This study attempts to accelerate the learning ability of an artificial electric field algorithm(AEFA)by attributing it with two mechanisms:elitism and opposition-based learning.Elitism advances the convergence of the...This study attempts to accelerate the learning ability of an artificial electric field algorithm(AEFA)by attributing it with two mechanisms:elitism and opposition-based learning.Elitism advances the convergence of the AEFA towards global optima by retaining the fine-tuned solutions obtained thus far,and opposition-based learning helps enhance its exploration ability.The new version of the AEFA,called elitist opposition leaning-based AEFA(EOAEFA),retains the properties of the basic AEFA while taking advantage of both elitism and opposition-based learning.Hence,the improved version attempts to reach optimum solutions by enabling the diversification of solutions with guaranteed convergence.Higher-order neural networks(HONNs)have single-layer adjustable parameters,fast learning,a robust fault tolerance,and good approximation ability compared with multilayer neural networks.They consider a higher order of input signals,increased the dimensionality of inputs through functional expansion and could thus discriminate between them.However,determining the number of expansion units in HONNs along with their associated parameters(i.e.,weight and threshold)is a bottleneck in the design of such networks.Here,we used EOAEFA to design two HONNs,namely,a pi-sigma neural network and a functional link artificial neural network,called EOAEFA-PSNN and EOAEFA-FLN,respectively,in a fully automated manner.The proposed models were evaluated on financial time-series datasets,focusing on predicting four closing prices,four exchange rates,and three energy prices.Experiments,comparative studies,and statistical tests were conducted to establish the efficacy of the proposed approach.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
Stock market and cryptocurrency forecasting is very important to investors as they aspire to achieve even the slightest improvement to their buy-or-hold strategies so that they may increase profitability.However,obtai...Stock market and cryptocurrency forecasting is very important to investors as they aspire to achieve even the slightest improvement to their buy-or-hold strategies so that they may increase profitability.However,obtaining accurate and reliable predictions is challenging,noting that accuracy does not equate to reliability,especially when financial time-series forecasting is applied owing to its complex and chaotic tendencies.To mitigate this complexity,this study provides a comprehensive method for forecasting financial time series based on tactical input–output feature mapping techniques using machine learning(ML)models.During the prediction process,selecting the relevant indicators is vital to obtaining the desired results.In the financial field,limited attention has been paid to this problem with ML solutions.We investigate the use of feature selection with annealing(FSA)for the first time in this field,and we apply the least absolute shrinkage and selection operator(Lasso)method to select the features from more than 1000 candidates obtained from 26 technical classifiers with different periods and lags.Boruta(BOR)feature selection,a wrapper method,is used as a baseline for comparison.Logistic regression(LR),extreme gradient boosting(XGBoost),and long short-term memory are then applied to the selected features for forecasting purposes using 10 different financial datasets containing cryptocurrencies and stocks.The dependent variables consisted of daily logarithmic returns and trends.The mean-squared error for regression,area under the receiver operating characteristic curve,and classification accuracy were used to evaluate model performance,and the statistical significance of the forecasting results was tested using paired t-tests.Experiments indicate that the FSA algorithm increased the performance of ML models,regardless of problem type.The FSA hybrid models showed better performance and outperformed the other BOR models on seven of the 10 datasets for regression and classification.FSA-based models also outperformed Lasso-based models on six of the 10 datasets for regression and four of the 10 datasets for classification.None of the hybrid BOR models outperformed the hybrid FSA models.Lasso-based models,excluding the LR type,were comparable to the best models for six of the 10 datasets for classification.Detailed experimental analysis indicates that the proposed methodology can forecast returns and their movements efficiently and accurately,providing the field with a useful tool for investors.展开更多
The financial health of leading enterprises has a significant impact on the sustainable development of the global economy.Most data-driven financial health forecasts are based on the direct use of small-scale machine ...The financial health of leading enterprises has a significant impact on the sustainable development of the global economy.Most data-driven financial health forecasts are based on the direct use of small-scale machine learning.In this study,we proposed the idea of optimization coupling learning to improve these machine learning models in financial health forecasting.It not only revealed lagging,immediate,continuous impacts of various indicators in different fiscal year,but also had the same low computational cost and complexity as known small-scale machine learning models.We used our optimization coupling learning to investigate 3424 leading enterprises in China and revealed inner triggering mechanisms and differences of enterprises’financial health status from individual behavior to macro level.展开更多
Deep learning enables neural networks to improve prediction performance through data supplementation.In financial time series forecasting,however,such data-driven approaches can encounter limitations where additional ...Deep learning enables neural networks to improve prediction performance through data supplementation.In financial time series forecasting,however,such data-driven approaches can encounter limitations where additional data degrade performance,contrary to common expectations.While more data can still be beneficial,it may introduce systemic concept drift due to the complex nonstationarities of stock price index time series,thereby exacerbating overfitting.One such drift is memory inconsistency:locally measured long memories fluctuate over time,alternately approaching and deviating from the random walk condition.We address this problem by typifying memory inconsistencies into two simplified forms:long-term dependentto-independent(D2I)and long-term independent-to-dependent(I2D)inconsistencies.The first experiment,which uses U.S.stock price indices,suggests that additional training examples may lead to performance deterioration of long short-term memory(LSTM)networks,especially when memory inconsistencies are prominent.Since stock markets are influenced by numerous unknown dynamics,the second experiment,which uses simulated mean-reverting time series derived from the fractional Ornstein–Uhlenbeck(fOU)process,is conducted to focus solely on challenges arising from memory inconsistencies.The experimental results demonstrate that memory inconsistencies disrupt the performance of LSTM networks.Theoretically,additional errors from D2I and I2D inconsistencies increase as the time lag increases.Since LSTM networks are inherently recurrent,causing information from distant steps to attenuate,they fail to effectively capture memory inconsistencies in practical offline learning schemes.Nonetheless,transplanting pretrained memory-consistent gate parameters into the LSTM model partially mitigates the performance deterioration caused by memory inconsistencies,suggesting that memory augmentation strategies have the potential to overcome this problem.As such a memory augmentation method,we propose the Gate-of-Gates(GoG)model,which extends the capacity of LSTM gates and demonstrates that it can mitigate additional errors arising from memory inconsistencies.展开更多
Text-mining technologies have substantially affected financial industries.As the data in every sector of finance have grown immensely,text mining has emerged as an important field of research in the domain of finance....Text-mining technologies have substantially affected financial industries.As the data in every sector of finance have grown immensely,text mining has emerged as an important field of research in the domain of finance.Therefore,reviewing the recent literature on text-mining applications in finance can be useful for identifying areas for further research.This paper focuses on the text-mining literature related to financial forecasting,banking,and corporate finance.It also analyses the existing literature on text mining in financial applications and provides a summary of some recent studies.Finally,the paper briefly discusses various text-mining methods being applied in the financial domain,the challenges faced in these applications,and the future scope of text mining in finance.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
Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selecti...Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability.Further,choosing the optimal number of hidden nodes for a network usually requires intensive human intervention,which may lead to an ill-conditioned situation.In this context,chemical reaction optimization(CRO)is a meta-heuristic paradigm with increased success in a large number of application areas.It is characterized by faster convergence capability and requires fewer tunable parameters.This study develops a learning framework combining the advantages of ELM and CRO,called extreme learning with chemical reaction optimization(ELCRO).ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy.We evaluate its performance by predicting the daily volatility and closing prices of BSE indices.Additionally,its performance is compared with three other similarly developed models—ELM based on particle swarm optimization,genetic algorithm,and gradient descent—and find the performance of the proposed algorithm superior.Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model.Hence,this model can be used as a promising tool for financial forecasting.展开更多
Accurate prediction of stock market behavior is a challenging issue for financial forecasting.Artificial neural networks,such as multilayer perceptron have been established as better approximation and classification m...Accurate prediction of stock market behavior is a challenging issue for financial forecasting.Artificial neural networks,such as multilayer perceptron have been established as better approximation and classification models for this domain.This study proposes a chemical reaction optimization(CRO)based neuro-fuzzy network model for prediction of stock indices.The input vectors to the model are fuzzified by applying a Gaussian membership function,and each input is associated with a degree of membership to different classes.A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model.CRO was chosen because it requires fewer control parameters and has a faster convergence rate.Five statistical parameters are used to evaluate the performance of the model,and the model is validated by forecasting the daily closing indices for five major stock markets.The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior.We conducted the Deibold-Mariano test to check the statistical significance of the proposed model,and it was found to be significant.This model can be used as a promising tool for financial forecasting.展开更多
基金supported by the Yonsei Fellow Program funded by Lee Youn Jae,Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government,Ministry of Science and ICT(MSIT)(No.2020-0-01361,Artificial Intelligence Graduate School Program(Yonsei University)No.2022-0-00113,Developing a Sustainable Collaborative Multi-modal Lifelong Learning Framework)the support of Teachers Associateship for Research Excellence(TARE)Fellowship(No.TAR/2021/00006)of the Science and Engineering Research Board(SERB),Government of India.
文摘This study attempts to accelerate the learning ability of an artificial electric field algorithm(AEFA)by attributing it with two mechanisms:elitism and opposition-based learning.Elitism advances the convergence of the AEFA towards global optima by retaining the fine-tuned solutions obtained thus far,and opposition-based learning helps enhance its exploration ability.The new version of the AEFA,called elitist opposition leaning-based AEFA(EOAEFA),retains the properties of the basic AEFA while taking advantage of both elitism and opposition-based learning.Hence,the improved version attempts to reach optimum solutions by enabling the diversification of solutions with guaranteed convergence.Higher-order neural networks(HONNs)have single-layer adjustable parameters,fast learning,a robust fault tolerance,and good approximation ability compared with multilayer neural networks.They consider a higher order of input signals,increased the dimensionality of inputs through functional expansion and could thus discriminate between them.However,determining the number of expansion units in HONNs along with their associated parameters(i.e.,weight and threshold)is a bottleneck in the design of such networks.Here,we used EOAEFA to design two HONNs,namely,a pi-sigma neural network and a functional link artificial neural network,called EOAEFA-PSNN and EOAEFA-FLN,respectively,in a fully automated manner.The proposed models were evaluated on financial time-series datasets,focusing on predicting four closing prices,four exchange rates,and three energy prices.Experiments,comparative studies,and statistical tests were conducted to establish the efficacy of the proposed approach.
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.
基金supported by THE SCIENTIFIC AND TECHNOLOGICAL RESEARCH COUNCIL OF TURKIYE.
文摘Stock market and cryptocurrency forecasting is very important to investors as they aspire to achieve even the slightest improvement to their buy-or-hold strategies so that they may increase profitability.However,obtaining accurate and reliable predictions is challenging,noting that accuracy does not equate to reliability,especially when financial time-series forecasting is applied owing to its complex and chaotic tendencies.To mitigate this complexity,this study provides a comprehensive method for forecasting financial time series based on tactical input–output feature mapping techniques using machine learning(ML)models.During the prediction process,selecting the relevant indicators is vital to obtaining the desired results.In the financial field,limited attention has been paid to this problem with ML solutions.We investigate the use of feature selection with annealing(FSA)for the first time in this field,and we apply the least absolute shrinkage and selection operator(Lasso)method to select the features from more than 1000 candidates obtained from 26 technical classifiers with different periods and lags.Boruta(BOR)feature selection,a wrapper method,is used as a baseline for comparison.Logistic regression(LR),extreme gradient boosting(XGBoost),and long short-term memory are then applied to the selected features for forecasting purposes using 10 different financial datasets containing cryptocurrencies and stocks.The dependent variables consisted of daily logarithmic returns and trends.The mean-squared error for regression,area under the receiver operating characteristic curve,and classification accuracy were used to evaluate model performance,and the statistical significance of the forecasting results was tested using paired t-tests.Experiments indicate that the FSA algorithm increased the performance of ML models,regardless of problem type.The FSA hybrid models showed better performance and outperformed the other BOR models on seven of the 10 datasets for regression and classification.FSA-based models also outperformed Lasso-based models on six of the 10 datasets for regression and four of the 10 datasets for classification.None of the hybrid BOR models outperformed the hybrid FSA models.Lasso-based models,excluding the LR type,were comparable to the best models for six of the 10 datasets for classification.Detailed experimental analysis indicates that the proposed methodology can forecast returns and their movements efficiently and accurately,providing the field with a useful tool for investors.
基金supported by the European Commission Horizon 2020 Framework Program No.861584the Taishan Distinguished Professor Fund No.20190910.
文摘The financial health of leading enterprises has a significant impact on the sustainable development of the global economy.Most data-driven financial health forecasts are based on the direct use of small-scale machine learning.In this study,we proposed the idea of optimization coupling learning to improve these machine learning models in financial health forecasting.It not only revealed lagging,immediate,continuous impacts of various indicators in different fiscal year,but also had the same low computational cost and complexity as known small-scale machine learning models.We used our optimization coupling learning to investigate 3424 leading enterprises in China and revealed inner triggering mechanisms and differences of enterprises’financial health status from individual behavior to macro level.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2023S1A5A8077102).
文摘Deep learning enables neural networks to improve prediction performance through data supplementation.In financial time series forecasting,however,such data-driven approaches can encounter limitations where additional data degrade performance,contrary to common expectations.While more data can still be beneficial,it may introduce systemic concept drift due to the complex nonstationarities of stock price index time series,thereby exacerbating overfitting.One such drift is memory inconsistency:locally measured long memories fluctuate over time,alternately approaching and deviating from the random walk condition.We address this problem by typifying memory inconsistencies into two simplified forms:long-term dependentto-independent(D2I)and long-term independent-to-dependent(I2D)inconsistencies.The first experiment,which uses U.S.stock price indices,suggests that additional training examples may lead to performance deterioration of long short-term memory(LSTM)networks,especially when memory inconsistencies are prominent.Since stock markets are influenced by numerous unknown dynamics,the second experiment,which uses simulated mean-reverting time series derived from the fractional Ornstein–Uhlenbeck(fOU)process,is conducted to focus solely on challenges arising from memory inconsistencies.The experimental results demonstrate that memory inconsistencies disrupt the performance of LSTM networks.Theoretically,additional errors from D2I and I2D inconsistencies increase as the time lag increases.Since LSTM networks are inherently recurrent,causing information from distant steps to attenuate,they fail to effectively capture memory inconsistencies in practical offline learning schemes.Nonetheless,transplanting pretrained memory-consistent gate parameters into the LSTM model partially mitigates the performance deterioration caused by memory inconsistencies,suggesting that memory augmentation strategies have the potential to overcome this problem.As such a memory augmentation method,we propose the Gate-of-Gates(GoG)model,which extends the capacity of LSTM gates and demonstrates that it can mitigate additional errors arising from memory inconsistencies.
文摘Text-mining technologies have substantially affected financial industries.As the data in every sector of finance have grown immensely,text mining has emerged as an important field of research in the domain of finance.Therefore,reviewing the recent literature on text-mining applications in finance can be useful for identifying areas for further research.This paper focuses on the text-mining literature related to financial forecasting,banking,and corporate finance.It also analyses the existing literature on text mining in financial applications and provides a summary of some recent studies.Finally,the paper briefly discusses various text-mining methods being applied in the financial domain,the challenges faced in these applications,and the future scope of text mining in finance.
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.
文摘Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability.Further,choosing the optimal number of hidden nodes for a network usually requires intensive human intervention,which may lead to an ill-conditioned situation.In this context,chemical reaction optimization(CRO)is a meta-heuristic paradigm with increased success in a large number of application areas.It is characterized by faster convergence capability and requires fewer tunable parameters.This study develops a learning framework combining the advantages of ELM and CRO,called extreme learning with chemical reaction optimization(ELCRO).ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy.We evaluate its performance by predicting the daily volatility and closing prices of BSE indices.Additionally,its performance is compared with three other similarly developed models—ELM based on particle swarm optimization,genetic algorithm,and gradient descent—and find the performance of the proposed algorithm superior.Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model.Hence,this model can be used as a promising tool for financial forecasting.
文摘Accurate prediction of stock market behavior is a challenging issue for financial forecasting.Artificial neural networks,such as multilayer perceptron have been established as better approximation and classification models for this domain.This study proposes a chemical reaction optimization(CRO)based neuro-fuzzy network model for prediction of stock indices.The input vectors to the model are fuzzified by applying a Gaussian membership function,and each input is associated with a degree of membership to different classes.A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model.CRO was chosen because it requires fewer control parameters and has a faster convergence rate.Five statistical parameters are used to evaluate the performance of the model,and the model is validated by forecasting the daily closing indices for five major stock markets.The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior.We conducted the Deibold-Mariano test to check the statistical significance of the proposed model,and it was found to be significant.This model can be used as a promising tool for financial forecasting.
