Price forecasting and risk portfolio optimization

Centeno, Virginia; Georgiev, Ivan; Mihova, Vesela; Pavlov, Velizar

doi:10.1063/1.5130808

Cited by 24 publications

(8 citation statements)

References 4 publications

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“…SQP combines an active set method and Newton's method for solving nonlinear optimization problems. Some works also use SQP to perform such optimization problems 32‐34 . The closing prices of the following assets trading in B3 are considered: VALE3, ITUB4, PETR4, B3SA3, BBDC4, PETR3, LAME3, JBSS3, WEGE3, and ODPV3.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Investment portfolio tracking using model predictive control

Melo

Cardoso

Jesus

et al. 2022

Optim Control Appl Methods

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The composition of an investment portfolio aiming to increase the financial returns while reducing the exposure to risk is a topic of growing interest in the world. In this direction, we propose a model predictive control (MPC) strategy in order to optimize the investment portfolio selection taking into account the tracking of a reference portfolio with desired return. In addition, an analysis comparing different sizes of the prediction horizon according to VPH-MPC (Varying Predictive Horizon-MPC) and FPH-MPC (Fixed Predictive Horizon-MPC) algorithms is conducted. Finally, we propose an optimal control problem using the tracking error as a function of loss of CVaR (Conditional Value at Risk) measurement. Numerical experiments are run based on Brazilian Stock Exchange data. The experimental results are compared with the Markowitz portfolio optimization model, a conventional tracking strategy, and benchmarks from the Brazilian financial market. This comparison indicates a good tracking performance obtained by the proposed MPC in the two versions while satisfying the constraints.

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Section: Numerical Experimentsmentioning

confidence: 99%

Investment portfolio tracking using model predictive control

Melo

Cardoso

Jesus

et al. 2022

Optim Control Appl Methods

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“…In fact, numerous investigations have been done regrading this interesting field. For example, Centeno and Jackson only researched the cumulative return, daily return and the maximum sharp ratio of the best venture capital portfolio based on ARIMA model [2][3]. Wang [4] and Sen, Dutta and Mehtab [5] only studied the advantages of LSTM model in deep learning of time series prediction in the financial field based on the data such as cumulative return and sharp rate.…”

Section: Introductionmentioning

confidence: 99%

A comparative research of portfolio return prediction based on the ARIMA and LSTM models

Liu¹

2022

BCPBM

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The ongoing development of deep learning or machine learning techniques makes time series prediction more precise. This technology has also achieved remarkable success in predicting the return of portfolio in the financial field, which means that the quantitative investment method continues to progress and shows strong applicability, and investors can obtain excess benefits in the market. This study seeks to anticipate portfolio return using deep learning and machine learning and compare and analyze the differences between them. This paper selects the stock data of six representative companies in various fields and calculates the maximum sharp ratio portfolio and then forecasts the return of the portfolio with ARIMA model and LSTM model respectively. The result indicates that, firstly, ARIMA model and LSTM model can be well applied to predict the future return of portfolio; Secondly, ARIMA model performs better in short-term prediction and stable data prediction. When it comes to long-term prediction, the LSTM model performs better. The research results may be useful for investors to choose appropriate time series models for portfolio prediction and analysis.

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“…R ECENTLY Monte Carlo (MC) and quasi-Monte Carlo (QMC) approaches have become a very attractive and necessary computational tools in finance [8]. The field of computational finance is becoming more complicated with increasing number of applications [2], [3]. The pricing of options is a very important in financial markets today and especially difficult when the dimension of the problem goes higher [2], [8], [9], [12].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Stochastic Techniques related to Option Pricing

Todorov¹,

Dimov²,

Fidanova³

et al. 2021

Annals of Computer Science and Information Systems

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ÐRecently stochastic methods have become very important tool for high performance computing of very high dimensional problems in computational finance. The advantages and disadvantages of the different highly efficient stochastic methods for multidimensional integrals related to evaluation of European style options will be analyzed. Multidimensional integrals up to 100 dimensions related to European options will be computed with highly efficient optimized lattice rules.

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Price forecasting and risk portfolio optimization

Cited by 24 publications

References 4 publications

Investment portfolio tracking using model predictive control

Investment portfolio tracking using model predictive control

A comparative research of portfolio return prediction based on the ARIMA and LSTM models

An Optimized Stochastic Techniques related to Option Pricing

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