Deep Learning for Global Tactical Asset Allocation

Chakravorty, Gaurav; Awasthi, Ankit; Silva, Brandon Da

doi:10.2139/ssrn.3242432

Cited by 13 publications

(7 citation statements)

References 9 publications

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“…Another useful application of DL architecture is to deal with investment decisions in conjunction with macroeconomic data. Chakravorty et al [94] used feed-forward neural network to perform tactical asset allocation while applying macroeconomic indicators and price-volume trend. They proposed two different methods in order to build a portfolio while the first one applied to estimate expected returns and uncertainty and the second approach used to obtain allocation directly using neural network architecture and optimize portfolio Sharpe.…”

Section: References Methods Application [87] Aementioning

confidence: 99%

Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

Mosavi

Ghamisi²,

Faghan³

et al. 2020

SSRN Journal

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Section: References Methods Application [87] Aementioning

confidence: 99%

Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

Mosavi

Ghamisi²,

Faghan³

et al. 2020

SSRN Journal

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“…Methods Application [86] Encoder-decoder Indicator prediction [87] Backpropagation Approach Forecasting inflation [88] Feed-Forward neural Network Asset allocation…”

Section: Referencementioning

confidence: 99%

“…Another useful application of DL architecture is to deal with investment decisions in conjunction with macroeconomic data. Chakravorty et al [88] used a feed-forward neural network to perform tactical asset allocation while applying macroeconomic indicators and price-volume trends. They proposed two different methods in order to build a portfolio; the first one estimated expected returns and uncertainty, and the second approach obtained allocation directly using neural network architecture and the optimized portfolio Sharpe.…”

Section: Referencementioning

confidence: 99%

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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“…A person who wants to extend their views and be open to new possibilities in their life needs to have the self-discipline of an accountant to do so. Upon completion of a thorough examination of their practices, accounting departments are obligated to document their ethics and social responsibility in the company's archives [ 24 ]. As a result of the requirement for a talent team to aid in the management of big data organizations, the formation of a talent pool becomes important when it comes to accounting for big data as well as when it comes to financial big data management.…”

Section: Introductionmentioning

confidence: 99%

The Application of the Unsupervised Migration Method Based on Deep Learning Model in the Marketing Oriented Allocation of High Level Accounting Talents

Liu¹,

Li²,

Zhang³

2022

Computational Intelligence and Neuroscience

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Deep learning is a branch of machine learning that uses neural networks to mimic the behaviour of the human brain. Various types of models are used in deep learning technology. This article will look at two important models and especially concentrate on unsupervised learning methodology. The two important models are as follows: the supervised and unsupervised models. The main difference is the method of training that they undergo. Supervised models are provided with training on a particular dataset and its outcome. In the case of unsupervised models, only input data is given, and there is no set outcome from which they can learn. The predicting/forecasting column is not present in an unsupervised model, unlike in the supervised model. Supervised models use regression to predict continuous quantities and classification to predict discrete class labels; unsupervised models use clustering to group similar models and association learning to find associations between items. Unsupervised migration is a combination of the unsupervised learning method and migration. In unsupervised learning, there is no need to supervise the models. Migration is an effective tool in processing and imaging data. Unsupervised learning allows the model to work independently to discover patterns and information that were previously undetected. It mainly works on unlabeled data. Unsupervised learning can achieve more complex processing tasks when compared to supervised learning. The unsupervised learning method is more unpredictable when compared with other types of learning methods. Some of the popular unsupervised learning algorithms include k-means clustering, hierarchal clustering, Apriori algorithm, clustering, anomaly detection, association mining, neural networks, etc. In this research article, we implement this particular deep learning model in the marketing oriented asset allocation of high level accounting talents. When the proposed unsupervised migration algorithm was compared to the existing Fractional Hausdorff Grey Model, it was discovered that the proposed system provided 99.12% accuracy by the high level accounting talented candidate in market-oriented asset allocation.

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Deep Learning for Global Tactical Asset Allocation

Cited by 13 publications

References 9 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

The Application of the Unsupervised Migration Method Based on Deep Learning Model in the Marketing Oriented Allocation of High Level Accounting Talents

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