Fundamental Factor Models Research Articles

Estimation Risk and Shrinkage in Vast-Dimensional Fundamental Factor Models

We investigate covariance matrix estimation in vast-dimensional spaces of 1,500 up to 2,000 stocks using fundamental factor models (FFMs). FFMs are the typical benchmark in the asset management industry and depart from the usual statistical factor models and the factor models with observed factors used in the statistical and finance literature. Little is known about estimation risk in FFMs in high dimensions. We investigate whether recent linear and non-linear shrinkage methods help to reduce the estimation risk in the asset return covariance matrix. Our findings indicate that modest improvements are possible using high-dimensional shrinkage techniques. The gains, however, are not realized using standard plug-in shrinkage parameters from the literature, but require sample dependent tuning.

Fundamental Factor Models Using Machine Learning

Fundamental factor models are one of the important methods for the quantitative active investors (Quants), so many investors and researchers use fundamental factor models in their work. But often we come up against the problem that highly effective factors do not aid in our portfolio performance. We think one of the reasons that why the traditional method is based on multiple linear regression. Therefore, in this paper, we tried to apply our machine learning methods to fundamental factor models as the return model. The results show that applying machine learning methods yields good portfolio performance and effectiveness more than the traditional methods.

Fundamental Factor Models Using Machine Learning

Fundamental factor models are one of the important methods for the quantitative active investors (Quants), so many investors and researchers use fundamental factor models in their work. But often we come up against the problem that highly effective factors do not aid in our portfolio performance. We think one of the reasons that why the traditional method is based on multiple linear regression. Therefore, in this paper, we tried to apply our machine learning methods to fundamental factor models as the return model. The results show that applying machine learning methods yields good portfolio performance and effectiveness more than the traditional methods.

Outliers:The Dangers of Not Being One of the Pack

Outliers are ubiquitous in modern datasets. Due to noise, uncertainty, and adversarial behavior, such observations are inherent to many real-world problems. In financial applications, outliers are very common not only in time series, but also in multivariate data used for the construction of fundamental factor models of stock returns. In many cases, failing to identify outliers may lead to wrong results and poor performance of the various models. Discriminating outliers from correct data have been studied extensively, in both statistics and machine learning. However, most financial practitioners rely on standard approaches like trimming and winsorization, which are trivial to implement and can easily be applied to large datasets. The aim of this article is to draw attention to the drawbacks of the standard methods and to present alternative ways by which outliers can be detected effectively in both the univariate and multivariate case.

The Fundamentals of Fundamental Factor Models (June 2010)

This paper highlights the fundamental-based origins of the factor models used at Barra. Barr Rosenberg and Vinay Marathe (1976) first discussed the theory that the effects of macroeconomic events on individual securities could be captured through microeconomic characteristics such as industry membership, financial structure, or growth orientation. This linkage between macroeconomic events and microeconomic (or fundamental) characteristics lies at the heart of the factor model. We discuss the intuition behind a fundamental factor model, showing how it is linked to traditional fundamental analysis, and point out the insights these models can provide. Our goal is to highlight the complementary role of the fundamental factor model to traditional security analysis.

Needles, Haystacks, and Hidden Factors

Statistical factor modeling is often described as a way to identify commonality among returns in a financial market. Statistical models examine returns over many time periods, and from them identify relationships between and among the different assets, unlike fundamental factor models, which from the outset group assets that are likely to experience similar returns. Yet the statistical approach is better at finding some types of factors than others. Statistical factors generally work best with high-frequency return data, and even then may not pick up distinctions that apply to a relatively small subset of assets (such as distinctions associated with industry membership); they are most useful when they supplement fundamental factors. We see this in a case study that adds statistical factors to an MSCI Barra fundamental factor risk model.

Zur empirischen Identifikation von Risikofaktoren bei Modellen der Arbitrage Pricing Theory (On the empirical identification of risk factors in arbitrage pricing models)

In the last ten years multiple factor models were widely used both in the academic and the real world as the leading paradigm describing the behavior of stock returns. Unfortunately, so far academic research has not succeeded in theoretically deriving which factors influence stock returns. An important class of APT models are the fundamental factor models. They rely on the empirical finding that company attributes such as market capitalization, book to market ratio, dividend yield, etc. are posited to have an impact on average stock returns. As a result of a multiple cross-sectional regression one determines which variables have discriminatory power, i.e. have significant factor returns. Another important class of APT models are the macroeconomic factor models. These models assume that the addition of macroeconomic variables leads to an improvement in the explanation of the cross-section of expected returns. A security’s sensitivities to the factors are called the factor betas of the security. The macroeconomic factor models estimate a firm’s factor betas by time-series regression. In the present paper it is shown that empirical tests of multiple factor models based on the cross-section of sample mean returns may be misleading. In general, the empirical studies are based on a pricing relation that contains idiosyncratic risk. Then, the correct econometric specification results in a cross-sectional regression model that can no longer be estimated because there are asset specific intercepts. Estimating a traditional cross-sectional relationship between sample mean returns and factor betas (including a single intercept) renders the least squares estimates of the regression coefficients biased. As a consequence conclusions about the significance of the included risk factors are wrong. Therefore, some of the „anomalies“ found in empirical studies may be due to this bias. Moreover, it is shown that even in the case of an exact pricing relation the underlying factors of the return generating process must be known. However, this contradicts the attempt to identify the risk factors empirically using a cross-sectional regression model. * Fur hilfreiche Hinweise zu einer fruheren Fassung dieses Aufsatzes mochten wir zwei anonymen Gutachtern danken. Operations Research Spektrum 20/2, 1998, 123-134.

The Three Types of Factor Models: A Comparison of Their Explanatory Power

Multifactor models of security market returns can be divided into three types: macroeconomic, fundamental, and statistical factor models. Macroeconomic factor models use observable economic time series, such as inflation and interest rates, as measures of the pervasive shocks to security returns. Fundamental factor models use the returns to portfolios associated with observed security attributes such as dividend yield, the book-to-market ratio, and industry identifiers. Statistical factor models derive their pervasive factors from factor analysis of the panel data set of security returns. This paper compares the explanatory power of these three approaches for U.S. equity returns.