Deep Investment in Financial Markets using Deep Learning Models

@article{Aggarwal2017DeepII,
  title={Deep Investment in Financial Markets using Deep Learning Models},
  author={Saurabh Aggarwal and Somya Aggarwal},
  journal={International Journal of Computer Applications},
  year={2017},
  volume={162},
  pages={40-43}
}
The aim of this paper is to layout deep investment techniques in financial markets using deep learning models. Financial prediction problems usually involve huge variety of data-sets with complex data interactions which makes it difficult to design an economic model. Applying deep learning models to such problems can exploit potentially non-linear patterns in data. In this paper author introduces deep learning hierarchical decision models for prediction analysis and better decision making for… 

Neural Network Predictive Modeling on Dynamic Portfolio Management—A Simulation-Based Portfolio Optimization Approach

This paper fusing a number of macroeconomic factors using Neural Networks to build an Economic Factor-based Predictive Model (EFPM) and combines it with the Copula-GARCH simulation model and the Mean-Conditional Value at Risk framework to derive an optimal portfolio comprised of six index funds.

Research on Decision-Making of Complex Venture Capital Based on Financial Big Data Platform

Under the financial big data platform, bootstrap resampling technology and long short-term memory (LSTM) are used to predict the value of the stock premium within 20 months and countermeasures for the company’s financial risk investment are provided.

Pattern Learning Via Artificial Neural Networks for Financial Market Predictions

This work compares empirical training and validation accuracies of both model architectures and reveals portfolio performance characteristics in terms of return and risk metrics for different portfolio sizes, trying to derive common patterns within the top and flop stocks.

Deep Learning for Financial Applications : A Survey

QuantNet: transferring learning across trading strategies

This paper introduces QuantNet: an architecture that learns market-agnostic trends and use these to learn superior market-specific trading strategies and evaluates QuantNet on historical data across 3103 assets in 58 global equity markets.

Threshold-based portfolio: the role of the threshold and its applications

It is found that the risk and return profile of the realized TBP represents a monotonically increasing frontier on the risk–return plane, where the equally weighted universe portfolio plays a role in the lower bound of TBPs.

Bankruptcy prediction using imaged financial ratios and convolutional neural networks

  • T. Hosaka
  • Computer Science
    Expert Syst. Appl.
  • 2019

A New CNN-Based Model for Financial Time Series: TAIEX and FTSE Stocks Forecasting

This paper suggests a new CNN-based forecasting model that can be applied on some time series and, can successfully extract the features of them in the forecasting process and has been applied to some of the most evaluated financial time series.

References

SHOWING 1-10 OF 11 REFERENCES

Deep Learning in Finance

This work explores the use of deep learning hierarchical models for problems in nancial prediction and classication and finds that deep learning can detect and exploit interactions in the data that are, at least currently, invisible to any existing nancial economic theory.

Deep Learning for Event-Driven Stock Prediction

This work proposes a deep learning method for event-driven stock market prediction that can achieve nearly 6% improvements on S&P 500 index prediction and individual stock prediction, respectively, compared to state-of-the-art baseline methods.

Dropout: a simple way to prevent neural networks from overfitting

It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.

Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification

This work presents a pipeline for learning deep feature representations from multiple domains with Convolutional Neural Networks with CNNs and proposes a Domain Guided Dropout algorithm to improve the feature learning procedure.

Rectified Linear Units Improve Restricted Boltzmann Machines

Restricted Boltzmann machines were developed using binary stochastic hidden units that learn features that are better for object recognition on the NORB dataset and face verification on the Labeled Faces in the Wild dataset.

An Artificial Neural Network Approach for Credit Risk Management Vincenzo Pacelli1

  • Michele Azzollini Journal of Intelligent Learning Systems and Applications
  • 2011

An Artificial Neural Network Approach for Credit Risk Management By-Vincenzo Pacelli1, Michele Azzollini

  • Journal of Intelligent Learning Systems and Applications
  • 2011

Shallow Networks: an Approximation Theory Perspective By Hrushikesh N. Mhaskar1 and Tomaso Poggio

  • Deep vs

Learning Deep Architectures for AI, Yoshua Bengio, Foundations and Trends in Machine Learning