Prediction of Stock Returns: Sum-of-the-Parts Method and Economic Constraint Method

Dai, Zhifeng; Zhou, Huiting

doi:10.3390/su12020541

Cited by 22 publications

(14 citation statements)

References 39 publications

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“…In addition, we also expect that our proposed method and its further modifications could produce new applications for problems in relevant areas of symmetric equations [51], image processing [52], and finance [53][54][55].…”

Section: Discussionmentioning

confidence: 97%

A Modified Hestenes-Stiefel-Type Derivative-Free Method for Large-Scale Nonlinear Monotone Equations

Dai

Zhu

2020

Mathematics

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The goal of this paper is to extend the modified Hestenes-Stiefel method to solve large-scale nonlinear monotone equations. The method is presented by combining the hyperplane projection method (Solodov, M.V.; Svaiter, B.F. A globally convergent inexact Newton method for systems of monotone equations, in: M. Fukushima, L. Qi (Eds.)Reformulation: Nonsmooth, Piecewise Smooth, Semismooth and Smoothing Methods, Kluwer Academic Publishers. 1998, 355-369) and the modified Hestenes-Stiefel method in Dai and Wen (Dai, Z.; Wen, F. Global convergence of a modified Hestenes-Stiefel nonlinear conjugate gradient method with Armijo line search. Numer Algor. 2012, 59, 79-93). In addition, we propose a new line search for the derivative-free method. Global convergence of the proposed method is established if the system of nonlinear equations are Lipschitz continuous and monotone. Preliminary numerical results are given to test the effectiveness of the proposed method.

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Section: Discussionmentioning

confidence: 97%

A Modified Hestenes-Stiefel-Type Derivative-Free Method for Large-Scale Nonlinear Monotone Equations

Dai

Zhu

2020

Mathematics

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“…In fact, we interviewed 16 scholars with a minimum of seven years of teaching and research in the fields of economics and finance, 5 industry managers with a minimum of 5 years in seniority position, and 5 professional stock traders. Based on the results of our interviews, the findings of recent studies in the fields of economics and finance ( Ang and Bekaert, 2007 ; Barinov et al., 2018 ; Bernanke and Gertler, 1999 ; Boons, 2016 ; Campbell and Yogo, 2006 ; Dai and Zhou, 2020 ; Dai and Zhu, 2020 ; Goyal and Welch, 2008 ; Gungor and Luger, 2019 ; Kroencke, 2017 ; Lee, 2019 ), and the availability of the related data, our study proposed adding three macroeconomic determinants/risks (the U.S. prime rate, the U.S. government long-term bond rate, and the exchange rate of USD/EUR) to the original CAPM to explain the nexus between the risks and the U.S. stock returns. These macroeconomic determinants are proxies for the macroeconomic state variables.…”

Section: Introductionmentioning

confidence: 99%

An augmented capital asset pricing model using new macroeconomic determinants

Pham¹,

Phuoc

2020

Heliyon

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Using the interview results of 26 experienced scholars, managers, and professional stock traders in conjunction with findings of recent studies in economics, we proposed an augmented asset pricing model using the macroeconomic determinants representing the macroeconomic state variables to explain the nexus between these risks and the U.S. stock returns. This non-traded factor model (MAPM) is inspired by and based on the macroeconomic theory and models and consists of the market return, U.S. prime rate, U.S. government long-term bond rate, and exchange rate of USD/EUR as in Eq. (1). Using the Bayesian approach (via two Bayes and t.Bayes estimators) and monthly returns of the S&P 500 stocks from 2007- 2019, our results showed the MAPM consistently yielded a statistically significant greater forecasting, explanatory power, and model adequacy compared to the most used capital asset pricing model (CAPM) in practice. Interestingly, our study found and confirmed ( t -statistic > 3) that the last two macroeconomic determinants have a statistically significant positive effect on the stock returns, which also supports the MAPM. These findings suggest the MAPM is a more efficient and advantageous model compared to the CAPM. So, practitioners would be better off employing the MAPM over CAPM in practice and research.

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“…Dai and Zhou [37] developed a new stock returns prediction model by combining sum-of-the-parts (SOP) method and three economic constraint methods, namely non-negative economic constraint strategy, momentum of return prediction strategy, and three-sigma strategy. The results showed that the model is robust and efficient.…”

Section: Introductionmentioning

confidence: 99%

Can International Market Indices Estimate TASI’s Movements? The ARIMA Model

Assous

Al-Rousan

AL-Najjar

et al. 2020

Journal of Open Innovation: Technology, Market, and Complexity

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This study investigates the effectiveness of six of the key international indices in estimating Saudi financial market (TADAWUL) index (TASI) movement. To investigate the relationship between TASI and other variables, six equations were built using two independent variables of time and international index, while TASI was the dependent variable. Linear, logarithmic, quadratic, cubic, power, and exponential equations were separately used to achieve the targeted results. The results reveal that power equation is the best equation for forecasting the TASI index with a low error rate and high determination coefficient. Additionally, findings of the AutoRegressive Integrated Moving Average (ARIMA) model represent the most important variables to use in order to build a prediction model that can estimate the TASI index. The ARIMA model (with Expert Modeler) coefficients are described as ARIMA (0,1,14). The results show that the SP500, NIKKEI, CAC40, and HSI indices are the most suitable variables for estimating TASI with an R2 and RMSE equal to 0.993 and 113, respectively. This relationship can be used on the previous day to estimate the opening price of TASI based on the closing prices of international indices.

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Prediction of Stock Returns: Sum-of-the-Parts Method and Economic Constraint Method

Cited by 22 publications

References 39 publications

A Modified Hestenes-Stiefel-Type Derivative-Free Method for Large-Scale Nonlinear Monotone Equations

A Modified Hestenes-Stiefel-Type Derivative-Free Method for Large-Scale Nonlinear Monotone Equations

An augmented capital asset pricing model using new macroeconomic determinants

Can International Market Indices Estimate TASI’s Movements? The ARIMA Model

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