs trading type of strategies.
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Zaimi, Wiam. "An Empirical Analysis of a Stock Market Index of a Developing Country: Case of the Main Index of the Casablanca Stock Exchange MASI." GLOBAL BUSINESS FINANCE REVIEW 27, no. 4 (August 31, 2022): 1–16. http://dx.doi.org/10.17549/gbfr.2022.27.4.1.
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Purpose: Managing stock market risk and making an optimal investment decision in a stock market requires study- ing the dynamics of this market and analyzing the fluctuations of its benchmark index in order to avoid heavy damage in the event of crises. This paper aims to study and analyze the fluctuations of the main index of the Casablanca Stock Exchange "MASI" to explore its efficiency and stability in the normal financial context (especially before the recent pandemic crisis).
Design/methodology/approach: To carry out this study, two methods are proposed, the first one, how evolves this index over time depending on some random data generation processes widely used for stock prices: A Random Walk with a drift RW(α) and an autoregressive process of order 1 AR(1). Based on the actual MASI returns series used (2007-2018), we estimate each equation parameter according to the process chosen to generate the artificial MASI returns series to know the most relevant data generation process in the case of The Moroccan financial market. The second method focuses on the technique of "simple moving average" as a method of stock prices fluctuation analysis to make its investment decision, choosing the proper order on the same series.
Findings: The results and findings of our econometric study show, in the first method, that either the RW(α) or the AR(1) process cannot adequately model MASI fluctuation. However, the results of the second method affirm the utility of the simple moving average to identify trends, their strength and buy / sell signals, using some techniques known in this field, in order to make decisions and draw interpretations in investment terms and risk management, which can prove that the market is less efficient and stable.
Research limitations/implications: An important implication of this study is the need to explore the efficient models to describe the MASI return series.
Originality/value: This study offers empirical evidence in relation to the estimation of the econometric model to describe MASI and how to make adequate investment decisions in the Moroccan stock market. Moreover, contributes to future research to find other more appropriate models.
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Neves, Maria Elisabete, Mário Abreu Pinto, Carla Manuela de Assunção Fernandes, and Elisabete Fátima Simões Vieira. "Value and growth stock returns: international evidence (JES)." International Journal of Accounting & Information Management 29, no. 5 (October 7, 2021): 698–733. http://dx.doi.org/10.1108/ijaim-05-2021-0097.
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Purpose This study aims to analyze the returns obtained from companies with strong growth potential (growth stocks) and the returns from companies with quite low stock prices, but with high value (value stocks). Design/methodology/approach The sample comprises monthly data, from January 2002 to December 2016, from seven countries, Germany, France, Switzerland, the UK, Portugal, the USA and Japan. The authors have used linear regression models for three different periods, the pre-crisis, subprime crisis and post-crisis period. Findings The results point out that the performance of value and growth stocks differs from different periods surrounding the global financial crisis. In fact, for six countries, value stocks outperformed growth stocks in the period that precedes the subprime crisis and during the crisis, this tendency remained only for France, Portugal and Japan. This trend changed in the period following the crisis. The results also show that investor sentiment has a robust significance in value and growth stock returns, mostly in the period before the crisis, highlighting that the investor sentiment is more significant in the moments that the value stocks outperformed. Originality/value As far as the authors know, this is the first work that, taking into account the future research lines of Capaul et al. (1993), investigates whether the results obtained by those authors remain current, meeting the authors’ challenge and covering the gap of recent studies on the performance of value and growth stocks. Besides, the authors have introduced a new country, heavily punished by both the global financial crisis and the sovereign debt crisis to understand whether there are significant differences in investment styles and whether this is related to the different economies. Also, in this context, the authors were pioneers in adding investor sentiment as an exogenous variable in the influence of stock returns.
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Akbulaev, N. N., F. S. Ahmadov, and M. R. Mammadova. "Analysis of the Impact of the COVID-19 Pandemic on Stock Exchange Indices in Italy." Economy of Region 18, no. 4 (2022): 1276–86. http://dx.doi.org/10.17059/ekon.reg.2022-4-22.
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The present paper investigates the impact of the COVID-19 pandemic on the prices of the Italian stock exchange indices. During the pandemic, the global economy as well as financial markets suffered due to isolation and social distancing. Paired models of the dependence of the key indices of the Italian stock exchange on the number of patients, recovered and died were analysed using the least squares method. Further, various tests were performed to verify the feasibility of the Gauss-Markov conditions by applying Gretl tools: White Test for heteroskedasticity of residues, Durbin-Watson test for autocorrelation of residuals and normality of distribution of residuals. Statistically significant regression models were constructed that characterise the impact of morbidity and mortality in the Italian population during the COVID-19 pandemic on the price of 11 key stock exchange indices. Based on this, the study examined the COVID-19 pandemic period in the spring of 2020 in Italy, the results of which revealed a loss in stock returns and high volatility in stock returns during this period compared to the normal study period. The econometric model shows that COVID-19 had a negative impact on stock returns and a number of other stock market indicators in Italy. It was revealed that the number of deaths from coronavirus is statistically significantly interconnected with all key stock exchange indices.
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Czinkan, Norbert, and Áron Horváth. "Determinants of housing prices from an urban economic point of view: evidence from Hungary." Journal of European Real Estate Research 12, no. 1 (May 7, 2019): 2–31. http://dx.doi.org/10.1108/jerer-10-2017-0041.
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Purpose The purpose of the paper is to investigate a cross section of Hungarian settlement-level unit housing prices with a special emphasis on measuring the effect of population and its growth, along with accessibility to the centre of an aggregated spatial unit such as a micro-region, county or region, for the period of 2001-2011. Design/methodology/approach The analysis uses cross-sectional ordinary least squares techniques with Moulton-corrected standard errors. The estimation is guided by the implications of a simplified monocentric urbanized area framework following the model of DiPasquale and Wheaton (1996), and the econometric model is augmented with population growth rate at the settlement level to bridge the theory explaining rents and data base containing prices instead. Findings The location is a key factor in determining housing prices: living 10 min further from the centre results in 11 per cent cheaper housing. When estimating bid-rent curves, results show that it is crucial to control for city size and the income effect. The elasticity of housing price with respect to city size is 0.09 according to our preferred model. Population growth has an asymmetric impact on housing prices: municipalities with positive expected population growth have higher prices today. Practical implications Estimating the quantitative relationship between commuting time and housing price is crucial for a cautious infrastructure development. The benefits of improved roads and faster access could be capitalized in appreciating the housing stock. Estimating the slope of the bid-rent curve is one possible ex ante quantification of the benefits of a public development. Originality/value One contribution of this research is providing empirical evidence to surprisingly limited applied work in the field of (monocentric) urban models using data from the CEE region. Second, to the best of the authors’ knowledge, this is the first study to investigate Hungarian settlement-level unit prices from an urban economic point of view.
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Volontyr, L., and L. Mykhalchyshyna. "Organizational and economic mechanism of grain sales: information component." Scientific Messenger of LNU of Veterinary Medicine and Biotechnologies 21, no. 92 (May 11, 2019): 81–89. http://dx.doi.org/10.32718/nvlvet-e9213.
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A significant part of the output of the agro-industrial complex of Ukraine is exported. Therefore, it is desirable to determine the optimal volume of products to be implemented each month. Prices for grain are formed depending on demand and supply, costs for production and sale, market fees, etc. The analysis of the price situation on the Ukrainian cities shows a large variation. The average price of 1 kg of grain crops does not give a full opportunity to characterize the price situation of the Ukrainian grain market. There is seasonal price cyclicality: their growth with the decrease of stocks and the reduction after harvesting, when mass sales of grain are carried out by producers who are not able to store the grown crops, and consumers make grain crops. In the article the solution of the economic-mathematical model of optimization of the calendar plan for the sale of agricultural products is developed and found. The model is considered from the standpoint of deterministic product prices and under the probabilistic nature of future market prices. The system of restrictions consists of two constraints: to determine the optimal size of grain crop harvesting of each type and the capacity of the warehouse. If future market prices are considered not deterministic, then the commodity producer always has the risk of receiving in the future revenue from the sale of products smaller than expected. A risk-averse person will be guided by two criteria when deciding to: maximize the expected total net income and minimize the dispersion of total net income. In this case, the model will be two-criterial and nonlinear. The method of supporting the process of determining the predominance of multi-criteria optimization is that the owner first of all has received information about the limits of the variation of the expected total net income and the standard deviation of income on the set of effective options for the calendar plan. The peculiarities of the individual attitude to risk are calculated by drawing information on the permissible levels of the indicated criterion. Further among all effective variants of the calendar plan of realization is calculated precisely the one that best reflects the individual predominance of the owner of the product. The following information is needed to construct a numerical model for grain sales: sales prices and the cost of storing 1 ton of grain crops to a certain month. The predicted values are based on a simple linear econometric model based on statistical sampling. The reliability of the econometric model is determined by the determination coefficient or on the basis of Fisher's F-criterion according to the theory of statistical hypotheses. Econometric models have weak extropolitic properties, so the forecast can be formed only short-term. The solution of the model showed: all kinds of grain crops, except for barley, are economically unprofitable to be implemented in such months as January, May, June, July and August. Wheat grades 3 and 6, corn is also unprofitable to be sold in September. Unlike other crops, barley is beneficial throughout the year. In February, the maximum sales of wheat is 2, 3 and 6 classes, in March the maximum sale of barley, and the minimum is in May. Maize has the maximum sales in May, and the minimum in September. The minimum sale of wheat depends on its class – September, April and December respectively 2, 3 and 6 classes. With such incomplete loading of warehouses, the profit from storage of grain crops will be 743 thousand. UAH. Thus, PJSC “Gnivan Grain Reciprocal Enterprise” is more likely to load its warehouses to improve its financial position. One of the ways of solving the problem of seasonal grain sales is to create a network of modern certified grain elevators, taking into account the logistically rational location, which will allow to keep enough grain in addition and of the proper quality. This will allow an increase in the efficiency of grain producers through the sale of grain at favorable market conditions in a wider range of time. Independent operators should also be encouraged to ensure that the quality of the grain is objectively measured. At present, the analysis of the work of the grain storage system shows that the high cost of services of active elevators is also a problem.
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Koulis, Alexandros, George Kaimakamis, and Christina Beneki. "Hedging effectiveness for international index futures markets." Economics and Business 32, no. 1 (July 31, 2018): 149–59. http://dx.doi.org/10.2478/eb-2018-0012.
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Abstract This paper investigates the hedging effectiveness of the International Index Futures Markets using daily settlement prices for the period 4 January 2010 to 31 December 2015. Standard OLS regressions, Error Correction Model (ECM), as well as Autoregressive Distributed Lag (ARDL) cointegration model are employed to estimate corresponding hedge ratios that can be employed in risk management. The analyzed sample consists of daily closing market rates of the stock market indexes of the USA and the European futures contracts. The findings indicate that the time varying hedge ratios, if estimated through the ARDL model, are more efficient than the fixed hedge ratios in terms of minimizing the risk. Additionally, there is evidence that the comparative advantage of advanced econometric approaches compared to conventional models is enhanced further for capital markets within peripheral EU countries
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S, Monish, Mridul Mohta, and Shanta Rangaswamy. "ETHEREUM PRICE PREDICTION USING MACHINE LEARNING TECHNIQUES – A COMPARATIVE STUDY." International Journal of Engineering Applied Sciences and Technology 7, no. 2 (June 1, 2022): 137–42. http://dx.doi.org/10.33564/ijeast.2022.v07i02.018.
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In recent years, popularity and use of cryptocurrencies has been rising along with their prices and Ethereum is the second most famous cryptocurrency after Bitcoin. Cryptocurrencies are based on blockchain, which is a distributed and empowered technology that has the power to transform any banking systems. It has become an attractive investment for traders as well as individuals looking to invest. The price of Ethereum varies and is controlled by different factors, such as the crypto market in which it is sold, supply and demand. Ethereum is so valuable because it could be used as cash, we could also pay a portion or part of Ethereum to someone in exchange and it is easily guaranteed by the blockchain. Unlike stocks, Ethereum price is much more variable, as it has a trading time of 24-hours a day without any close time. The paper compares the results of three different models, namely Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs) and Bi-directional Long Short-Term Memory (Bi-LSTMs). The dataset consists of the closing price for the last 2000 days that is used to predict both short-term (30 days) and long-term (90 days) Ethereum prices. These prices are being fetched from an API which is in JSON format and are updated every day.
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Rege, Sameer, and Samuel Gil Martín. "PORTUGUESE STOCK MARKET: A LONG-MEMORY PROCESS?" Business: Theory and Practice 12, no. 1 (March 10, 2011): 75–84. http://dx.doi.org/10.3846/btp.2011.08.
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This paper gives a basic overview of the various attempts at modelling stochastic processes for stock markets with a specific application to the Portuguese stock market data. Long-memory dependence in the stock prices would completely alter the data generation process and econometric models not considering the long-range dependence would exhibit poor forecasting abilities. The Hurst exponent is used to identify the presence of long-memory or fractal behaviour of the data generation process for the daily returns to ascertain if the process follows a fractional brownian motion. Detrended fluctuation analysis (DFA) using linear and quadratic trends and the Geweke Porter-Hudak methods are applied to detect the presence of long-memory or persistence. We find that the daily returns exhibit a small amount of long memory and that the quadratic trend used in the DFA overestimates the value of the Hurst exponent. These findings are corroborated by the use of the Geweke Porter-Hudak method wherein the Hurst exponent is close to the DFA using the linear trend.
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Coen-Pirani, Daniele. "Markups, Aggregation, and Inventory Adjustment." American Economic Review 94, no. 5 (November 1, 2004): 1328–53. http://dx.doi.org/10.1257/0002828043052376.
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In this paper I suggest a unified explanation for two puzzles in the inventory literature: first, estimates of inventory speeds of adjustment in aggregate data are very small relative to the apparent rapid reaction of stocks to unanticipated variations in sales. Second, estimates of inventory speeds of adjustment in firm-level data are significantly higher than in aggregate data. The paper develops a multi-sector model where inventories are held to avoid stockouts, and price markups vary along the business cycle. The omission of countercyclical markup variations from inventory targets introduces a downward bias in estimates of adjustment speeds obtained from partial adjustment models. When the cyclicality of markups differs across sectors, this downward bias is shown to be more severe with aggregate rather than firm-level data. Similar results apply not only to inventories, but also to labor and prices. Montercarlo simulations of a calibrated version of the model suggest that these biases are quantitatively significant.
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Yan, Runze. "Option pricing and risk hedging for Visa." BCP Business & Management 32 (November 22, 2022): 203–10. http://dx.doi.org/10.54691/bcpbm.v32i.2889.
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As the core of the option transaction, the option price changing with the supply and demand in the market is a variable which affects the profit and loss of both trading sides directly. In the 20th century, multitudinous econometric pricing models proposed lacked universal recognition until the Black Scholes Merton model came out. This paper focuses on the stocks and options from Visa Inc. to do the article consisting of calibration, option pricing and hedging using fundamental Black Scholes Merton model and the extensive jump model mainly under the seldom used method. The article demonstrates that calibrated parameters in Black Scholes Merton model perform better than that of the jump diffusion model with the same method, and the hedging portfolio based on the Black Scholes Merton model do keeps the profit and loss at a steady level though it should not be a preference at the certain circumstance. The results in this paper are beneficial for investors to forecast the price of option with the optimal model and describes the nature for option selection.
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TRIVEDI, JATIN, MOHD AFJAL, CRISTI SPULBAR, RAMONA BIRAU, KRISHNA MURTHY INUMULA, and NARCIS EDUARD MITU. "Investigating the impact of COVID-19 pandemic on volatility patterns and its global implication for textile industry: An empirical case study for Shanghai Stock Exchange of China." Industria Textila 73, no. 04 (August 31, 2022): 365–76. http://dx.doi.org/10.35530/it.073.04.202148.
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This research paper aims to examine the impact of the COVID-19 pandemic on volatility patterns and its global implication for the textile industry in China. The COVID-19 pandemic has generated a global health crisis with profound economic, social and financial implications, but also has triggered a ruthless global recession. The global economic recovery as a result of the COVID-19 pandemic can also generate significant investment opportunities for the textile industry in China. In this paper, the application of empirical methods could explain historical prices, the movement dynamics of financial assets, and investigate various important characteristics of asset pricing that explore details of the Chinese stock market. The econometric framework includes the following: symmetric Generalize Autoregressive Conditional Heteroscedastic GARCH (1, 1) model, asymmetric GARCH models such as EGARCH and GJR models. The main aim is to identify the asymmetric volatility effect, and impact of news on the SSE Composite Index and investigate long memory properties in volatility using daily data for the sample period from 19th December 1990 to 31st December 2020. This empirical study contributes to the existing literature on the impact of the COVID-19 pandemic on international stock markets, by investigating symmetric and asymmetric volatility patterns in the case of the Shanghai Stock Exchange from China
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Phuong, Lai Cao Mai. "Investor Sentiment by Money Flow Index and Stock Return." International Journal of Financial Research 12, no. 4 (March 18, 2021): 33. http://dx.doi.org/10.5430/ijfr.v12n4p33.
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Factors affecting stock prices have been studied by many scholars on different stock markets. However, the number of empirical studies applying technical analysis indicators to measure investor sentiment is quite limited. To explore this interesting topic, this study uses the Money Flow Index (MFI) indicator to measure an investor's sentiment by various thresholds and to test its effect on the excess return on Vietnam stock market. Data series including market, interest rate, finance and transaction data of 138 companies listed on the Ho Chi Minh City Stock Exchange from 2015 to June 2020 are used in the equations Regression. The study's findings show that, after controlling for market factors, individual characteristics and liquidity of each company, investor sentiment as measured by the MFI indicator still has a significant impact on the return of stocks at all thresholds. In addition, when the MFI value area is near the starting and ending point of the scale (less than 20, greater than 80), the regression coefficients of these two thresholds and control variables both increase compared to the remaining models, return and significant effect to the excess return of the securities.
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Karmakar, Madhusudan. "Modeling Conditional Volatility of the Indian Stock Markets." Vikalpa: The Journal for Decision Makers 30, no. 3 (July 2005): 21–38. http://dx.doi.org/10.1177/0256090920050303.
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Traditional econometric models assume a constant one period forecast variance. However, many financial time series display volatility clustering, that is, autoregressive conditional heteroskedasticity (ARCH). The aim of this paper is to estimate conditional volatility models in an effort to capture the salient features of stock market volatility in India and evaluate the models in terms of out-ofsample forecast accuracy. The paper also investigates whether there is any leverage effect in Indian companies. The estimation of volatility is made at the macro level on two major market indices, namely, S&P CNX Nifty and BSE Sensex. The fitted model is then evaluated in terms of its forecasting accuracy on these two indices. In addition, 50 individual companies' share prices currently included in S&P CNX Nifty are used to examine the heteroskedastic behaviour of the Indian stock market at the micro level. The vanilla GARCH (1, 1) model has been fitted to both the market indices. We find: a strong evidence of time-varying volatility a tendency of the periods of high and low volatility to cluster a high persistence and predictability of volatility. Conditional volatility of market return series from January 1991 to June 2003 shows a clear evidence of volatility shifting over the period where violent changes in share prices cluster around the boom of 1992. Though the higher price movement started in response to strong economic fundamentals, the real cause for abrupt movement appears to be the imperfection of the market. The forecasting ability of the fitted GARCH (1, 1) model has been evaluated by estimating parameters initially over trading days of the in-sample period and then using the estimated parameters to later data, thus forming out-of-sample forecasts on two market indices. These out-of-sample volatility forecasts have been compared to true realized volatility. Three alternative methods have been followed to measure three pairs of forecast and realized volatility. In each method, the volatility forecasts are evaluated and compared through popular measures. To examine the information content of forecasts, a regression-based efficiency test has also been performed. It is observed that the GARCH (1, 1) model provides reasonably good forecasts of market volatility. While turning to 50 individual underlying shares, it is observed that the GARCH (1, 1) model has been fitted for almost all companies. Only for four companies, GARCH models of higher order may be more successful. In general, volatility seems to be of a persistent nature. Only eight out of 50 shares show significant leverage effects and really need an asymmetric GARCH model such as EGARCH to capture their volatility clustering which is left for future research. The implications of the study are as follows: The various GARCH models provide good forecasts of volatility and are useful for portfolio allocation, performance measurement, option valuation, etc. Given the anticipated high growth of the economy and increasing interest of foreign investors towards the country, it is important to understand the pattern of stock market volatility in India which is time-varying, persistent, and predictable. This may help diversify international portfolios and formulate hedging strategies.
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Fationa Halili. ""The Impact of Macroeconomic Factors on the Change of Residential Prices" The case study of Albania." International Journal of Applied Research in Management and Economics 5, no. 4 (January 7, 2023): 29–44. http://dx.doi.org/10.33422/ijarme.v5i4.946.
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The housing market is a very important sector worldwide and occupies a significant part of their capital stock. Given that the economy is experiencing a difficult situation, because of the Covid-19 pandemic crisis, also based on the financial crisis of 2007-2008, their impact has been immediate in the change in house prices. Moreover, this paper will contain the investigation of some of the macroeconomic variables that affect housing prices in Albania. The study is built on econometric models, using bound test to understand what correlation relationship exists between GDP, inflation and mortgage rate and evaluating the "Autoregressive Distributive Lags" model in the short-term period, as well as looking at the perspective of a long-term period according to the "Vector Error Correction" (VEC) model. From the result obtained from E-views software, it is shown that depending on the time periods, the impact of these macro variables is different. If in the short term it seems that the impact of the mortgage rate is not a very substantial variable or inflation can have a negative impact, in long-terms is the opposite. To have a model that is as reliable and strong as possible, "battery" tests have also been analyzed to diagnose its constituent elements such as heteroskedasticity, autocorrelation, normality and the CUSUM / CUSUM square test for stability of the model. This paper applies to all areas, whose political implications can be found mainly for the bank, various investors or even policy makers, which may be the Bank of Albania, which should pursue policies to stimulate economic growth and show caution in the level of inflation created or setting the mortgage threshold.
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Dell’Anna, Federico. "What Advantages Do Adaptive Industrial Heritage Reuse Processes Provide? An Econometric Model for Estimating the Impact on the Surrounding Residential Housing Market." Heritage 5, no. 3 (July 6, 2022): 1572–92. http://dx.doi.org/10.3390/heritage5030082.
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When industrial relics, such as obsolete buildings, sites, and infrastructures, enter into a process of adaptive reuse, they become transformation engines capable of shaping the urban fabric. They provide tangible and intangible links to our past and have the potential to play a significant role in today’s cities’ futures. One unresolved issue is the quantification of the externalities of these transformation processes. If undertaken correctly, adaptive reuse can contribute to the development of social and cultural capital, environmental sustainability, urban regeneration, and, most importantly, economic benefits to the surrounding community. In this sense, understanding the value of heritage is particularly important in light of the new European urban environmental policy movement based on the circular economy, which aims to change the way Member States consume and produce materials and energy. After a review of the externalities generated by the adaptive reuse of disused industrial heritage, the paper will concentrate on the estimation of economic benefits given by a transformation process that affected Turin’s Aurora district (Northern Italy) during the last years. The hedonic pricing method (HPM) was used to investigate the effects of the construction of new headquarters and the redevelopment of an old power plant converted into a museum and conference center. This study used econometric models to identify a significant increase in market prices within 800 m of the site and calculated a EUR 16,650,445 capitalized benefit from the transformation on the surrounding residential building stock. The study thus contributed to the awareness that reused heritage not only improves the lives of residents, but it also has a positive impact on the real estate market, in terms of transactions, as well as market values.