Journal articles on the topic 'Stock exchanges Forecasting Econometric models'
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1
Chlebus, Marcin, Michał Dyczko, and Michał Woźniak. "Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem." Central European Economic Journal 8, no. 55 (January 1, 2021): 44–62. http://dx.doi.org/10.2478/ceej-2021-0004.
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Abstract Statistical learning models have profoundly changed the rules of trading on the stock exchange. Quantitative analysts try to utilise them predict potential profits and risks in a better manner. However, the available studies are mostly focused on testing the increasingly complex machine learning models on a selected sample of stocks, indexes etc. without a thorough understanding and consideration of their economic environment. Therefore, the goal of the article is to create an effective forecasting machine learning model of daily stock returns for a preselected company characterised by a wide portfolio of strategic branches influencing its valuation. We use Nvidia Corporation stock covering the period from 07/2012 to 12/2018 and apply various econometric and machine learning models, considering a diverse group of exogenous features, to analyse the research problem. The results suggest that it is possible to develop predictive machine learning models of Nvidia stock returns (based on many independent environmental variables) which outperform both simple naïve and econometric models. Our contribution to literature is twofold. First, we provide an added value to the strand of literature on the choice of model class to the stock returns prediction problem. Second, our study contributes to the thread of selecting exogenous variables and the need for their stationarity in the case of time series models.
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Ni, Zhehan, and Weilun Chen. "A Comparative Analysis of the Application of Machine Learning Algorithms and Econometric Models in Stock Market Prediction." BCP Business & Management 34 (December 14, 2022): 879–90. http://dx.doi.org/10.54691/bcpbm.v34i.3108.
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Forecasting the future price trend of a stock traded on a financial exchange is the aim of stock market prediction. In recent decades, stock market prediction has been a fascinating topic in the domain of Data Science and Finance. In reality, the stock movement is ambiguous and chaotic due to various influencing factors such as government policy, current events, interest rates Etc. At the same time, accurate enough forecasting of stock price movement leads to substantial benefits for investors. This paper provides a comprehensive review of the application and comparison of Machine Learning (ML) algorithms and Econometric Models in stock market prediction. The mentioned models are categorized into (i) ML algorithms, including Linear Regression (LR), K-nearest neighbors (KNN), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM). (ii) Econometric Models, including Autoregressive Integrated Moving Average (ARIMA) Model, Capital Asset Pricing Model (CAPM), and Fama-French (FF) Factor Model.
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Chambi Condori, Pedro Pablo. "Financial contagion: The impact of the volatility of global stock exchanges on the Lima-Peru Stock Exchange." Economía & Negocios 1, no. 1 (June 24, 2020): 13–27. http://dx.doi.org/10.33326/27086062.2019.1.896.
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What happens in the international financial markets in terms of volatility, have an impact on the results of the local stock market financial markets, as a result of the spread and transmission of larger stock market volatility to smaller markets such as the Peruvian, assertion that goes in accordance with the results obtained in the study in reference. The statistical evaluation of econometric models, suggest that the model obtained can be used for forecasting volatility expected in the very short term, very important estimates for agents involved, because these models can contribute to properly align the attitude to be adopted in certain circumstances of high volatility, for example in the input, output, refuge or permanence in the markets and also in the selection of best steps and in the structuring of the portfolio of investment with equity and additionally you can view through the correlation on which markets is can or not act and consequently the best results of profitability in the equity markets. This work comprises four well-defined sections; a brief history of the financial volatility of the last 15 years, a tight summary of the background and a dense summary of the methodology used in the process of the study, exposure of the results obtained and the declaration of the main conclusions which led us mention research, which allows writing, evidence of transmission and spread of the larger stock markets toward the Peruvian stock market volatility, as in the case of the American market to the market Peruvian stock market with the coefficient of dynamic correlation of 0.32, followed by the Spanish market and the market of China. Additionally, the coefficient of interrelation found by means of the model dcc mgarch is a very important indicator in the structure of portfolios of investment with instruments that they quote on the financial global markets.
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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 (June 20, 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 Classifier (XG), Bagging Classifier (BC), AdaBoost Classifier (Ada), Extra Trees Classifier (ET), and Voting Classifier (VC)) in forecasting the direction of stock price movement. Eight different stock data from three stock exchanges (NYSE, NASDAQ, and NSE) are randomly collected and used for the study. Each data set is split into training and test set. Ten-fold cross validation accuracy is used to evaluate the ML models on the training set. In addition, the ML models are evaluated on the test set using accuracy, precision, recall, F1-score, specificity, and area under receiver operating characteristics curve (AUC-ROC). Kendall W test of concordance is used to rank the performance of the tree-based ML algorithms. For the training set, the AdaBoost model performed better than the rest of the models. For the test set, accuracy, precision, F1-score, and AUC metrics generated results significant to rank the models, and the Extra Trees classifier outperformed the other models in all the rankings.
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Rudzkis, Rimantas, Roma Valkavičienė, and Virmantas Kvedaras. "Prediction of Baltic Sectorial Share Price Indices." Lietuvos statistikos darbai 53, no. 1 (December 20, 2014): 53–59. http://dx.doi.org/10.15388/ljs.2014.13894.
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Extending the research started in [31], the paper uses econometric methods for the short-term forecasting of quarterly values of sector indexes of stock prices from the OMX Baltic stock exchange. The ARMA models and modelling methodology that was used to build the statistical models in the previous paper are now augmented with the algorithms of time series aggregation and identification of special features of the series. Here, the search for informative factors relies on the study of related literature. The specification of models is further tailored using the traditional significance (p-value) analysis of regressors and a cross-validation analysis. The latter is implemented in this paper using the Jack-knife approach. The data period analysed covers the years 2000–2013. The results of the analysis indicate that the inclusion not only of recent autoregressive terms but also of some aggregated characteristics (as certain special features of indexes) improves the precision of forecasting substantially. The calculations were performed using the statistical analysis software SAS.
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Manikandan, Narayanan, and Srinivasan Subha. "Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks." Scientific World Journal 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/6709352.
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Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.
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Sheng, Yankai, and Ding Ma. "Stock Index Spot–Futures Arbitrage Prediction Using Machine Learning Models." Entropy 24, no. 10 (October 13, 2022): 1462. http://dx.doi.org/10.3390/e24101462.
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With the development of quantitative finance, machine learning methods used in the financial fields have been given significant attention among researchers, investors, and traders. However, in the field of stock index spot–futures arbitrage, relevant work is still rare. Furthermore, existing work is mostly retrospective, rather than anticipatory of arbitrage opportunities. To close the gap, this study uses machine learning approaches based on historical high-frequency data to forecast spot–futures arbitrage opportunities for the China Security Index (CSI) 300. Firstly, the possibility of spot–futures arbitrage opportunities is identified through econometric models. Then, Exchange-Traded-Fund (ETF)-based portfolios are built to fit the movements of CSI 300 with the least tracking errors. A strategy consisting of non-arbitrage intervals and unwinding timing indicators is derived and proven profitable in a back-test. In forecasting, four machine learning methods are adopted to predict the indicator we acquired, namely Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), Back Propagation Neural Network (BPNN), and Long Short-Term Memory neural network (LSTM). The performance of each algorithm is compared from two perspectives. One is an error perspective based on the Root-Mean-Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and goodness of fit (R2). Another is a return perspective based on the trade yield and the number of arbitrage opportunities captured. Finally, a performance heterogeneity analysis is conducted based on the separation of bull and bear markets. The results show that LSTM outperforms all other algorithms over the entire time period, with an RMSE of 0.00813, MAPE of 0.70 percent, R2 of 92.09 percent, and an arbitrage return of 58.18 percent. Meanwhile, in certain market conditions, namely both the bull market and bear market separately with a shorter period, LASSO can outperform.
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Moћић, Брaнимир Д. "Крaткoрoчнo прeдвиђaњe принoсa бeрзaнскoг индeксa Рeпубликe Српскe (БИРС) // Short-term return forecasti ng of the Stock Exchange Index of Republic of Srpska (BIRS)." ACTA ECONOMICA 10, no. 17 (June 10, 2012): 155. http://dx.doi.org/10.7251/ace1217155m.
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Резиме: Aктивнo учeствoвaњe рaциoнaлнoг инвeститoрa нa финaнсиjскoм тр-жишту пoдрaзумjeвa њeгoву спoсoбнoст дa приликoм избoрa финaнсиjскихиструмeнaтa зa oдрeђeни пeриoд улaгaњa, бирa oнe инструмeнтe кojипoсjeдуjу нajвeћи oчeкивaни принoс зa дaти нивo ризикa. Имajући у видудa je риjeч o oчeкивaним вриjeднoстимa пaрaмeтaрa, њихoвe вриjeднoсти нису унaприjeд пoзнaтe, стoгa сe oнe мoрajу прeдвидjeти. Oснoви прeдмeтистрaживaњa у oвoм рaду oднoси сe нa упoтрeбу aутoрeгрeсиoних мoдeлa пoкрeтних срeдинa (Aутoрeгрeссивe мoвинг aвeрaгe - AРMA) зa крaткoрoч-нo прeдвиђaњa вриjeднoсти принoсa бeрзaнскoг индeксa Рeпубликe Српскe(БИРС). Oснoвни циљ истрaживaњa jeстe дa сe сaглeдa стeпeн eфикaснoстиу прeдвиђaњу принoсa БИРС-a нa oснoву oвих мoдeлa, тe дa сe крoз стaтистичкo-eкoнoмeтриjску aнaлизу финaнсиjскo тржиштe Рeпубликe Српскeучини инфoрмaциoнo aфирмaтивниjим. Summary: Active participation of rational investors in the fi nanci al markets imply its abilityto select fi nancial instruments that have the highest expected return for a givenlevel of risk for a certain investment period. Bearing in mind that these returns arethe expected values of the parameters, their values are not known in advance, sothey must be forecasted. Main subject of this research refers to the use Autoregressivemodels (Autoregressive moving average - ARMA) in process of short term returnforecasting of the Stock Exchange Index of Republic of Srpska (BIRS). Th e mainobjective of this research is to examine the effi ciency of return forecasting based onautoregressive models, and trough comprehensive statistical-econometric analysis,make fi nancial market of Republic of Srpska more informational affi rmative.
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Markowski, Łukasz, and Jakub Keller. "Fear Anatomy – an Attempt to Assess the Impact of Selected Macroeconomic Variables on the Variability of the VIX S&P 500 Index." Annales Universitatis Mariae Curie-Skłodowska, sectio H – Oeconomia 54, no. 2 (June 29, 2020): 41. http://dx.doi.org/10.17951/h.2020.54.2.41-51.
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<p>This article deals with the subject of volatility of financial markets in relation to the US stock market and its volatility index, i.e. the VIX index. The authors analyzed previous studies on the VIX index and based on them, defined a research gap that relates to the problem of market response to emerging macroeconomic information about the US economy. The vast majority of research on the VIX index relates to its forecasting based on mathematical models not taking into account current market data. The authors attempted to assess the impact of emerging macro data on the variability of the VIX index, thus illustrating the magnitude of the impact of individual variables on the so-called US Stock Exchange fear index. The study analysed 80 macroeconomic variables in the period from January 2009 to June 2019 in order to check which of them cause the greatest market volatility. The study was based on correlation study and econometric modeling. The obtained results allowed to formulate conclusions indicating the most important macroeconomic parameters that affect the perception of the market by investors through the pricing of options valuation on the S&P 500 index. The authors managed to filter the most important variables for predicting the change of VIX level. In the eyes of the authors, the added value of the article is to indicate the relationship between macro variables and market volatility illustrated by the VIX index, which has not been explored in previous studies. The analyzes carried out are part of the research trend on market information efficiency and broaden knowledge in the area of capital investments.</p>
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Razzaq Al Rababa’a, Abdel, Zaid Saidat, and Raed Hendawi. "Forecasting stock returns on the Amman Stock Exchange: Do neural networks outperform linear regressions?" Investment Management and Financial Innovations 18, no. 4 (December 1, 2021): 280–96. http://dx.doi.org/10.21511/imfi.18(4).2021.24.
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Different models have been used in the finance literature to predict the stock market returns. However, it remains an open question whether non-linear models can outperform linear models while providing accurate predictions for future returns. This study examines the prediction of the non-linear artificial neural network (ANN) models against the baseline linear regression models. This study aims specifically to compare the prediction performance of regression models with different specifications and static and dynamic ANN models. Thus, the analysis was conducted on a growing market, namely the Amman Stock Exchange. The results show that the trading volume and interest rates on loans tend to explain the monthly returns the most, compared to other predictors in the regressions. Moreover, incorporating more variables is not found to help in explaining the fluctuations in the stock market returns. More importantly, using the root mean square error (RMSE), as well as the mean absolute error statistical measures, the static ANN becomes the most preferred model for forecasting. The associated forecasting errors from these metrics become equal to 0.0021 and 0.0005, respectively. Lastly, the analysis conducted with the dynamic ANN model produced the highest RMSE value of 0.0067 since November 2018 following the amendment to the Jordanian income tax law. The same observation is also seen since the emerging of the COVID-19 outbreak (RMSE = 0.0042).
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Dritsakis, Nikolaos, and Georgios Savvas. "Forecasting Volatility Stock Return: Evidence from the Nordic Stock Exchanges." International Journal of Economics and Finance 9, no. 2 (January 11, 2017): 15. http://dx.doi.org/10.5539/ijef.v9n2p15.
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The purpose of this study is to explore the volatility and secondary effects in the four Nordic stock exchanges of Norway: Oslo Bors Linked all-share index AXLT Denmark: OMX Copenhagen 20, Sweden: OMX Stockholm 30 and Finland: OMX Helsinki 25. Keeping in mind that there is an ARCH effect in the returns of the four stock exchanges, we move on to the evaluation to the evaluation of models ARCH (q), GARCH (p, q) GARCH-M (p, q). Evaluating the parameters became possible through the use of the maximum likelihood method using the BHHH algorithm of (Berndt et al., 1974) and the three distributions (normal, t-Student, and the Generalized normal distribution GED). The results of this study indicate model ARMA(0,1)-GARCH-Μ(1,1) with t-student distribution as the appropriate one to describe the returns of the all Nordic stock exchanges except that of Sweden, where model ARMA(0,3)-GARCH-Μ(1,1) describes it best. Lastly, for forecasting the models ARMA(0,1)-GARCH-Μ(1,1) and ARMA(0,3)-GARCH-Μ(1,1) of the current stock exchanges we use both the dynamic and static process. The results of this study indicate that the static process forecasts better than the corresponding dynamic.
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Olena Nikolaieva, Anzhela Petrova, and Rostyslav Lutsenko. "FORECASTING OF THE STOCK RATE OF LEADING WORLD COMPANIES USING ECONOMETRIC METHODS AND DCF ANALYSIS." International Journal of Innovative Technologies in Economy, no. 2(29) (May 31, 2020): 33–41. http://dx.doi.org/10.31435/rsglobal_ijite/31052020/7067.
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In this article, we will cover various models for forecasting the stock price of global companies, namely the DCF model, with well-reasoned financial analysis and the ARIMA model, an integrated model of autoregression − moving average, as an econometric mechanism for point and interval forecasting. The main goal is to compare the obtained forecasting results and evaluate their real accuracy. The article is based on forecasting stock prices of two companies: Coca-Cola HBC AG (CCHGY) and Nestle S.A. (NSRGF). At the moment, it is not determined which approach is better for predicting the stock price − the analysis of financial indicators or the use of econometric data analysis methods.
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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 (December 2006): 283–95. http://dx.doi.org/10.1177/097265270600500305.
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YAO, JINGTAO, CHEW LIM TAN, and HEAN-LEE POH. "NEURAL NETWORKS FOR TECHNICAL ANALYSIS: A STUDY ON KLCI." International Journal of Theoretical and Applied Finance 02, no. 02 (April 1999): 221–41. http://dx.doi.org/10.1142/s0219024999000145.
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This paper presents a study of artificial neural nets for use in stock index forecasting. The data from a major emerging market, Kuala Lumpur Stock Exchange, are applied as a case study. Based on the rescaled range analysis, a backpropagation neural network is used to capture the relationship between the technical indicators and the levels of the index in the market under study over time. Using different trading strategies, a significant paper profit can be achieved by purchasing the indexed stocks in the respective proportions. The results show that the neural network model can get better returns compared with conventional ARIMA models. The experiment also shows that useful predictions can be made without the use of extensive market data or knowledge. The paper, however, also discusses the problems associated with technical forecasting using neural networks, such as the choice of "time frames" and the "recency" problems.
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Chalissery, Neenu, Mosab I. Tabash, Mohamed Nishad T., and Maha Rahrouh. "Modeling asymmetric volatility of financial assets using univariate GARCH models: An Indian perspective." Investment Management and Financial Innovations 19, no. 4 (December 6, 2022): 244–59. http://dx.doi.org/10.21511/imfi.19(4).2022.20.
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In recent years, numerous models with various amounts of variance have been developed to estimate and forecast important characteristics of time series data. While there are many studies on asymmetric volatility and accuracy testing of univariate Generalized Autoregressive Conditional Heteroscedasticity models, there are no parallel studies involving multiple financial assets and different heteroscedastic models and density functions. The objective of this study is to contrast the forecasting accuracy of univariate volatility models with Normal and Student-t distributions in forecasting the volatility of stock, gold futures, crude futures, exchange rate, and bond yield over a 10-year time span from January 2010 through December 2021 in Indian market. The results of exponential, threshold and asymmetric power models show that the volatility stock (–0.12047, 0.17433, 0.74020 for Nifty, and –0.1153, 0.1676, 0.7372 for Sensex), exchange rate (–0.0567, 0.0961,0.9004), crude oil futures (-0.0411, 0.0658, 0.2130), and bond yield (–0.0193, 0.0514 and –0.0663) react asymmetrically to good and bad news. In case of gold futures, an inverse asymmetric effect (0.0537, –0.01217, –0.1898) is discovered; positive news creates higher variance in gold futures than bad news. The Exponential model captures the asymmetric volatility effect in all asset classes better than any other asymmetric models. This opens the door for many studies in Indian financial market.
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Banik, Shipra, Mohammed Anwer, and A. F. M. Khodadad Khan. "Modeling Chaotic Behavior of Chittagong Stock Indices." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/410832.
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Stock market prediction is an important area of financial forecasting, which attracts great interest to stock buyers and sellers, stock investors, policy makers, applied researchers, and many others who are involved in the capital market. In this paper, a comparative study has been conducted to predict stock index values using soft computing models and time series model. Paying attention to the applied econometric noises because our considered series are time series, we predict Chittagong stock indices for the period from January 1, 2005 to May 5, 2011. We have used well-known models such as, the genetic algorithm (GA) model and the adaptive network fuzzy integrated system (ANFIS) model as soft computing forecasting models. Very widely used forecasting models in applied time series econometrics, namely, the generalized autoregressive conditional heteroscedastic (GARCH) model is considered as time series model. Our findings have revealed that the use of soft computing models is more successful than the considered time series model.
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Jia, Fang, and Boli Yang. "Forecasting Volatility of Stock Index: Deep Learning Model with Likelihood-Based Loss Function." Complexity 2021 (February 25, 2021): 1–13. http://dx.doi.org/10.1155/2021/5511802.
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Volatility is widely used in different financial areas, and forecasting the volatility of financial assets can be valuable. In this paper, we use deep neural network (DNN) and long short-term memory (LSTM) model to forecast the volatility of stock index. Most related research studies use distance loss function to train the machine learning models, and they gain two disadvantages. The first one is that they introduce errors when using estimated volatility to be the forecasting target, and the second one is that their models cannot be compared to econometric models fairly. To solve these two problems, we further introduce a likelihood-based loss function to train the deep learning models and test all the models by the likelihood of the test sample. The results show that our deep learning models with likelihood-based loss function can forecast volatility more precisely than the econometric model and the deep learning models with distance loss function, and the LSTM model is the better one in the two deep learning models with likelihood-based loss function.
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Lascsáková, Marcela. "Improving Accuracy of the Numerical Model Forecasting Commodity Prices." Applied Mechanics and Materials 708 (December 2014): 251–56. http://dx.doi.org/10.4028/www.scientific.net/amm.708.251.
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In mathematical models, for forecasting prices on commodity exchanges different mathematical methods are used. In the paper the numerical model based on the exponential approximation of commodity stock exchanges was derived. The price prognoses of aluminium on the London Metal Exchange were determined as numerical solution of the Cauchy initial problem for the 1st order ordinary differential equation. To make the numerical model more accurate the idea of the modification of the initial condition value by the stock exchange was realized. The derived numerical model was observed to determine the influence of the decreased size of the limiting value error causing the modification of the initial condition value by the chosen stock exchange on the accuracy of the obtained prognoses. The advantage of the chosen sizes of the limiting value error 7 % and 8 % within different movements of aluminium prices was studied.
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Angelidis,, Dimitrios, Athanasios Koulakiotis, and Apostolos Kiohos. "Feedback Trading Strategies: The Case of Greece and Cyprus." South East European Journal of Economics and Business 13, no. 1 (June 1, 2018): 93–99. http://dx.doi.org/10.2478/jeb-2018-0006.
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Abstract This paper examines whether or not feedback trading strategies are present in the Athens (ASE) and Cyprus Stock Exchanges (CSE). The analysis employs two econometric models: the feedback trading strategy model, introduced by Sentana and Wadhwani (1992), and the exponential autoregressive model, proposed by LeBaron (1992). These two theoretical frameworks, separately, were joined with the FIGARCH (1, d, 1) approach. Both models assume two different groups of traders - the “rational” investors that build their portfolio by following the firms’ fundamentals and the “noise” speculators that ignore stock fundamentals and focus on a positive (negative) feedback trading strategy. The empirical results revealed that negative feedback trading strategies exist in the two underlying stock markets
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Majewski, Sebastian, Waldemar Tarczynski, and Malgorzata Tarczynska-Luniewska. "Measuring investors’ emotions using econometric models of trading volume of stock exchange indexes." Investment Management and Financial Innovations 17, no. 3 (September 30, 2020): 281–91. http://dx.doi.org/10.21511/imfi.17(3).2020.21.
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Traditional finance explains all human activity on the ground of rationality and suggests all decisions are rational because all current information is reflected in the prices of goods. Unfortunately, the development of information technology and a growth of demand for new, attractive possibilities of investment caused the process of searching new, unique signals supporting investment decisions. Such a situation is similar to risk-taking, so it must elicit the emotional reactions of individual traders.The paper aims to verify the question that the market risk may be the determinant of traders’ emotions, and if volatility is a useful tool during the investment process as the measure of traders’ optimism, similarly to Majewski’s work (2019). Likewise, various econometric types of models of estimation of the risk parameter were used in the research: classical linear using OLS, general linear using FGLS, and GARCH(p, q) models using maximum likelihood method. Hypotheses were verified using the data collected from the most popular world stock exchanges: New York, Frankfurt, Tokyo, and London. Data concerned stock exchange indexes such as SP500, DAX, Nikkei, and UK100.
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Shi, Chao, and Xiaosheng Zhuang. "A Study Concerning Soft Computing Approaches for Stock Price Forecasting." Axioms 8, no. 4 (October 18, 2019): 116. http://dx.doi.org/10.3390/axioms8040116.
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Financial time-series are well known for their non-linearity and non-stationarity nature. The application of conventional econometric models in prediction can incur significant errors. The fast advancement of soft computing techniques provides an alternative approach for estimating and forecasting volatile stock prices. Soft computing approaches exploit tolerance for imprecision, uncertainty, and partial truth to progressively and adaptively solve practical problems. In this study, a comprehensive review of latest soft computing tools is given. Then, examples incorporating a series of machine learning models, including both single and hybrid models, to predict prices of two representative indexes and one stock in Hong Kong’s market are undertaken. The prediction performances of different models are evaluated and compared. The effects of the training sample size and stock patterns (viz. momentum and mean reversion) on model prediction are also investigated. Results indicate that artificial neural network (ANN)-based models yield the highest prediction accuracy. It was also found that the determination of optimal training sample size should take the pattern and volatility of stocks into consideration. Large prediction errors could be incurred when stocks exhibit a transition between mean reversion and momentum trend.
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Rawlin, Rajveer S., and Satya Surya Narayana Raju Pakalapati. "Forecasting Stock Prices of Select Indian Private Sector Banks – A Time Series Approach." SDMIMD Journal of Management 13, no. 1 (March 1, 2022): 35. http://dx.doi.org/10.18311/sdmimd/2022/29270.
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<p>Forecasting stock markets and individual stocks has been a well-researched area in the world of finance. Fundamental and technical analysis is widely used by investors in analysing stock prices. Researchers have used various methods to predict stock prices such as Hidden Markov models, genetic algorithms and neural networks (Enke, Grauer, and Mehdiyev, 2011; Hassan, Nath, and Kirley 2007). Time series analysis is used in forecasting asset prices (Long et al, 2021; Eita, 2012). Indian private sector banks are among the best-performing stocks on the Indian stock exchanges over the last decade, as they have consistently captured market share from their public sector counterparts. ARIMA is a useful technique to forecast stock and stock index prices (Box and Jenkins, 1970). This study aimed to evaluate the effectiveness of the ARIMA model in forecasting private bank stock prices in India. Forecasted values differed from actual prices, suggesting markets may be efficient and other variables may also prove to be influential in forecasting Indian private bank stock prices.</p>
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de Marcos, Rodrigo, Antonio Bello, and Javier Reneses. "Short-Term Electricity Price Forecasting with a Composite Fundamental-Econometric Hybrid Methodology." Energies 12, no. 6 (March 20, 2019): 1067. http://dx.doi.org/10.3390/en12061067.
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Various power exchanges are nowadays being affected by a plethora of factors that, as a whole, cause considerable instabilities in the system. As a result, traders and practitioners must constantly adapt their strategies and look for support for their decision-making when operating in the market. In many cases, this calls for suitable electricity price forecasting models that can account for relevant aspects for electricity price forecasting. Consequently, fundamental-econometric hybrid approaches have been developed by many authors in the literature, although these have rarely been applied in short-term contexts, where other considerations and issues must be addressed. Therefore, this work aims to develop a robust hybrid methodology that is capable of making the most of the advantages fundamental and the hybrid model in a synergistic manner, while also providing insight as to how well these models perform across the year. Several methods have been utilised in this work in order to modify the hybridisation approach and the input datasets for enhanced predictive accuracy. The performance of this proposal has been analysed in the real case study of the Iberian power exchange and has outperformed other well-recognised and traditional methods.
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Khoa, Bui Thanh, and Tran Trong Huynh. "Forecasting stock price movement direction by machine learning algorithm." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (December 1, 2022): 6625. http://dx.doi.org/10.11591/ijece.v12i6.pp6625-6634.
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<p><span lang="EN-US">Forecasting stock price movement direction (SPMD) is an essential issue for short-term investors and a hot topic for researchers. It is a real challenge concerning the efficient market hypothesis that historical data would not be helpful in forecasting because it is already reflected in prices. Some commonly-used classical methods are based on statistics and econometric models. However, forecasting becomes more complicated when the variables in the model are all nonstationary, and the relationships between the variables are sometimes very weak or simultaneous. The continuous development of powerful algorithms features in machine learning and artificial intelligence has opened a promising new direction. This study compares the predictive ability of three forecasting models, including <a name="_Hlk106797328"></a>support vector machine (SVM), artificial neural networks (ANN), and logistic regression. The data used is those of the stocks in the VN30 basket with a holding period of one day. With the rolling window method, this study got a highly predictive SVM with an average accuracy of 92.48%.</span></p>
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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 presidential election, stock market volatility decreases. Using two data sets from the 2000 U.S. presidential election, we test our claim by estimating several GARCH, exponential GARCH (EGARCH), fractionally integrated exponential GARCH (FIEGARCH), and Markov-switching models. We also conduct extensive forecasting tests—including RMSE and MAE statistics as well as realized volatility regressions—to evaluate these competing statistical models. Results from forecasting tests show, in contrast to prevailing claims, that GARCH and EGARCH models provide substantially more accurate forecasts than the Markov-switching models. Estimates from all the statistical models support our key prediction that stock market volatility decreases when traders anticipate a Democratic victory.
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Budzyńska, Anna, and Mirosław Piotr Urbanek. "Prediction of global sugar prices after abolition of EU sugar quotas." Przegląd Prawno-Ekonomiczny, no. 2 (June 17, 2021): 9–24. http://dx.doi.org/10.31743/ppe.12450.
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In the article two main goals were indicated. The first is to verify the hypothesis that there is not a relevant relationship between limiting the impact of state intervention mechanisms and sugar prices on world exchanges. The second goal is to choose the best model for forecasting sugar prices after the abolition of the sugar quotas on domestic markets of sugar producers. The starting point for building the model was the time series of sugar prices on a monthly basis on world stock exchanges – London and New York in 1990–2020. One of the three models was used for forecasting. Sugar prices on world stock exchanges showed large fluctuations amounting to USD cents 28 per pound of sugar for white sugar, while for raw sugar the figure was slightly lower and reached USD cents 26 per pound. On average, in 1990–2020, the nominal price for white sugar was 16 cents per pound, and for raw sugar -12 cents per pounds. However, the level of sugar prices in the world is determined primarily by market factors, rather than administrative constraints.
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Zhang, Fengyi, Zhigao Liao, and Hongping Hu. "Application of Multi-Input Hamacher-ANFIS Ensemble Model on Stock Price Forecast." Advances in Data Science and Adaptive Analysis 11, no. 01n02 (April 2019): 1950004. http://dx.doi.org/10.1142/s2424922x19500049.
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The stock market is a complex, evolving, and nonlinear dynamic system. Forecasting stock prices has been regarded as one of the most challenging applications of modern time series forecasting. This paper proposes a novel multi-input Hamacher-ANFIS (adaptive network-based fuzzy inference system based on Hamacher operator) ensemble model to forecast stock prices in China’s stock market and achieve good prediction performance. We selected five stocks with the largest total market capitalization from the Shanghai and Shenzhen Stock Exchanges, measured their historical volatility over the same time period, and weighed the performance of each stock forecasting model based on the above volatility. Then, the experiment was repeated 100 times for each data set, and we calculated the comprehensive [Formula: see text] of the testing set according to the weight that we obtained earlier. The statistical test of the experimental results shows that: (1) In terms of comprehensive [Formula: see text] of the stock price, the multi-input Hamacher-ANFIS model is superior to other conventional models; (2) when compared with the nonensemble forecasting strategy, the ensemble strategy of the Hamacher-ANFIS model has significant advantages.
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Lukasevich, I. "Assessment and modeling of volatility in the Russian stock market: empirical study." Management and Business Administration, no. 1 (March 30, 2022): 134–49. http://dx.doi.org/10.33983/2075-1826-2022-1-134-149.
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The work is devoted to an empirical study of volatility in the Russian stock market. The indicators of volatility assessment are considered, approaches to its assessment and modeling are analyzed. Based on the values of the stock indices MOEX10 and MOEXBMI for a ten-year period from 2012 to 2021, various indicators of their volatility were determined, its properties and features were identified. Econometric models are proposed that describe the behavior of the volatility of the MOEX10 and MOEXBMI indices and allow for short-term and long-term forecasting of their values. The indicators, models and approaches used in this article can be used by investors to assess and predict risks when conducting transactions in the stock market.
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DeJong, David N., and Charles H. Whiteman. "Modeling Stock Prices without Knowing How to Induce Stationarity." Econometric Theory 10, no. 3-4 (August 1994): 701–19. http://dx.doi.org/10.1017/s0266466600008732.
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Bayesian procedures for evaluating linear restrictions imposed by economic theory on dynamic econometric models are applied to a simple class of presentvalue models of stock prices. The procedures generate inferences that are not conditional on ancillary assumptions regarding the nature of the nonstationarity that characterizes the data. Inferences are influenced by prior views concerning nonstationarity, but these views are formally incorporated into the analysis, and alternative views are easily adopted. Viewed in light of relatively tight prior distributions that have proved useful in forecasting, the present-value model seems at odds with the data. Researchers less certain of the interaction between dividends and prices would find little reason to look beyond the present-value model.
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Serrano Bautista, Ramona, and José Antonio Núñez Mora. "Value-at-risk predictive performance: a comparison between the CaViaR and GARCH models for the MILA and ASEAN-5 stock markets." Journal of Economics, Finance and Administrative Science 26, no. 52 (November 24, 2021): 197–221. http://dx.doi.org/10.1108/jefas-03-2021-0009.
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PurposeThis paper tests the accuracies of the models that predict the Value-at-Risk (VaR) for the Market Integrated Latin America (MILA) and Association of Southeast Asian Nations (ASEAN) emerging stock markets during crisis periods.Design/methodology/approachMany VaR estimation models have been presented in the literature. In this paper, the VaR is estimated using the Generalized Autoregressive Conditional Heteroskedasticity, EGARCH and GJR-GARCH models under normal, skewed-normal, Student-t and skewed-Student-t distributional assumptions and compared with the predictive performance of the Conditional Autoregressive Value-at-Risk (CaViaR) considering the four alternative specifications proposed by Engle and Manganelli (2004).FindingsThe results support the robustness of the CaViaR model in out-sample VaR forecasting for the MILA and ASEAN-5 emerging stock markets in crisis periods. This evidence is based on the results of the backtesting approach that analyzed the predictive performance of the models according to their accuracy.Originality/valueAn important issue in market risk is the inaccurate estimation of risk since different VaR models lead to different risk measures, which means that there is not yet an accepted method for all situations and markets. In particular, quantifying and forecasting the risk for the MILA and ASEAN-5 stock markets is crucial for evaluating global market risk since the MILA is the biggest stock exchange in Latin America and the ASEAN region accounted for 11% of the total global foreign direct investment inflows in 2014. Furthermore, according to the Asian Development Bank, this region is projected to average 7% annual growth by 2025.
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Lei, Bolin, Boyu Zhang, and Yuping Song. "Volatility Forecasting for High-Frequency Financial Data Based on Web Search Index and Deep Learning Model." Mathematics 9, no. 4 (February 5, 2021): 320. http://dx.doi.org/10.3390/math9040320.
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The existing index system for volatility forecasting only focuses on asset return series or historical volatility, and the prediction model cannot effectively describe the highly complex and nonlinear characteristics of the stock market. In this study, we construct an investor attention factor through a Baidu search index of antecedent keywords, and then combine other trading information such as the trading volume, trend indicator, quote change rate, etc., as input indicators, and finally employ the deep learning model via temporal convolutional networks (TCN) to forecast the volatility under high-frequency financial data. We found that the prediction accuracy of the TCN model with investor attention is better than those of the TCN model without investor attention, the traditional econometric model as the generalized autoregressive conditional heteroscedasticity (GARCH), the heterogeneous autoregressive model of realized volatility (HAR-RV), autoregressive fractionally integrated moving average (ARFIMA) models, and the long short-term memory (LSTM) model with investor attention. Compared with the traditional econometric models, the multi-step prediction results for the TCN model remain robust. Our findings provide a more accurate and robust method for volatility forecasting for big data and enrich the index system of volatility forecasting.
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Huang, Chun-Kai, Venelle Pather, Jahvaid Hammujuddy, and Knowledge Chinhamu. "Extreme Risk In Resource Indices And The Generalized Logistic Distribution." Journal of Applied Business Research (JABR) 33, no. 2 (March 1, 2017): 283–96. http://dx.doi.org/10.19030/jabr.v33i2.9899.
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The resource sector accounts for a substantial proportion of market capitalization on the US and South African stock exchanges. Hence, severe movements in related stock prices can drastically affect the risk profile of the entire market. Extreme value theory provides a basis for evaluating and forecasting such sporadic occurrences. In this article, we compare performances of classical extreme value models against the recently suggested generalized logistic distribution, for estimating value-at-risk and expected shortfall in resource indices. Our results suggest a significant difference in risk behavior between the two markets and the generalized logistic distribution does not always outperform classical models, as previous work may have suggested.
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Nandy, Ankita. "Forecasting the Movement of Renewables Stocks Using BSE Energy Index1." International Journal of Research in Science and Technology 12, no. 01 (2022): 07–18. http://dx.doi.org/10.37648/ijrst.v12i01.002.
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Coincident to the dip in the demand of conventional sources of energy like coal, oil and gas as the pandemic progressed has been a surge in the global demand for environment friendly practices, putting the spotlight on energy generated from renewable sources. The Renewables sector has found favor and is witnessing steady rise on a global level. Though a minor contributor to the power generation in India, this sector is deemed to grow in the coming years as India strives to reduce its CO2 emissions, making the related instruments lucrative investment options. Stock exchanges are critical to the economic health of a nation and the pandemic led to major crashes in several exchanges around the world. Investment firms can employ deep learning models to forecast the movement of the market and thus assure their customers of high returns in the high-risk environment, cutting through the general pessimism pervading the investment sphere post-pandemic. This work builds forecasting models for two such stocks using neural networks. Selecting the BSE as the universe of study, two companies are selected and modelled across two techniques: LSTM and Bidirectional LSTM, employing three different feature sets. The inclusion of BSE Energy Index in the models alongside the historical prices enables capturing the influence of external elements on the energy market.
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Petrušková, Kristína, and Erika Liptáková. "Development of electricity prices in the V4 countries and econometric model of forecasting spot electricity prices in Slovakia." Advances in Thermal Processes and Energy Transformation 3, no. 1 (2020): 14–24. http://dx.doi.org/10.54570/atpet2020/03/01/0014.
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In this article, the authors focused on the development of electricity prices on wholesale and retail markets in the Visegrad Four countries (V4). The main goal of the paper is to design an econometric model of predicting spot electricity prices in Slovakia. Therefore, the model of multiple linear regression is used, based on knowledge from the literature and existing studies. The model can be used to predict the possible development of electricity prices depending on several relevant variables - coal prices, gas prices and the share of renewable energy sources. The data were obtained from Eurostat and OECD databases, investment sites, which showed the development of commodity prices on wholesale market, or from power exchanges or a short-term electricity market operator. RStudio software was then used to evaluate created models. The result was price forecasts for further periods, which were also compared with the actual spot prices.
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Rege, Sameer, and Samuel Gil Martín. "PORTUGUESE STOCK MARKET: A LONG-MEMORY PROCESS?" Business: Theory and Practice 12, no. 1 (March 10, 2011): 75–84. http://dx.doi.org/10.3846/btp.2011.08.
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This paper gives a basic overview of the various attempts at modelling stochastic processes for stock markets with a specific application to the Portuguese stock market data. Long-memory dependence in the stock prices would completely alter the data generation process and econometric models not considering the long-range dependence would exhibit poor forecasting abilities. The Hurst exponent is used to identify the presence of long-memory or fractal behaviour of the data generation process for the daily returns to ascertain if the process follows a fractional brownian motion. Detrended fluctuation analysis (DFA) using linear and quadratic trends and the Geweke Porter-Hudak methods are applied to detect the presence of long-memory or persistence. We find that the daily returns exhibit a small amount of long memory and that the quadratic trend used in the DFA overestimates the value of the Hurst exponent. These findings are corroborated by the use of the Geweke Porter-Hudak method wherein the Hurst exponent is close to the DFA using the linear trend.
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Lascsáková, Marcela. "IMPACT OF DIFFERENT PRICE MOVEMENTS ON THE ACCURACY OF NUMERICAL PRICE FORECASTING." Acta logistica 8, no. 4 (December 31, 2021): 435–43. http://dx.doi.org/10.22306/al.v8i4.250.
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The focus of this paper aims at comparison of two prognostic numerical models with different strategies for accuracy improvement. To verify prediction performance of proposed models, the forecasts of aluminium stock exchanges on the London Metal Exchange were carried out as numerical solution of the Cauchy initial problem for the first-order ordinary differential equation. Two techniques for accuracy improvement were utilized, replacing the initial condition value by the nearest known stock exchange and a modification of the differential equation in solved Cauchy initial problem by means of two known initial values. We dealt with an idea of how different price development affected the accuracy of proposed strategies. With regard to obtained results, it was found that the prognoses obtained by using two known initial values were more increasing or decreasing than prognoses calculated by utilizing the initial condition drift. The strategy of a changing form of the differential equation in the Cauchy initial problem can be considered slightly more accurate. Faster increased prognoses were more advantageous especially at a steep price increase and within a price increase following the price decline. A moderate increase of the prognoses determined by the initial condition drift fit reasonably well a price fluctuation and a price decline following the price increase.
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Garafutdinov, Robert. "APPLICATION OF THE LONG MEMORY MODELS FOR RETURNS FORECASTING IN THE FORMATION OF INVESTMENT PORTFOLIOS." Applied Mathematics and Control Sciences, no. 2 (August 16, 2021): 171–91. http://dx.doi.org/10.15593/2499-9873/2021.2.10.
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This paper continues research within the framework of the scientific direction in econophysics at the Department of Information Systems and Mathematical Methods in Economics of the faculty of Economics of PSU. This paper describes the method of formation investment portfolios of four assets based on forecasted returns obtained using long memory econometric models and tests the hypotheses that the optimization of portfolio structure by forecasted returns obtained using such models (by the example of ARFIMA) allows to improve portfolio characteristics in comparison to the optimization by historical returns. Different variants of portfolios of four financial instruments were formed to test the method and test the hypotheses. The study obtained the following results. Portfolio parameters do not deteriorate, on average, when optimized by forecasted data and, in some cases, they improve because the optimizer identifies the most profitable assets more often and gives them more weight. The optimizer is better at identifying the most profitable assets based on the forecasted returns than the least risky ones because the autoregressive models predict the trend of the index rather than its volatility. Finally in the paper there are formulated the possible directions for further research: improving the methodology, namely, performing preliminary fractal analysis of series, imposing stricter restrictions on risk, using other forecasting models, rebalancing the portfolio; conducting research on data from the U.S. stock market, which is certainly more developed in comparison with Russia; using stock indices as a benchmark for assessing the effectiveness of portfolios.
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Zhang, Yuruixian, Wei Chong Choo, Yuhanis Abdul Aziz, Choy Leong Yee, Cheong Kin Wan, and Jen Sim Ho. "Effects of Multiple Financial News Shocks on Tourism Demand Volatility Modelling and Forecasting." Journal of Risk and Financial Management 15, no. 7 (June 23, 2022): 279. http://dx.doi.org/10.3390/jrfm15070279.
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Even though both symmetric and asymmetric conceptions of news impacts are well-established in the disciplines of economics and financial markets, the effects of combining multiple news shocks on the volatility of tourism demand have not yet been delved into or gauged in any tourist destination. This work hypothesises and verifies that the news impact curve (NIC), conditional heteroscedastic volatility models, and multiple news shocks are suitable for forecasting the volatility of the Malaysian tourist industry. Among them, three primarily volatility models (GARCH, EGARCH, and GJRGARCH) are used in conjunction with five financial news shocks (FFNSs), namely the Kuala Lumpur Composite Index (KLCI), the United States Dollar Index (DXY), the stock performance of 500 large companies listed on stock exchanges (S&P500), Crude Oil (CO), and Gold Price (GP). Among the most significant findings of this study are the demonstration of monthly seasonality using conditional mean equations, asymmetry effects in EGARCH-FFNSs, and GJRGARCH-FFNSs models in conditional variance equations and 50 NICs, and the GARCH-FFNSs model’s evaluation of the persistence influence of news shocks on monthly visitor arrivals in Malaysia. The GJRGARCH-FFNSs model is the best model for Malaysian tourism demand volatility forecasting accuracy. Furthermore, KLCI and Gold Price have the most substantial impact on the number of tourists to Malaysia. In addition, it should be emphasised that the methodological framework utilised in this study can be a useful tool for creating and forecasting the performance of symmetry and asymmetry impacts on tourism demand volatility.
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Moskalenko, Valentyna, Anastasija Santalova, and Nataliia Fonta. "STUDY OF NEURAL NETWORKS FOR FORECASTING THE VALUE OF COMPANY SHARES IN AN UNSTABLE ECONOMY." Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, no. 2 (8) (December 23, 2022): 16–23. http://dx.doi.org/10.20998/2079-0023.2022.02.03.
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These studies deal with analysis and selection of neural networks with various architectures and hybrid models, which include neural networks, to predict the market value of shares in the stock market of a country that is in the process of unstable development. Analysis and forecasting of such stock markets cannot be carried out using classical methods. The relevance of the research topic is due to the need to develop software systems that implement algorithmic support for predicting the market value of shares in Ukraine. The introduction of such software systems in the circuit of investment decisionmaking in companies that are interested in increasing the information transparency of the Ukrainian stock market will improve the forecasts of the market value of shares. This, in turn, will help improve the investment climate and ensure the growth of investment in the Ukrainian economy. The analysis of the results of existing studies on the use of neural networks and other methods of computational intelligence for modeling the behavior of stock market participants and market forecasting has been carried out. The article presents the results of a study for the using of neural networks with various architectures for predicting the market value of shares in the stock markets of Ukraine. Four shares of the Ukrainian Stock Exchange were chosen for forecasting: Centrenergo (CEEN); Ukrtelecom (UTLM); Kriukivs’kyi Vahonobudivnyi Zavod PAT (KVBZ); Raiffeisen Bank Aval (BAVL). The following models were chosen for the experimental study: long short-term memory LSTM; convolutional neural network CNN; a hybrid model combining two neural networks CNN and LSTM; a hybrid model consisting of a variational mode decomposition algorithm and a long-term memory neural network (VMD-LSTM); hybrid VMD-CNN-LSTM deep learning model based on variational mode (VMD) and two neural networks. Estimates of forecast quality based on various metrics were calculated. It is concluded that the use of the hybrid model VMD-CNN-LSTM gives the minimum error in predicting the market value of the shares of Ukrainian enterprises. It is also advisable to use the VMD-LSTM model to predict the stock exchanges of countries with an unstable economy.
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Hami, Mustapha El, and Ahmed Hefnaoui. "Analysis of Herding Behavior in Moroccan Stock Market." Journal of Economics and Behavioral Studies 11, no. 1(J) (March 10, 2019): 181–90. http://dx.doi.org/10.22610/jebs.v11i1(j).2758.
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Frontier markets, particularly the Moroccan financial market, are characterized by a narrowness of market, inability to absorb erratic price fluctuations and the low liquidity of securities that encourage investors to herd and imitate those who have all the information about the market. A quantitative research approach was used to analyze the existence of herding n Moroccan stock market. The daily data used in this study concerns the period from 04/01/2010 to 29/12/2017 and contains the daily returns of the MASI and a total of 43 traded stocks. Statistical and econometric methods such as multidimensional scaling and Cross-sectional absolute deviation were used. Subsequently, after the regression models were examined, findings indicated that the first stocks with the highest similarity to the index return are BMCE, BCP, IAM, ATW and CMSR, and the first stocks with the highest dissimilarity are PAP, IBC and SNP, This will have to allow investors to choose profitable alternatives and avoid those that present a possible risk. The results did also show the existence of herding in the Moroccan stock market both upward and downward. This finding was supported by the clear existence of a non-linearity between market performance and CSAD measurement, which confirms the prediction of a non-linear inversion relationship between CSAD and ð‘…ð‘š. This could be due to the low level of transparency that prevails in frontier stock exchanges and reduces the quality of their information environment, which leads investors not to react rationally and to draw information from the transactions of their peers.
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Jiang, Xiaoquan, and Qiang Kang. "Cross-Sectional PEG Ratios, Market Equity Premium, and Macroeconomic Activity." Journal of Accounting, Auditing & Finance 35, no. 3 (January 8, 2018): 471–500. http://dx.doi.org/10.1177/0148558x17748277.
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This article explores the information content of PEG ratios (price/earnings to growth ratios) for future aggregate returns and economic fundamentals. We first establish an analytic link between PEG ratios and time-varying expected returns of stocks. We then combine the link with empirical asset pricing models to extract market-wide information from cross-sectional PEG ratios. The resultant cross-section estimates of the risk premiums on stock betas serve as proxies for market-wide information. The proxies contain salient information about future market equity premiums and macroeconomic activity both in-sample and out-of-sample. Moreover, the proxies outperform aggregate PEG ratios and the cross-section beta-premium estimate based on conventional valuation ratios and retain incremental power in forecasting future market equity premiums. The results are robust to using various econometric methods for standard error adjustments.
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Buczyński, Mateusz, and Marcin Chlebus. "Comparison of Semi-Parametric and Benchmark Value-At-Risk Models in Several Time Periods with Different Volatility Levels." e-Finanse 14, no. 2 (June 1, 2018): 67–82. http://dx.doi.org/10.2478/fiqf-2018-0013.
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AbstractIn the literature, there is no consensus as to which Value-at-Risk forecasting model is the best for measuring market risk in banks. In the study an analysis of Value-at-Risk forecasting model quality over varying economic stability periods for main indices from stock exchanges was conducted. The VaR forecasts from GARCH(1,1), GARCH-t(1,1), GARCH-st(1,1), QML-GARCH(1,1), CAViaR and historical simulation models in periods with contrasting volatility trends (increasing, constantly high and decreasing) for countries economically developed (the USA – S&P 500, Germany - DAX and Japan – Nikkei 225) and economically developing (China – SSE COMP, Poland – WIG20 and Turkey – XU100) were compared. The data samples used in the analysis were selected from the period 01.01.1999 – 24.03.2017. To assess the VaR forecast quality: excess ratio, Basel traffic light test, coverage tests (Kupiec test, Christoffersen test), Dynamic Quantile test, cost functions and Diebold-Marino test were used. Obtained results show that the quality of Value-at-Risk forecasts for the models varies depending on a volatility trend. However, GARCH-st (1,1) and QML-GARCH(1,1) were found to be the most robust models in the different volatility periods. The results show as well that the CAViaR model forecasts were less appropriate in the increasing volatility period. Moreover, no significant differences for the VaR forecast quality were found for the developed and developing countries.
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Ercegovac, Roberto, Mario Pečarić, and Ivica Klinac. "Bank Risk Profiles and Business Model Characteristics." Journal of Central Banking Theory and Practice 9, no. 3 (September 1, 2020): 107–21. http://dx.doi.org/10.2478/jcbtp-2020-0039.
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AbstractCurrent research, especially after the financial crisis, highlights different key determinants of high risk bank profiles. The main aim of this paper is to test, through an empirical model, the impact of various determinants of bank business models on the bank risk with the purpose of enabling early identification of signals of risk and timely application of prudential measures. There are two basic business models for banks: market-oriented wholesale bank business model and client-oriented bank business model. In the wholesale model, a significant share of the assets is comprised of securities in the trade portfolio, the bank is strongly involved in the international financial markets, while on the income side of the bank profile, a large part is related to non-interest income. In the client related business model, classical banking is dominant, which is visible in the high share of loan-related assets, a larger share of self-financing and a larger share of income from interest-operational income in the total income structure of the bank. In the panel analysis of the empirical data, as an indicator of the bank risk profile, the stock market price to stock market price volatility ratio was used with the presumption that the market price and its volatility, with sufficiently liquid shares listed on public stock exchanges, is representative of bank risk. The analysis is conducted on a homogenous example of 20 European banks in the period 2002-2017. Following the econometric analysis, the conclusion is that banks in which business model wholesale characteristics are dominant are more exposed to business risk in periods of market shocks and, as such, represent a danger for the long-term stability of the financial sector.
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Karmakar, Madhusudan. "Modeling Conditional Volatility of the Indian Stock Markets." Vikalpa: The Journal for Decision Makers 30, no. 3 (July 2005): 21–38. http://dx.doi.org/10.1177/0256090920050303.
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Traditional econometric models assume a constant one period forecast variance. However, many financial time series display volatility clustering, that is, autoregressive conditional heteroskedasticity (ARCH). The aim of this paper is to estimate conditional volatility models in an effort to capture the salient features of stock market volatility in India and evaluate the models in terms of out-ofsample forecast accuracy. The paper also investigates whether there is any leverage effect in Indian companies. The estimation of volatility is made at the macro level on two major market indices, namely, S&P CNX Nifty and BSE Sensex. The fitted model is then evaluated in terms of its forecasting accuracy on these two indices. In addition, 50 individual companies' share prices currently included in S&P CNX Nifty are used to examine the heteroskedastic behaviour of the Indian stock market at the micro level. The vanilla GARCH (1, 1) model has been fitted to both the market indices. We find: a strong evidence of time-varying volatility a tendency of the periods of high and low volatility to cluster a high persistence and predictability of volatility. Conditional volatility of market return series from January 1991 to June 2003 shows a clear evidence of volatility shifting over the period where violent changes in share prices cluster around the boom of 1992. Though the higher price movement started in response to strong economic fundamentals, the real cause for abrupt movement appears to be the imperfection of the market. The forecasting ability of the fitted GARCH (1, 1) model has been evaluated by estimating parameters initially over trading days of the in-sample period and then using the estimated parameters to later data, thus forming out-of-sample forecasts on two market indices. These out-of-sample volatility forecasts have been compared to true realized volatility. Three alternative methods have been followed to measure three pairs of forecast and realized volatility. In each method, the volatility forecasts are evaluated and compared through popular measures. To examine the information content of forecasts, a regression-based efficiency test has also been performed. It is observed that the GARCH (1, 1) model provides reasonably good forecasts of market volatility. While turning to 50 individual underlying shares, it is observed that the GARCH (1, 1) model has been fitted for almost all companies. Only for four companies, GARCH models of higher order may be more successful. In general, volatility seems to be of a persistent nature. Only eight out of 50 shares show significant leverage effects and really need an asymmetric GARCH model such as EGARCH to capture their volatility clustering which is left for future research. The implications of the study are as follows: The various GARCH models provide good forecasts of volatility and are useful for portfolio allocation, performance measurement, option valuation, etc. Given the anticipated high growth of the economy and increasing interest of foreign investors towards the country, it is important to understand the pattern of stock market volatility in India which is time-varying, persistent, and predictable. This may help diversify international portfolios and formulate hedging strategies.
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Kouki, Ahmed. "IFRS and value relevance." Journal of Applied Accounting Research 19, no. 1 (February 12, 2018): 60–80. http://dx.doi.org/10.1108/jaar-05-2015-0041.
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Purpose The purpose of this paper is to compare the value relevance of accounting information between International Financial Reporting Standards (IFRS)-firms and non-IFRS-firms over five years before mandatory IFRS adoption from 2000 to 2004 and six years after IFRS adoption from 2006 to 2011. Design/methodology/approach The sample includes 1166 firm-year observations that cover firms from three Europeans countries. Different econometric tests, multivariate and panel regressions have been used to verify the hypotheses. Findings In the pre-IFRS period, voluntary IFRS adoption did not improve the value relevance of accounting information. The results indicate that the information contents of non-IFRS-firms in the post-adoption period have higher quality than in the pre-adoption period. The findings show a higher association between accounting information, stock prices and stock returns over both periods, however, the difference in results is not statistically significant. Research limitations/implications This study was not generalized to other stock exchanges that have a significant weight in the European Union, such as the FTSE 100 companies or the SP/MIB. Practical implications This study has some implications for standards setters, firms and practitioners. The transition to IFRS reduces the diversity of accounting systems and institutional conditions (capital market structure, Taxation systems). In addition, mandatory IFRS adoption engendered changes in firms’ business and organizational models that led accountants to improve their educational and training programs. Originality/value This paper contributes to the value relevance as well as IFRS literature by using a sample from code-law origin countries that switched from a debt-oriented system to shareholder-oriented system. It offers a comparative approach between IFRS-firms and Non-IFRS-firms in the pre- and post-adoption periods. In contrast, prior studies focused on the comparison during only one period. This empirical evidence should be of interest to investors and policymakers in other markets.
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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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AlKhouri, Ritab, and Houda Arouri. "The effect of diversification on risk and return in banking sector." International Journal of Managerial Finance 15, no. 1 (February 4, 2019): 100–128. http://dx.doi.org/10.1108/ijmf-01-2018-0024.
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PurposeThe purpose of this paper is to investigate the effect of revenue diversification, non-interest income and asset diversification on the performance and stability of the Gulf Cooperation Council (GCC) conventional and Islamic banking systems.Design/methodology/approachThe authors implement a panel of 69 conventional and Islamic banks listed in six GCC markets over the period of 2003–2015, using the System Generalized Method of Moments methodology.FindingsNon-interest income diversification has a negative impact on GCC banks’ performance, while asset-based diversification affects banks performance positively. However, Investors tend to penalize the value of the banks’ assets, which are highly diversified. Government intervention, lack of competition, legal protection and high control of Central banks on GCC banks’ have positive impact on performance. Contrary to the results on conventional banks, asset diversification adds value to Islamic banks. Overall, both banks’ revenue and non-interest diversification have negative impact on GCC banks’ stability, while asset diversification improves Islamic banks’ stability.Research limitations/implicationsThe analysis is limited to a sample of banks, which are listed in the GCC stock exchanges. The lack of data on private and foreign banks operating in the region made the analysis and, consequently, the results specific to shareholding companies. Also, the authors’ measures of bank stability might not be appropriate to use for Islamic banks, given their banking models implemented.Practical implicationsResearch results provide important implications for regulators, bank managers and policy makers, as to the expected ways to support economic diversification through bank diversification strategies.Originality/valueUnlike related studies, the authors’ sample of homogeneous banks has a market structure that is different from the samples in the literature covering either developed countries or heterogeneous samples from both developed and developing countries. Furthermore, using an efficient econometric methodology, the authors deal with two types of banks: conventional banks and Islamic banks. The research determines which type of bank is more able to benefit from different types of diversification. Unlike previous research, this research explores the sensitivity of the results both to the regulatory environment of the GCC market and to general market conditions.
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Proulx, Pierre-Paul, Luce Bourgault, and Jean-François Manegre. "Candide-Cofor et la prévision de besoins en main-d’oeuvre par occupation et par industrie au Canada." Relations industrielles 32, no. 1 (April 12, 2005): 108–26. http://dx.doi.org/10.7202/028767ar.
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The authors present a review and an assessment of the Candide1and Cofor2models as instruments for estimating manpower requirements at the industry and provincial levels. In summary form the approach is as follows. Following upon a forecast of Real Domestic Product by industry generated by Candide, Cofor allows the preparation of estimates of total employment by industry at the national level by making use of productivity equations of the following form: In Y/L = f (T) where Y is Real Domestic Product, L is employment and T is a time trend. In certain instances K (capital stock) is used instead of T. Then total employment by industry is estimated at the provincial level by extrapolating the ratio of total employment in the industry by province to that at the national level. Finally employment by occupation is obtained by applying the 1971 Census occupational distribution of experienced labour force by industry at the provincial level. Adjustments are made for death and retirement rates as observed at the all industry and Canada levels. The paper then illustrates the use of the models with results obtained for the Canadian industrial chemicals and Québec textiles and total Québec industries. Comments are then made concerning the strenght and weaknesses of the models. Among these are: 1) The use of average productivity estimates to examine manpower requirements in industries contemplating large scale projects. 2) An implicit hypothesis to the effect that capacity is utilized fully. 3) The aging of the occupational distributions, and the use of experienced labour force rather than employment in the analysis of occupational distributions. 4) Estimates for both sexes together rather than by sex. 5) Lack of adjustments to reflect the age-experience profiles by industry. 6) Lack of adjustment for recent significant increases in turnover rates. 7) Insufficient adjustment for cyclical effects. 8) Inadequate disaggregation at the provincial level, etc.. 1 Canadian Disaggregated Interdepartmental Econometric Model operated by the Economic Council of Canada. 2 Canadian Occupational Forecasting Model developed and operated by the Canadian Department of Manpower and Immigration.
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Domanski, Pawel D., and Mateusz Gintrowski. "Alternative approaches to the prediction of electricity prices." International Journal of Energy Sector Management 11, no. 1 (April 3, 2017): 3–27. http://dx.doi.org/10.1108/ijesm-06-2013-0001.
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Purpose This paper aims to present the results of the comparison between different approaches to the prediction of electricity prices. It is well-known that the properties of the data generation process may prefer some modeling methods over the others. The data having an origin in social or market processes are characterized by unexpectedly wide realization space resulting in the existence of the long tails in the probabilistic density function. These data may not be easy in time series prediction using standard approaches based on the normal distribution assumptions. The electricity prices on the deregulated market fall into this category. Design/methodology/approach The paper presents alternative approaches, i.e. memory-based prediction and fractal approach compared with established nonlinear method of neural networks. The appropriate interpretation of results is supported with the statistical data analysis and data conditioning. These algorithms have been applied to the problem of the energy price prediction on the deregulated electricity market with data from Polish and Austrian energy stock exchanges. Findings The first outcome of the analysis is that there are several situations in the task of time series prediction, when standard modeling approach based on the assumption that each change is independent of the last following random Gaussian bell pattern may not be a true. In this paper, such a case was considered: price data from energy markets. Electricity prices data are biased by the human nature. It is shown that more relevant for data properties was Cauchy probabilistic distribution. Results have shown that alternative approaches may be used and prediction for both data memory-based approach resulted in the best performance. Research limitations/implications “Personalization” of the model is crucial aspect in the whole methodology. All available knowledge should be used on the forecasted phenomenon and incorporate it into the model. In case of the memory-based modeling, it is a specific design of the history searching routine that uses the understanding of the process features. Importance should shift toward methodology structure design and algorithm customization and then to parameter estimation. Such modeling approach may be more descriptive for the user enabling understanding of the process and further iterative improvement in a continuous striving for perfection. Practical implications Memory-based modeling can be practically applied. These models have large potential that is worth to be exploited. One disadvantage of this modeling approach is large calculation effort connected with a need of constant evaluation of large data sets. It was shown that a graphics processing unit (GPU) approach through parallel calculation on the graphical cards can improve it dramatically. Social implications The modeling of the electricity prices has big impact of the daily operation of the electricity traders and distributors. From one side, appropriate modeling can improve performance mitigating risks associated with the process. Thus, the end users should receive higher quality of services ultimately with lower prices and minimized risk of the energy loss incidents. Originality/value The use of the alternative approaches, such as memory-based reasoning or fractals, is very rare in the field of the electricity price forecasting. Thus, it gives a new impact for further research enabling development of better solutions incorporating all available process knowledge and customized hybrid algorithms.
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"Financial Forecasting Model in Developed and Developing Economies." International Journal of Recent Technology and Engineering 8, no. 3S3 (December 16, 2019): 291–96. http://dx.doi.org/10.35940/ijrte.c1067.1183s319.
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The study focused on the volatility forecasting in developed and developing share market. The objective of the study was to evaluate the ability of six different statistical and econometric volatility forecasting models in the context of India, Brazil, Japan and US stock market from November 1994 till February 2005 on the basis of four evaluation error measures statistics which are mean absolute error (MAE), root mean square error (RMSE), Theil’s U (TU) and MAPE. The monthly data of stock market index of India, Brazil, Japan and US were collected from January 1992 till April 2005 and also monthly data of stock market index, discount rate, consumer price index (CPI), industrial production and foreign exchange reserves of India, Brazil, Japan and US respectively were collected. Then further analysis was done using four forecasting models which were moving average, exponential weighted moving average, multiple regression, GARCH. The study found out that GARCH and MAE forecasting models are superior in developed market as well as developing market like India.