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Hjalmarsson, Erik. "New Methods for Inference in Long-Horizon Regressions." Journal of Financial and Quantitative Analysis 46, no. 3 (February 18, 2011): 815–39. http://dx.doi.org/10.1017/s0022109011000135.
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AbstractI develop new results for long-horizon predictive regressions with overlapping observations. I show that rather than using autocorrelation robust standard errors, the standard t-statistic can simply be divided by the square root of the forecasting horizon to correct for the effects of the overlap in the data. Further, when the regressors are persistent and endogenous, the long-run ordinary least squares (OLS) estimator suffers from the same problems as the short-run OLS estimator, and it is shown how similar corrections and test procedures as those proposed for the short-run case can also be implemented in the long run. An empirical application to stock return predictability shows that, contrary to many popular beliefs, evidence of predictability does not typically become stronger at longer forecasting horizons.
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Cochrane, John H., and Monika Piazzesi. "Bond Risk Premia." American Economic Review 95, no. 1 (February 1, 2005): 138–60. http://dx.doi.org/10.1257/0002828053828581.
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We study time variation in expected excess bond returns. We run regressions of one-year excess returns on initial forward rates. We find that a single factor, a single tent-shaped linear combination of forward rates, predicts excess returns on one-to five-year maturity bonds with R2 up to 0.44. The return-forecasting factor is countercyclical and forecasts stock returns. An important component of the return-forecasting factor is unrelated to the level, slope, and curvature movements described by most term structure models. We document that measurement errors do not affect our central results.
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Hjalmarsson, Erik. "Predicting Global Stock Returns." Journal of Financial and Quantitative Analysis 45, no. 1 (November 26, 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 regression, and an alternative robust estimator is proposed. The empirical results indicate that the short interest rate and the term spread are fairly robust predictors of stock returns in developed markets. In contrast, no strong or consistent evidence of predictability is found when considering the EP and DP ratios as predictors.
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Alwagdani, Othman. "Dynamic Return-Volume Relations in the Saudi Stock Market: Evidence from Quantiles Regressions." International Journal of Economics and Finance 7, no. 11 (October 27, 2015): 84. http://dx.doi.org/10.5539/ijef.v7n11p84.
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This paper examines the causality patterns between the lagged trading volume and returns of the Saudi stock market (TASI) for the period from2003:01 to April 2013:05, along with two consecutive sub-periods to account for pre- and post- market collapse of 2006. Using the quantile regression approach, the study finds that the return-volume relations are heterogeneous across quantiles with symmetric tendency across the mean for the full sample period. On the contrary, the study could not support the heterogeneous and symmetric effects for the first sub-sample period. The second sub-sample period is characterized by homogenous across quantiles with statistical evidence of symmetry. Thus, the study concludes that the dependence structure between the lagged volume and subsequent market returns seems to be randomly relying on the chosen period which makes volume unsuitable to be used as explanatory power for returns forecasting.
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Caldeira, João F., Rangan Gupta, and Hudson S. Torrent. "Forecasting U.S. Aggregate Stock Market Excess Return: Do Functional Data Analysis Add Economic Value?" Mathematics 8, no. 11 (November 16, 2020): 2042. http://dx.doi.org/10.3390/math8112042.
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This paper analyzes the forecast performance of historical S&P500 and Dow Jones Industrial Average (DJIA) excess returns while using nonparametric functional data analysis (NP-FDA). The empirical results show that the NP-FDA forecasting strategy outperforms not only the the prevailing-mean model, but also the traditional univariate predictive regressions with standard predictors used in the literature and, most cases, also combination approaches that use all predictors jointly. In addition, our results clearly have important implications for investors, from an asset allocation perspective, a mean-variance investor realizes substantial economic gains. Indeed, our results show that NP-FDA is the only one individual model that can overcome the historical average forecasts for excess returns in statistically and economically significant manners for both S&P500 and DJIA during the entire period, NBER recession, and expansions periods.
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MILACEK, TRENT T., and B. WADE BRORSEN. "TRADING BASED ON KNOWING THE WASDE REPORT IN ADVANCE." Journal of Agricultural and Applied Economics 49, no. 3 (April 4, 2017): 400–415. http://dx.doi.org/10.1017/aae.2017.8.
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AbstractPast research shows that prices move in response to World Agricultural Supply and Demand Estimates (WASDE) reports immediately prior to and after a report. This research develops trading models based on knowing the next WASDE report in advance. This should help traders evaluate investments to predict information contained within the report and in determining how best to use such forecasts. The price-forecasting models use regressions against the ratios of ending stocks to use. Results show a steady increasing return to trading over the report month. The highest returns are produced by trading during the growing and harvest seasons.
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Elgammal, Mohammed Mohammed, Fatma Ehab Ahmed, and David Gordon McMillan. "The predictive ability of stock market factors." Studies in Economics and Finance 39, no. 1 (October 21, 2021): 111–24. http://dx.doi.org/10.1108/sef-01-2021-0010.
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Purpose This paper aims to ask whether a range of stock market factors contain information that is useful to investors by generating a trading rule based on one-step-ahead forecasts from rolling and recursive regressions. Design/methodology/approach Using USA data across 3,256 firms, the authors estimate stock returns on a range of factors using both fixed-effects panel and individual regressions. The authors use rolling and recursive approaches to generate time-varying coefficients. Subsequently, the authors generate one-step-ahead forecasts for expected returns, simulate a trading strategy and compare its performance with realised returns. Findings Results from the panel and individual firm regressions show that an extended Fama-French five-factor model that includes momentum, reversal and quality factors outperform other models. Moreover, rolling based regressions outperform recursive ones in forecasting returns. Research limitations/implications The results support notable time-variation in the coefficients on each factor, whilst suggesting that more distant observations, inherent in recursive regressions, do not improve predictive power over more recent observations. Results support the ability of market factors to improve forecast performance over a buy-and-hold strategy. Practical implications The results presented here will be of interest to both academics in understanding the dynamics of expected stock returns and investors who seek to improve portfolio performance through highlighting which factors determine stock return movement. Originality/value The authors investigate the ability of risk factors to provide accurate forecasts and thus have economic value to investors. The authors conducted a series of moving and expanding window regressions to trace the dynamic movements of the stock returns average response to explanatory factors. The authors use the time-varying parameters to generate one-step-ahead forecasts of expected returns and simulate a trading strategy.
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Pohlman, Lawrence, and Lingjie Ma. "Return Forecasting by Quantile Regression." Journal of Investing 19, no. 4 (November 30, 2010): 116–21. http://dx.doi.org/10.3905/joi.2010.19.4.116.
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Benavides, Guillermo. "PREDICTIVE ACCURACY OF FUTURES OPTIONS IMPLIED VOLATILITY: THE CASE OF THE EXCHANGE RATE FUTURES MEXICAN PESO-US DOLLAR." PANORAMA ECONÓMICO 5, no. 9 (April 26, 2017): 41. http://dx.doi.org/10.29201/pe-ipn.v5i9.83.
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There has been substantial research effort aimed to forecast futures price return volatilities of financial assets. A significant part of the literature shows that volatility forecast accuracy is not easy to estimate regardless of the forecasting model applied. This paper examines the volatility accuracy of several volatility forecast models for the case of the Mexican peso-USD exchange rate futures returns. The models applied here are a univariate GARCH, a multivariate ARCH (the BEKK model), two option implied volatility models and a composite forecast model. The composite model includes time-series (historical) and option implied volatility forecasts. Different to other works in the literature, in this paper there is a more rigorous analysis of the option implied volatilities calculations. The results show that the option implied models are superior to the historical models in terms of accuracy and that the composite forecast model was the most accurate one (compared to the alternative models) having the lowest mean-squared-errors. However, the results should be taken with caution given that the coefficient of determination in the regressions was relatively low. According to these findings it is recommended to use a composite forecast model if both types of data are available i.e. the time-series (historical) and the option implied.
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Georgiou, Catherine. "The British Stock Market under the Structure of Market Capitalization Value: New Evidence on its Predictive Content." International Journal of Business and Economic Sciences Applied Research 13, no. 3 (2020): 57–70. http://dx.doi.org/10.25103/ijbesar.133.05.
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Purpose: The aim of our paper is twofold. First, we examine the predictive ability of log book-market, dividend-price, earnings-price and dividend-earnings ratios on the most recent data set of the strongest securities in the UK economy; unlike the majority of the studies in this data set, our analysis is not limited on returns but further investigates dividend and earnings growth predictability under the presence of the most recent global financial recession. Second, we exploit the long-run equilibrium relationship in two systems, [p_t,d_t,e_t] and [p_t,b_t,e_t] and examine the predictive ability of our newly formed variables, namely 〖pde〗_t and 〖pbe〗_t. Design/methodology/approach: In this study, we examine the most recent data set of Financial Times Stock Exchange 100 (FTSE 100) and analyze it based on the formation of size portfolios. The main focus is placed on the index’s returns, dividend and earnings growth rates and the predictive ability of the four financial ratios we have selected following their reputation as strong predictors. We also formulate two extra ratios based on their long-run equilibrium relationship. Finding: Our study’s main findings can be summarized as following. First, we retrieve evidence that in-sample return predictability is evident in the medium and large-sized portfolios and is better captured by 〖pde〗_t at 35% and 47% equivalently. Second, forecasts on dividend growth are even more linked to the size criterion we employ. Third, in-sample regressions of continuously compounded earnings growth rate show that most predictive benefits are obtained by 〖dp〗_t in the medium portfolio with an R^2 of 45%. Research limitations/implications: A first constraint is the forecasters we employ; we have used the most indicative ones due to their popularity in similar data sets but there are other macroeconomic variables such as spreads and interest rates that could be tested in future research. Also, we could examine the sensitivity of our results on whether we use nominal, excess or real returns and then, attempt to alter our data’s frequency so as to address the seasonality effect observed mainly in dividends and earnings. Originality/value: We believe that our paper contributes to the ongoing debate of the traits that make return predictable and the information included in either dividends or earnings to explain that predictability. Finally, the novelty of this paper lies in the links it tries to retrieve among market capitalization value and predictability in a market whose predictive components have not been entirely explored. Our paper may prove informative to investors focused on short-term forecasting and interested in the effects of size in portfolio formation.
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Stetsenko, Sergey, Nadiia Bolila, Lesya Sorokina, Tetiana Tsyfra, and Olena Molodid. "MONITORING MECHANISM OF RESILIENCE OF THE ANTI-CRISIS POTENTIAL SYSTEM OF THE CONSTRUCTION ENTERPRISE IN THE LONG-TERM PERIOD." ECONOMICS, FINANCE AND MANAGEMENT REVIEW, no. 3 (October 1, 2020): 29–40. http://dx.doi.org/10.36690/2674-5208-2020-3-29.
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The anti-crisis potential of enterprises belonging to industries with a long production cycle is unstable, as the time interval between incoming cash flows is much longer than between outgoing ones. This problem is especially actual for construction companies, whose income largely depends on the state of investment demand. Therefore, there is a need to develop an effective mechanism to control the resilience of the anti-crisis system of the construction company to environmental conditions. According to many scientists, anti-crisis potential is associated with the financial sanation. Given the undeniable usefulness of the analyzed developments, there has been noted that they are unsuitable for managing the financial sanation capacity and anti-crisis potential of construction companies. After all, most of them are suggested for agricultural or industrial ones. Given the definition of "financial sanation", the implementation of measures aimed at restoring business is impossible without investment. Investing funds in any business project, including those aimed at strengthening the financial condition of the enterprise, will be efficient only when they provide an economic effect. Thus, the requirement of return on investment is indisputable, but for high-risk activities, including construction, there is an additional condition, to get a return on investment as soon as possible: from 3 to 10 years. After all, this is the duration period of different types of macroeconomic cycles, during which the vast majority of construction companies go through all stages of economic development: from growth to decline. The dependence of the payback period of equity capital on other factors of the internal and external environment of business systems has been studied in order to manage the rehabilitation and anti-crisis potential of a construction company. General scientific methods such as analysis, synthesis, deduction, induction, analogy as means of studying and generalizing theoretical approaches to providing anti-crisis potential; Васkwаrd Stерwisе method (construction of multifactor linear regressions Stаtistіса 8.0), providing the sequential construction of equations in which the set of input factors decreases by removing the least significant to explain the variation of the independent variate. In order to increase the efficiency of decision-making on the feasibility of additional equity attraction by construction companies there has been developed a methodological approach to forecasting the level of financial sanation capacity of the construction company. It is a complex; a multifactor model - a linguistic scale, allows to identify changes in the payback period of equity in the medium term using quantitative and linguistic estimates and can be used as a functional module of digitized economic management of the enterprise.
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Ma, Guozhen, Ning Pang, Zeya Zhang, Yongli Wang, Chen Liu, Suhang Yao, and Siyi Tao. "Power Load Forecasting Model Based on Grey Neural Network Regression Combination." E3S Web of Conferences 213 (2020): 03006. http://dx.doi.org/10.1051/e3sconf/202021303006.
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Due to the limitations of a single power load forecasting model, the power load forecasting cannot be performed well. In order to obtain a greater closeness to predict results with actual data, this paper presents the power load forecasting model based on gray neural network combined return to Guangzhou, 2010 - 2019 on actual data for example, the results show that: As used herein, the combined model method has high accuracy and strong use value.
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Silva, Nuno. "Time-varying stock return predictability: the Eurozone case." Notas Económicas, no. 41 (June 1, 2015): 28–38. http://dx.doi.org/10.14195/2183-203x_41_3.
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In this paper, we test the existence of predictability in eleven Eurozone stock markets, using both regressions with constant coefficients and with time-varying coefficients. Our results show that there is statistical evidence of predictability in some countries. The economic value of the forecasting models is much stronger than what could be inferred, based on the statistical tests. A meanvariance investor could have obtained substantial utility gains in most countries. Overall, models with time-varying parameters perform slightly better than models with constant coefficients.http://dx.doi.org/10.14195/2183‑203X_41_4
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Qu, Hui, and Yu Zhang. "A New Kernel of Support Vector Regression for Forecasting High-Frequency Stock Returns." Mathematical Problems in Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/4907654.
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This paper investigates the value of designing a new kernel of support vector regression for the application of forecasting high-frequency stock returns. Under the assumption that each return is an event that triggers momentum and reversal periodically, we decompose each future return into a collection of decaying cosine waves that are functions of past returns. Under realistic assumptions, we reach an analytical expression of the nonlinear relationship between past and future returns and introduce a new kernel for forecasting future returns accordingly. Using high-frequency prices of Chinese CSI 300 index from January 4, 2010, to March 3, 2014, as empirical data, we have the following observations: (1) the new kernel significantly beats the radial basis function kernel and the sigmoid function kernel out-of-sample in both the prediction mean square error and the directional forecast accuracy rate. (2) Besides, the capital gain of a simple trading strategy based on the out-of-sample predictions with the new kernel is also significantly higher. Therefore, we conclude that it is statistically and economically valuable to design a new kernel of support vector regression for forecasting high-frequency stock returns.
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Zrazhevsky, Grigoriy, and Vira Zrazhevska. "Quintile regression based approach for dynamical VaR and CVaR forecasting using metalog distribution." System research and information technologies, no. 1 (July 11, 2021): 139–50. http://dx.doi.org/10.20535/srit.2308-8893.2021.1.12.
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The paper proposes a new method of dynamic VaR and CVaR (ES) risk measures forecasting. Quantile linear GARCH model is chosen as the main forecasting model for time series quantiles. To build a forecast, the values of quantiles are approximated by the metalog distribution, which makes it possible to use analytical formulas to evaluate risk measures. The method of VaR and CVaR forecasting is formulated as a step-by-step algorithm. At the first stage, an initial model is built to obtain variance estimates. The predicted variance values obtained from the constructed model are used at the second stage to find the QLGARCH model coefficients by solving the minimization problem. At the third stage, the QLGARCH models are estimated on a non uniform quantile grid. The obtained predicted values of quantiles are used to estimate the approximating metalog distribution. The investigated theory is applied to VaR and CVaR forecasting for time series of daily log return of the DJI index.
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Chen, Qian, Xiang Gao, Xiaoxuan Huang, and Xi Li. "Multiple-step value-at-risk forecasts based on volatility-filtered MIDAS quantile regression: Evidence from major investment assets." Investment Management and Financial Innovations 18, no. 3 (September 20, 2021): 372–84. http://dx.doi.org/10.21511/imfi.18(3).2021.31.
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Forecasting multiple-step value-at-risk (VaR) consistently across asset classes is hindered by the limited sample size of low-frequency returns and the potential model misspecification when assuming identical return distributions over different holding periods. This paper hence investigates the predictive power for multi-step VaR of a framework that models separately the volatility component and the error term of the return distribution. The proposed model is illustrated with ten asset returns series including global stock markets, commodity futures, and currency exchange products. The estimation results confirm that the volatility-filter residuals demonstrate distinguished tail dynamics to that of the return series. The estimation results suggest that volatility-filtered residuals may have either negative or positive tail dependence, unlike the unanimous negative tail dependence in the return series. By comparing the proposed model to several alternative approaches, the results from both the formal and informal tests show that the specification under concern performs equivalently well if not better than its top competitors at the 2.5% and 5% risk level in terms of accuracy and validity. The proposed model also generates more consistent VaR forecasts under both the 5-step and 10-step setup than the MIDAS-Q model. AcknowledgmentThe authors are grateful to the editor and an anonymous referee. This research is sponsored by the National Natural Science Foundation of China (Award Number: 71501117). All remaining errors are our own.
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Maio, Paulo, and Pedro Santa-Clara. "Dividend Yields, Dividend Growth, and Return Predictability in the Cross Section of Stocks." Journal of Financial and Quantitative Analysis 50, no. 1-2 (April 2015): 33–60. http://dx.doi.org/10.1017/s0022109015000058.
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AbstractThere is a generalized conviction that variation in dividend yields is exclusively related to expected returns and not to expected dividend growth, for example, Cochrane’s (2011) presidential address. We show that this pattern, although valid for the aggregate stock market, is not true for portfolios of small and value stocks, where dividend yields are related mainly to future dividend changes. Thus, the variance decomposition associated with the aggregate dividend yield has important heterogeneity in the cross section of equities. Our results are robust to different forecasting horizons, econometric methodology (long-horizon regressions or first-order vector autoregression), and alternative decomposition based on excess returns.
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Loo, Wei Kang. "Predictability of HK-REITs returns using artificial neural network." Journal of Property Investment & Finance 38, no. 4 (November 14, 2019): 291–307. http://dx.doi.org/10.1108/jpif-07-2019-0090.
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Purpose The purpose of this paper is to determine if artificial neural network (ANN) works better than linear regression in predicting Hong Kong real estate investment trusts’ (REITs) excess return. Design/methodology/approach Both ANN and the regression were applied in this study to forecast the Hong Kong REITs’ (HK-REITs) return using the capital asset pricing model and Fama and French’s three-factor models. Each result was further split into annual time series as a measure to investigate the consistency of the performance across time. Findings ANN had produced a better forecasting results than the regression based on their trading performance. However, the forecasting performance varied across individual REITs and time periods. Practical implications ANN should be considered for use when one were to attempt forecasting the HK-REITs excess returns. However, the trading performance should be always compared with buy and hold strategy prior to make any investment decisions. Originality/value This paper tested the predicting power of ANN on the HK-REITs and the consistency of its predicting power.
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Chindia, E. W. "Forecasting Techniques and Accuracy of Performance Forecasting." International Journal of Management Excellence 7, no. 2 (August 31, 2016): 813–20. http://dx.doi.org/10.17722/ijme.v7i2.851.
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This article explores the impact of the different forecasting methods (FMs) on the accuracy of performance forecasting (APF) in large manufacturing firms (LMFs), in Kenya. The objective of the study was to assess if the different forecasting methods have an influence on any of the aspects of measures of APF. APF, in manufacturing operations, is seldom derived accurately. However, LMFs tend to hire skilled forecasters, to a great extent, to ensure APF when preparing future budgets. The different types of forecasting techniques have been known to influence the behavior of operations resulting in the formulation of either accurate or inaccurate forecasts resulting in either adverse or favorable organizational performance. The study used the three known forecasting methods, objective, subjective and combined forecasting techniques against measures of APF, expected value, growth in market share, return on assets and return on sales. Regression analysis was used applying data collected through a structured questionnaire administered among randomly selected LMFs. Results indicated that there was evidence that APF is influenced by each of the forecasting methods in different ways.
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Jurevičienė, Daiva, and Darius Rauličkis. "FORECASTING BANKS RETURN ON EQUITY USING LEADING ECONOMIC INDICATORS." Business: Theory and Practice 21, no. 2 (June 30, 2020): 460–68. http://dx.doi.org/10.3846/btp.2020.12664.
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The research examines an approach to forecast return on equity using leading economic indicators for short periods in banks. ROE is one of the most important ratios for performance measurement. Its adequacy is necessary for competitiveness, attract funding in financial markets, accumulate reserve for future turbulences, secure compliance with supervisory requirements and maintain positive signals for the market. There is still a debate in the literature on factors of commercial banks’ profitability forecasting, techniques, and most appropriate models to improve the correctness of predicting and acquiring more accurate signals for communication on targets. The problems are still relevant from both a theoretical perspective and practical implementation. This research aims to prove the necessity to include leading economic indicators for short term ROE forecasting. It conducts investigations for the relevant studies, using regression analysis, necessary tests, ascertains opportunities and limitations of using these indicators and develops a conceptual model and its assessment major Baltic banks. The results show verification of approach to forecast ROE using leading economic indicators for short periods. Such study complements signalling theory with a new approach, how to predict and acquire signal not only using economic indicators as a general group but sub-group them into coinciding, lagging and leading.
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Fałdziński, Marcin, Piotr Fiszeder, and Witold Orzeszko. "Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression." Energies 14, no. 1 (December 22, 2020): 6. http://dx.doi.org/10.3390/en14010006.
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We compare the forecasting performance of the generalized autoregressive conditional heteroscedasticity (GARCH) -type models with support vector regression (SVR) for futures contracts of selected energy commodities: Crude oil, natural gas, heating oil, gasoil and gasoline. The GARCH models are commonly used in volatility analysis, while SVR is one of machine learning methods, which have gained attention and interest in recent years. We show that the accuracy of volatility forecasts depends substantially on the applied proxy of volatility. Our study confirms that SVR with properly determined hyperparameters can lead to lower forecasting errors than the GARCH models when the squared daily return is used as the proxy of volatility in an evaluation. Meanwhile, if we apply the Parkinson estimator which is a more accurate approximation of volatility, the results usually favor the GARCH models. Moreover, it is difficult to choose the best model among the GARCH models for all analyzed commodities, however, forecasts based on the asymmetric GARCH models are often the most accurate. While, in the class of the SVR models, the results indicate the forecasting superiority of the SVR model with the linear kernel and 15 lags, which has the lowest mean square error (MSE) and mean absolute error (MAE) among the SVR models in 92% cases.
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Kim, Jong-Min, Leixin Xia, Iksuk Kim, Seungjoo Lee, and Keon-Hyung Lee. "Finding Nemo: Predicting Movie Performances by Machine Learning Methods." Journal of Risk and Financial Management 13, no. 5 (May 9, 2020): 93. http://dx.doi.org/10.3390/jrfm13050093.
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Analyzing the success of movies has always been a popular research topic in the film industry. Artificial intelligence and machine learning methods in the movie industry have been applied to modeling the financial success of the movie industry. The new contribution of this research combined Bayesian variable selection and machine learning methods for forecasting the return on investment (ROI). We also attempt to compare machine learning methods including the quantile regression model with movie performance data in terms of in-sample and out of sample forecasting.
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Valluri, Subhakara. "Commodity Indices Risk and Return Analysis Against Libor Benchmark." Applied Studies in Agribusiness and Commerce 12, no. 3-4 (December 13, 2018): 55–66. http://dx.doi.org/10.19041/apstract/2018/3-4/7.
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This study analyze the risk and return characteristics of commodity index investments against the LIBOR benchmark. Commodity-based asset allocation strategies can be optimized by benchmarking the risk and return characteristics of commodity indices with LIBOR index rate. In this study, we have considered agriculture, energy, and precious metals commodity indices and LIBOR index to determine the risk and return characteristics using estimation techniques in terms of expected return, standard deviation, and geometric mean. We analyzed the publicly available daily market data from 10/9/2001 to 12/30/2016 for benchmarking commodity indices against LIBOR. S&P GSCI Agriculture Index (SGK), S&P GSCI Energy Index (SGJ), and S&P GSCI Precious Metals Index (SGP) are taken to represent each category of widely traded commodities in the regression analysis. Our study uses time series data based on daily prices. Alternative forecasting methodologies for time series analysis are used to cross-check the results. The forecasting techniques used are Holt-Winters Exponential Smoothing and ARIMA. This methodology predicts forecasts using smoothening parameters. The empirical research has shown that the risk of each of the commodity index that represents agriculture, energy, and precious metals sector is smaller compared to its return, whereas LIBOR based interest rate benchmark shows higher risk compared to its return in recession, non-recession and overall periods.
JEL Classification: C43, G13, G15
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Schnytzer, Adi, and Janez Janez Sustersic. "THE REGRESSION TOURNAMENT: A NOVEL APPROACH TO PREDICTION MODEL ASSESSMENT." Journal of Prediction Markets 5, no. 2 (December 19, 2012): 32–43. http://dx.doi.org/10.5750/jpm.v5i2.488.
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Standard methods to assess the statistical quality of econometric models implicitly assume there is only one person in the world, namely the forecaster with her model(s), and that there exists an objective and independent reality to which the model predictions may be compared. However, on many occasions, the reality with which we compare our predictions and in which we take our actions is co-determined and changed constantly by actions taken by other actors based on their own models. We propose a new method, called a regression tournament, to assess the utility of forecasting models and taking these interactions into account. We present an empirical case of betting on Australian Rules Football matches where the most accurate predictive model does not yield the highest betting return, or, in our terms, does not win a regression tournament.
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Guo, Han, Martha Conklin, Tessa Maurer, Francesco Avanzi, Kevin Richards, and Roger Bales. "Valuing Enhanced Hydrologic Data and Forecasting for Informing Hydropower Operations." Water 13, no. 16 (August 19, 2021): 2260. http://dx.doi.org/10.3390/w13162260.
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Climate change is rapidly modifying historic river flows and snowpack conditions in the Sierra Nevada in California and other seasonally snow-covered mountains. Statistical forecasting methods based on regressing summer flow against spring snow water equivalent, precipitation, and antecedent runoff are thus becoming increasingly inadequate for water-resources decision making, which can lead to missed opportunities in maximizing beneficial uses, including the value of hydropower resources. An enhanced forecasting method using a process-based model and spatially distributed wireless sensor data offers more accurate runoff forecasts. In this paper, we assessed the forecasting accuracy of these two forecasting methods by applying them to two tributaries within the North Fork Feather River basin in California. The result shows the enhanced forecasting method having better accuracy than the statistical model. In addition, a hydropower simulation showed a considerable increase in energy value with the enhanced forecasting informing reservoir operations. The investment analysis on applying this method shows an average internal rate of return of 31% across all scenarios, making this forecasting method an attractive way to better inform water-related decisions for hydropower generation in the context of climate change.
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Ilyas, Qazi Mudassar, Khalid Iqbal, Sidra Ijaz, Abid Mehmood, and Surbhi Bhatia. "A Hybrid Model to Predict Stock Closing Price Using Novel Features and a Fully Modified Hodrick–Prescott Filter." Electronics 11, no. 21 (November 3, 2022): 3588. http://dx.doi.org/10.3390/electronics11213588.
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Forecasting stock market prices is an exciting knowledge area for investors and traders. Successful predictions lead to high financial revenues and prevent investors from market risks. This paper proposes a novel hybrid stock prediction model that improves prediction accuracy. The proposed method consists of three main components, a noise-filtering technique, novel features, and machine learning-based prediction. We used a fully modified Hodrick–Prescott filter to smooth the historical stock price data by removing the cyclic component from the time series. We propose several new features for stock price prediction, including the return of firm, return open price, return close price, change in return open price, change in return close price, and volume per total. We investigate traditional and deep machine learning approaches for prediction. Support vector regression, auto-regressive integrated moving averages, and random forests are used for conventional machine learning. Deep learning techniques comprise long short-term memory and gated recurrent units. We performed several experiments with these machine learning algorithms. Our best model achieved a prediction accuracy of 70.88%, a root-mean-square error of 0.04, and an error rate of 0.1.
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Lutey, Matthew, and Dave Rayome. "Ichimoku Cloud Forecasting Returns in the U.S." GLOBAL BUSINESS FINANCE REVIEW 27, no. 5 (October 31, 2022): 17–26. http://dx.doi.org/10.17549/gbfr.2022.27.5.17.
Full textAbstract:
Purpose: We show that the Ichimoku Cloud can forecast stock returns in the U.S., Canada, Germany, and U.K.
Design/methodology/approach: We use a regression of next months index return regressed on the Ichimoku Cloud entry signal for price crossing above 9 periods, 26 period, 52 periods and a crossover between 9 and 26 periods. The regression slope coefficient is recorded as the risk premium return. We also record the t-statistic and R2 of the model. We note that T-statistics of 1.65 are statistically significant. R2 is economically significant with a value above .5 percent.
Findings: This is showing real-time application how the current Ichimoku Cloud signal can predict tomorrow’s stock return. The strongest results occur for lagged values one period in the U.S. which shows initial justification to using the Ichimoku Cloud. We additionally show the Ichimoku Cloud entry signals are strong in regards to T-statistics and R2 when benchmarked on each of the equity markets in the U.S., Canada, Germany, and U.K.
Research limitation/implications: The model only considers technical indicators for forecasting risk premium and could benefit from additional indicators or macro fundamentals.
Originality/value: This is the first paper to use Ichimoku Cloud in the risk premium forecast framework.
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Yeh, Hsiang-Yuan, Yu-Ching Yeh, and Da-Bai Shen. "Word Vector Models Approach to Text Regression of Financial Risk Prediction." Symmetry 12, no. 1 (January 2, 2020): 89. http://dx.doi.org/10.3390/sym12010089.
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Linking textual information in finance reports to the stock return volatility provides a perspective on exploring useful insights for risk management. We introduce different kinds of word vector representations in the modeling of textual information: bag-of-words, pre-trained word embeddings, and domain-specific word embeddings. We apply linear and non-linear methods to establish a text regression model for volatility prediction. A large number of collected annually-published financial reports in the period from 1996 to 2013 is used in the experiments. We demonstrate that the domain-specific word vector learned from data not only captures lexical semantics, but also has better performance than the pre-trained word embeddings and traditional bag-of-words model. Our approach significantly outperforms with smaller prediction error in the regression task and obtains a 4%–10% improvement in the ranking task compared to state-of-the-art methods. These improvements suggest that the textual information may provide measurable effects on long-term volatility forecasting. In addition, we also find that the variations and regulatory changes in reports make older reports less relevant for volatility prediction. Our approach opens a new method of research into information economics and can be applied to a wide range of financial-related applications.
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Khan, Umair, Farhan Aadil, Mustansar Ali Ghazanfar, Salabat Khan, Noura Metawa, Khan Muhammad, Irfan Mehmood, and Yunyoung Nam. "A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation Between Stock Markets." Sustainability 10, no. 10 (October 15, 2018): 3702. http://dx.doi.org/10.3390/su10103702.
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Knowledge-based decision support systems for financial management are an important part of investment plans. Investors are avoiding investing in traditional investment areas such as banks due to low return on investment. The stock exchange is one of the major areas for investment presently. Various non-linear and complex factors affect the stock exchange. A robust stock exchange forecasting system remains an important need. From this line of research, we evaluate the performance of a regression-based model to check the robustness over large datasets. We also evaluate the effect of top stock exchange markets on each other. We evaluate our proposed model on the top 4 stock exchanges—New York, London, NASDAQ and Karachi stock exchange. We also evaluate our model on the top 3 companies—Apple, Microsoft, and Google. A huge (Big Data) historical data is gathered from Yahoo finance consisting of 20 years. Such huge data creates a Big Data problem. The performance of our system is evaluated on a 1-step, 6-step, and 12-step forecast. The experiments show that the proposed system produces excellent results. The results are presented in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).
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Zaremba, Adam. "QUALITY INVESTING IN CEE EMERGING MARKETS." Business, Management and Education 12, no. 2 (December 23, 2014): 159–80. http://dx.doi.org/10.3846/bme.2014.241.
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Using sorting, cross-sectional tests, regression, and tests of a monotonic relation, the study examines the return patterns related to seven distinct quality characteristics: accruals, bid-ask spread, balance sheet liquidity, profitability, leverage, payout ratio and turnover. The investigation of more than 1.300 stocks from 11 Central and Eastern European countries for the period 2002–2014 documents a strong gross-profitability premium and an inverted liquidity premium. Profitable and not heavily leveraged companies provide a partial hedge against market distress. Finally, the paper proposes quality spreads as a forecasting tool and shows that they have predictive abilities over quality premiums related to leverage, profitability and bid-ask spread.
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Naveed Jan, Muhammad, and Usman Ayub. "DO THE FAMA AND FRENCH FIVE-FACTOR MODEL FORECAST WELL USING ANN?" Journal of Business Economics and Management 20, no. 1 (February 27, 2019): 168–91. http://dx.doi.org/10.3846/jbem.2019.8250.
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Forecasting the stock returns in the emerging markets is challenging due to their peculiar characteristics. These markets exhibit linear as well as nonlinear features and Conventional forecasting methods partially succeed in dealing with the nonlinear nature of stock returns. Contrarily, Artificial Neural Networks (ANN) is a flexible machine learning tool which caters both the linear and nonlinear markets. This paper investigates the forecasting ability of ANN by using Fama and French five-factor model. We construct ANN’s based on the composite factors of the FF5F model to predict portfolio returns in two stages; in stage one, the study identifies the best-fit combination of training, testing, and validation along with the number of neurons full sample period. In stage two, the study uses this best combination to forecast the model under 48-months rolling window analysis. In-sample and out-sample comparisons, regression, and goodness of fit test and actual and predicted values of the stock returns of our ANN model reveal that the proposed model accurately predicts the one-month ahead returns. Our findings reinforce the investment concept that the markets compensate the high-risk portfolios more than mid and low beta portfolios and the methodology will significantly improve the return on investment of the investors.
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Amini, Nuzulia, Bambang Setiono, Christian Haposan Pangaribuan, and Elfindah Princes. "The Impact of Cash Management Practices toward Financial Performance of Small and Medium Enterprises in Indonesia." Journal of Business, Management, and Social Studies 1, no. 1 (May 11, 2021): 35–47. http://dx.doi.org/10.53748/jbms.v1i1.7.
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Objective – The importance of SMEs in Indonesia has been becoming the center of attention for the government to help the national economy grow. However, there are still many problems that hinder the potential of SMEs to develop. One of the problems is financial Management in terms of cash management, which can help SMEs have better Management over cash and better performance. Therefore, this study aims to analyze the current two elements of cash management practice, forecasting (FOR) and cash mobilization (CML) done by SMEs in Indonesia and its impact on Return on Assets (ROA) and Gross Profit Margin (GPM) as the financial performance measurements.
Methodology – The research uses a quantitative approach from 90 SMEs in Java and Bali islands from April until July 2018. The data were analyzed descriptively using a 4-point scale questionnaire. A regression analysis was added to find out significant relationships between the variables.
Findings – The research found that SMEs owners/managers often do forecasting and rarely do cash mobilization practices. The regression analysis shows a significant relationship between cash management practices and ROA but a non-significant relationship between cash management practices and Gross Profit Margin (GPM).
Novelty – This research provides an insight of how cash management practices influence the financial performance in the context of SMEs.
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Hamdallah, Madher Ebrahim, and Anan Fathi Srouji. "The influence of sustainable innovation on financial entrepreneurship performance: Growth and prediction in an emerging market." Journal of Governance and Regulation 11, no. 1 (2022): 27–37. http://dx.doi.org/10.22495/jgrv11i1art3.
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This study aims to perceive the effect of financial entrepreneurship performance (FEP) over sustainable innovation (SI) disclosure in an emerging market. Jordanian banks are tested based on a multiple regression analysis for the periods 2008 and 2018 and a time series forecasting webinar analysis for the period from 2019 to 2029 based on data ranging from 2008 to 2018. Innovation is indicated through disclosed intangible assets (IA), and items related to research and development (R&D) costs. As organizations anticipate stability by concentrating on technological awareness to influence higher innovative performance (Guo, Guo, Zhou, & Wu, 2020), this study came to converse the relationships between previous literature variables; Hussain (2015) as well as Lassala, Apetrei, and Sapena (2017) revealed through the regression models that there is a relationship between FEP and SI. Meanwhile, bank FEP is directed by return on assets (ROA) and return on equity (ROE). Results reveal that bank FEP affects SI disclosure in a positive manner for the period 2008 and at a higher significant level than 2018. In the meantime, the growth prediction analyses divulge that both ROA and ROE are expected to decrease rapidly within a coming couple of years and then increase promptly.
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Chen, Yi-Chang, Hung-Che Wu, Yuanyuan Zhang, and Shih-Ming Kuo. "A Transmission of Beta Herding during Subprime Crisis in Taiwan’s Market: DCC-MIDAS Approach." International Journal of Financial Studies 9, no. 4 (December 11, 2021): 70. http://dx.doi.org/10.3390/ijfs9040070.
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The aim of this study is to investigate the herding of beta transmission between return and volatility. We have used the dynamic conditional correlation model with the mixed-data sampling (DCC-MIDAS) model for the analysis. The evidence demonstrates that herding is a key transmitter in Taiwan’s stock market. The significant estimation of DCC-MIDAS explains that the herding phenomenon is highly dynamic and time-varying in herding behavior. By means of time-varying beta of herding based on our rolling forecasting method and robustness check of the Markov-switching regression approach using four types of portfolios, the evidence indicates that there are conditional correlations between betas and herding. In addition, it also reveals that herding forms in Taiwan’s markets during the subprime crisis period.
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Dai, Zhifeng, Huiting Zhou, Xiaodi Dong, and Jie Kang. "Forecasting Stock Market Volatility: A Combination Approach." Discrete Dynamics in Nature and Society 2020 (June 5, 2020): 1–9. http://dx.doi.org/10.1155/2020/1428628.
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We find that combining two important predictors, stock market implied volatility and oil volatility, can improve the predictability of stock return volatility. We also document that the stock market implied volatility provides far more significant predictability than the oil volatility and other nonoil macroeconomic and financial variables. The empirical results show the “kitchen sink” combination approach that using two predictors jointly performs better than not only the univariate regression models which use oil volatility or stock market implied volatility separately but also convex combination of the individual forecasts. This improvement of predictability is also remarkable when we consider the business cycle. Furthermore, the robust test based on different lag lengths and different macroinformation shows that our forecasting strategy is efficient.
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Muhammad Basysyar, Fadhil, and Gifthera Dwilestari. "COMPARISON OF MACHINE LEARNING ALGORITHMS FOR PREDICTING DIAMOND PRICES BASED ON EXPLORATORY DATA ANALYSIS." International Journal of Engineering Applied Sciences and Technology 7, no. 5 (September 1, 2022): 71–79. http://dx.doi.org/10.33564/ijeast.2022.v07i05.012.
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Diamonds are a unique commodity whose socially generated notions significantly influence perceived value. To study how a diamond's physical attributes might predict its price, a massive dataset of loose diamonds scraped from an online diamond store is subjected to data mining, which reveals that diamond weight, color, and clarity are the most influential determinants of diamond pricing. Therefore, submit a proposal for an Exploratory Data Analysis that includes a component that analyses various parts of news articles using LASSO Regression, ElasticNet Regression, and Random Forest Regression. This system is trained on past data to forecast diamond prices while retaining an easily interpretable trading approach concerning rule complexity. The suggested strategy beats cutting-edge methods for prediction accuracy and interpretability, such as extreme learning machines using deep learning. Our data indicate that the news impact factor is crucial for forecasting. Demonstrate that the suggested system outperforms the average yearly return while offering a set of language trading rules that are interpretable. This has substantial repercussions for investors. A significant degree of subjectivity in diamond pricing may result from diamond dealers' price concealment techniques.
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Seckin, Neslihan. "Modeling flood discharge at ungauged sites across Turkey using neuro-fuzzy and neural networks." Journal of Hydroinformatics 13, no. 4 (November 22, 2010): 842–49. http://dx.doi.org/10.2166/hydro.2010.046.
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One of the most important problems in hydrology is the reliable forecasting of maximum discharge at an ungauged site of interest. Statistical techniques are commonly used for finding the maximum discharge and return period relationship. However, these techniques are generally considered to be inadequate because of the complexity of the problem. Hence, neural network techniques are preferred. In this study, two different neural network models developed based on the following techniques – a multi-layer perceptron neural network with Levenberg–Marquardt algorithm and a radial basis neural network behind an adaptive neuro-fuzzy inference system – are employed in order to capture the nonlinear relationship between discharge and five independent variables – drainage area (km2), elevation (m), latitude, longitude, return period (year) and maximum discharge (m3/s). For a modeling study, watershed data from 543 catchments across Turkey were used. Statistical models with regression techniques were also applied to the same data, providing a wider comparison. The results of the models were then compared and assessed with respect to mean square errors, mean absolute error, mean absolute relative error and determination coefficient. Based on these results, it was found that the neural network techniques demonstrated better performance in predicting the maximum discharge based on five independent variables than the regression techniques, and were comparable to the adaptive neuro-fuzzy inference system.
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Pradhan, Kailash. "The Hedging Effectiveness of Stock Index Futures: Evidence for the S&P CNX Nifty Index Traded in India." South East European Journal of Economics and Business 6, no. 1 (April 1, 2011): 111–23. http://dx.doi.org/10.2478/v10033-011-0010-2.
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The Hedging Effectiveness of Stock Index Futures: Evidence for the S&P CNX Nifty Index Traded in IndiaThis study evaluates optimal hedge ratios and the hedging effectiveness of stock index futures. The optimal hedge ratios are estimated from the ordinary least square (OLS) regression model, the vector autoregression model (VAR), the vector error correction model (VECM) and multivariate generalized autoregressive conditional heteroskedasticity (M-GARCH) models such as VAR-GARCH and VEC-GARCH using the S&P CNX Nifty index and its futures index. Hedging effectiveness is measured in terms of within sample and out of sample risk-return trade-off at various forecasting horizons. The analysis found that the VEC-GARCH time varying hedge ratio provides the greatest portfolio risk reduction and generates the highest portfolio returns.
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P. Bauman, Mark. "Forecasting operating profitability with DuPont analysis." Review of Accounting and Finance 13, no. 2 (May 6, 2014): 191–205. http://dx.doi.org/10.1108/raf-11-2012-0115.
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Purpose – The purpose of this study is to re-examine the relation between changes in profit margin (ΔPM) and changes in return on net operating assets (ΔRNOA) by partitioning on the direction of the change in PM. DuPont analysis provides a means of disaggregating a firm’s return on net operating assets (RNOA) into asset turnover (ATO) and profit margin (PM) components to gain insights into the underlying drivers of operating profitability. Prior research finds that changes in ATO are informative about one-year-ahead changes in RNOA, while changes in PM are not. Design/methodology/approach – Consistent with prior research, regression analysis is used to develop a predictive model for one-year-ahead changes in RNOA. Results based on in-sample parameter estimates are used to examine the out-of-sample forecasting accuracy of alternative model specifications. Findings – The results are consistent with significant forecast improvement resulting from considering the impact on future RNOA of the direction of the ΔPM. Originality/value – The study contributes to the literature on the determinants of profitability ratios by providing further guidance on how financial statement information can be utilized to improve forecasts of firm performance.
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Mundi, Hardeep Singh, and Parmjit Kaur. "Impact of CEO Overconfidence on Firm Performance: An Evidence from S&P BSE 200." Vision: The Journal of Business Perspective 23, no. 3 (July 18, 2019): 234–43. http://dx.doi.org/10.1177/0972262919850935.
Full textAbstract:
The current research article considers the impact of CEO overconfidence on firm performance for S&P BSE 200 firms. The CEO overconfidence is measured using revealed beliefs (holder 67, long holder and net buyer), press coverage and forecasting error proxies of CEO overconfidence. CEO Overconfidence measures are constructed as per the methodology of Malmendier and Tate (2005b, 2008). Firm performance is measured using Tobin’s Q and return on assets. The data are collected from the Centre for Monitoring Indian Economy (CMIE) prowess, S&P Capital IQ and the annual reports of the sample firms over a period of 15 years starting from 1 April 2000 to 31 March 2015. Regression results for each of the proxy of CEO overconfidence with the proxies of firm performance indicate that large Indian firms with overconfident CEOs enjoy a higher return on assets and Tobin’s Q as compared to the full sample firms. Overconfident CEOs consider themselves better-than-average, are involved with over-investment and show superior performance for the firm. The overconfident CEOs increase firm performance by following optimal levels of investments in the firm.
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Pripoaie, Rodica, Carmen-Mihaela Cretu, Anca-Gabriela Turtureanu, Carmen-Gabriela Sirbu, Emanuel Ştefan Marinescu, Laurentiu-Gabriel Talaghir, Florentina Chițu, and Daniela Monica Robu. "A Statistical Analysis of the Migration Process: A Case Study—Romania." Sustainability 14, no. 5 (February 27, 2022): 2784. http://dx.doi.org/10.3390/su14052784.
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The research aims at studying and predicting the migration process in Romania over the last 20 years and at identifying the impact of the COVID-19 pandemic. The study analyzes several models for estimating migration through linear regression, but also a VAR (Vector autoregression) analysis, as the variables can influence each other. Vector autoregression (VAR) is also used to model multivariate time series, and it can analyze the dynamics of a migration process. Therefore, the best model for forecasting the migration process in Romania is Model 1 of linear regression. This phenomenon generates many positive and negative economic, demographic and political effects. The migration process has become particularly important for Romania in the last 20 years, and its socio-economic, political and cultural effects affect the Romanian state. That is why flexible policies are needed in order to be coherent, to have as main purpose keeping specialists in the country in certain basic economic fields, as well to implement measures to determine the return of specialists and students who have left to study abroad.
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Banerjee, Arindam. "Can Ratios Predict the Financial Performance in Banks: A Case of National Banks in United Arab Emirates (U.A.E)." International Journal of Accounting and Financial Reporting 8, no. 4 (October 11, 2018): 227. http://dx.doi.org/10.5296/ijafr.v8i4.13802.
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A country’s banking sector plays a dominant and important role in its financial growth and economic progress. The prime objective of this research paper is aimed towards evaluating the performance of 12 selected banks in United Arab Emirates (UAE) through various financial ratios. The paper highlights the various financial parameters such as adequacy of risk based capital, credit growth, concentration of credit, non performing position of loans, liquidity gap analysis, liquidity ratios, return on assets, return on equity, net interest margin in analysing the financial performance of the selected banks. The analysis of ratio helps to develop an insight to the extent the various financial variable impact the profitability and the productivity of the selected National Commercial Banks in U.A.E. The purpose of this paper is to examine the future financial performance of selected U.A.E National commercial banks using three indicators; Internal–based performance measured by Return on Assets, Market-based performance measured by Tobin’s Q model (Price / Book value of Equity) and Economic–based performance measured by Economic Value add. The financial data has been adopted from the audited financial statements of the sampled banks for the period of 2014 till 2017. Statistical tools used in the study include multiple regression analysis that captures the impact of the individual size of the bank, the credit risk, efficiency in operations and the asset management on the financial performance followed by forecasting the Future Trend.
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Gunawan, Mudita, and Achmad Herlanto Anggono. "Cryptocurrency Safe Haven Property against Indonesian Stock Market During COVID-19." Journal of Economics, Business, & Accountancy Ventura 24, no. 1 (July 30, 2021): 121. http://dx.doi.org/10.14414/jebav.v24i1.2661.
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Safe-haven assets conserve their value or grow against another asset or portfolioduring market turmoil. Indonesian stock market, represented by the Jakarta composite index (JKSE), plunged in price because of COVID-19, pushing investors to look for safe-havens. The cryptocurrency began to be perceived as a store of value as indicated by the transaction volume increase; hence it was expected to be a safe haven asset. However, cryptocurrency’s high price volatility cast doubts on its store of value effectiveness, prompting inspection for its safe haven property as well. This research aimed to predict the assets' risk and return plus investigate whether cryptocurrency is safe haven assets against the Indonesian stock market during COVID- 19. Daily closing prices of JKSE, Bitcoin, Ethereum, Litecoin, and Ripple were used, then the GARCH model was implemented in the forecasting. DCC-GARCH model, followed by dummy variable regression, will be applied to the return data to evaluate the safe haven property. The prediction projected Bitcoin as the most profitable asset andRipple as the riskiest. The analysis and robustness test suggested that none of these cryptocurrencies were safe haven assets during the whole observation. This indicates that investors who intend to seek safe haven investments were advised against investing in these cryptocurrencies.
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Gunawan, Mudita, and Achmad Herlanto Anggono. "Cryptocurrency Safe Haven Property against Indonesian Stock Market During COVID-19." Journal of Economics, Business, & Accountancy Ventura 24, no. 1 (July 30, 2021): 121. http://dx.doi.org/10.14414/jebav.v24i1.2661.
Full textAbstract:
Safe-haven assets conserve their value or grow against another asset or portfolioduring market turmoil. Indonesian stock market, represented by the Jakarta composite index (JKSE), plunged in price because of COVID-19, pushing investors to look for safe-havens. The cryptocurrency began to be perceived as a store of value as indicated by the transaction volume increase; hence it was expected to be a safe haven asset. However, cryptocurrency’s high price volatility cast doubts on its store of value effectiveness, prompting inspection for its safe haven property as well. This research aimed to predict the assets' risk and return plus investigate whether cryptocurrency is safe haven assets against the Indonesian stock market during COVID- 19. Daily closing prices of JKSE, Bitcoin, Ethereum, Litecoin, and Ripple were used, then the GARCH model was implemented in the forecasting. DCC-GARCH model, followed by dummy variable regression, will be applied to the return data to evaluate the safe haven property. The prediction projected Bitcoin as the most profitable asset andRipple as the riskiest. The analysis and robustness test suggested that none of these cryptocurrencies were safe haven assets during the whole observation. This indicates that investors who intend to seek safe haven investments were advised against investing in these cryptocurrencies.
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De OLIVEIRA, Guilherme Garcia, Dejanira Luderitz SALDANHA, and Laurindo Antonio GUASSELLI. "MODELS FOR SPATIALIZATION AND FORECASTING OF FLOODED AREAS IN THE SÃO SEBASTIÃO DO CAÍ URBAN ZONE, RIO GRANDE DO SUL STATE, BRAZIL." Pesquisas em Geociências 38, no. 2 (August 31, 2011): 132. http://dx.doi.org/10.22456/1807-9806.26379.
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The study aims at developing models for the spatialization and forecasting of floods in the urban area of São Sebastião do Caí, RS, Brazil. For the calculation of return period (RP), and in order to analyze the seasonality of floods, streamflow data from the station located in the city were used. However, for the development of a mathematical model for flood forecasting, the time series of a station upstream was also used in order to perform a regression with the quotas recorded in both seasons. For the identification of flood plains, a digital terrain model was produced based on elevation data in scales between 1:2,000 and 1:10,000. The QuickBird satellite image (spatial resolution of 0.61 m) was used only for the spatialization of the land use and land cover reached by each flood scenario. Mapping and 3D simulation of the areas affected by flooding were obtained for RP of 2, 5, 10 and 30 years. The following results are most significant: i) the river water level rises between 9.28 m and 11.98 m for RP of 2 to 30 years; ii) along the historical series, 75% of floods have occurred between June and October; iii) the mathematical model for flood forecasting showed an average error of 0.72 m, and the accuracy varies between 0.62 m and 1.84 m, according to the expected magnitude; iv) it was observed that 93 hectares of urban area in São Sebastião do Caí are hit by floods with a RP of 30 years (23% of the urban area); v) modelling of a recent flood event dated of 24/09/2007 has resulted in similar values for the simulated and observed flooded area.
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Huang, Tsui-Hua, Yungho Leu, and Wen-Tsao Pan. "Constructing ZSCORE-based financial crisis warning models using fruit fly optimization algorithm and general regression neural network." Kybernetes 45, no. 4 (April 4, 2016): 650–65. http://dx.doi.org/10.1108/k-08-2015-0208.
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Purpose – In order to avoid enterprise crisis and cause the domino effect, which influences the investment return of investors, the national economy, and financial crisis, establishing a complete set of feasible financial early warning model can help to prevent the possibility of enterprise crisis in advance, and thus, reduce the influence on society and the economy. The purpose of this paper is to develop an efficient financial crisis warning model. Design/methodology/approach – First, the fruit fly optimization algorithm (FOA) is used to adjust the coefficients of the parameters in the ZSCORE model (we call it the FOA_ZSCORE model), and the difference between the forecasted value and the real target value is calculated. Afterward, the generalized regressive neural network (GRNN model), with optimized spread by FOA (we call it FOA_GRNN model), is used to forecast the difference to promote the forecasting accuracy. Various models, including ZSCORE, FOA_ZSCORE, FOA_ZSCORE+GRNN, and FOA_ZSCORE+FOA_GRNN, are trained and tested. Finally, different models are compared based on their prediction accuracies and ROC curves. Furthermore, more appropriate parameters, which are different from the parameters in the original ZSCORE model, are selected by using the multivariate adaptive regression splines (MARS) method. Findings – The hybrid model of the FOA_ZSCORE together with the FOA_GRNN offers the highest prediction accuracy, compared to other models; the MARS can be used to select more appropriate parameters to further improve the performance of the prediction models. Originality/value – This paper proposes a hybrid model, FOA_ZSCORE+FOA_GRNN which offers better performance than the original ZSCORE model.
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Sohibien, Gama Putra Danu, Lilis Laome, Achmad Choiruddin, and Heri Kuswanto. "COVID-19 Pandemic’s Impact on Return on Asset and Financing of Islamic Commercial Banks: Evidence from Indonesia." Sustainability 14, no. 3 (January 19, 2022): 1128. http://dx.doi.org/10.3390/su14031128.
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The aim of this study is to propose appropriate models to forecast Return on Asset (ROA) and financing of Indonesia Islamic Commercial Banks during COVID-19 pandemic. In particular, we study the models which involve reciprocal relation between ROA and financing and incorporate COVID-19 pandemic’s impact. It is crucial because the government would benefit from forecasting results to formulate the policy for the banks related to ROA and financing. We consider two models: Vector Autoregressive with exogenous variable (VARX) and spline regression, since both models are able to exploit the multivariate structure of ROA and financing and to include COVID-19 impact as predictor. The results show that the VARX outperforms spline regression in terms of RMSE. Using VARX, we deduce that ROA and financing have a positive reciprocal relationship, meaning that when ROA increases, financing would increase, and vice versa. In addition, the pandemic has significant impact on the decline of the ROA. We recommend that banks conduct an in-depth analysis to determine the appropriate form of restructuring for debtors so that it does not have a significant impact on the decrease in ROA.
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Loukianova, Anna, Egor Nikulin, and Andrey Zinchenko. "Forecasting the level of earnings management of Russian and Chinese companies." Investment Management and Financial Innovations 14, no. 2 (July 27, 2017): 264–80. http://dx.doi.org/10.21511/imfi.14(2-1).2017.11.
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The purpose of the current paper is to elaborate a model to forecast a particular type of earnings management by companies: upward earnings management, downward earnings management or the absence of significant manipulation. The sample analyzed in the current paper comprises 664 Russian and 2,380 Chinese public companies for the period 2009-2014. The forecast was made for 2014 based on annual accounting data for 2009-2013. Regression analysis, as well as Classification and Regression Tree modelling (CART), were used. The data forecast for 2014 was compared with actual data for that year, and the accuracy of the forecasting model was assessed. The paper outlines the main conditions under which a particular type of earnings manipulation is expected to take place in a company in the accounting period following the current one. It is shown that the main factor influencing the company’s level of earnings manipulation of the next accounting period for both Russian and Chinese companies is the debt ratio calculated as the ratio of total liabilities to total assets. The other important factors are: the company size, return on equity, earnings persistence, the level of earnings manipulation in the current period and stock emission.
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Doan, Thanh Nga, and Thu Trang Ta. "Factors of fraud triangle affecting the likelihood of material misstatements in financial statements: An empirical study." Journal of Governance and Regulation 12, no. 1 (2023): 82–92. http://dx.doi.org/10.22495/jgrv12i1art8.
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This study aims to identify and examine the three components of the fraud triangle theory that affect the likelihood of material misstatements in financial statements. With a sample of 150 listed companies from two stock exchanges in Vietnam, Ho Chi Minh City (HOSE) and Hanoi Stock Exchange (HNX) in 2019, this study uses multinomial logistic regression analysis to examine the relationship among factors. This study shows the impact of using the elements of the fraud triangle theory in forecasting the likelihood of material misstatement (Cressey, 1953; Romney et al., 1980). The results indicate that the following factors affect the possibility of material misstatements in financial statements of companies: debt ratio, return on assets, independence of the board members, selection of an audit firm, auditor change in comparison with the previous year, and historical financial statements with material misstatements. These findings of the study can be utilized to develop strategies to help identify companies that are likely to have material misstatements in their financial statements.
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Synenko, Oleksandr, Kateryna Yarema, and Yuliia Bezsmertna. "Solow economy model." Problems of Innovation and Investment Development, no. 21 (December 27, 2019): 150–57. http://dx.doi.org/10.33813/2224-1213.21.2019.15.
Full textAbstract:
The subject of the research is the approach to the possibility of using the Solow model to perform the regression analysis on the example of the Ukrainian economy model. The purpose of writing this article is to investigate the notion of regres- sion analysis, Solow’s economy model, algorithm for performing regression analy- sis on the example of Ukraine’s economy model. This model can be adapted for the economy of enterprises. Methodology. The research methodology is system-struc- tural and comparative analyzes (to study the structure of GDP); monograph (when studying methods of regression analysis on the example of the Ukrainian economy); economic analysis (when assessing the impact of factors on Ukraine’s GDP). The scientific novelty consists the features of the use of the Solow model on the ex- ample of Ukrainian economy are determined. An algorithm for calculating the basic parameters of a model using the Excel application package is disclosed. The main recommendations on the development of the national economy and economic growth through the use of macroeconomic instruments are given. Conclusions. The use of the Solow model enables forecasting and analysis. The results obtained re- vealed the problem of low resource return of capital as a resource, along with the means of macroeconomic regulation of the investment process, using which can improve the situation. A special place in these funds belongs to the accelerated depreciation and interest rate policies.