The Random Neural Network with a Genetic Algorithm and Deep Learning Clusters in Fintech: Smart Investment

Serrano, Will

doi:10.1007/978-3-319-92007-8_26

Cited by 8 publications

(10 citation statements)

References 47 publications

(41 reference statements)

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“…Methods Application [93] LSTM and AE Market investment [94] Hyper-parameter Option pricing in finance [95] LSTM and SVR Quantitative strategy in investment [96] R-NN and genetic method Smart financial investment Aggarwal and Aggarwal [93] designed a deep learning model applied to an economic investment problem with the capability of extracting nonlinear data patterns. They presented a decision model using neural network architecture such as LSTM, auto-encoding, and smart indexing to better estimate the risk of portfolio selection with securities for the investment problem.…”

Section: Referencementioning

confidence: 99%

“…Based on the results, the single DL approach outperforms the traditional RF approach with higher return in adopted investment strategy, but its deviation is considerably higher than that for the hybrid DL (LSTM-SVR) method. A novel learning Genetic Algorithm is proposed by Serrano [96] concerning Smart Investment that uses the R-NN model to emulate human behavior. The model makes use of complicated deep learning architecture where reinforcement learning occurs for fast decision-making purposes, deep learning for constructing stock identity, clusters for the overall decision-making purpose, and genetics for the transfer purpose.…”

Section: Deep Learning In Investmentmentioning

confidence: 99%

“…The authors in [96] constructed a complex model while the genetic algorithm using network weights was used to imitate the human brain where applying the following formula for the encoding-decoding organism, Equation ( 28):…”

Section: Deep Learning In Investmentmentioning

confidence: 99%

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Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

et al. 2020

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The popularity of deep reinforcement learning (DRL) applications in economics has increased exponentially. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated dynamic economics systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into the state-of-the-art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems in the presence of risk parameters and the ever-increasing uncertainties.

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Section: Referencementioning

confidence: 99%

Section: Deep Learning In Investmentmentioning

confidence: 99%

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Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

et al. 2020

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“…Deviation results for the study by Culkin and Das [100] Based on results, the single DL approach outperforms the traditional RF approach with higher return in adopted investment strategy, but its deviation was considerably higher than that for the hybrid DL (LSTM-SVR) method. A novel learning Genetic Algorithm is proposed by Serrano [102] concerning Smart Investment that use R-NN model to emulate the human behavior. The model makes usage of complicated deep learning architecture where reinforcement learning installs for the fast decisions-making purpose, deep learning for constructing stock identity, clusters for the overall decisions-making purpose and genetic for transferring purpose.…”

Section: Deep Learning In Investmentmentioning

confidence: 99%

Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

Mosavi¹,

Ghamisi²,

Faghan³

et al. 2020

Preprint

View full text Add to dashboard Cite

The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL through a wide range of capabilities from reinforcement learning (RL) and deep learning (DL) for handling sophisticated dynamic business environments offers vast opportunities. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this work, we first consider a brief review of DL, RL, and deep RL methods in diverse applications in economics providing an in-depth insight into the state of the art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher accuracy as compared to the traditional algorithms while facing real economic problems at the presence of risk parameters and the ever-increasing uncertainties.

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“…[25]. At present it has been widely applied to a variety of fields, including government policy [27] [40], and so on, and financial technology (FinTech) [41] is also one of popular applications. In addition, the series of decision tree models usually perform well in processing mid-size data, such as classification and regression trees (CART) [42], ID3 [43], and C4.5 [44].…”

Section: B Machine Learning Application In Decision Makingmentioning

confidence: 99%

A Boosting Regression-Based Method to Evaluate the Vital Essence in Semiconductor Industry Performance

et al. 2020

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In accordance with the statistical analysis, the industrial performance is usually related to research and development (R&D) intensity, and this factor indeed plausibly brings the biggest profit with patents and supporting products to the development of semiconductor industry. How to evaluate the completive performance of modern industries is an increasing issue, especially for the semiconductor industries in these decades. However, almost every traditional statistical model is deterred by the hypothesis of population and independent correlation among each feature, and this makes the result of typical regression model potentially lose reliability. To avoid this weakness, this paper therefore applies a gradient boosting based method-XGBoost to evaluate the feature importance of semiconductor industries. In the simulation experiments, different findings revel certain information, apart from R&D intensity, actually sway the gross net value in the annual financial announcement of semiconductor industries. Moreover, this paper proposes another concept to evaluate the essential factor contributing the development of semiconductor industries. Instead of only focusing on the effect of R&D intensity, this paper also predicts the future growth rate (GR) of net value by applying the greedy search of XGBoost Regression. INDEX TERMS XGBoost, Boosting Regression, Semiconductor Industry I. INOTRODUCTION Research and development (R&D) management is one of important issues in the research field of human resource management (HRM), and how to make proper investment in the expense of R&D management has become an increasing issue. The most works reveal a common conclusion in the literature that R&D has a positive effect on the performance of industries, so the term "R&D Intensity" is primarily applied in the analysis of relevant works and defined as the

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The Random Neural Network with a Genetic Algorithm and Deep Learning Clusters in Fintech: Smart Investment

Cited by 8 publications

References 47 publications

Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

A Boosting Regression-Based Method to Evaluate the Vital Essence in Semiconductor Industry Performance

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