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

By Sang Il Lee, Seong Joon Yoo. 2017

50 citations recorded by the source corpus.

This paper aims at developing a new method by which to build a data-driven portfolio featuring a target risk–return. We first present a comparative study of recurrent neural network models (RNNs), including a simple RNN, long short-term memory (LSTM), and gated recurrent unit. The models are applied to the investment universe consisted of 10 stocks in the S&P500\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ S \& P500$$\end{document}. The experimental results show that the LSTM-based prediction model outperforms the others in terms of hit ratio of 1-month-ahead forecasts. We then build predictive threshold-based portfolios (TBPs) that are subsets of the universe satisfying given threshold criteria for the LSTM-based return forecasts. The TBPs are rebalanced monthly to restore equal weight to the constituents of the TBPs. We find 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. This shows the availability of TBPs in targeting specific risk–return levels, and the EWP of an universe plays a role in the reference portfolio of the TBPs. In the process, thresholds play dominant roles in characterizing risk, return, and the prediction accuracy of the TBPs. The TBP is more data-driven in designing portfolio return and risk than existing ones, in the sense that it requires no prior knowledge of finance such as financial assumptions, financial mathematics, or expert insights. For practical uses, we present a multiperiod TBP management method and also discuss the application of TBP to mean–variance portfolios to reduce estimation risk.

Subfields: Electrical and Electronic Engineering, Finance, Management Science and Operations Research, Economics & Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. A. Ozbayoglu
2. M. Ugur Gudelek
3. Omer Berat Sezer
4. Longbing Cao
5. Weiwei Jiang

In Finance

1. M. Ugur Gudelek
2. A. Ozbayoglu
3. Omer Berat Sezer
4. Weiwei Jiang
5. Longbing Cao

In Management Science and Operations Research

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Weiwei Jiang
5. Longbing Cao

In Economics & Operations Research

1. A. Ozbayoglu
2. M. Ugur Gudelek
3. Omer Berat Sezer

Citations in the corpus

• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “[84] compared LSTM, RNN, CNN, MLP, whereas in [85] RNN, LSTM, CNN, Autoregressive Integrated Moving Average (ARIMA) were preferred, Lee and Yoo [86] compared 3 RNN models (SRNN, LSTM, GRU) for stock price prediction and then constructed a threshold based portfolio with selecting stocks according to ”
• Deep Learning for Financial Applications : A Survey (2020, methodology): “[136] 10 stocks in S&P500 1997-2016 OCHLV, Price data RNN, LSTM, GRU Accuracy, Monthly return Keras, Tensorflow”