In the last decade,market financial forecasting has attracted high interests amongst the researchers in pattern recognition.Usually,the data used for analysing the market,and then gamble on its future trend,are provid...In the last decade,market financial forecasting has attracted high interests amongst the researchers in pattern recognition.Usually,the data used for analysing the market,and then gamble on its future trend,are provided as time series;this aspect,along with the high fluctuation of this kind of data,cuts out the use of very efficient classification tools,very popular in the state of the art,like the well known convolutional neural networks(CNNs)models such as Inception,Res Net,Alex Net,and so on.This forces the researchers to train new tools from scratch.Such operations could be very time consuming.This paper exploits an ensemble of CNNs,trained over Gramian angular fields(GAF)images,generated from time series related to the Standard&Poor's 500 index future;the aim is the prediction of the future trend of the U.S.market.A multi-resolution imaging approach is used to feed each CNN,enabling the analysis of different time intervals for a single observation.A simple trading system based on the ensemble forecaster is used to evaluate the quality of the proposed approach.Our method outperforms the buyand-hold(B&H)strategy in a time frame where the latter provides excellent returns.Both quantitative and qualitative results are provided.展开更多
Background:Improving financial time series forecasting is one of the most challenging and vital issues facing numerous financial analysts and decision makers.Given its direct impact on related decisions,various attemp...Background:Improving financial time series forecasting is one of the most challenging and vital issues facing numerous financial analysts and decision makers.Given its direct impact on related decisions,various attempts have been made to achieve more accurate and reliable forecasting results,of which the combining of individual models remains a widely applied approach.In general,individual models are combined under two main strategies:series and parallel.While it has been proven that these strategies can improve overall forecasting accuracy,the literature on time series forecasting remains vague on the choice of an appropriate strategy to generate a more accurate hybrid model.Methods:Therefore,this study’s key aim is to evaluate the performance of series and parallel strategies to determine a more accurate one.Results:Accordingly,the predictive capabilities of five hybrid models are constructed on the basis of series and parallel strategies compared with each other and with their base models to forecast stock price.To do so,autoregressive integrated moving average(ARIMA)and multilayer perceptrons(MLPs)are used to construct two series hybrid models,ARIMA-MLP and MLP-ARIMA,and three parallel hybrid models,simple average,linear regression,and genetic algorithm models.Conclusion:The empirical forecasting results for two benchmark datasets,that is,the closing of the Shenzhen Integrated Index(SZII)and that of Standard and Poor’s 500(S&P 500),indicate that although all hybrid models perform better than at least one of their individual components,the series combination strategy produces more accurate hybrid models for financial time series forecasting.展开更多
Financial time series forecasting could be beneficial for individual as well as institutional investors. But, the high noise and complexity residing in the financial data make this job extremely challenging. Over the ...Financial time series forecasting could be beneficial for individual as well as institutional investors. But, the high noise and complexity residing in the financial data make this job extremely challenging. Over the years, many researchers have used support vector regression (SVR) quite successfully to conquer this challenge. In this paper, an SVR based forecasting model is proposed which first uses the principal component analysis (PCA) to extract the low-dimensional and efficient feature information, and then uses the independent component analysis (ICA) to preprocess the extracted features to nullify the influence of noise in the features. Experiments were carried out based on 16 years’ historical data of three prominent stocks from three different sectors listed in Dhaka Stock Exchange (DSE), Bangladesh. The predictions were made for 1 to 4 days in advance targeting the short term prediction. For comparison, the integration of PCA with SVR (PCA-SVR), ICA with SVR (ICA-SVR) and single SVR approaches were applied to evaluate the prediction accuracy of the proposed approach. Experimental results show that the proposed model (PCA-ICA-SVR) outperforms the PCA-SVR, ICA-SVR and single SVR methods.展开更多
This paper aims to find evidence for the improvements on the present earnings forecast models through analyzing the correlation among financial ratios, auditor opinion of listed companies and their future earnings. Th...This paper aims to find evidence for the improvements on the present earnings forecast models through analyzing the correlation among financial ratios, auditor opinion of listed companies and their future earnings. This paper uses two statistical regression methods including Logistic model and Linear model to examine the inner interaction between financial ratios and future earnings from qualitative and quantitative perspectives respectively. Empirical tests find that financial ratios, especially ROE, can help to predict future earnings. Then we add auditor opinion variable into Logistic model to test whether going concern opinion in the auditor reports can be helpful for earnings forecast. Result shows the degree of optimistic statement of going concern opinion is significantly correlated with future earnings but with the disturbance of earnings management.展开更多
Financial crisis prediction(FCP)received significant attention in the financial sector for decision-making.Proper forecasting of the number of firms possible to fail is important to determine the growth index and stre...Financial crisis prediction(FCP)received significant attention in the financial sector for decision-making.Proper forecasting of the number of firms possible to fail is important to determine the growth index and strength of a nation’s economy.Conventionally,numerous approaches have been developed in the design of accurate FCP processes.At the same time,classifier efficacy and predictive accuracy are inadequate for real-time applications.In addition,several established techniques carry out well to any of the specific datasets but are not adjustable to distinct datasets.Thus,there is a necessity for developing an effectual prediction technique for optimum classifier performance and adjustable to various datasets.This paper presents a novel multi-vs.optimization(MVO)based feature selection(FS)with an optimal variational auto encoder(OVAE)model for FCP.The proposed multi-vs.optimization based feature selection with optimal variational auto encoder(MVOFS-OVAE)model mainly aims to accomplish forecasting the financial crisis.For achieving this,the proposed MVOFS-OVAE model primarily pre-processes the financial data using min-max normalization.In addition,the MVOFS-OVAE model designs a feature subset selection process using the MVOFS approach.Followed by,the variational auto encoder(VAE)model is applied for the categorization of financial data into financial crisis or non-financial crisis.Finally,the differential evolution(DE)algorithm is utilized for the parameter tuning of the VAE model.A series of simulations on the benchmark dataset reported the betterment of the MVOFS-OVAE approach over the recent state of art approaches.展开更多
In the context of the debate on the role of cryptocurrencies in the economy as well as their dynamics and forecasting,this brief study analyzes the predictability of Bitcoin volume and returns using Google search valu...In the context of the debate on the role of cryptocurrencies in the economy as well as their dynamics and forecasting,this brief study analyzes the predictability of Bitcoin volume and returns using Google search values.We employed a rich set of established empirical approaches,including a VAR framework,a copulas approach,and non-parametric drawings,to capture a dependence structure.Using a weekly dataset from 2013 to 2017,our key results suggest that the frequency of Google searches leads to positive returns and a surge in Bitcoin trading volume.Shocks to search values have a positive effect,which persisted for at least a week.Our findings contribute to the debate on cryptocurrencies/Bitcoins and have profound implications in terms of understanding their dynamics,which are of special interest to investors and economic policymakers.展开更多
This paper deals with the development of wage distribution by gender in the Czech and Slovak Republics in the years of 2005-2012. Special attention is given to changes in the behavior of wage distribution in relation ...This paper deals with the development of wage distribution by gender in the Czech and Slovak Republics in the years of 2005-2012. Special attention is given to changes in the behavior of wage distribution in relation to the onset of the global economic recession. The different behavior of the wage distribution of Czech and Slovak employees during the period is the subject of research. The article discusses the differences in the wage level between men and women in the Czech and Slovak Republics. There are the total wage distributions of men and women together, both in the Czech Republic and in the Slovak Republic on one hand, and wage distributions according to the gender separately for men and women on the other hand. Special attention was paid to the development of Gini coefficient of the concentration in both countries according to the gender in the period under review, too.展开更多
In this research we are going to define two new concepts: a) “The Potential of Events” (EP) and b) “The Catholic Information” (CI). The term CI derives from the ancient Greek language and declares all the Catholic...In this research we are going to define two new concepts: a) “The Potential of Events” (EP) and b) “The Catholic Information” (CI). The term CI derives from the ancient Greek language and declares all the Catholic (general) Logical Propositions (<img src="Edit_5f13a4a5-abc6-4bc5-9e4c-4ff981627b2a.png" width="33" height="21" alt="" />) which will true for every element of a set A. We will study the Riemann Hypothesis in two stages: a) By using the EP we will prove that the distribution of events e (even) and o (odd) of Square Free Numbers (SFN) on the axis Ax(N) of naturals is Heads-Tails (H-T) type. b) By using the CI we will explain the way that the distribution of prime numbers can be correlated with the non-trivial zeros of the function <em>ζ</em>(<em>s</em>) of Riemann. The Introduction and the Chapter 2 are necessary for understanding the solution. In the Chapter 3 we will present a simple method of forecasting in many very useful applications (e.g. financial, technological, medical, social, etc) developing a generalization of this new, proven here, theory which we finally apply to the solution of RH. The following Introduction as well the Results with the Discussion at the end shed light about the possibility of the proof of all the above. The article consists of 9 chapters that are numbered by 1, 2, …, 9.展开更多
Through study,it is found that since 1952,there has been a long-run equilibrium relationship between China's rural financial market growth and rural economic growth,the government-led rural financial market growth...Through study,it is found that since 1952,there has been a long-run equilibrium relationship between China's rural financial market growth and rural economic growth,the government-led rural financial market growth has effectively supported rural economic growth,and increasing the farmers' financing ratio has always helped to boost long-term growth of the rural economy.However,dominated by market mechanism from 1978,there is only one-way support relationship:rural economic growth brings about quantitative growth of rural financial market.展开更多
The fixed annual budget process can be a cumbersome and static process, often failing to deliver intended benefits. Typically detached from business operations and strategic planning goals, the annual budget suffers f...The fixed annual budget process can be a cumbersome and static process, often failing to deliver intended benefits. Typically detached from business operations and strategic planning goals, the annual budget suffers from inherent weaknesses caused by a lack of business intelligence regarding its underlying assumptions. This weakness is well documented in existing literature and there is ample evidence of improved alternatives to static corporate financial planning. One such alternative utilizes business intelligence as an essential component in the annual budget process, along with rolling forecasts as a critical tool. Utilizing business intelligence supported, driver-based rolling forecasting can align an organization’s budget process with strategic objectives and can further the operational and financial strength of an organization, as well as maximize shareholder value. In order to fully explore this topic, this article will present a review of the conventional annual budget process and the manner in which an approach that bases financial forecasts on business intelligence drivers can align operations with strategic objectives and add value to an organization. An assessment of intelligence-supported, driver-based rolling forecasting will also be presented, demonstrating an im- proved approach to the traditional annual budgeting process.展开更多
Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification.There are several forecasting techniques in the literature for obtaining accurate forecasts for inv...Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification.There are several forecasting techniques in the literature for obtaining accurate forecasts for investment decision making.Numerous empirical studies have employed such methods to investigate the returns of different individual stock indices.However,there have been very few studies of groups of stock markets or indices.The findings of previous studies indicate that there is no single method that can be applied uniformly to all markets.In this context,this study aimed to examine the predictive performance of linear,nonlinear,artificial intelligence,frequency domain,and hybrid models to find an appropriate model to forecast the stock returns of developed,emerging,and frontier markets.We considered the daily stock market returns of selected indices from developed,emerging,and frontier markets for the period 2000–2018 to evaluate the predictive performance of the above models.The results showed that no single model out of the five models could be applied uniformly to all markets.However,traditional linear and nonlinear models outperformed artificial intelligence and frequency domain models in providing accurate forecasts.展开更多
This paper explores the power of stock trading trend using an integrated New ThresholdFuzzy Cognitive Maps(NTFCMs)Markov chain model.This new model captures thepositive as well as the negative jumps and predicts the t...This paper explores the power of stock trading trend using an integrated New ThresholdFuzzy Cognitive Maps(NTFCMs)Markov chain model.This new model captures thepositive as well as the negative jumps and predicts the trend for different stocks over 4months which follow an uptrend,downtrend and a mixed trend.The mean absolute percent error(MAPE)tolerance limits,the root mean square error(RMSE)tolerance limits aredetermined for various stock indices over a multi-timeframe period and observed for theexisting methods lying within the defined limits.The results show for every‘n’number ofpredictions made,the predicted close value of the day’s stock price was within tolerancelimit with 0%error and with 100%accuracy in predicting the future trend.展开更多
For the purposes of this research, the optimal MLP neural network topology has been designed and tested by means the specific genetic algorithm multi-objective Pareto-Based. The objective of the research is to predict...For the purposes of this research, the optimal MLP neural network topology has been designed and tested by means the specific genetic algorithm multi-objective Pareto-Based. The objective of the research is to predict the trend of the ex-change rate Euro/USD up to three days ahead of last data available. The variable of output of the ANN designed is then the daily exchange rate Euro/Dollar and the frequency of data collection of variables of input and the output is daily. By the analysis of the data it is possible to conclude that the ANN model developed can largely predict the trend to three days of exchange rate Euro/USD.展开更多
The aim of this paper is to show how qualitative and quantitative approaches can be complementary to study internet financial communication in a thesis by papers and how grounded theory (GT) can be the link among th...The aim of this paper is to show how qualitative and quantitative approaches can be complementary to study internet financial communication in a thesis by papers and how grounded theory (GT) can be the link among the different papers of the thesis. The study context of our thesis was the unregulated markets of New York Stock Exchange (NYSE) Euronext Brussels and the problematic rose from this context: What is the voluntary effort of communication when there is no obligation of internet financial communication? Four papers tried to answer this central question and other following research questions. To answer those research questions, several methodological approaches were used: content analysis of websites and scoring, linear regression, paired sample, and interviews. At the end of our thesis by papers, we discovered that GT was the general methodological travel among the papers: Every article had for vocation to try to answer the questions raised by the previous article.展开更多
In this study,the impact of business and financial information integration(BFⅡ)on the voluntary management earnings forecasts(VMEFs)of listed firms in China between 2008 and 2018 is investigated.Drawing on litigation...In this study,the impact of business and financial information integration(BFⅡ)on the voluntary management earnings forecasts(VMEFs)of listed firms in China between 2008 and 2018 is investigated.Drawing on litigation cost and ability signaling theories,we find that the adoption of BFⅡencourages top managers to disclose VMEFs.BFⅡfirms are identified through the textual analysis of management discussion and analysis(MD&A)reports,and the empirical results indicate that BFⅡfirms have a higher probability and frequency of issuing VMEFs than non-BFⅡfirms.The results remain robust after we identify causality by applying a propensity score matching and difference-in-differences(PSM-DID)test and use an alternate measure of BFⅡ.Further tests show that BFⅡfirms issue more accurate VMEFs and are able to issue them at an earlier stage.We also find that the positive relationship between BFⅡand VMEFs is weakened if the media expresses concern about the uncertainty of BFⅡadoption.展开更多
The financial market volatility forecasting is regarded as a challenging task because of irreg ularity, high fluctuation, and noise. In this study, a multiscale ensemble forecasting model is proposed. The original fin...The financial market volatility forecasting is regarded as a challenging task because of irreg ularity, high fluctuation, and noise. In this study, a multiscale ensemble forecasting model is proposed. The original financial series are decomposed firstly different scale components (i.e., approximation and details) using the maximum overlap discrete wavelet transform (MODWT). The approximation is pre- dicted by a hybrid forecasting model that combines autoregressive integrated moving average (ARIMA) with feedforward neural network (FNN). ARIMA model is used to generate a linear forecast, and then FNN is developed as a tool for nonlinear pattern recognition to correct the estimation error in ARIMA forecast. Moreover, details are predicted by Elman neural networks. Three weekly exchange rates data are collected to establish and validate the forecasting model. Empirical results demonstrate consistent better performance of the proposed approach.展开更多
基金supported by the“Bando Aiuti per progetti di Ricerca e Sviluppo-POR FESR 2014-2020-Asse 1,Azione 1.1.3.Project AlmostAnOracle-AI and Big Data Algorithms for Financial Time Series Forecasting”。
文摘In the last decade,market financial forecasting has attracted high interests amongst the researchers in pattern recognition.Usually,the data used for analysing the market,and then gamble on its future trend,are provided as time series;this aspect,along with the high fluctuation of this kind of data,cuts out the use of very efficient classification tools,very popular in the state of the art,like the well known convolutional neural networks(CNNs)models such as Inception,Res Net,Alex Net,and so on.This forces the researchers to train new tools from scratch.Such operations could be very time consuming.This paper exploits an ensemble of CNNs,trained over Gramian angular fields(GAF)images,generated from time series related to the Standard&Poor's 500 index future;the aim is the prediction of the future trend of the U.S.market.A multi-resolution imaging approach is used to feed each CNN,enabling the analysis of different time intervals for a single observation.A simple trading system based on the ensemble forecaster is used to evaluate the quality of the proposed approach.Our method outperforms the buyand-hold(B&H)strategy in a time frame where the latter provides excellent returns.Both quantitative and qualitative results are provided.
文摘Background:Improving financial time series forecasting is one of the most challenging and vital issues facing numerous financial analysts and decision makers.Given its direct impact on related decisions,various attempts have been made to achieve more accurate and reliable forecasting results,of which the combining of individual models remains a widely applied approach.In general,individual models are combined under two main strategies:series and parallel.While it has been proven that these strategies can improve overall forecasting accuracy,the literature on time series forecasting remains vague on the choice of an appropriate strategy to generate a more accurate hybrid model.Methods:Therefore,this study’s key aim is to evaluate the performance of series and parallel strategies to determine a more accurate one.Results:Accordingly,the predictive capabilities of five hybrid models are constructed on the basis of series and parallel strategies compared with each other and with their base models to forecast stock price.To do so,autoregressive integrated moving average(ARIMA)and multilayer perceptrons(MLPs)are used to construct two series hybrid models,ARIMA-MLP and MLP-ARIMA,and three parallel hybrid models,simple average,linear regression,and genetic algorithm models.Conclusion:The empirical forecasting results for two benchmark datasets,that is,the closing of the Shenzhen Integrated Index(SZII)and that of Standard and Poor’s 500(S&P 500),indicate that although all hybrid models perform better than at least one of their individual components,the series combination strategy produces more accurate hybrid models for financial time series forecasting.
文摘Financial time series forecasting could be beneficial for individual as well as institutional investors. But, the high noise and complexity residing in the financial data make this job extremely challenging. Over the years, many researchers have used support vector regression (SVR) quite successfully to conquer this challenge. In this paper, an SVR based forecasting model is proposed which first uses the principal component analysis (PCA) to extract the low-dimensional and efficient feature information, and then uses the independent component analysis (ICA) to preprocess the extracted features to nullify the influence of noise in the features. Experiments were carried out based on 16 years’ historical data of three prominent stocks from three different sectors listed in Dhaka Stock Exchange (DSE), Bangladesh. The predictions were made for 1 to 4 days in advance targeting the short term prediction. For comparison, the integration of PCA with SVR (PCA-SVR), ICA with SVR (ICA-SVR) and single SVR approaches were applied to evaluate the prediction accuracy of the proposed approach. Experimental results show that the proposed model (PCA-ICA-SVR) outperforms the PCA-SVR, ICA-SVR and single SVR methods.
基金This paper is sponsored by National Natural Science Foundation of China (No.70172023) and Education Department of China (01JA630019). The author is grateful to Prof. Minghai Wei of Sun Yat-sen University and Prof.
文摘This paper aims to find evidence for the improvements on the present earnings forecast models through analyzing the correlation among financial ratios, auditor opinion of listed companies and their future earnings. This paper uses two statistical regression methods including Logistic model and Linear model to examine the inner interaction between financial ratios and future earnings from qualitative and quantitative perspectives respectively. Empirical tests find that financial ratios, especially ROE, can help to predict future earnings. Then we add auditor opinion variable into Logistic model to test whether going concern opinion in the auditor reports can be helpful for earnings forecast. Result shows the degree of optimistic statement of going concern opinion is significantly correlated with future earnings but with the disturbance of earnings management.
文摘Financial crisis prediction(FCP)received significant attention in the financial sector for decision-making.Proper forecasting of the number of firms possible to fail is important to determine the growth index and strength of a nation’s economy.Conventionally,numerous approaches have been developed in the design of accurate FCP processes.At the same time,classifier efficacy and predictive accuracy are inadequate for real-time applications.In addition,several established techniques carry out well to any of the specific datasets but are not adjustable to distinct datasets.Thus,there is a necessity for developing an effectual prediction technique for optimum classifier performance and adjustable to various datasets.This paper presents a novel multi-vs.optimization(MVO)based feature selection(FS)with an optimal variational auto encoder(OVAE)model for FCP.The proposed multi-vs.optimization based feature selection with optimal variational auto encoder(MVOFS-OVAE)model mainly aims to accomplish forecasting the financial crisis.For achieving this,the proposed MVOFS-OVAE model primarily pre-processes the financial data using min-max normalization.In addition,the MVOFS-OVAE model designs a feature subset selection process using the MVOFS approach.Followed by,the variational auto encoder(VAE)model is applied for the categorization of financial data into financial crisis or non-financial crisis.Finally,the differential evolution(DE)algorithm is utilized for the parameter tuning of the VAE model.A series of simulations on the benchmark dataset reported the betterment of the MVOFS-OVAE approach over the recent state of art approaches.
文摘In the context of the debate on the role of cryptocurrencies in the economy as well as their dynamics and forecasting,this brief study analyzes the predictability of Bitcoin volume and returns using Google search values.We employed a rich set of established empirical approaches,including a VAR framework,a copulas approach,and non-parametric drawings,to capture a dependence structure.Using a weekly dataset from 2013 to 2017,our key results suggest that the frequency of Google searches leads to positive returns and a surge in Bitcoin trading volume.Shocks to search values have a positive effect,which persisted for at least a week.Our findings contribute to the debate on cryptocurrencies/Bitcoins and have profound implications in terms of understanding their dynamics,which are of special interest to investors and economic policymakers.
文摘This paper deals with the development of wage distribution by gender in the Czech and Slovak Republics in the years of 2005-2012. Special attention is given to changes in the behavior of wage distribution in relation to the onset of the global economic recession. The different behavior of the wage distribution of Czech and Slovak employees during the period is the subject of research. The article discusses the differences in the wage level between men and women in the Czech and Slovak Republics. There are the total wage distributions of men and women together, both in the Czech Republic and in the Slovak Republic on one hand, and wage distributions according to the gender separately for men and women on the other hand. Special attention was paid to the development of Gini coefficient of the concentration in both countries according to the gender in the period under review, too.
文摘In this research we are going to define two new concepts: a) “The Potential of Events” (EP) and b) “The Catholic Information” (CI). The term CI derives from the ancient Greek language and declares all the Catholic (general) Logical Propositions (<img src="Edit_5f13a4a5-abc6-4bc5-9e4c-4ff981627b2a.png" width="33" height="21" alt="" />) which will true for every element of a set A. We will study the Riemann Hypothesis in two stages: a) By using the EP we will prove that the distribution of events e (even) and o (odd) of Square Free Numbers (SFN) on the axis Ax(N) of naturals is Heads-Tails (H-T) type. b) By using the CI we will explain the way that the distribution of prime numbers can be correlated with the non-trivial zeros of the function <em>ζ</em>(<em>s</em>) of Riemann. The Introduction and the Chapter 2 are necessary for understanding the solution. In the Chapter 3 we will present a simple method of forecasting in many very useful applications (e.g. financial, technological, medical, social, etc) developing a generalization of this new, proven here, theory which we finally apply to the solution of RH. The following Introduction as well the Results with the Discussion at the end shed light about the possibility of the proof of all the above. The article consists of 9 chapters that are numbered by 1, 2, …, 9.
文摘Through study,it is found that since 1952,there has been a long-run equilibrium relationship between China's rural financial market growth and rural economic growth,the government-led rural financial market growth has effectively supported rural economic growth,and increasing the farmers' financing ratio has always helped to boost long-term growth of the rural economy.However,dominated by market mechanism from 1978,there is only one-way support relationship:rural economic growth brings about quantitative growth of rural financial market.
文摘The fixed annual budget process can be a cumbersome and static process, often failing to deliver intended benefits. Typically detached from business operations and strategic planning goals, the annual budget suffers from inherent weaknesses caused by a lack of business intelligence regarding its underlying assumptions. This weakness is well documented in existing literature and there is ample evidence of improved alternatives to static corporate financial planning. One such alternative utilizes business intelligence as an essential component in the annual budget process, along with rolling forecasts as a critical tool. Utilizing business intelligence supported, driver-based rolling forecasting can align an organization’s budget process with strategic objectives and can further the operational and financial strength of an organization, as well as maximize shareholder value. In order to fully explore this topic, this article will present a review of the conventional annual budget process and the manner in which an approach that bases financial forecasts on business intelligence drivers can align operations with strategic objectives and add value to an organization. An assessment of intelligence-supported, driver-based rolling forecasting will also be presented, demonstrating an im- proved approach to the traditional annual budgeting process.
文摘Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification.There are several forecasting techniques in the literature for obtaining accurate forecasts for investment decision making.Numerous empirical studies have employed such methods to investigate the returns of different individual stock indices.However,there have been very few studies of groups of stock markets or indices.The findings of previous studies indicate that there is no single method that can be applied uniformly to all markets.In this context,this study aimed to examine the predictive performance of linear,nonlinear,artificial intelligence,frequency domain,and hybrid models to find an appropriate model to forecast the stock returns of developed,emerging,and frontier markets.We considered the daily stock market returns of selected indices from developed,emerging,and frontier markets for the period 2000–2018 to evaluate the predictive performance of the above models.The results showed that no single model out of the five models could be applied uniformly to all markets.However,traditional linear and nonlinear models outperformed artificial intelligence and frequency domain models in providing accurate forecasts.
文摘This paper explores the power of stock trading trend using an integrated New ThresholdFuzzy Cognitive Maps(NTFCMs)Markov chain model.This new model captures thepositive as well as the negative jumps and predicts the trend for different stocks over 4months which follow an uptrend,downtrend and a mixed trend.The mean absolute percent error(MAPE)tolerance limits,the root mean square error(RMSE)tolerance limits aredetermined for various stock indices over a multi-timeframe period and observed for theexisting methods lying within the defined limits.The results show for every‘n’number ofpredictions made,the predicted close value of the day’s stock price was within tolerancelimit with 0%error and with 100%accuracy in predicting the future trend.
文摘For the purposes of this research, the optimal MLP neural network topology has been designed and tested by means the specific genetic algorithm multi-objective Pareto-Based. The objective of the research is to predict the trend of the ex-change rate Euro/USD up to three days ahead of last data available. The variable of output of the ANN designed is then the daily exchange rate Euro/Dollar and the frequency of data collection of variables of input and the output is daily. By the analysis of the data it is possible to conclude that the ANN model developed can largely predict the trend to three days of exchange rate Euro/USD.
文摘The aim of this paper is to show how qualitative and quantitative approaches can be complementary to study internet financial communication in a thesis by papers and how grounded theory (GT) can be the link among the different papers of the thesis. The study context of our thesis was the unregulated markets of New York Stock Exchange (NYSE) Euronext Brussels and the problematic rose from this context: What is the voluntary effort of communication when there is no obligation of internet financial communication? Four papers tried to answer this central question and other following research questions. To answer those research questions, several methodological approaches were used: content analysis of websites and scoring, linear regression, paired sample, and interviews. At the end of our thesis by papers, we discovered that GT was the general methodological travel among the papers: Every article had for vocation to try to answer the questions raised by the previous article.
基金financial support from the National Natural Science Foundation of China(Grant No.71902210)the Youth Research Fund of the Ministry of Education for Humanities and Social Sciences(Grant No.19YJC630092)+2 种基金the Program for Innovation Research in Central University of Finance and Economics(Grant No.CUFE 20190111)Social Science Foundation of Guangdong Province of China(Grant No.GD19CGL05)Graduate Research and Innovation Fund Project of Central University of Finance and Economics(Grant No.20182Y006)
文摘In this study,the impact of business and financial information integration(BFⅡ)on the voluntary management earnings forecasts(VMEFs)of listed firms in China between 2008 and 2018 is investigated.Drawing on litigation cost and ability signaling theories,we find that the adoption of BFⅡencourages top managers to disclose VMEFs.BFⅡfirms are identified through the textual analysis of management discussion and analysis(MD&A)reports,and the empirical results indicate that BFⅡfirms have a higher probability and frequency of issuing VMEFs than non-BFⅡfirms.The results remain robust after we identify causality by applying a propensity score matching and difference-in-differences(PSM-DID)test and use an alternate measure of BFⅡ.Further tests show that BFⅡfirms issue more accurate VMEFs and are able to issue them at an earlier stage.We also find that the positive relationship between BFⅡand VMEFs is weakened if the media expresses concern about the uncertainty of BFⅡadoption.
基金supported by the Humanities and Social Sciences Youth Foundation of the Ministry of Education of PR of China under Grant No.11YJC870028the Selfdetermined Research Funds of CCNU from the Colleges’Basic Research and Operation of MOE under Grant No.CCNU13F030+1 种基金China Postdoctoral Science Foundation under Grant No.2013M530753National Science Foundation of China under Grant No.71390335
文摘The financial market volatility forecasting is regarded as a challenging task because of irreg ularity, high fluctuation, and noise. In this study, a multiscale ensemble forecasting model is proposed. The original financial series are decomposed firstly different scale components (i.e., approximation and details) using the maximum overlap discrete wavelet transform (MODWT). The approximation is pre- dicted by a hybrid forecasting model that combines autoregressive integrated moving average (ARIMA) with feedforward neural network (FNN). ARIMA model is used to generate a linear forecast, and then FNN is developed as a tool for nonlinear pattern recognition to correct the estimation error in ARIMA forecast. Moreover, details are predicted by Elman neural networks. Three weekly exchange rates data are collected to establish and validate the forecasting model. Empirical results demonstrate consistent better performance of the proposed approach.