Equity Style Payoffs And Stock Return Predictability: Evidence From The South African JSE Securities Exchange

This paper undertakes to examine the structure of the payoffs to style attributes on the JSE Securities Exchange over the period from 1997 to 2007. Adopting the methodology of Haugen and Baker (1996), two expected return multifactor models are constructed to predict forward monthly returns of sample stocks using style attributes as model inputs. The Grinold model has an objective of selecting model inputs that will maximize the in-sample Grinold (1989) information ratio while the QH model has an objective of maximizing the in-sample Qian and Hua (2003) information ratio. The out-of-sample predictions of the models and their corresponding model inputs are evaluated over the period from 2002 to 2007. The permutations of the style attributes for the models are updated every 12 months based on training over the prior 60 months under the stepwise variable selection procedure proposed by Van Rensburg and Robertson (2003). The examination of the style attributes selected by the respective models reveal that most of the style attributes that exhibit significant univariate mean payoffs in our prior study are selected by the expected return multifactor models in this research. In addition, the value attributes with the most consistent univariate payoffs are found to be the most important contributors to equity return forecasting. It is also found that models that account for multicollinearity tend to outperform the models that fail to address this issue over the out-of-sample period. Overall, the QH model outperforms the Grinold model in terms of both information ratios over the out-of-sample period. The out-of-sample performance scores of the respective models are consistent with their corresponding in-sample performance scores, which suggest that successful expected return multifactor models, based on style attributes, can be developed to forecast ex-ante JSE equity returns.

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A Stepwise Variable Selection Procedure for Nonlinear Regression Models

Biometrics ◽

10.2307/2530219 ◽

1980 ◽

Vol 36 (3) ◽

pp. 511 ◽

Cited By ~ 54

Author(s):

P. N. Peduzzi ◽

R. J. Hardy ◽

T. R. Holford

Keyword(s):

Variable Selection ◽

Nonlinear Regression ◽

Regression Models ◽

Selection Procedure ◽

Nonlinear Regression Models ◽

Stepwise Variable Selection ◽

Variable Selection Procedure

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Determinants of Malnutrition Among Ever-married Women in Bangladesh

Dhaka University Journal of Science ◽

10.3329/dujs.v69i1.54618 ◽

2021 ◽

Vol 69 (1) ◽

pp. 7-13

Author(s):

Md Abdus Salam Akanda ◽

Most Sonia Khatun ◽

AHM Musfiqur Rahman Nabeen

Keyword(s):

Multinomial Logistic Regression ◽

Selection Procedure ◽

Wealth Index ◽

Stepwise Variable Selection ◽

Variable Selection Procedure ◽

Younger Age ◽

The Government ◽

Sylhet Region ◽

Overweight Or Obesity ◽

Status Of Women

Underweight and overweight problems have serious consequences on the health status of women in Bangladesh. The objective of this study is to find the important factors that may influence a woman for being underweight, overweight and obese. Multinomial logistic regression model is fitted for this purpose. The stepwise variable selection procedure is used to select covariates for the model. Information of ever-married 15,323 non-pregnant women is extracted from Bangladesh Demographic and Health Survey, 2014 data. Seven covariates (region, living place, wealth index, respondent‟s marital status, current working status, education, and current age) are selected finally for the model from the initially considered thirteen variables. The results of the study demonstrate that the women living in Sylhet region, rural area, widowed or divorced, having less education and younger age are more likely to become underweight. Conversely, the women are living in Khulna region, urban area, married, not working, having more than 10 years of schooling and age 35-49 are at higher risk of experiencing overweight or obesity. Thus, the Government of Bangladesh should take proper initiatives to improve underweight and overweight problem of women considering the findings of this study. Dhaka Univ. J. Sci. 69(1): 7-13, 2021 (January)

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Nonlinearities In Stock Return Prediction: A Blended Approach

Journal of Applied Business Research (JABR) ◽

10.19030/jabr.v29i1.7551 ◽

2012 ◽

Vol 29 (1) ◽

pp. 7

Author(s):

Kathleen Hodnett ◽

Heng-Hsing Hsieh ◽

Paul Van Rensburg

Keyword(s):

South African ◽

Stock Return ◽

Nonlinear Models ◽

Font Size ◽

Stock Selection ◽

Information Ratio ◽

Stock Return Prediction ◽

Out Of Sample ◽

Linear And Nonlinear ◽

Forecasting Volatility

Our prior research indicates that there are periods within which nonlinear stock selection models outperform their linear counterparts in the South African equity market. In order to explore the nonlinearities in stock return prediction, we propose a blended stock selection technique that has the potential of diversifying the risk of inaccurate forecasts of the linear and nonlinear models. The proposed technique has an objective of optimizing the Qian and Hua (2003) information ratio, which constitutes to the maximization of the forecasting accuracy per unit of forecasting volatility. The blended stock selection model is found to outperform the respective linear and nonlinear models in an out-of-sample fractile analysis on a risk-adjusted basis for South African stocks over the period from 2002 to 2007.

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Out-of-sample stock return predictability in Australia

Australian Journal of Management ◽

10.1177/0312896211428493 ◽

2012 ◽

Vol 37 (3) ◽

pp. 461-479 ◽

Cited By ~ 12

Author(s):

Yiwen (Paul) Dou ◽

David R. Gallagher ◽

David Schneider ◽

Terry S. Walter

Keyword(s):

Stock Return ◽

Return Predictability ◽

Stock Return Predictability ◽

Out Of Sample

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Closing a Bitcoin Trade Optimally under Partial Information: Performance Assessment of a Stochastic Disorder Model

Mathematics ◽

10.3390/math10010157 ◽

2022 ◽

Vol 10 (1) ◽

pp. 157

Author(s):

Zehra Eksi ◽

Daniel Schreitl

Keyword(s):

Performance Analysis ◽

Performance Assessment ◽

Partial Information ◽

Exit Time ◽

Exit Strategy ◽

Random Drift ◽

Expected Return ◽

Drift Model ◽

Out Of Sample ◽

Partially Observable

The Bitcoin market exhibits characteristics of a market with pricing bubbles. The price is very volatile, and it inherits the risk of quickly increasing to a peak and decreasing from the peak even faster. In this context, it is vital for investors to close their long positions optimally. In this study, we investigate the performance of the partially observable digital-drift model of Ekström and Lindberg and the corresponding optimal exit strategy on a Bitcoin trade. In order to estimate the unknown intensity of the random drift change time, we refer to Bitcoin halving events, which are considered as pivotal events that push the price up. The out-of-sample performance analysis of the model yields returns values ranging between 9% and 1153%. We conclude that the return of the initiated Bitcoin momentum trades heavily depends on the entry date: the earlier we entered, the higher the expected return at the optimal exit time suggested by the model. Overall, to the extent of our analysis, the model provides a supporting framework for exit decisions, but is by far not the ultimate tool to succeed in every trade.

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Application Of Tactical Style Allocation For Global Equity Portfolios

International Business & Economics Research Journal (IBER) ◽

10.19030/iber.v11i7.7061 ◽

2012 ◽

Vol 11 (7) ◽

pp. 745

Author(s):

Heng-Hsing Hsieh ◽

Kathleen Hodnett ◽

Paul Van Rensburg

Keyword(s):

Financial Market ◽

Weighted Least Squares ◽

Sample Period ◽

Value Stocks ◽

Style Rotation ◽

Out Of Sample ◽

Study Support ◽

Equity Portfolios ◽

Investment Styles ◽

Specific Timing

Our earlier study suggests that there exists specific timing for the two prominent investment styles, value and momentum. We extend our prior research to test and evaluate a tactical style allocation (TSA) model based on the weighted least squares (WLS) technique for global equities over the out-of-sample period from 1994 through 2008. Two TSA style-based portfolios are constructed in this research, namely, a portfolio with the risk-free proxy (cash component), the global momentum index and the global value index as its constituents, and a portfolio that is comprised of only the global momentum index and the global value index. The optimized portfolios based on the TSA model outperform the MSCI World Index, the global value index and the global momentum index on a risk-adjusted basis over the examination period. The cash component of the style-based portfolio appears to provide necessary protection during financial market crises. The results of our study support the use of the proposed TSA model to perform active style rotation between value stocks and momentum stocks for global equity portfolios.

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In-sample vs. out-of-sample tests of stock return predictability in the context of data mining

Journal of Empirical Finance ◽

10.1016/j.jempfin.2005.08.001 ◽

2006 ◽

Vol 13 (2) ◽

pp. 231-247 ◽

Cited By ~ 142

Author(s):

David E. Rapach ◽

Mark E. Wohar

Keyword(s):

Data Mining ◽

Stock Return ◽

Return Predictability ◽

Stock Return Predictability ◽

Out Of Sample

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Regime Shifts in Excess Stock Return Predictability: An Out-of-Sample Portfolio Analysis

The Journal of Portfolio Management ◽

10.3905/jpm.2018.44.3.010 ◽

2018 ◽

Vol 44 (3) ◽

pp. 10-24

Author(s):

Giulia Dal Pra ◽

Massimo Guidolin ◽

Manuela Pedio ◽

Fabiola Vasile

Keyword(s):

Stock Return ◽

Return Predictability ◽

Portfolio Analysis ◽

Regime Shifts ◽

Stock Return Predictability ◽

Out Of Sample

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Investor Sentiment and Stock Returns

Handbook of Research on Behavioral Finance and Investment Strategies - Advances in Finance, Accounting, and Economics ◽

10.4018/978-1-4666-7484-4.ch019 ◽

2015 ◽

pp. 315-328

Author(s):

Serkan Yılmaz Kandır ◽

Veli Akel ◽

Murat Çetin

Keyword(s):

Stock Returns ◽

Investor Sentiment ◽

Consumer Confidence ◽

Cointegration Test ◽

Sample Period ◽

Monthly Data ◽

Out Of Sample ◽

Confidence Index ◽

The Relationship

In this chapter, the authors investigate the relationship between investor sentiment and stock returns in an out of sample market, namely Borsa Istanbul. The authors use the Consumer Confidence Index as an investor sentiment proxy, while utilizing BIST Second National Index as a measure of small capitalized stock returns. The sample period spans from January 2004 to May 2014. By using monthly data, the authors employ cointegration test and error–correction based Granger causality models. The authors' findings suggest that there is a long-term relationship between investor sentiment and stock returns in Borsa Istanbul. Moreover, a unidirectional causal relationship from investor sentiment to stock returns is also found.

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A pseudo knockoff filter for correlated features

Information and Inference A Journal of the IMA ◽

10.1093/imaiai/iay012 ◽

2018 ◽

Vol 8 (2) ◽

pp. 313-341

Author(s):

Jiajie Chen ◽

Anthony Hou ◽

Thomas Y Hou

Keyword(s):

Variable Selection ◽

False Discovery Rate ◽

Numerical Experiments ◽

Selection Procedure ◽

Numerical Examples ◽

False Discovery ◽

Variable Selection Procedure ◽

Partial Analysis ◽

False Discovery Proportion

Abstract In Barber & Candès (2015, Ann. Statist., 43, 2055–2085), the authors introduced a new variable selection procedure called the knockoff filter to control the false discovery rate (FDR) and proved that this method achieves exact FDR control. Inspired by the work by Barber & Candès (2015, Ann. Statist., 43, 2055–2085), we propose a pseudo knockoff filter that inherits some advantages of the original knockoff filter and has more flexibility in constructing its knockoff matrix. Moreover, we perform a number of numerical experiments that seem to suggest that the pseudo knockoff filter with the half Lasso statistic has FDR control and offers more power than the original knockoff filter with the Lasso Path or the half Lasso statistic for the numerical examples that we consider in this paper. Although we cannot establish rigourous FDR control for the pseudo knockoff filter, we provide some partial analysis of the pseudo knockoff filter with the half Lasso statistic and establish a uniform false discovery proportion bound and an expectation inequality.

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