2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), 2014
We extend Neural Network (NN) trading models with an innovative and efficient volatility filter b... more We extend Neural Network (NN) trading models with an innovative and efficient volatility filter based on fuzzy c-means clustering algorithm, where the choice for the number of clusters, a frequent problem with cluster analysis, is selected by optimizing a global risk-return performance measure. Our algorithm automatically extracts fuzzy rules from past trades by taking into account the predicted return size and intraday time varying realized volatility, the latter used as a proxy for uncertainty. The model identifies unique intraday scenarios and subsequently creates a dynamic and visually apprehensible risk-return search space to control algorithmic trading decisions. Our results show that this method can be successfully applied to support high-frequency intraday trading strategies, outperforming both standard NN and buy-and-hold models.
International Journal of Artificial Intelligence & Applications
With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinT... more With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. However, despite the evergrowing achievements, the biggest obstacle in most AI systems is their lack of interpretability. This deficiency of transparency limits their application in different domains including credit scoring. Credit scoring systems help financial experts make better decisions regarding whether or not to accept a loan application so that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. A recently introduced con...
Non-Fungible Tokens (NFTa) can either represent an original digital artwork, or act as a digital ... more Non-Fungible Tokens (NFTa) can either represent an original digital artwork, or act as a digital reference to the actual work. In both as digital references to the actual work. In both cases the record in the distributed ledger, mostly a blockchain-based database, intends to serve as a proof of ownership or transfer of rights. NFTs might also add a further purpose, which in blockchain terms is referred to as “a utility", such as access to special websites, chats or clubs in emerging metaverse platforms. This use-case paper presents a first introduction of two early stage demonstrators, set outside the common use of art images or images of historical events as NFTs. The first case shows how educational credentials can be created, in which different teachers contribute to assessment achievements. We elaborate how these partial achievements are verified separately within the actual credentials. In the second case study, we build on previous research in regard to NFTs in the music ...
Computer Science & Information Technology (CS & IT), 2020
With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusi... more With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusiasm in Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. Credit scoring helps financial experts make better decisions regarding whether or not to accept a loan application, such that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. An interesting concept that has been recently introduced is eXplainable AI (XAI), which focuses on making black-box models more interpretable. In this work, we present a credit scoring model that is both accurate and inter...
We present a performance comparison of risk-adjusted intraday trading strategies based on dynamic... more We present a performance comparison of risk-adjusted intraday trading strategies based on dynamic non-linear models using the more traditional Articial Neural Network, as well as Adaptive Neuro-Fuzzy Systems (ANFIS) and Dynamic Evolving Neuro Fuzzy Systems (DENFIS). The model selection process takes into account the risk-return measures together with exible position holding periods and a return band lter, employing a dynamic combination of moving average signals. Our results show that these models can be successfully applied to support intraday trading strategies, especially when considering constraints such as transaction costs and trading hours, which existing approaches in the literature do not account for.
International Journal of Artificial Intelligence & Applications (IJAIA), 2021
With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinT... more With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. However, despite the evergrowing achievements, the biggest obstacle in most AI systems is their lack of interpretability. This deficiency of transparency limits their application in different domains including credit scoring. Credit scoring systems help financial experts make better decisions regarding whether or not to accept a loan application so that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. A recently introduced con...
With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusi... more With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusiasm in Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. Credit scoring helps financial experts make better decisions regarding whether or not to accept a loan application, such that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. An interesting concept that has been recently introduced is eXplainable AI (XAI), which focuses on making black-box models more interpretable. In this work, we present a credit scoring model that is both accurate and inter...
2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), 2014
We extend Neural Network (NN) trading models with an innovative and efficient volatility filter b... more We extend Neural Network (NN) trading models with an innovative and efficient volatility filter based on fuzzy c-means clustering algorithm, where the choice for the number of clusters, a frequent problem with cluster analysis, is selected by optimizing a global risk-return performance measure. Our algorithm automatically extracts fuzzy rules from past trades by taking into account the predicted return size and intraday time varying realized volatility, the latter used as a proxy for uncertainty. The model identifies unique intraday scenarios and subsequently creates a dynamic and visually apprehensible risk-return search space to control algorithmic trading decisions. Our results show that this method can be successfully applied to support high-frequency intraday trading strategies, outperforming both standard NN and buy-and-hold models.
International Journal of Artificial Intelligence & Applications
With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinT... more With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. However, despite the evergrowing achievements, the biggest obstacle in most AI systems is their lack of interpretability. This deficiency of transparency limits their application in different domains including credit scoring. Credit scoring systems help financial experts make better decisions regarding whether or not to accept a loan application so that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. A recently introduced con...
Non-Fungible Tokens (NFTa) can either represent an original digital artwork, or act as a digital ... more Non-Fungible Tokens (NFTa) can either represent an original digital artwork, or act as a digital reference to the actual work. In both as digital references to the actual work. In both cases the record in the distributed ledger, mostly a blockchain-based database, intends to serve as a proof of ownership or transfer of rights. NFTs might also add a further purpose, which in blockchain terms is referred to as “a utility", such as access to special websites, chats or clubs in emerging metaverse platforms. This use-case paper presents a first introduction of two early stage demonstrators, set outside the common use of art images or images of historical events as NFTs. The first case shows how educational credentials can be created, in which different teachers contribute to assessment achievements. We elaborate how these partial achievements are verified separately within the actual credentials. In the second case study, we build on previous research in regard to NFTs in the music ...
Computer Science & Information Technology (CS & IT), 2020
With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusi... more With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusiasm in Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. Credit scoring helps financial experts make better decisions regarding whether or not to accept a loan application, such that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. An interesting concept that has been recently introduced is eXplainable AI (XAI), which focuses on making black-box models more interpretable. In this work, we present a credit scoring model that is both accurate and inter...
We present a performance comparison of risk-adjusted intraday trading strategies based on dynamic... more We present a performance comparison of risk-adjusted intraday trading strategies based on dynamic non-linear models using the more traditional Articial Neural Network, as well as Adaptive Neuro-Fuzzy Systems (ANFIS) and Dynamic Evolving Neuro Fuzzy Systems (DENFIS). The model selection process takes into account the risk-return measures together with exible position holding periods and a return band lter, employing a dynamic combination of moving average signals. Our results show that these models can be successfully applied to support intraday trading strategies, especially when considering constraints such as transaction costs and trading hours, which existing approaches in the literature do not account for.
International Journal of Artificial Intelligence & Applications (IJAIA), 2021
With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinT... more With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. However, despite the evergrowing achievements, the biggest obstacle in most AI systems is their lack of interpretability. This deficiency of transparency limits their application in different domains including credit scoring. Credit scoring systems help financial experts make better decisions regarding whether or not to accept a loan application so that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. A recently introduced con...
With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusi... more With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusiasm in Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. Credit scoring helps financial experts make better decisions regarding whether or not to accept a loan application, such that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. An interesting concept that has been recently introduced is eXplainable AI (XAI), which focuses on making black-box models more interpretable. In this work, we present a credit scoring model that is both accurate and inter...
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