Fusing Economic Indicators for Portfolio Optimization - A Simulation-Based Approach

2019 ◽  
Author(s):  
Jiayang Yu
2020 ◽  
Vol 13 (11) ◽  
pp. 285
Author(s):  
Jiayang Yu ◽  
Kuo-Chu Chang

Portfolio optimization and quantitative risk management have been studied extensively since the 1990s and began to attract even more attention after the 2008 financial crisis. This disastrous occurrence propelled portfolio managers to reevaluate and mitigate the risk and return trade-off in building their clients’ portfolios. The advancement of machine-learning algorithms and computing resources helps portfolio managers explore rich information by incorporating macroeconomic conditions into their investment strategies and optimizing their portfolio performance in a timely manner. In this paper, we present a simulation-based approach by fusing a number of macroeconomic factors using Neural Networks (NN) to build an Economic Factor-based Predictive Model (EFPM). Then, we combine it with the Copula-GARCH simulation model and the Mean-Conditional Value at Risk (Mean-CVaR) framework to derive an optimal portfolio comprised of six index funds. Empirical tests on the resulting portfolio are conducted on an out-of-sample dataset utilizing a rolling-horizon approach. Finally, we compare its performance against three benchmark portfolios over a period of almost twelve years (01/2007–11/2019). The results indicate that the proposed EFPM-based asset allocation strategy outperforms the three alternatives on many common metrics, including annualized return, volatility, Sharpe ratio, maximum drawdown, and 99% CVaR.


2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Claudio Leiva ◽  
Víctor Flores ◽  
Carolina Aguilar

The integration of both sensors and simulation in some industrial processes has received a large increase in the recent years, thanks to results such as increase in production indices or improvement in economic indicators. This document describes the development and validation of a simulation-based computer process for generating sulphuric acid. For this process, the data generated by simulation between the fixed beds of a catalytic reactor versus the results obtained using real data from a sulphuric acid production plant in the Antofagasta Region, Chile, have been used. This sulphuric acid production plant is designed for producing 720,000 tons of sulphuric acid annually, with a production capacity of 26 MW, which is used for both its own consumption and the Big North Interconnected System (SING, for its acronym in Spanish) and a sulphur consumption of 240,000 tons/year. For the simulation process, converter input variables such as temperature and gas flow to later observe the oxidation behavior under different operational scenarios were considered. To do it, a working method has been proposed and the software Aspen HYSYS® was used for the simulation. The simulation result was validated using design operational data provided by the company. The real results show a 99.9% of adjustment concerning the values obtained using the simulation. Based on the findings, a new operational scenario was created, and the economic indicators of the simulator implementation were determined: NPV=CLP 161,695,000 and IRR=53% with a 6% monthly production increase.


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