A Long Short-Term Memory Approach Towards Stock Selection and Portfolio Optimization

2020 ◽  
Author(s):  
Sumit Mahlawat ◽  
Utkarsh Prabhakar ◽  
Nishank Goyal ◽  
Praket Parth ◽  
Varun Ramamohan
Keyword(s):  
Portfolio Optimization ◽  
Short Term Memory ◽  
Stock Selection ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Portfolio Optimization-Based Stock Prediction Using Long-Short Term Memory Network in Quantitative Trading

2020 ◽  
Vol 10 (2) ◽  
pp. 437 ◽  
Author(s):  
Van-Dai Ta ◽  
CHUAN-MING Liu ◽  
Direselign Addis Tadesse
Keyword(s):  
Prediction Model ◽  
Portfolio Optimization ◽  
Short Term Memory ◽  
Historical Data ◽  
Optimization Techniques ◽  
Support Vector ◽  
Stock Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

In quantitative trading, stock prediction plays an important role in developing an effective trading strategy to achieve a substantial return. Prediction outcomes also are the prerequisites for active portfolio construction and optimization. However, the stock prediction is a challenging task because of the diversified factors involved such as uncertainty and instability. Most of the previous research focuses on analyzing financial historical data based on statistical techniques, which is known as a type of time series analysis with limited achievements. Recently, deep learning techniques, specifically recurrent neural network (RNN), has been designed to work with sequence prediction. In this paper, a long short-term memory (LSTM) network, which is a special kind of RNN, is proposed to predict stock movement based on historical data. In order to construct an efficient portfolio, multiple portfolio optimization techniques, including equal-weighted modeling (EQ), simulation modeling Monte Carlo simulation (MCS), and optimization modeling mean variant optimization (MVO), are used to improve the portfolio performance. The results showed that our proposed LSTM prediction model works efficiently by obtaining high accuracy from stock prediction. The constructed portfolios based on the LSTM prediction model outperformed other constructed portfolios-based prediction models such as linear regression and support vector machine. In addition, optimization techniques showed a significant improvement in the return and Sharpe ratio of the constructed portfolios. Furthermore, our constructed portfolios beat the benchmark Standard and Poor 500 (S&P 500) index in both active returns and Sharpe ratios.

Incorporating Financial News for Forecasting Bitcoin Prices Based on Long Short-Term Memory Networks

2020 ◽  
Author(s):  
Abdolreza Nazemi ◽  
Johannes Jakubik ◽  
Andreas Geyer-Schulz ◽  
Frank J. Fabozzi
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Financial News ◽  
Long Short Term Memory

Unit Selection with Hierarchical Cascaded Long Short Term Memory Bidirectional Recurrent Neural Nets

2017 ◽  
Author(s):  
Vincent Pollet ◽  
Enrico Zovato ◽  
Sufian Irhimeh ◽  
Pier Batzu
Keyword(s):  
Short Term Memory ◽  
Neural Nets ◽  
Short Term ◽  
Unit Selection ◽  
Term Memory ◽  
Long Short Term Memory

Articulatory-to-speech Conversion Using Bi-directional Long Short-term Memory

2018 ◽  
Author(s):  
Fumiaki Taguchi ◽  
Tokihiko Kaburagi
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Dam Deformation Interpretation and Prediction Based on a Long Short-Term Memory Model Coupled with an Attention Mechanism

2021 ◽  
Vol 11 (14) ◽  
pp. 6625
Author(s):  
Yan Su ◽  
Kailiang Weng ◽  
Chuan Lin ◽  
Zeqin Chen
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Impact Factors ◽  
Nonlinear Prediction ◽  
Time Dimension ◽  
Short Term ◽  
Deformation Prediction ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Dam Deformation

An accurate dam deformation prediction model is vital to a dam safety monitoring system, as it helps assess and manage dam risks. Most traditional dam deformation prediction algorithms ignore the interpretation and evaluation of variables and lack qualitative measures. This paper proposes a data processing framework that uses a long short-term memory (LSTM) model coupled with an attention mechanism to predict the deformation response of a dam structure. First, the random forest (RF) model is introduced to assess the relative importance of impact factors and screen input variables. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) method is used to identify and filter the equipment based abnormal values to reduce the random error in the measurements. Finally, the coupled model is used to focus on important factors in the time dimension in order to obtain more accurate nonlinear prediction results. The results of the case study show that, of all tested methods, the proposed coupled method performed best. In addition, it was found that temperature and water level both have significant impacts on dam deformation and can serve as reliable metrics for dam management.

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