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Journal articles on the topic 'Econometrics. Stock price forecasting'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

He, Ling T. "Forecasting of housing stock returns and housing prices." Journal of Financial Economic Policy 7, no. 2 (2015): 90–103. http://dx.doi.org/10.1108/jfep-01-2014-0004.

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Purpose – The purpose of this paper is to create an endurance index of housing investor sentiment and use it to forecast housing stock returns. This study performs not only in-sample and out-of-sample forecasting, like many previous studies did, but also a true forecasting by using all lag terms of independent variables. In addition, an evaluation procedure is applied to quantify the quality of forecasts. Design/methodology/approach – Using a binomial probability distribution model, this paper creates an endurance index of housing investor sentiment. The index reflects the probability of the h

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2

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 pr

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3

Lim, G. C., Vance L. Martin, and Leslie E. Teo. "ENDOGENOUS JUMPING AND ASSET PRICE DYNAMICS." Macroeconomic Dynamics 2, no. 2 (1998): 213–37. http://dx.doi.org/10.1017/s1365100598007044.

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A model of asset price dynamics is derived in which large jumps in stock prices are determined endogenously. An important property of the model is that it can lead to asset price distributions that are multimodal. The model can explain how relatively small changes in dividends can lead to relatively large changes in asset prices and it can be used to identify the time period in which bubbles begin and end. The framework is applied to modeling the U.S. stock market crash in October 1987. Some forecasting experiments also are conducted with the result that the model is able to predict the size o

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4

Endress, Tobias. "“Deliberated Intuition” in Stock Price Forecasting." Economics & Sociology 11, no. 3 (2018): 11–27. http://dx.doi.org/10.14254/2071-789x.2018/11-3/1.

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5

Harel, Arie, and Giora Harpaz. "Forecasting stock prices." International Review of Economics & Finance 73 (May 2021): 249–56. http://dx.doi.org/10.1016/j.iref.2020.12.033.

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Shi, Chao, and Xiaosheng Zhuang. "A Study Concerning Soft Computing Approaches for Stock Price Forecasting." Axioms 8, no. 4 (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

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Babirath, Julia, Karel Malec, Rainer Schmitl, Kamil Maitah, and Mansoor Maitah. "Forecasting based on spectral time series analysis: prediction of the Aurubis stock price." Investment Management and Financial Innovations 17, no. 4 (2020): 215–27. http://dx.doi.org/10.21511/imfi.17(4).2020.20.

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The attempt to predict stock price movements has occupied investors ever since. Reliable forecasts are a basis for investment management, and improved forecasting results lead to enhanced portfolio performance and sound risk management. While forecasting using the Wiener process has received great attention in the literature, spectral time series analysis has been disregarded in this respect. The paper’s main objective is to evaluate whether spectral time series analysis can produce reliable forecasts of the Aurubis stock price. Aurubis poses a suitable candidate for an investor’s portfolio du

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Lin, Yu, Yan Yan, Jiali Xu, Ying Liao, and Feng Ma. "Forecasting stock index price using the CEEMDAN-LSTM model." North American Journal of Economics and Finance 57 (July 2021): 101421. http://dx.doi.org/10.1016/j.najef.2021.101421.

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9

Sadik, Zryan A., Paresh M. Date, and Gautam Mitra. "Forecasting crude oil futures prices using global macroeconomic news sentiment." IMA Journal of Management Mathematics 31, no. 2 (2019): 191–215. http://dx.doi.org/10.1093/imaman/dpz011.

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Abstract We propose a method of incorporating macroeconomic news into a predictive model for forecasting prices of crude oil futures contracts. Since these futures contracts are more liquid than the underlying commodity itself, accurate forecasting of their prices is of great value to multiple categories of market participants. We utilize the Kalman filtering framework for forecasting arbitrage-free (futures) prices and assume that the volatility of oil (futures) price is influenced by macroeconomic news. The impact of quantified news sentiment on the price volatility is modelled through a par

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10

Dechow, Patricia M., and Haifeng You. "Understanding the Determinants of Analyst Target Price Implied Returns." Accounting Review 95, no. 6 (2020): 125–49. http://dx.doi.org/10.2308/tar-2015-0265.

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ABSTRACT We investigate the determinants of analysts' target price implied returns and the implication of our findings for investment decision-making. We identify four broad sets of factors that help explain the cross-sectional variation in target price implied returns: future realized stock returns, errors in forecasting fundamentals, errors in forecasting the expected return to risk, and biases relating to analysts' incentives. Our results suggest that all four sets help explain target price implied returns, with errors in forecasting the expected return to empirical risk proxies having the

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11

Piotroski, Joseph D., and Darren T. Roulstone. "The Influence of Analysts, Institutional Investors, and Insiders on the Incorporation of Market, Industry, and Firm-Specific Information into Stock Prices." Accounting Review 79, no. 4 (2004): 1119–51. http://dx.doi.org/10.2308/accr.2004.79.4.1119.

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We investigate the extent to which the trading and trade-generating activities of three informed market participants—financial analysts, institutional investors, and insiders—influence the relative amount of firm-specific, industry-level, and market-level information impounded into stock prices, as measured by stock return synchronicity. We find that stock return synchronicity is positively associated with analyst forecasting activities, consistent with analysts increasing the amount of industry-level information in prices through intra-industry information transfers. In contrast, stock return

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12

Lee, Chien-Chiang, Ching-Chuan Tsong, and Cheng-Feng Lee. "TESTING FOR THE EFFICIENT MARKET HYPOTHESIS IN STOCK PRICES: INTERNATIONAL EVIDENCE FROM NONLINEAR HETEROGENEOUS PANELS." Macroeconomic Dynamics 18, no. 4 (2013): 943–58. http://dx.doi.org/10.1017/s1365100512000697.

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Using international data, this paper explores whether the efficient market hypothesis for real stock prices is supported for different panels. The stationarity of a real stock price has important implications for modeling and forecasting financial activities. On a global scale, we implement the recently developed nonlinear heterogeneous panel unit root test, which allows us to account for possible nonlinearity and cross-section dependence and to identify how many and which countries of the panel contain a unit root. The primary conclusion is that the stationarity of real stock prices varies be

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13

Xaba, Diteboho, Ntebogang Dinah Moroke, Johnson Arkaah, and Charlemagne Pooe. "A Comparative Study Of Stock Price Forecasting Using Nonlinear Models." Risk Governance and Control: Financial Markets and Institutions 7, no. 2 (2017): 7–17. http://dx.doi.org/10.22495/rgcv7i2art1.

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This study compared the in-sample forecasting accuracy of three forecasting nonlinear models namely: the Smooth Transition Regression (STR) model, the Threshold Autoregressive (TAR) model and the Markov-switching Autoregressive (MS-AR) model. Nonlinearity tests were used to confirm the validity of the assumptions of the study. The study used model selection criteria, SBC to select the optimal lag order and for the selection of appropriate models. The Mean Square Error (MSE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) served as the error measures in evaluating the forecasting a

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14

Najand, Mohammad. "Forecasting Stock Index Futures Price Volatility: Linear vs. Nonlinear Models." Financial Review 37, no. 1 (2002): 93–104. http://dx.doi.org/10.1111/1540-6288.00006.

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15

Dutta, Goutam, Pankaj Jha, Arnab Kumar Laha, and Neeraj Mohan. "Artificial Neural Network Models for Forecasting Stock Price Index in the Bombay Stock Exchange." Journal of Emerging Market Finance 5, no. 3 (2006): 283–95. http://dx.doi.org/10.1177/097265270600500305.

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16

Ocran, Matthew. "South Africa and United States stock prices and the Rand/Dollar exchange rate." South African Journal of Economic and Management Sciences 13, no. 3 (2010): 362–75. http://dx.doi.org/10.4102/sajems.v13i3.106.

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This paper seeks to examine the dynamic causal relations between the two major financial assets, stock prices of the US and South Africa and the rand/US$ exchange rate. The study uses a mixed bag of time series approaches such as cointegration, Granger causality, impulse response functions and forecasting error variance decompositions. The paper identifies a bi-directional causality from the Standard & Poor’s 500 stock price index to the rand/US$ exchange rate in the Granger sense. It was also found that the Standard & Poor’s stock price index accounts for a significant portion of the

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17

Plastun, Alex, Inna Makarenko, Lyudmila Khomutenko, Yanina Belinska, and Maryna Domashenko. "Exploring frequency of price overreactions in the Ukrainian stock market." Investment Management and Financial Innovations 15, no. 3 (2018): 157–68. http://dx.doi.org/10.21511/imfi.15(3).2018.13.

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This paper explores the frequency of price overreactions in the Ukrainian stock market by focusing on the PFTS Index over the period 2006–2017 and UX index over the period 2008–2017, as well as some “blue chips” (BAVL, UNAF, MSICH, CEEN) for the period of 2013–2015. Using static approach to detect overreactions, a number of hypotheses are tested: the frequency of price overreactions is informative about crisis events in the economy (H1), can be used for price prediction purposes (H2), and exhibits seasonality (H3). To do this, various statistical tests (both parametric and non-parametric), inc

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18

Hjalmarsson, Erik. "Predicting Global Stock Returns." Journal of Financial and Quantitative Analysis 45, no. 1 (2009): 49–80. http://dx.doi.org/10.1017/s0022109009990469.

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AbstractI test for stock return predictability in the largest and most comprehensive data set analyzed so far, using four common forecasting variables: the dividend-price (DP) and earnings-price (EP) ratios, the short interest rate, and the term spread. The data contain over 20,000 monthly observations from 40 international markets, including 24 developed and 16 emerging economies. In addition, I develop new methods for predictive regressions with panel data. Inference based on the standard fixed effects estimator is shown to suffer from severe size distortions in the typical stock return regr

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19

Kurov, Alexander, Alessio Sancetta, Georg Strasser, and Marketa Halova Wolfe. "Price Drift Before U.S. Macroeconomic News: Private Information about Public Announcements?" Journal of Financial and Quantitative Analysis 54, no. 1 (2018): 449–79. http://dx.doi.org/10.1017/s0022109018000625.

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We examine stock index futures and Treasury futures around the release time of 30 U.S. macroeconomic announcements. Nine of the 20 announcements that move markets show evidence of substantial informed trading before the official release time. Prices begin to move in the “correct” direction approximately 30 minutes before the release time. The preannouncement price drift accounts on average for approximately 40% of the total price adjustment. This implies that some traders have private information about macroeconomic fundamentals. Preannouncement drift might originate from a combination of info

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20

Rudzkis, Rimantas, Roma Valkavičienė, and Virmantas Kvedaras. "Prediction of Baltic Sectorial Share Price Indices." Lietuvos statistikos darbai 53, no. 1 (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

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21

Meng, Kyle C. "Using a Free Permit Rule to Forecast the Marginal Abatement Cost of Proposed Climate Policy." American Economic Review 107, no. 3 (2017): 748–84. http://dx.doi.org/10.1257/aer.20150781.

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This paper develops a method for forecasting the marginal abatement cost (MAC) of climate policy using three features of the failed Waxman-Markey bill. First, the MAC is revealed by the price of traded permits. Second, the permit price is estimated using a regression discontinuity design (RDD) comparing stock returns of firms on either side of the policy's free permit cutoff rule. Third, because Waxman-Markey was never implemented, I extend the RDD approach to incorporate prediction market prices which normalize estimates by policy realization probabilities. A final bounding analysis recovers

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22

Chu, Xiaojun, and Jianying Qiu. "Forecasting Volatility with Price Limit Hits—Evidence from Chinese Stock Market." Emerging Markets Finance and Trade 55, no. 5 (2018): 1034–50. http://dx.doi.org/10.1080/1540496x.2018.1532888.

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23

Byun, Sung Je. "The usefulness of cross-sectional dispersion for forecasting aggregate stock price volatility." Journal of Empirical Finance 36 (March 2016): 162–80. http://dx.doi.org/10.1016/j.jempfin.2016.01.013.

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24

Parlagutan Pulungan, Dolly, Sugeng Wahyudi, Suharnomo Suharnomo, and Harjum Muharam. "Technical analysis testing in forecasting Socially Responsible Investment Index in Indonesia Stock Exchange." Investment Management and Financial Innovations 15, no. 4 (2018): 135–43. http://dx.doi.org/10.21511/imfi.15(4).2018.11.

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This study aims to examine whether the Autoregressive Integrated Moving Average (ARIMA) model is appropriate to be applied in the Indonesia Stock Exchange, especially for the socially resposible investment stocks. For the ARIMA model combines the autoregressive and moving average method, so it is viewed as a useful tool to predict the stock prices. Those methods are frequently used methods to forecast the stock prices. The data used in this study were daily SRI-KEHATI Index during the period of June 8, 2009 to July 17, 2017. The results showed that the daily SRI-KEHATI Index data were not stat

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Agarwalla, Megha, Tarak Nath Sahu, and Shib Sankar Jana. "Dynamics of oil price shocks and emerging stock market volatility: a generalized VAR approach." Vilakshan - XIMB Journal of Management 18, no. 2 (2021): 106–21. http://dx.doi.org/10.1108/xjm-07-2020-0018.

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Purpose This study aims to establish the dynamic relationship between international crude oil prices and Indian stock prices represented by the Bombay Stock Exchange (BSE) energy index. Design/methodology/approach Using Johansen’s cointegration test, vector error correction (VEC) model, impulse response function and variance decomposition test the study tries to ascertain the short-term and long-term dynamic association between the oil price shock and the movement of stock price and Granger causality test is applied to find out the nature of causality. Findings Considering vector autoregressio

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Bali, Turan G., K. Ozgur Demirtas, and Hassan Tehranian. "Aggregate Earnings, Firm-Level Earnings, and Expected Stock Returns." Journal of Financial and Quantitative Analysis 43, no. 3 (2008): 657–84. http://dx.doi.org/10.1017/s0022109000004245.

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AbstractThis paper provides an analysis of the predictability of stock returns using market-, industry-, and firm-level earnings. Contrary to Lamont (1998), we find that neither dividend payout ratio nor the level of aggregate earnings can forecast the excess market return. We show that these variables do not have robust predictive power across different stock portfolios and sample periods. In contrast to the aggregate-level findings, earnings yield has significant explanatory power for the time-series and cross-sectional variation in firmlevel stock returns and the 48 industry portfolio retur

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27

Sharma, Prateek, and Vipul _. "Forecasting stock index volatility with GARCH models: international evidence." Studies in Economics and Finance 32, no. 4 (2015): 445–63. http://dx.doi.org/10.1108/sef-11-2014-0212.

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Purpose – The purpose of this paper is to compare the daily conditional variance forecasts of seven GARCH-family models. This paper investigates whether the advanced GARCH models outperform the standard GARCH model in forecasting the variance of stock indices. Design/methodology/approach – Using the daily price observations of 21 stock indices of the world, this paper forecasts one-step-ahead conditional variance with each forecasting model, for the period 1 January 2000 to 30 November 2013. The forecasts are then compared using multiple statistical tests. Findings – It is found that the stand

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Uematsu, Yoshimasa, and Shinya Tanaka. "High‐dimensional macroeconomic forecasting and variable selection via penalized regression." Econometrics Journal 22, no. 1 (2019): 34–56. http://dx.doi.org/10.1111/ectj.12117.

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Summary This study examines high-dimensional forecasting and variable selection via folded-concave penalized regressions. The penalized regression approach leads to sparse estimates of the regression coefficients and allows the dimensionality of the model to be much larger than the sample size. First, we discuss the theoretical aspects of a penalized regression in a time series setting. Specifically, we show the oracle inequality with ultra-high-dimensional time-dependent regressors. Then we show the validity of the penalized regression using two empirical applications. First, we forecast quar

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29

Randolph, W. L., and Mohammad Najand. "A test of two models in forecasting stock index futures price volatility." Journal of Futures Markets 11, no. 2 (1991): 179–90. http://dx.doi.org/10.1002/fut.3990110205.

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30

Kumar, Gourav, Uday Pratap Singh, and Sanjeev Jain. "Hybrid evolutionary intelligent system and hybrid time series econometric model for stock price forecasting." International Journal of Intelligent Systems 36, no. 9 (2021): 4902–35. http://dx.doi.org/10.1002/int.22495.

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31

Favero, Carlo A., Arie E. Gozluklu, and Andrea Tamoni. "Demographic Trends, the Dividend-Price Ratio, and the Predictability of Long-Run Stock Market Returns." Journal of Financial and Quantitative Analysis 46, no. 5 (2011): 1493–520. http://dx.doi.org/10.1017/s0022109011000329.

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AbstractThis paper documents the existence of a slowly evolving trend in the log dividend-price ratio, DPt, determined by a demographic variable, MYt: the middle-aged to young ratio. Deviations of DPtfrom this long-run component explain transitory but persistent fluctuations in stock market returns. The relation between MYtand DPtis a prediction of an overlapping generation model. The joint significance of MY and DPtin long-horizon forecasting regressions for market returns explains the mixed evidence on the ability of DPtto predict stock returns and provide a model-based interpretation of sta

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Ampomah, Ernest Kwame, Zhiguang Qin, and Gabriel Nyame. "Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement." Information 11, no. 6 (2020): 332. http://dx.doi.org/10.3390/info11060332.

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Forecasting the direction and trend of stock price is an important task which helps investors to make prudent financial decisions in the stock market. Investment in the stock market has a big risk associated with it. Minimizing prediction error reduces the investment risk. Machine learning (ML) models typically perform better than statistical and econometric models. Also, ensemble ML models have been shown in the literature to be able to produce superior performance than single ML models. In this work, we compare the effectiveness of tree-based ensemble ML models (Random Forest (RF), XGBoost C

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33

Prime, Sunantha. "Forecasting the changes in daily stock prices in Shanghai Stock Exchange using Neural Network and Ordinary Least Squares Regression." Investment Management and Financial Innovations 17, no. 3 (2020): 292–307. http://dx.doi.org/10.21511/imfi.17(3).2020.22.

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The research focuses on finding a superior forecasting technique to predict stock movement and behavior in the Shanghai Stock Exchange. The author’s interest is in stock market activities during high volatility, specifically 13 years from 2002 to 2015. This volatile period, fueled by events such as the dot-com bubble, SARS outbreak, political leadership transitions, and the global financial crisis, is of interest. The study aims to analyze changes in stock prices during an unstable period. The author used advanced computer sciences, Machine Learning through information processing and training,

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34

Hsing, Yu. "Tests of the Functional Form, the Wealth Effect, Currency Substitution, and Capital Mobility for Taiwan's Money Demand Function." Review of Pacific Basin Financial Markets and Policies 10, no. 03 (2007): 329–39. http://dx.doi.org/10.1142/s0219091507001094.

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The Box–Cox transformation indicates that the log-linear form for M2 demand cannot be rejected while the Fair (1987) specification and the linear form can be rejected at the 5% level in favor of general functional form. M2 demand is positively influenced by real GDP, the deposit rate, and the real stock price and negatively affected by the nominal exchange rate, the foreign interest rate, and the expected inflation rate. Hence, the wealth effect of an increased real stock price is greater than the substitution effect. The substitution effect of the deprecation of the New Taiwan dollar dominate

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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 (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 activit

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Kumar Meher, Bharat, Iqbal Thonse Hawaldar, Cristi Spulbar, and Ramona Birau. "Forecasting stock market prices using mixed ARIMA model: a case study of Indian pharmaceutical companies." Investment Management and Financial Innovations 18, no. 1 (2021): 42–54. http://dx.doi.org/10.21511/imfi.18(1).2021.04.

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Many investors in order to predict stock prices use various techniques like fundamental analysis and technical analysis and sometimes rely on the discussions provided by various stock market analysts. ARIMA is a part of time-series analysis under prediction algorithms, and this paper attempts to predict the share prices of selected pharmaceutical companies in India, listed under NIFTY100, using the ARIMA model. A sample size of 782 time-series observations from January 1, 2017 to December 31, 2019 for each selected pharmaceutical firm has been considered to frame the ARIMA model. ADF test is u

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Shafer, Carl E. "Price and Value Effects of Pecan Crop Forecasts, 1971–1987." Journal of Agricultural and Applied Economics 21, no. 1 (1989): 97–103. http://dx.doi.org/10.1017/s0081305200000959.

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AbstractPrice equations incorporating USDA October crop forecasts and June pecan stocks provided reasonable formulations for pecan price explanation and forecasting. USDA crop forecasts exceeded final reported production in 12 of the 18 seasons from 1970 to 1987, probably resulting in slightly lower prices and crop values. Large crop forecast errors in both direction and level in 1986 and 1987 confounded the price determination process. Nevertheless, producer prices may have been lower absent the October crop forecasts, which somewhat reduce buyers' uncertainty regarding supply. Early crop est

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Leblang, David, and Bumba Mukherjee. "Presidential Elections and the Stock Market: Comparing Markov-Switching and Fractionally Integrated GARCH Models of Volatility." Political Analysis 12, no. 3 (2004): 296–322. http://dx.doi.org/10.1093/pan/mph020.

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Existing research on electoral politics and financial markets predicts that when investors expect left parties—Democrats (US), Labor (UK)—to win elections, market volatility increases. In addition, current econometric research on stock market volatility suggests that Markov-switching models provide more accurate volatility forecasts and fit stock price volatility data better than linear or nonlinear GARCH (generalized autoregressive conditional heteroskedasticity) models. Contrary to the existing literature, we argue here that when traders anticipate that the Democratic candidate will win the

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39

DeJong, David N., and Charles H. Whiteman. "Modeling Stock Prices without Knowing How to Induce Stationarity." Econometric Theory 10, no. 3-4 (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 forec

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40

Carlston, Benjamin. "Can stock market liquidity and volatility predict business cycles?" Studies in Economics and Finance 35, no. 1 (2018): 81–96. http://dx.doi.org/10.1108/sef-05-2016-0131.

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Purpose The purpose of this paper is to predict real gross domestic product (GDP) growth and business cycles by using information from both liquidity and volatility measures. Design/methodology/approach The paper estimates liquidity and volatility measures from over 5,000 NYSE rms and extracts a common factor, which the paper calls uncertainty. In-sample and out-of-sample forecasting tests are used to determine the ability of the uncertainty factor to predict growth in real GDP, industrial production, consumer price index, real consumption and changes in real investment. Findings The paper fin

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Debata, Byomakesh, and Jitendra Mahakud. "Interdependence between Monetary Policy and Stock Liquidity: A Panel VAR Approach." Margin: The Journal of Applied Economic Research 12, no. 4 (2018): 387–413. http://dx.doi.org/10.1177/0973801018786270.

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This article examines the relationship between monetary policy and individual stock liquidity in an order-driven emerging stock market like India. This study considers stocks listed in National Stock Exchange of India (NSE) and continuously traded from April 2002 to March 2015. Considering the multiple dimensions of liquidity, this study uses five different liquidity proxies to capture the various facets of liquidity such as trading activity, price impact and transaction cost. An array of macroeconomic and firm-specific control variables are used while analysing the liquidity and monetary poli

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Kamel Al Zobi, Mo’taz, and Othman Hel Al-Dhaimesh. "The impact of cash flow statement components on stock volatility: Evidence from Qatar." Investment Management and Financial Innovations 18, no. 2 (2021): 365–73. http://dx.doi.org/10.21511/imfi.18(2).2021.29.

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The published financial statements are considered one of the most important sources of information that investors rely on in forecasting stock performance or even judging the organization’s ability to cover short-run liabilities. Cash flows play a core role in maintaining a high market value for its shares. Hence, this study came to analyze the explanatory value of the cash flow statement in explaining stock volatility (SV) in the Qatar financial market. Study data were collected using published financial statements from a sample of 44 Qatari-listed companies throughout 2013–2019. A panel cros

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MacKinnon, Douglas, and Martin Pavlovič. "A Bayesian analysis of hop price fluctuations." Agricultural Economics (Zemědělská ekonomika) 66, No. 12 (2020): 519–26. http://dx.doi.org/10.17221/239/2020-agricecon.

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This paper quantifies the correlation between U.S. season average prices for hops with U.S. hop stocks and U.S. hop hectarage. The Hop Equilibrium Ratio, a measure of the supply/demand relationship for U.S. hops, was introduced. Through the Bayesian inference method, the authors used these data to calculate the effect an incremental change to one metric had on the probability of directional changes of future U.S. season average prices (SAP). Between 2010 and 2020, the dominance of proprietary varieties created unprecedented cartel-like powers offering opportunities for supply- and price-manage

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Beyer, Anne. "Capital Market Prices, Management Forecasts, and Earnings Management." Accounting Review 84, no. 6 (2009): 1713–47. http://dx.doi.org/10.2308/accr.2009.84.6.1713.

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ABSTRACT: I analyze a manager's optimal earnings forecasting strategy and optimal earnings management policy in a setting where both the mean and the variance of the distribution generating the firm's cash flows are unknown. The analysis shows that the equilibrium price of the firm is a function of the manager's forecast, the firm's reported earnings, and the squared error in the manager's earnings forecast. The model contains several predictions, including: (1) the manager manipulates earnings to reduce his forecast error at the earnings announcement date; (2) the firm's stock price is more s

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CHEN, SHIU-SHENG. "FORECASTING CRUDE OIL PRICE MOVEMENTS WITH OIL-SENSITIVE STOCKS." Economic Inquiry 52, no. 2 (2014): 830–44. http://dx.doi.org/10.1111/ecin.12053.

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Hunton, James E., Tanya Benford, Vicky Arnold, and Steve G. Sutton. "The Impact of Electronic Commerce Assurance on Financial Analysts' Earnings Forecasts and Stock Price Estimates (Retracted)." AUDITING: A Journal of Practice & Theory 19, s-1 (2000): 5–22. http://dx.doi.org/10.2308/aud.2000.19.s-1.5.

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The objective of this study is to assess the impact of electronic commerce (EC) assurance on earnings forecasts and stock price estimates of financial analysts. The theoretical foundation of the current study is based on the information hypothesis (Fama and Laffer 1971; Wallace 1980), which supports the notion that EC assurance is a means of reducing information asymmetry and uncertainty for financial market participants. In the first phase of this study, a survey of 37 financial analysts indicates the importance of vendor- and outcome-based risk factors when forecasting the financial performa

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Azhar, Rialdi, Fajrin Satria Dwi Kesumah, Ambya Ambya, Febryan Kusuma Wisnu, and Edwin Russel. "APPLICATION OF SHORT-TERM FORECASTING MODELS FOR ENERGY ENTITY STOCK PRICE (STUDY ON INDIKA ENERGI TBK, JII)." International Journal of Energy Economics and Policy 10, no. 1 (2020): 294–301. http://dx.doi.org/10.32479/ijeep.8715.

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JUJIE, WANG, CHEN YU, QIU SHIYAO, and CUI QUAN. "Cuckoo Search Optimized Integrated Framework Based on Feature Clustering and Deep Learning for Daily Stock Price Forecasting." ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH 55, no. 3/2021 (2021): 55–70. http://dx.doi.org/10.24818/18423264/55.3.21.04.

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Patatoukas, Panos N., Richard G. Sloan, and Jenny Zha. "On the Pricing of Mandatory DCF Disclosures: Evidence from Oil and Gas Royalty Trusts." Accounting Review 90, no. 6 (2015): 2449–82. http://dx.doi.org/10.2308/accr-51128.

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ABSTRACT We identify a setting in which firms are required to disclose discounted cash flow (DCF) estimates relating to the value of their primary assets. ASC 932 (formerly SFAS No. 69) has mandated DCF disclosures for proved oil and gas reserves since 1982, and these reserves constitute the primary assets of oil and gas royalty trusts. For a hand-collected sample of oil and gas royalty trusts, we find that (1) the mandatory DCF disclosures are incrementally value-relevant over historical cost accounting variables, (2) investors misprice royalty trust units because they underweight the disclos

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Klerck, W. G. "Forecasting share prices on The Johannesburg Stock Exchange using multivariate time series analysis." Investment Analysts Journal 15, no. 28 (1986): 27–33. http://dx.doi.org/10.1080/10293523.1986.11082251.

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