Predicting Financial Returns

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Financial Markets".

Deadline for manuscript submissions: closed (27 May 2022) | Viewed by 3788

Special Issue Editors


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Guest Editor
Department of Statistics, University Carlos III Madrid, 28093 Getafe (Madrid), Spain
Interests: commodities modelling; financial econometrics; high frequency data; volatility modelling

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Guest Editor
Department of Finance and Economics, University of Deusto, Av. de las Universidades, 24, 48007 Bilbao, Spain
Interests: econometrics: semiparametric and nonparametric estimation; time series econometrics: volatility and interest rates estimation; simulation and modelling
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Special Issue Information

Dear colleagues,

A recurring question that arises in the financial literature is whether stock returns are predictable. Essentially, the debate is divided into studies searching for the best predictors of stock returns and studies stating their unpredictability. In addition, recent results have indicated that the predictability of stock returns significantly increases when the variance risk premium is considered in predictive models of stock market returns. Predictability rates are still small; however, they can often be increased by adding other predictors, such as short and long interest rate values, in the models. Further, new policies that are driven by climate change will likely affect financial markets whose risk assessment may benefit from incorporating new predictors, such as carbon emissions or electricity spot prices, into their models.

The aim of this Special Issue is to shed more light onto the problem of the predictability of stock returns by accumulating novel research on this matter. We are eager to receive new advances in modelling, estimation and forecasting that have been applied to the predictability of stock returns.  We encourage submissions of theoretical and empirical research on these issues, including parametric, nonparametric and machine learning tools.

Dr. Helena Veiga
Dr. Isabel Casas
Guest Editors

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Published Papers (1 paper)

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Research

25 pages, 2276 KiB  
Article
An Application of Portfolio Mean-Variance and Semi-Variance Optimization Techniques: A Case of Fiji
by Ronald Ravinesh Kumar, Peter Josef Stauvermann and Aristeidis Samitas
J. Risk Financial Manag. 2022, 15(5), 190; https://doi.org/10.3390/jrfm15050190 - 19 Apr 2022
Cited by 5 | Viewed by 3183
Abstract
In this paper, we apply the Markowitz portfolio optimization technique based on mean-variance and semi-variance as measures of risk on stocks listed on the South Pacific Stock Exchange, Fiji. We document key market characteristics and consider monthly returns data from SEP-2019 to FEB-2022 [...] Read more.
In this paper, we apply the Markowitz portfolio optimization technique based on mean-variance and semi-variance as measures of risk on stocks listed on the South Pacific Stock Exchange, Fiji. We document key market characteristics and consider monthly returns data from SEP-2019 to FEB-2022 (T = 30) of 17/19 listed companies on the stock exchange to construct various portfolios like 1/N (naïve), maximum return, and market and minimum-variance with and without short-selling constraints. Additionally, we compute each stock’s beta using the market capitalization-weighted stock price index data. We note that well-diversified portfolios (market portfolio and minimum-variance portfolio) with short-selling constraints have relatively higher expected returns with lower risk. Moreover, well-diversified portfolios perform better than the naïve and maximum portfolios in terms of risk. Moreover, we find that both the mean-variance and the semi-variance measures of risk yields a unique market portfolio in terms of expected returns, although the latter has a lower standard deviation and a higher Sharpe ratio. However, for the minimum-variance portfolios and market portfolios without short selling, we find relatively higher returns and risks using the mean-variance than the semi-variance approach. The low beta of individual stock indicates the low sensitivity of its price to the movement of the market index. The study is an initial attempt to provide potential investors with some practical strategies and tools in developing a diversified portfolio. Since not all the portfolios based on mean-variance and the semi-variance analyses are unique, additional methods of investment analysis and portfolio construction are recommended. Subsequently, for investment decisions, our analysis can be complemented with additional measures of risk and an in-depth financial statement/company performance analysis. Full article
(This article belongs to the Special Issue Predicting Financial Returns)
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