Portfolio Optimization with 2D Relative-Attentional Gated Transformer

@article{Kim2020PortfolioOW,
  title={Portfolio Optimization with 2D Relative-Attentional Gated Transformer},
  author={Tae Wan Kim and Matloob Khushi},
  journal={2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)},
  year={2020},
  pages={1-6}
}
  • Tae Wan Kim, Matloob Khushi
  • Published 16 December 2020
  • Computer Science
  • 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)
Portfolio optimization is one of the most attentive fields that have been researched with machine learning approaches. Many researchers attempted to solve this problem using deep reinforcement learning due to its efficient inherence that can handle the property of financial markets. However, most of them can hardly be applicable to real-world trading since they ignore or extremely simplify the realistic constraints such as transaction costs or slippage. These constraints have a significantly… 

Figures and Tables from this paper

Event-Driven LSTM For Forex Price Prediction
TLDR
The experiment results show that the proposed event-driven feature selection together with the proposed models can form a robust prediction system which supports accurate trading strategies with minimal risk.
Feature importance recap and stacking models for forex price prediction
TLDR
A novel approach of feature selection called “feature importance recap” is proposed which combines the feature importance score from treebased model with the performance of deep learning model and a stacking model is developed to further improve the performance.
Clustering and attention model based for Intelligent Trading
TLDR
This work chose several pairs of foreign currency historical data and derived technical indicators from 2005 to 2021 as the dataset and established different machine learning models for event driven price prediction for oversold scenario.
Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating
TLDR
The proposed methodology with the combination of purposefully picked features shows an improvement over the previous studies, and the model predicts the direction of 1% price changes on the 10th day with high confidence and with enough buffer to even build a robotic trading system.
Text Mining of Stocktwits Data for Predicting Stock Prices
TLDR
FinALBERT, an ALBERT based model trained to handle financial domain text classification tasks by labelling Stocktwits text data based on stock price change is introduced, which can help analyse the historical data effectively and the mathematical function can be easily customised to predict stock movement.
A Survey of Forex and Stock Price Prediction Using Deep Learning
TLDR
The trend of using deep-learning-based methods for financial modeling is rising exponentially, and recent models combining LSTM with other methods, for example, DNN, are widely researched.

References

SHOWING 1-10 OF 31 REFERENCES
Portfolio management via two-stage deep learning with a joint cost
Deep Robust Reinforcement Learning for Practical Algorithmic Trading
TLDR
This paper proposes a novel trading agent, based on deep reinforcement learning, to autonomously make trading decisions and gain profits in the dynamic financial markets and designs several elaborate mechanisms to make the trading agent more practical to the real trading environment.
Deep Direct Reinforcement Learning for Financial Signal Representation and Trading
TLDR
This work introduces a recurrent deep neural network for real-time financial signal representation and trading and proposes a task-aware backpropagation through time method to cope with the gradient vanishing issue in deep training.
Event-Driven LSTM For Forex Price Prediction
TLDR
The experiment results show that the proposed event-driven feature selection together with the proposed models can form a robust prediction system which supports accurate trading strategies with minimal risk.
Learning to trade via direct reinforcement
TLDR
It is demonstrated how direct reinforcement can be used to optimize risk-adjusted investment returns (including the differential Sharpe ratio), while accounting for the effects of transaction costs.
Adaptive Portfolio Asset Allocation Optimization with Deep Learning
TLDR
The conclusion that a Long Short-Term Memory model can generate better risk-adjusted returns than conventional strategic passive portfolio management is provided.
Performance functions and reinforcement learning for trading systems and portfolios
We propose to train trading systems and portfolios by optimizing objective functions that directly measure trading and investment performance. Rather than basing a trading system on forecasts or
Wavelet Denoising and Attention-based RNN- ARIMA Model to Predict Forex Price
TLDR
A novel approach that integrates the wavelet denoising, Attention-based Recurrent Neural Network (ARNN), and Autoregressive Integrated Moving Average (ARIMA) models is proposed, capable of modelling dynamic systems such as the forex market.
GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTrading
TLDR
An automatic robotic trading (RoboTrading) strategy is designed with the proposed Genetic Algorithm Maximizing Sharpe and Sterling Ratio model (GA-MSSR) model, which achieves the best performance on risk factors, including maximum drawdowns and variance in return, comparing to benchmark models.
...
...