Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on f...Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks.展开更多
In this paper, we study the price of catastrophe Options with counterparty credit risk in a reduced form model. We assume that the loss process is generated by a doubly stochastic Poisson process, the share price proc...In this paper, we study the price of catastrophe Options with counterparty credit risk in a reduced form model. We assume that the loss process is generated by a doubly stochastic Poisson process, the share price process is modeled through a jump-diffusion process which is correlated to the loss process, the interest rate process and the default intensity process are modeled through the Vasicek model： We derive the closed form formulae for pricing catastrophe options in a reduced form model. Furthermore, we make some numerical analysis on the explicit formulae.展开更多
In this paper,we consider the price of catastrophe options with credit risk in a regime-switching model.We assume that the macroeconomic states are described by a continuous-time Markov chain with a finite state space...In this paper,we consider the price of catastrophe options with credit risk in a regime-switching model.We assume that the macroeconomic states are described by a continuous-time Markov chain with a finite state space.By using the measure change technique,we derive the price expressions of catastrophe put options.Moreover,we conduct some numerical analysis to demonstrate how the parameters of the model affect the price of the catastrophe put option.展开更多
For this research,we examined the influence of access to domestic and international financing on sustainability performance with a mediating role of innovative performance and a moderating role of access to government...For this research,we examined the influence of access to domestic and international financing on sustainability performance with a mediating role of innovative performance and a moderating role of access to government support.Data were collected from 317 small and medium-sized enterprises(SMEs)through structured questionnaires.The results indicated that access to domestic and international financing significantly contributes to sustainability and innovative performances.Accordingly,we found a partial mediating role of innovative performance between access to domestic financing and sustainability performance as well as between access to international financing and sustainability performance.Access to government support significantly moderates the relationship between access to domestic finances and innovative performance as well as between access to international finances and innovative performance.Practitioners and policymakers should encourage national and international financial institutions and banks to facilitate SMEs by lending them funds for innovative activities and sustainability performance.Moreover,the government should support SMEs,so that they can contribute to economic growth and the gross domestic product.The implications from these matters will be further discussed in this paper.展开更多
Hedge funds have traditionally served wealthy individuals and institutional investors with the promise of delivering protection of capital and uncorrelated positive returns irrespective of market direction,allowing th...Hedge funds have traditionally served wealthy individuals and institutional investors with the promise of delivering protection of capital and uncorrelated positive returns irrespective of market direction,allowing them to better manage portfolio risk.However,the financial crisis of 2008 has heightened investor sensitivity to the high fees,illiquidity,lack of transparency,and lockup periods typically associated with hedge funds.Hedge fund replication products,or clones,seek to answer these challenges by providing daily liquidity,transparency,and immediate exposure to a desired hedge fund strategy.Nonetheless,although lowering cost and adding simplicity by using a common set of factors,traditional replication products might offer lower risk-reward performance compared to hedge funds.This research explores hedge fund replication further by examining the importance of constructing clones with specific factors relevant to each hedge fund strategy,and then compares the strategy specific clone risk and reward performance against both actual hedge fund performance and hedge fund clones constructed using a more general set of common factors.Testing shows that using strategy specific factors to replicate common hedge fund strategies can offer superior risk-reward performance compared to previous general model clones.展开更多
As rule-based systems (RBS) technology gains wider acceptance, the need to create and maintain large knowledge bases will assume greater importance. Demonstrating a rule base to be free from error remains one of the o...As rule-based systems (RBS) technology gains wider acceptance, the need to create and maintain large knowledge bases will assume greater importance. Demonstrating a rule base to be free from error remains one of the obstacles to the adoption of this technology. In the past several years, a vast body of research has been carried out in developing various graphical techniques such as utilizing Petri Nets to analyze structural errors in rule-based systems, which utilize propositional logic. Four typical errors in rule-based systems are redundancy, circularity, incompleteness, and inconsistency. Recently, a DNA-based computing approach to detect these errors has been proposed. That paper presents algorithms which are able to detect structural errors just for special cases. For a rule base, which contains multiple starting nodes and goal nodes, structural errors are not removed correctly by utilizing the algorithms proposed in that paper and algorithms lack generality. In this study algorithms mainly based on Adleman’s operations, which are able to detect structural errors, in any form that they may arise in rule base, are presented. The potential of applying our algorithm is auspicious giving the operational time complexity of O(n*(Max{q, K, z})), in which n is the number of fact clauses;q is the number of rules in the longest inference chain;K is the number of tubes containing antecedents which are comprised of distinct number of starting nodes;and z denotes the maximum number of distinct antecedents comprised of the same number of starting nodes.展开更多
This paper applies deep learning models to predict Bitcoin price directions and the subsequent profitability of trading strategies based on these predictions.The study compares the performance of the convolutional neu...This paper applies deep learning models to predict Bitcoin price directions and the subsequent profitability of trading strategies based on these predictions.The study compares the performance of the convolutional neural network-long short-term memory(CNN–LSTM),long-and short-term time-series network,temporal convolutional network,and ARIMA(benchmark)models for predicting Bitcoin prices using on-chain data.Feature-selection methods—i.e.,Boruta,genetic algorithm,and light gradient boosting machine—are applied to address the curse of dimensionality that could result from a large feature set.Results indicate that combining Boruta feature selection with the CNN-LSTM model consistently outperforms other combinations,achieving an accuracy of 82.44%.Three trading strategies and three investment positions are examined through backtesting.The long-and-short buy-and-sell investment approach generated an extraordinary annual return of 6654% when informed by higher-accuracy price-direction predictions.This study provides evidence of the potential profitability of predictive models in Bitcoin trading.展开更多
The contagion credit risk model is used to describe the contagion effect among different financial institutions. Under such a model, the default intensities are driven not only by the common risk factors, but also by ...The contagion credit risk model is used to describe the contagion effect among different financial institutions. Under such a model, the default intensities are driven not only by the common risk factors, but also by the defaults of other considered firms. In this paper, we consider a two-dimensional credit risk model with contagion and regime-switching. We assume that the default intensity of one firm will jump when the other firm defaults and that the intensity is controlled by a Vasicek model with the coefficients allowed to switch in different regimes before the default of other firm. By changing measure, we derive the marginal distributions and the joint distribution for default times. We obtain some closed form results for pricing the fair spreads of the first and the second to default credit default swaps （CDSs）. Numerical results are presented to show the impacts of the model parameters on the fair spreads.展开更多
文摘Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks.
基金supported by the National Natural Science Foundation of China(11371274)
文摘In this paper, we study the price of catastrophe Options with counterparty credit risk in a reduced form model. We assume that the loss process is generated by a doubly stochastic Poisson process, the share price process is modeled through a jump-diffusion process which is correlated to the loss process, the interest rate process and the default intensity process are modeled through the Vasicek model： We derive the closed form formulae for pricing catastrophe options in a reduced form model. Furthermore, we make some numerical analysis on the explicit formulae.
基金supported by the Jiangsu University Philosophy and Social Science Research Project(Grant No.2019SJA1326).
文摘In this paper,we consider the price of catastrophe options with credit risk in a regime-switching model.We assume that the macroeconomic states are described by a continuous-time Markov chain with a finite state space.By using the measure change technique,we derive the price expressions of catastrophe put options.Moreover,we conduct some numerical analysis to demonstrate how the parameters of the model affect the price of the catastrophe put option.
文摘For this research,we examined the influence of access to domestic and international financing on sustainability performance with a mediating role of innovative performance and a moderating role of access to government support.Data were collected from 317 small and medium-sized enterprises(SMEs)through structured questionnaires.The results indicated that access to domestic and international financing significantly contributes to sustainability and innovative performances.Accordingly,we found a partial mediating role of innovative performance between access to domestic financing and sustainability performance as well as between access to international financing and sustainability performance.Access to government support significantly moderates the relationship between access to domestic finances and innovative performance as well as between access to international finances and innovative performance.Practitioners and policymakers should encourage national and international financial institutions and banks to facilitate SMEs by lending them funds for innovative activities and sustainability performance.Moreover,the government should support SMEs,so that they can contribute to economic growth and the gross domestic product.The implications from these matters will be further discussed in this paper.
基金The Department of Engineering Management and Systems Engineering at the Missouri University of Science and Technology provided graduate assistantship funding for Mr.Sujit Subhash.
文摘Hedge funds have traditionally served wealthy individuals and institutional investors with the promise of delivering protection of capital and uncorrelated positive returns irrespective of market direction,allowing them to better manage portfolio risk.However,the financial crisis of 2008 has heightened investor sensitivity to the high fees,illiquidity,lack of transparency,and lockup periods typically associated with hedge funds.Hedge fund replication products,or clones,seek to answer these challenges by providing daily liquidity,transparency,and immediate exposure to a desired hedge fund strategy.Nonetheless,although lowering cost and adding simplicity by using a common set of factors,traditional replication products might offer lower risk-reward performance compared to hedge funds.This research explores hedge fund replication further by examining the importance of constructing clones with specific factors relevant to each hedge fund strategy,and then compares the strategy specific clone risk and reward performance against both actual hedge fund performance and hedge fund clones constructed using a more general set of common factors.Testing shows that using strategy specific factors to replicate common hedge fund strategies can offer superior risk-reward performance compared to previous general model clones.
文摘As rule-based systems (RBS) technology gains wider acceptance, the need to create and maintain large knowledge bases will assume greater importance. Demonstrating a rule base to be free from error remains one of the obstacles to the adoption of this technology. In the past several years, a vast body of research has been carried out in developing various graphical techniques such as utilizing Petri Nets to analyze structural errors in rule-based systems, which utilize propositional logic. Four typical errors in rule-based systems are redundancy, circularity, incompleteness, and inconsistency. Recently, a DNA-based computing approach to detect these errors has been proposed. That paper presents algorithms which are able to detect structural errors just for special cases. For a rule base, which contains multiple starting nodes and goal nodes, structural errors are not removed correctly by utilizing the algorithms proposed in that paper and algorithms lack generality. In this study algorithms mainly based on Adleman’s operations, which are able to detect structural errors, in any form that they may arise in rule base, are presented. The potential of applying our algorithm is auspicious giving the operational time complexity of O(n*(Max{q, K, z})), in which n is the number of fact clauses;q is the number of rules in the longest inference chain;K is the number of tubes containing antecedents which are comprised of distinct number of starting nodes;and z denotes the maximum number of distinct antecedents comprised of the same number of starting nodes.
文摘This paper applies deep learning models to predict Bitcoin price directions and the subsequent profitability of trading strategies based on these predictions.The study compares the performance of the convolutional neural network-long short-term memory(CNN–LSTM),long-and short-term time-series network,temporal convolutional network,and ARIMA(benchmark)models for predicting Bitcoin prices using on-chain data.Feature-selection methods—i.e.,Boruta,genetic algorithm,and light gradient boosting machine—are applied to address the curse of dimensionality that could result from a large feature set.Results indicate that combining Boruta feature selection with the CNN-LSTM model consistently outperforms other combinations,achieving an accuracy of 82.44%.Three trading strategies and three investment positions are examined through backtesting.The long-and-short buy-and-sell investment approach generated an extraordinary annual return of 6654% when informed by higher-accuracy price-direction predictions.This study provides evidence of the potential profitability of predictive models in Bitcoin trading.
基金Acknowledgements The authors cordially thank the anonymous reviewers for valuable comments to improve the earlier version of the paper. This work was supported by the National Natural Science Foundation of China （Grant Nos. 11371274, 11671291）, the Natural Science Foundation of Jiangsu Province （Grant No. BK20160300）, and the Open Project of Jiangsu Key Laboratory of Financial Engineering （Grant No. NSK2015-05）.
文摘The contagion credit risk model is used to describe the contagion effect among different financial institutions. Under such a model, the default intensities are driven not only by the common risk factors, but also by the defaults of other considered firms. In this paper, we consider a two-dimensional credit risk model with contagion and regime-switching. We assume that the default intensity of one firm will jump when the other firm defaults and that the intensity is controlled by a Vasicek model with the coefficients allowed to switch in different regimes before the default of other firm. By changing measure, we derive the marginal distributions and the joint distribution for default times. We obtain some closed form results for pricing the fair spreads of the first and the second to default credit default swaps （CDSs）. Numerical results are presented to show the impacts of the model parameters on the fair spreads.
