scholarly journals How to Explain the Cross-Section of Equity Returns through Common Principal Components

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1011
Author(s):  
José Manuel Cueto ◽  
Aurea Grané ◽  
Ignacio Cascos
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Principal Components ◽  
Equity Returns ◽  
Block Bootstrap ◽  
Time Series Regression ◽  
Cross Sectional ◽  
Multifactor Models ◽  
Multifactor Model ◽  
Common Principal Components

In this paper, we propose a procedure to obtain and test multifactor models based on statistical and financial factors. A major issue in the factor literature is to select the factors included in the model, as well as the construction of the portfolios. We deal with this matter using a dimensionality reduction technique designed to work with several groups of data called Common Principal Components. A block-bootstrap methodology is developed to assess the validity of the model and the significance of the parameters involved. Data come from Reuters, correspond to nearly 1250 EU companies, and span from October 2009 to October 2019. We also compare our bootstrap-based inferential results with those obtained via classical testing proposals. Methods under assessment are time-series regression and cross-sectional regression. The main findings indicate that the multifactor model proposed improves the Capital Asset Pricing Model with regard to the adjusted-R2 in the time-series regressions. Cross-section regression results reveal that Market and a factor related to Momentum and mean of stocks’ returns have positive risk premia for the analyzed period. Finally, we also observe that tests based on block-bootstrap statistics are more conservative with the null than classical procedures.

scholarly journals Models for Expected Returns with Statistical Factors

2020 ◽  
Vol 13 (12) ◽  
pp. 314
Author(s):  
José Manuel Cueto ◽  
Aurea Grané ◽  
Ignacio Cascos
Keyword(s):  
Time Series ◽  
Coefficient Of Variation ◽  
Stock Prices ◽  
Bootstrap Method ◽  
Block Bootstrap ◽  
Time Series Regression ◽  
Cross Sectional ◽  
Multifactor Models ◽  
Two Factors ◽  
Resampling Procedure

In this paper, we propose multifactor models for the pan-European Equity Market using a block-bootstrap method and compare the results with those of traditional inferential techniques. The new factors are built from statistical measurements on stock prices—in particular, coefficient of variation, skewness, and kurtosis. Data come from Reuters, correspond to nearly 2000 EU companies, and span from January 2008 to February 2018. Regarding methodology, we propose a non-parametric resampling procedure that accounts for time dependency in order to test the validity of the model and the significance of the parameters involved. We compare our bootstrap-based inferential results with classical proposals (based on F-statistics). Methods under assessment are time-series regression, cross-sectional regression, and the Fama–MacBeth procedure. The main findings indicate that the two factors that better improve the Capital Asset Pricing Model with regard to the adjusted R2 in the time-series regressions are the skewness and the coefficient of variation. For this reason, a model including those two factors together with the market is thoroughly studied. We also observe that our block-bootstrap methodology seems to be more conservative with the null of the GRS test than classical procedures.

scholarly journals Forecasting using cross-section average–augmented time series regressions

2020 ◽  
Author(s):  
Hande Karabiyik ◽  
Joakim Westerlund
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Regression Models ◽  
Common Factor ◽  
Principal Component ◽  
Small Sample ◽  
Interactive Effects ◽  
Cross Sectional ◽  
Second Development ◽  
The Cross

Summary There is a large and growing body of literature concerned with forecasting time series variables by the use of factor-augmented regression models. The workhorse of this literature is a two-step approach in which the factors are first estimated by applying the principal components method to a large panel of variables, and the forecast regression is then estimated, conditional on the first-step factor estimates. Another stream of research that has attracted much attention is concerned with the use of cross-section averages as common factor estimates in interactive effects panel regression models. The main justification for this second development is the simplicity and good performance of the cross-section averages when compared with estimated principal component factors. In view of this, it is quite surprising that no one has yet considered the use of cross-section averages for forecasting. Indeed, given the purpose to forecast the conditional mean, the use of the cross-sectional average to estimate the factors is only natural. The present paper can be seen as a reaction to this. The purpose is to investigate the asymptotic and small-sample properties of forecasts based on cross-section average–augmented regressions. In contrast to most existing studies, the investigation is carried out while allowing the number of factors to be unknown.

scholarly journals Unemployement and Labour Force Participation : The Canadian Experience

2005 ◽  
Vol 28 (1) ◽  
pp. 56-75
Author(s):  
Robert Swidinsky
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Labour Force ◽  
Labour Force Participation ◽  
Negative Response ◽  
Time Series Regression ◽  
Canadian Experience ◽  
Short Run ◽  
The Cross ◽  
Force Time

In an analysis of the short-run sensitivity of the Canadian labour force time series regression results appear inconclusive whereas cross-section regression results suggest a strong negative response to unemployment. Generally, the findings from the cross-section are comparable neither qualitatively nor quantitatively with those from the time series.

Time‐Series Cross‐Section Methods

2009 ◽  
Author(s):  
Nathaniel Beck
Keyword(s):  
Time Series ◽  
Spatial Autocorrelation ◽  
Cross Section ◽  
Fixed Effects ◽  
Event History ◽  
Random Coefficient Models ◽  
Cross Sectional ◽  
Standard Set ◽  
Critical Insight ◽  
Series Component

This article outlines the literature on time-series cross-sectional (TSCS) methods. First, it addresses time-series properties including issues of nonstationarity. It moves to cross-sectional issues including heteroskedasticity and spatial autocorrelation. The ways that TSCS methods deal with heterogeneous units through fixed effects and random coefficient models are shown. In addition, a discussion of binary variables and their relationship to event history models is provided. The best way to think about modeling single time series is to think about modeling the time-series component of TSCS data. On the cross-sectional side, the best approach is one based on thinking about cross-sectional issues like a spatial econometrician. In general, the critical insight is that TSCS and binary TSCS data present a series of interesting issues that must be carefully considered, and not a standard set of nuisances that can be dealt with by a command in some statistical package.

The Determinants of Idiosyncratic Volatility

2017 ◽  
Vol 25 (4) ◽  
pp. 509-545
Author(s):  
Jaeuk Khil ◽  
Song Hee Kim ◽  
Eun Jung Lee
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Cross Section ◽  
Growth Rates ◽  
Idiosyncratic Volatility ◽  
Return Volatility ◽  
Firm Level ◽  
Cross Sectional ◽  
Asset Growth ◽  
The Cross

We investigate the cross-sectional and time-series determinants of idiosyncratic volatility in the Korean market. In particular, we focus on the empirical relation between firms’ asset growth rate and idiosyncratic stock return volatility. We find that, in the cross-section, companies with high idiosyncratic volatility tend to be small and highly leveraged, have high variance of ROE and Market to Book ratio, high turnover rate, and pay no dividends. Furthermore, firms with extreme (either high positive or negative) asset growth rates have high idiosyncratic return volatility than firms with moderate growth rates, suggesting the V-shaped relation between asset growth rate and idiosyncratic return volatility. We find that the V-shaped relation is robust even after controlling for other factors. In time-series, we find that firm-level idiosyncratic volatility is positively related to the dispersion of the cross-sectional asset growth rates. As a result, this study is contributed to show that the asset growth is the most important predictor of firm-level idiosyncratic return volatility in both the cross-section and the time-series in the Korean stock market. In addition, we show how the effect of risk factors varies with industries.

scholarly journals The Cross Section of Country Equity Returns: A Review of Empirical Literature

2019 ◽  
Vol 12 (4) ◽  
pp. 165 ◽  
Author(s):  
Zaremba
Keyword(s):  
Asset Pricing ◽  
Cross Section ◽  
Equity Returns ◽  
Empirical Literature ◽  
Fund Flows ◽  
Cross Sectional ◽  
Preparation Methods ◽  
Stock Level ◽  
Country Level ◽  
The Cross

The last three decades brought mounting evidence regarding the cross-sectional predictability of country equity returns. The studies not only documented country-level counterparts of well-established stock-level anomalies, such as size, value, or momentum, but also demonstrated some unique return-predicting signals such as fund flows or political regimes. Nonetheless, the different studies vary remarkably in terms of their dataset and methods employed. This study aims to provide a comprehensive review of the current literature on the cross-section of country equity returns. We focus on three particular aspects of the asset pricing literature. First, we study the choice of dataset and sample preparation methods. Second, we survey different aspects of the methodological approaches. Last but not least, we review the country-level equity anomalies discovered so far. The discussed cross-sectional return patterns not only provide new insights into international asset pricing but can also be potentially translated into effective country allocation strategies.

On a model for a time series of cross-sections

1986 ◽  
Vol 23 (A) ◽  
pp. 113-125 ◽  
Author(s):  
P. M. Robinson
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Cross Sections ◽  
Standard Form ◽  
Cross Sectional ◽  
Aggregated Data ◽  
Section Data ◽  
Infinite Population ◽  
The Cross ◽  
Observation Times

Dynamic stationary models for mixed time series and cross-section data are studied. The models are of simple, standard form except that the unknown coefficients are not assumed constant over the cross-section; instead, each cross-sectional unit draws a parameter set from an infinite population. The models are framed in continuous time, which facilitates the handling of irregularly-spaced series, and observation times that vary over the cross-section, and covers also standard cases in which observations at the same regularly-spaced times are available for each unit. A variety of issues are considered, in particular stationarity and distributional questions, inference about the parameter distributions, and the behaviour of cross-sectionally aggregated data.

The cross-section of Johannesburg Securities Exchange listed equity returns (1994-2011)

2015 ◽  
Vol 32 (4) ◽  
pp. 422-444 ◽  
Author(s):  
Jakobus Daniel Van Heerden ◽  
Paul Van Rensburg
Keyword(s):  
Cross Section ◽  
Equity Returns ◽  
Research Approach ◽  
Momentum Effect ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
Specific Factors ◽  
The Cross ◽  
The Impact

Purpose – The aim of this study is to examine the impact of technical and fundamental (referred to as firm-specific) factors on the cross-sectional variation in equity returns on the Johannesburg Securities Exchange (JSE). Design/methodology/approach – To reach the objective, the study follows an empirical research approach. Cross-sectional regression analyses, factor-portfolio analyses and multifactor analyses are performed using 50 firm-specific factors for listed shares over three sample periods during 1994 to 2011. Findings – The results suggest that a strong value and momentum effect is present and robust on the JSE, while a size effect is present but varies over time. Multifactor analyses show that value and momentum factors are collectively significant in explaining the cross-section of returns. The results imply that the JSE is either not an efficient market or that current market risk models are incorrectly specified. Practical implications – The findings of the study offers practical application possibilities to investment analysts and portfolio managers. Originality/value – To the authors’ knowledge, this is the first study to use such a comprehensive data set for the specific analyses on the JSE over such a long period. All previously identified statistical biases are addressed in this study. Different approaches are applied to compare results and test for robustness for the first time.

scholarly journals INFLUENCE OF MACROECONOMIC FACTORS ON STOCK PRICES IN POLAND – CROSS SECTION AND TIME SERIES ANALYSIS

2018 ◽  
Author(s):  
Tomasz Schabek ◽  
Nijolė Maknickienė
Keyword(s):  
Time Series ◽  
Stock Returns ◽  
Cross Section ◽  
Stock Prices ◽  
Cross Sectional ◽  
Macroeconomic Factors ◽  
Book Value ◽  
Long Time ◽  
Market Variables ◽  
Macro Variables

The purpose of the study is to determine if the macroeconomic factors influence rates of returns from broad index of stocks in Poland. The study investigates stability of relation between macroeconomic and stock market variables in short and long time period. After running time series regressions we check if selected macro variables are still significant in cross-section of stock returns including control variables like price to book value, capitalization and momentum. The study is based on large sample of individual rates of returns and macroeconomic variables describing real sphere of the economy. Mine findings suggest that the short and long term relation is statistically and economically significant although not stable in the both analysed time horizons. Macroeconomic beta parameter (sensitivity to macro variables measure) is not significant in cross-sectional test proving that traditionally accepted variables (in our study only price to book-value and momentum) still better explain the expected re-turns.

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