Relevant bibliographies by topics / Stocks - Prices - Econometric models

Academic literature on the topic 'Stocks - Prices - Econometric models'

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Published: 4 June 2021
Last updated: 30 January 2023

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Journal articles on the topic "Stocks - Prices - Econometric models"

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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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Dissertations / Theses on the topic "Stocks - Prices - Econometric models"

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Oliveira, Lima Jorge Claudio Cavalcante de. "Fractional integration and long memory models of stock price volatility : the evidence of the emerging markets." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=38164.

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Following the important work on unit roots and cointegration which started in the mid-1980s, a great deal of econometric works has been devoted to the study of the subtleties and varieties of near nonstationarity and persistence that characterize so many economic and financial time series. In recent years research activity has gained importance with outstanding contributions made on estimation and testing of a wide variety of long memory processes, together with many interesting and imaginative applications over a wide variety of different fields of economics and finance. For these reasons, this study provides empirical evidence to an aspect of fractional differencing and long memory processes, or the long memory of volatility. Evidence of long memory persistence is explored using stock price indices for eight emerging economies in both Asian and Latin American markets. The concern with the presence of long memory in higher moments of return series was first drawn by Ding, Granger and Engle (1993), using asset returns. Baillie, Bollerslev and Mikkelsen (1996) developed the fractionally integrated GARCH, or FIGARCH, process to represent long memory in volatility. The measure of long-memory persistence in the volatility is employed either using the original rescaled range statistic by Hurst (1951) and its modified version proposed by Lo (1991). Further analysis of the presence of long memory persistence is conducted using autocorrelation analysis. All the findings point in the same direction, that is, the existence of long memory in volatility irrespective of the measure chosen. Estimation of different models of volatility is undertaken beginning with the ARCH specification and until the FIGARCH model. The results show the effects to be higher in Latin American countries than in the Asian ones. This result seems consistent with the degree of intervention in the Latin American markets, known to be much higher.
Other possible explanations for the occurrence of long term persistence are also pursued such as the Regime Switching modelisation proposed first by Hamilton and Susnel (1994) with the SWARCH approach. Results show that this approach can bring another possible explanation for persistence, specially in economies like Brazil that, have very different regimes for the period covered in this study.
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Magliolo, Jacques. "The relevance and fairness of the JSE ALTX PRE-IPO share pricing methodologies." Thesis, Nelson Mandela Metropolitan University, 2012. http://hdl.handle.net/10948/d1018652.

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This three year indepth study was prompted after a decade of working as a corporate advisor for numerous stockbroking firms' corporate advisory and listing divisions. An overwhelming lack of discernible pricing methodology for IPOs on the JSE's Main Board and failed Venture Capital and Development Capital Markets was transferred to the new Alternative Exchange (AltX). This prompted lengthly discussions with former head of JSE's AltX Noah Greenhill. Such discussions are set out in this dissertation and relate to pricing methodologies and the lack of guidance or legislation as set out in the JSE's schedule 21 of Listing requirements. The focus of this dissertation is thus centred on whether the current adopted methodologies to establish a fair and reasonable pre-IPO share price is effective. To achieve this, global pricing methodologies were assessed within the framework of various valuation techniques used by South African Designated Advisors.
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Milunovich, George Economics Australian School of Business UNSW. "Modelling and valuing multivariate interdependencies in financial time series." Awarded by:University of New South Wales. School of Economics, 2006. http://handle.unsw.edu.au/1959.4/25162.

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This thesis investigates implications of interdependence between stock market prices in the context of several financial applications including: portfolio selection, tests of market efficiency and measuring the extent of integration among national stock markets. In Chapter 2, I note that volatility spillovers (transmissions of risk) have been found in numerous empirical studies but that no one, to my knowledge, has evaluated their effects in the general portfolio framework. I dynamically forecast two multivariate GARCH models, one that accounts for volatility spillovers and one that does not, and construct optimal mean-variance portfolios using these two alternative models. I show that accounting for volatility spillovers lowers portfolio risk with statistical significance and that risk-averse investors would prefer realised returns from portfolios based on the volatility spillover model. In Chapter 3, I develop a structural MGARCH model that parsimoniously specifies the conditional covariance matrix and provides an identification framework. Using the model to investigate interdependencies between size-sorted portfolios from the Australian Stock Exchange, I gain new insights into the issue of asymmetric dependence. My findings not only confirm the observation that small stocks partially adjust to market-wide news embedded in the returns to large firms but also present evidence that suggests that small firms in Australia fail to even partially adjust (with statistical significance) to large firms??? shocks contemporaneously. All adjustments in small capitalisation stocks occur with a lag. Chapter 4 uses intra-daily data and develops a new method for measuring the extent of stock market integration that takes into account non-instantaneous adjustments to overnight news. This approach establishes the amounts of time that the New York, Tokyo and London stock markets take to fully adjust to overnight news and then uses this This thesis investigates implications of interdependence between stock market prices in the context of several financial applications including: portfolio selection, tests of market efficiency and measuring the extent of integration among national stock markets. In Chapter 2, I note that volatility spillovers (transmissions of risk) have been found in numerous empirical studies but that no one, to my knowledge, has evaluated their effects in the general portfolio framework. I dynamically forecast two multivariate GARCH models, one that accounts for volatility spillovers and one that does not, and construct optimal mean-variance portfolios using these two alternative models. I show that accounting for volatility spillovers lowers portfolio risk with statistical significance and that risk-averse investors would prefer realised returns from portfolios based on the volatility spillover model. In Chapter 3, I develop a structural MGARCH model that parsimoniously specifies the conditional covariance matrix and provides an identification framework. Using the model to investigate interdependencies between size-sorted portfolios from the Australian Stock Exchange, I gain new insights into the issue of asymmetric dependence. My findings not only confirm the observation that small stocks partially adjust to market-wide news embedded in the returns to large firms but also present evidence that suggests that small firms in Australia fail to even partially adjust (with statistical significance) to large firms??? shocks contemporaneously. All adjustments in small capitalisation stocks occur with a lag. Chapter 4 uses intra-daily data and develops a new method for measuring the extent of stock market integration that takes into account non-instantaneous adjustments to overnight news. This approach establishes the amounts of time that the New York, Tokyo and London stock markets take to fully adjust to overnight news and then uses this
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Yang, Wenling. "M-GARCH Hedge Ratios And Hedging Effectiveness In Australian Futures Markets." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2000. https://ro.ecu.edu.au/theses/1530.

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This study deals with the estimation of the optimal hedge ratios using various econometric models. Most of the recent papers have demonstrated that the conventional ordinary least squares (OLS) method of estimating constant hedge ratios is inappropriate, other more complicated models however seem to produce no more efficient hedge ratios. Using daily AOIs and SPI futures on the Australian market, optimal hedge ratios are calculated from four different models: the OLS regression model, the bivariate vector autoaggressive model (BVAR), the error-correction model (ECM) and the multivariate diagonal Vcc GARCH Model. The performance of each hedge ratio is then compared. The hedging effectiveness is measured in terms of ex-post and ex-ante risk-return traHe-off at various forcasting horizons. It is generally found that the GARCH time varying hedge ratios provide the greatest portfolio risk reduction, particularly for longer hedging horizons, but hey so not generate the highest portfolio return.
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Fodor, Bryan D. "The effect of macroeconomic variables on the pricing of common stock under trending market conditions." Thesis, Department of Business Administration, University of New Brunswick, 2003. http://hdl.handle.net/1882/49.

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Thesis (MBA) -- University of New Brunswick, Faculty of Administration, 2003.
Typescript. Bibliography: leaves 83-84. Also available online through University of New Brunswick, UNB Electronic Theses & Dissertations.
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Eadie, Edward Norman. "Small resource stock share price behaviour and prediction." Title page, contents and abstract only, 2002. http://web4.library.adelaide.edu.au/theses/09CM/09cme11.pdf.

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King, Daniel Jonathan. "Modelling stock return volatility dynamics in selected African markets." Thesis, Rhodes University, 2013. http://hdl.handle.net/10962/d1006452.

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Stock return volatility has been shown to occasionally exhibit discrete structural shifts. These shifts are particularly evident in the transition from ‘normal’ to crisis periods, and tend to be more pronounced in developing markets. This study aims to establish whether accounting for structural changes in the conditional variance process, through the use of Markov-switching models, improves estimates and forecasts of stock return volatility over those of the more conventional single-state (G)ARCH models, within and across selected African markets for the period 2002-2012. In the univariate portion of the study, the performances of various Markov-switching models are tested against a single-state benchmark model through the use of in-sample goodness-of-fit and predictive ability measures. In the multivariate context, the single-state and Markov-switching models are comparatively assessed according to their usefulness in constructing optimal stock portfolios. It is found that, even after accounting for structural breaks in the conditional variance process, conventional GARCH effects remain important to capturing the heteroscedasticity evident in the data. However, those univariate models which include a GARCH term are shown to perform comparatively poorly when used for forecasting purposes. Additionally, in the multivariate study, the use of Markov-switching variance-covariance estimates improves risk-adjusted portfolio returns when compared to portfolios that are constructed using the more conventional single-state models. While there is evidence that the use of some Markov-switching models can result in better forecasts and higher risk-adjusted returns than those models which include GARCH effects, the inability of the simpler Markov-switching models to fully capture the heteroscedasticity in the data remains problematic.
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Mnjama, Gladys Susan. "Exchange rate pass-through to domestic prices in Kenya." Thesis, Rhodes University, 2011. http://hdl.handle.net/10962/d1002709.

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In 1993, Kenya liberalised its trade policy and allowed the Kenyan Shillings to freely float. This openness has left Kenya's domestic prices vulnerable to the effects of exchange rate fluctuations. One of the objectives of the Central Bank of Kenya is to maintain inflation levels at sustainable levels. Thus it has become necessary to determine the influence that exchange rate changes have on domestic prices given that one of the major determinants of inflation is exchange rate movements. For this reason, this thesis examines the magnitude and speed of exchange rate pass-through (ERPT) to domestic prices in Kenya. In addition, it takes into account the direction and size of changes in the exchange rates to determine whether the exchange rate fluctuations are symmetric or asymmetric. The thesis uses quarterly data ranging from 1993:Ql - 2008:Q4 as it takes into account the period when the process of liberalization occurred. The empirical estimation was done in two stages. The first stage was estimated using the Johansen (1991) and (1995) co integration techniques and a vector error correction model (VECM). The second stage entailed estimating the impulse response and variance decomposition functions as well as conducting block exogeneity Wald tests. In determining the asymmetric aspect of the analysis, the study followed Pollard and Coughlin (2004) and Webber (2000) frameworks in analysing asymmetry with respect to appreciation and depreciation and large and small changes in the exchange rate to import prices. The results obtained showed that ERPT to Kenya is incomplete but relatively low at about 36 percent in the long run. In terms of asymmetry, the results showed that ERPT is found to be higher in periods of appreciation than depreciation. This is in support of market share and binding quantity constraints theory. In relation to size changes, the results show that size changes have no significant impact on ERPT in Kenya.
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Humpe, Andreas. "Macroeconomic variables and the stock market : an empirical comparison of the US and Japan." Thesis, St Andrews, 2008. http://hdl.handle.net/10023/464.

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Clayton, Maya. "Econometric forecasting of financial assets using non-linear smooth transition autoregressive models." Thesis, University of St Andrews, 2011. http://hdl.handle.net/10023/1898.

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Following the debate by empirical finance research on the presence of non-linear predictability in stock market returns, this study examines forecasting abilities of nonlinear STAR-type models. A non-linear model methodology is applied to daily returns of FTSE, S&P, DAX and Nikkei indices. The research is then extended to long-horizon forecastability of the four series including monthly returns and a buy-and-sell strategy for a three, six and twelve month holding period using non-linear error-correction framework. The recursive out-of-sample forecast is performed using the present value model equilibrium methodology, whereby stock returns are forecasted using macroeconomic variables, in particular the dividend yield and price-earnings ratio. The forecasting exercise revealed the presence of non-linear predictability for all data periods considered, and confirmed an improvement of predictability for long-horizon data. Finally, the present value model approach is applied to the housing market, whereby the house price returns are forecasted using a price-earnings ratio as a measure of fundamental levels of prices. Findings revealed that the UK housing market appears to be characterised with asymmetric non-linear dynamics, and a clear preference for the asymmetric ESTAR model in terms of forecasting accuracy.
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Books on the topic "Stocks - Prices - Econometric models"

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Lo, Andrew W. Econometric models of limit-order executions. Cambridge, MA: National Bureau of Economic Research, 1997.

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Kelly, Morgan. Do noise traders influence stock prices? Dublin: University College Dublin, Department of Economics, 1996.

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Campbell, John Y. Inflation illusion and stock prices. Cambridge, MA: National Bureau of Economic Research, 2004.

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Asquith, Paul. Short interest and stock returns. Cambridge, MA: National Bureau of Economic Research, 2004.

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Grinblatt, Mark. What do we really know about the cross-sectional relation between past and expected returns? Cambridge, MA: National Bureau of Economic Research, 2002.

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Majnoni, Giovanni. Share prices and trading volume: Indications of stock exchange efficiency. Roma: Banca d'Italia, 1996.

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Guidolin, Massimo. Size and value anomalies under regime shifts. [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2005.

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Weil, Philippe. On the possibility of price decreasing bubbles. Cambridge, MA: National Bureau of Economic Research, 1989.

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Grinblatt, Mark. The disposition effect and momentum. Cambridge, MA: National Bureau of Economic Research, 2002.

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Ghysels, Eric. There is a risk-return tradeoff after all. Cambridge, MA: National Bureau of Economic Research, 2004.

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Book chapters on the topic "Stocks - Prices - Econometric models"

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Lowry, Mark Newton. "Futures prices and hidden stocks of refined oil products." In International Commodity Market Models, 263–73. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3084-4_14.

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Rakpho, Pichayakone, Woraphon Yamaka, and Songsak Sriboonchitta. "Markov Switching Quantile Model Unknown tau Energy Stocks Price Index Thailand." In Structural Changes and their Econometric Modeling, 488–96. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04263-9_38.

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Thongon, Arjaree, Songsak Sriboonchitta, and Yongyut Laosiritaworn. "Effect of Markets Temperature on Stock-Price: Monte Carlo Simulation on Spin Model." In Modeling Dependence in Econometrics, 445–53. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03395-2_28.

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Tran, Hien D., Son P. Nguyen, Hoa T. Le, and Uyen H. Pham. "An Alternative to p-Values in Hypothesis Testing with Applications in Model Selection of Stock Price Data." In Robustness in Econometrics, 305–19. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50742-2_18.

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Huy, Dinh Tran Ngoc, Vo Kim Nhan, Nguyen Thi Ngoc Bich, Nguyen Thi Phuong Hong, Nham Thanh Chung, and Pham Quang Huy. "Impacts of Internal and External Macroeconomic Factors on Firm Stock Price in an Expansion Econometric model—A Case in Vietnam Real Estate Industry." In Data Science for Financial Econometrics, 189–205. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48853-6_14.

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Mokhtarzadeh, Fatemeh. "A global vector autoregression model for softwood lumber trade." In International trade in forest products: lumber trade disputes, models and examples, 174–93. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789248234.0008.

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Abstract A novel econometric approach is developed in this chapter, namely, the Global Vector Autoregressive (GVAR) model. It provides a comprehensive framework for analyzing the country-level impacts of various domestic, foreign, and/or global shocks on softwood lumber trade. The GVAR approach is applied to Canada-U.S. trade in softwood lumber and used to analyze the effect of external shocks on Canadian lumber prices. Findings indicate that Canada's export prices are positively correlated to U.S. housing starts and real GDP. Further, using impulse response functions, it is used to examine the effects on regional lumber export prices in Canada of: (1) a change in U.S. housing starts; (2) a reduction in U.S. GDP by one standard deviation; (3) a COVID-19 induced decline in U.S. GDP (of three standard deviations); (4) an increase in global oil prices; and, in the Appendix, (5) an increase in the long-term interest rate. Price impacts vary a great deal by Canadian region depending on the type of shock, with the propagation mechanism in Alberta significantly different from that in other regions. For example, with an oil price shock and because Alberta is a major exporter of oil, the lumber export price remains high even as the shock dissipates over time.
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Mokhtarzadeh, Fatemeh. "A global vector autoregression model for softwood lumber trade." In International trade in forest products: lumber trade disputes, models and examples, 174–93. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789248234.0174.

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Abstract A novel econometric approach is developed in this chapter, namely, the Global Vector Autoregressive (GVAR) model. It provides a comprehensive framework for analyzing the country-level impacts of various domestic, foreign, and/or global shocks on softwood lumber trade. The GVAR approach is applied to Canada-U.S. trade in softwood lumber and used to analyze the effect of external shocks on Canadian lumber prices. Findings indicate that Canada's export prices are positively correlated to U.S. housing starts and real GDP. Further, using impulse response functions, it is used to examine the effects on regional lumber export prices in Canada of: (1) a change in U.S. housing starts; (2) a reduction in U.S. GDP by one standard deviation; (3) a COVID-19 induced decline in U.S. GDP (of three standard deviations); (4) an increase in global oil prices; and, in the Appendix, (5) an increase in the long-term interest rate. Price impacts vary a great deal by Canadian region depending on the type of shock, with the propagation mechanism in Alberta significantly different from that in other regions. For example, with an oil price shock and because Alberta is a major exporter of oil, the lumber export price remains high even as the shock dissipates over time.
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Kileber, Solange, Javier Toro, Matias Rebello Cardomingo, Luciano José da Silva, Marcio Issao Nakane, and Virginia Parente. "Determinants of Carbon Emission Prices." In Handbook of Research on Energy and Environmental Finance 4.0, 354–76. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8210-7.ch014.

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Carbon emission allowances are considered an important policy instrument to prevent an undesirable increase in the Earth's temperature caused by the excess of greenhouse gases in the atmosphere. Most of the existing literature modeled the behavior of allowance prices before the implementation of regulatory measures such as the market stability reserve mechanism. In this chapter, the main determinants of the carbon emissions allowance prices in the European Union are examined, applying econometric models—ARCH and GARCH—that take into consideration the allowances supply in the future market, the energy prices, the stock indices, and the regulatory measures. The results depicted that the most relevant variables affecting the allowance prices were the regulatory measures that mainly restrict the number of allowances available. Understanding the dynamics of the variables that impact these prices can help policymakers to address the oversupply of allowances by sending correct price signals to the market participants.
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Jawad, Muhammad, and Munazza Naz. "An Econometric Investigation of Market Volatility and Efficiency: A Study of Small Cap’s Stock Indices." In Linear and Non-Linear Financial Econometrics -Theory and Practice [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94119.

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By utilization the context of econometric models, this chapter investigates three significant research parameters and tries to find out the positive outcome for further studies. The first question, is the volatility of Small Cap foreseeable?. The second question, does the volatility of Small Cap exhibition the same pragmatic regularities stated in the literature about the behavior of further stock prices?, The third and Final question, can Small Cap clear the test of market efficiency?. The results of these research questions will provide the answers of following objectives: First, economic representatives investing in Small Cap Stock markets. Second, the business professors/professionals/educationist is more concerned in Small Cap for their teaching and research. Third, the policy makers who are observing the stock market volatilities because of its significances and impulsive behavior to invest for more incentives among other consequences.
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Oluseun Olayungbo, David. "Volatility Effects of the Global Oil Price on Stock Price in Nigeria: Evidence from Linear and Non-Linear GARCH." In Linear and Non-Linear Financial Econometrics -Theory and Practice. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.93497.

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This present study examines the volatility effects of the oil price on the stock price returns in Nigeria from the period of 2000M(12) to 2020M(4) on a monthly data using both standard GARCH and non-linear GARCH models. The motivation for the present study is the recent fall in the global oil price of Brent Crude to US$15.25 per barrel due to the outbreak of the Corona Virus (COVID-19). Consequentially, the Nigerian stock market (NSE) responded with a fall of 4172 point or by a fall of 15.53%. After establishing the presence of heteroscedasticity through the ARCH test and volatility clustering through the returns, the outcome of the study contributes to knowledge by providing financial information and signals to investors about the best GARCH model response to proactively and successfully use to model global oil price shocks so as to reduce financial risk in Nigeria’s stock market.
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Conference papers on the topic "Stocks - Prices - Econometric models"

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Кудрявцев, Олег, Oleg Kudryavtsev, Кирилл Мозолев, Kirill Mozolev, Артур Чивчян, Arthur Chivchyan, Хейрулла Мамедзаде, and Kheyrulla Mamedzade. "THE ECONOMETRIC ANALYSIS OF THE DYNAMICS OF ETHEREUM IN THE SHORT-TERM PERIOD." In Mathematics in Economics. AUS PUBLISHERS, 2018. http://dx.doi.org/10.26526/conferencearticle_5c24b1d2cf9e41.19868561.

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The article presents an econometric analysis of the effect of stock indicators, such as Comex Gold futures, Dow Jones Industrial Average index and NASDAQ Composite, on the Ethereum cryptocurrency dynamics in the 100-day period. As part of the study, an econometric model of the dynamics of e-currency was built. The survey results show that when the Comex gold futures price changes by 1% on average, the Ethereum price changes by 5.01% in the same direction, when the Dow Jones Industrial Average index changes by 1%, the Ethereum price is 10.897%, and when the NASDAQ Composite index changes, the Ethereum price will change in the opposite direction to 3.59%
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Chatterjee, Ananda, Hrisav Bhowmick, and Jaydip Sen. "Stock Price Prediction Using Time Series, Econometric, Machine Learning, and Deep Learning Models." In 2021 IEEE Mysore Sub Section International Conference (MysuruCon). IEEE, 2021. http://dx.doi.org/10.1109/mysurucon52639.2021.9641610.

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Reiter, Doris F., and Michael J. Economides. "Prediction of Short-term Natural Gas Prices Using Econometric and Neural Network Models." In SPE Hydrocarbon Economics and Evaluation Symposium. Society of Petroleum Engineers, 1999. http://dx.doi.org/10.2118/52960-ms.

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Gui, Jiyuan, and Xiaoyun Wu. "Forecasting the stock price of vaccine manufacturers in China using machine learning and econometrics model." In International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), edited by Yuanchang Zhong. SPIE, 2022. http://dx.doi.org/10.1117/12.2647506.

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Mohammed, Salahadin. "The Validity of Using Technical Indicators When forecasting Stock Prices Using Deep Learning Models: Empirical Evidence Using Saudi Stocks." In 2022 14th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2022. http://dx.doi.org/10.1109/cicn56167.2022.10008298.

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Xie, Xiaoxia. "Research on the explanation power of accounting information of annual report of listed companies of stock price—Based on econometric model concept of empirical accounting theory." In 2011 International Conference on Business Management and Electronic Information (BMEI). IEEE, 2011. http://dx.doi.org/10.1109/icbmei.2011.5920522.

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Hacıoğlu Deniz, Müjgan, and Kutluk Kağan Sümer. "The Effects of Oil Price Volatility on Foreign Trade Revenue and National Income: A Comparative Analysis on Selected Eurasian Economies." In International Conference on Eurasian Economies. Eurasian Economists Association, 2015. http://dx.doi.org/10.36880/c06.01362.

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The aim of this study is to identify the effects of the volatility of oil prices and exchange rates on foreign trade revenue of a few selected Eurasian Economies. These countries are oil and natural gas exporting countries and getting most of their trade revenue from exporting these commodities. The effects of sharply falling oil prices since June 2014 and depreciating exchange rates on these countries’ external trade were analyzed by using alternative econometric models. The sample of this analysis covered the period from June 2014 when oil prices has started falling sharply – till June 2015 in which still world oil price is lower than the price of 140-150 dollars for per gallon in the previous years. Decreasing prices basically destabilize the revenues of these states since approximately two third (2/3) of their export revenue and substantial part of their budget revenue that comes from oil and natural gas. In Russian economy falling prices of oil depreciates both public revenue and economic activity. This means predominantly depending on one commodity for export and foreign trade makes these countries’ economies in dependence of that commodity’s price and makes these economies so vulnerable to global crisis and price volatilities. In order to avoid from this situation, these countries should divert their production and increase in variety for exporting goods.
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Bai, Muqing, and Yu Sun. "An Intelligent and Social-Oriented Sentiment Analytical Model for Stock Market Prediction using Machine Learning and Big Data Analysis." In 8th International Conference on Artificial Intelligence and Applications (AI 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121819.

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In an era of machine learning, many fields outside of computer science have implemented machine learning as a tool [5]. In the financial world, a variety of machine learning models are used to predict the future prices of a stock in order to optimize profit. This paper preposes a stock prediction algorithm that focuses on the correlation between the price of a stock and its public sentiments shown on social media [6].We trained different machine learning algorithms to find the best model at predicting stock prices given its sentiment. And for the public to access this model, a web-based server and a mobile application is created. We used Thunkable, a powerful no code platform, to produce our mobile application [7]. It allows anyone to check the predictions of stocks, helping people with their investment decisions.
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Takara, Lucas de Azevedo, Viviana Cocco Mariani, and Leandro dos Santos Coelho. "Autoencoder Neural Network Approaches for Anomaly Detection in IBOVESPA Stock Market Index." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-37.

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Anomalies are patterns in data that do not conform to a well-defined notion of normal behavior. Anomaly detection has been applied to many problems such as bank fraud, fault detection, noise reduction, among many others. Some approaches to detect anomalies include classical statistical econometric methods such as AutoRegressive Moving Average (ARMA) and AutoRegressive Integrated Moving Average (ARIMA) approaches. More recently, with the progress of artificial intelligence and more specifically, machine learning, new algorithms such as one-class support vector machines, isolation forest, gradient boosting, and deep neural networks were applied to such tasks. This paper focuses on propose an anomaly detection framework for the Índice da Bolsa de Valores de São Paulo (IBOVESPA). It is a major stock market index that tracks the performance of around 50 most liquid stocks traded on the São Paulo Stock Exchange in Brazil. Exploring unsupervised autoencoder neural network algorithms, we compare the long short-term autoencoder, bidirectional long short-term autoencoder, and convolutional autoencoder models, aiming to explore the performance of these architectures for anomaly detection. Due to the ability of autoencoders to learn a compressed representation of their respective input, we train these models with standard data by minimizing the mean absolute error (MAE) loss function and evaluate them with anomalous inputs. We set a reconstruction error threshold, and in case that the reconstruction error of the test data sample is beyond it, anomalies are detected. Our results show that these models perform quite well and can be applied to real stock market data.
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Mohite, S. D. D. "Downstream Refining and Petrochemicals Challenges - Future Configuration." In SPE Energy Resources Conference. SPE, 2014. http://dx.doi.org/10.2118/spe-169979-ms.

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Abstract Precise predictions and solutions for tomorrow's needs are the key to building a growing, sustainable business. This requires a mixture of vision, strategic risk taking business model and investment in new technology. Refining trends forecast is useful for predicting possible landscape, where in challenge would be to meet twice the energy levels from today with half the CO2 emissions by 2030. Increasing and diversification of world's energy supplies to support the population of over 8 billion then would be a mammoth task, given that the triangle of energy, food and water will be crucial. Three fundamental factors that will influence and shape this setting are: Global products demand will rise by 1.1% - 1.3% annually by 2030 to over 115 million barrels per day, with marginal influence of crude oil prices;Reinforced legislation targeting reduction of GHG emissions, requiring improved clean transportation and bunker fuels - accounting 2/3rd of total demand and growth;Refining and Petrochemicals form the backbone of global economics and meeting demand with inevitable steady profitability is a major task possibly also using alternative unconventional sources. In competitive context – innovation, operational excellence and implementation of robust strategies are critical for sustenance and growth. Project returns can however be enhanced by incorporating integration principles and model at the design stage itself. Whilst development pace of new technologies would accelerate which can radically alter business structure in certain geographies, question remains on what makes a successful project come to fruition. The presentation discusses futuristic economic unlocking of value by application of technology models and best practices by utilizing various feed-stocks, including natural gas as a main competitor and maximum upgrading bottom-of-the-barrel. Besides, novel process designs and operational control would be squeezed as it is invariably the last fraction which is most difficult to remove! This paper contains forward-looking scenario about global Refining strategy, Petrochemicals feed-stock cost advantages, technology diversification routes to maximize returns from cheaper sources, financial performance and economics, growth opportunities in various countries, sectors or markets, besides a focus on Europe and GCC regions and current projects in Kuwait. However, these involve uncertainty as they depend mainly on future circumstances like commercializing R&D, not all of which can be controlled or accurately predicted, hence are directional for investment decisions.
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