Characteristic-based Model Research Articles

Factor Timing with Portfolio Characteristics

Abstract In a factor timing context, academic research has focused on identifying a set of predictors that can explain the dynamics of factor portfolios. We propose an alternative approach for timing factor portfolio returns by exploiting the information from their portfolio characteristics. Different combinations of dimension reduction techniques are employed to independently reduce the number of both predictors and portfolios to predict. Characteristic-based models outperform existing methods in terms of exact predictability, as well as investment performance. (JEL G10, G11, C52, C55)

Radiomic Signature Based on Dynamic Contrast-Enhanced MRI for Evaluation of Axillary Lymph Node Metastasis in Breast Cancer.

Background To construct and validate a radiomic-based model for estimating axillary lymph node (ALN) metastasis in patients with breast cancer by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods In this retrospective study, a radiomic-based model was established in a training cohort of 236 patients with breast cancer. Radiomic features were extracted from breast DCE-MRI scans. A method named the least absolute shrinkage and selection operator (LASSO) was applied to select radiomic features based on highly reproducible features. A radiomic signature was built by a support vector machine (SVM). Multivariate logistic regression analysis was adopted to establish a clinical characteristic-based model. The performance of models was analysed through discrimination ability and clinical benefits. Results The radiomic signature comprised 6 features related to ALN metastasis and showed significant differences between the patients with ALN metastasis and without ALN metastasis (P < 0.001). The area under the curve (AUC) of the radiomic model was 0.990 and 0.858, respectively, in the training and validation sets. The clinical feature-based model, including MRI-reported status and palpability, performed slightly worse, with an AUC of 0.784 in the training cohort and 0.789 in the validation cohort. The radiomic signature was confirmed to provide more clinical benefits by decision curve analysis. Conclusions The radiomic-based model developed in this study can successfully diagnose the status of lymph nodes in patients with breast cancer, which may reduce unnecessary invasive clinical operations.

An approach for increase of digital/IT innovation adoption for sustainable development of transportation system

In order to survive the COVID pandemic that starter in 2020 and is still ongoing companies see a challenge to rebuild their business so it will be flexible and sustainable. It is especially important for companies from traditional verticals like transport who are not used to be digital. Innovation adoption will allow companies to face future crises with more agility and smaller profit losses. Even though the nowadays there are a lot of innovations to choose the level of innovation adoption rate is still relatively low. Most of them can be categorized as digital/IT because the companies need to execute the digital transformation process quite fast. The well-known innovation adoption models use general characteristics of an innovation such as trialability, complex in use and in design etc. but do not provide the users with practical recommendations and give less attention to the digital/IT element. Digital maturity models on the other hand result in practical recommendations on how to improve the adoption of digital and information technologies. In this article we suggest a conceptual approach - add a digital/IT maturity element to a characteristic-based model of innovation adoption. As a methodological support to a model several questionnaires for assessment the model’s elements as well as the further research direction and design have been developed.

Factor Timing with Portfolio Characteristics

Factor momentum has formed the basis of factor timing strategies. We propose an alternative approach for timing factor portfolio returns by exploiting the information from their portfolio characteristics. Different combinations of dimension reduction techniques are employed to independently reduce the number of predictors and portfolios to predict. Characteristic-based models outperform factor momentum in terms of exact predictability as well as investment performance.

Nexus between running costs and building characteristics of commercial buildings: hedonic regression modelling

PurposeTraditionally, early-stage investment decisions on buildings purely based initial capital costs and simply ignored running costs and total lifecycle cost. This was basically due to the absence of estimating models that yield running costs at the early design stage. Often, when the design of a building, which is responsible for 10–15% of its total cost, is completed, 80% of the total cost is committed. This study aims to develop a building characteristic-based model, which is an early-stage determinant of running costs of buildings, to predict the running costs of commercial buildings.Design/methodology/approachA desk study was carried out to collect running costs data and building characteristics of 35 commercial buildings in Sri Lanka. A Pareto analysis, bivariate correlation analysis and hedonic regression modelling were performed on collected data.FindingsAccording to Pareto analysis, utilities, services, admin work and cleaning are four main cost constituents, responsible for 80% of running costs, which can be represented by highly correlated building characteristics of building height, number of floors and size. Approximately 94% of the variance in annual running costs/sq. m is expressed by variables of number of floors, net floor area and working hours/day together with a mean prediction accuracy of 2.89%.Research limitations/implicationsThe study has utilised a sample of 35 commercial buildings due to non-availability and difficulty in accessing running cost data.Originality/valueEarly-stage supportive running costs estimation model proposed by the study would enable construction professionals to benchmark the running costs and thereby optimise the building design. The developed hedonic model illustrated the variance of running costs concerning the changes in characteristics of a building.

A large-scale approach for evaluating asset pricing models

Recent studies show that the standard test portfolios do not contain sufficient information to discriminate between asset pricing models. To address this issue, we develop a large-scale approach that expands the cross-section to several thousand portfolios. Our novel approach is simple, widely applicable, and allows for formal evaluation/comparison tests. Its benefits are confirmed in empirical tests of CAPM- and characteristic-based models. While these models are all misspecified, we uncover striking performance differences between them. In particular, the human capital and conditional CAPMs largely outperform the CAPM, which suggests that labor income and time-varying recession risks are primary concerns for investors.

Using Abnormal Analyst Coverage to Unlock New Evidence on Stock Price Crash Risk

We employ a characteristic-based model to decompose total analyst coverage into abnormal and expected components and show that abnormal coverage contains valuable information about individual firm ex-ante crash risk (proxied by implied volatility smirk from options data). Specifically, one standard deviation increase in unexpected or abnormal coverage is associated with a 5.5% decrease in the ex-ante crash risk. The abnormal coverage signal is more useful in firms with a more transparent information environment, proxied by lower analyst dispersed opinions, lower financial opacity, and more comparable financial statements. Collectively, the results suggest that options market investors utilise abnormal coverage to identify and assess crash risk of mispriced firms.

International Characteristic-Based Asset Pricing

In this paper, we develop characteristic-based asset-pricing models for international stocks. We price stocks using benchmark portfolios created based on observable characteristics: market capitalization, book-to-market, prior-year return, growth of total assets, and operating profitability. Benchmark portfolios are created for each stock within each geographical region or country. As such, our approach allows for segmentation in characteristic-based asset pricing among regions. Using a resampling micro-portfolio approach recently introduced by Barras (2018), we find that market capitalization is the most powerful single characteristic in pricing international stocks, and that a three-characteristic model based on market capitalization, book-to-market, and prior-year return has the lowest pricing errors. Meanwhile, micro-portfolio tests and bootstrap simulation analysis also provide evidence that characteristic-based models perform significantly better than global or regional factor-based models in pricing international stocks. We further apply our characteristic-based models to the worldwide equity holdings of U.S.-domiciled mutual funds that mainly invest in international stocks. International equity index funds exhibit zero characteristic-based alphas, reinforcing that our characteristic-based model prices international stocks accurately. Further, we find strong evidence that actively managed funds that mainly invest in emerging markets or in international small/mid-cap stocks exhibit positive alphas. Our results indicate that U.S.-domiciled active managers are able to generate alphas in less-efficient sectors of international stock markets, when expected returns are measured using characteristic-based pricing.

Uncovering expected returns: Information in analyst coverage proxies

We show that analyst coverage proxies contain information about expected returns. We decompose analyst coverage into abnormal and expected components using a simple characteristic-based model and show that firms with abnormally high analyst coverage subsequently outperform firms with abnormally low coverage by approximately 80 basis points per month. We also show abnormal coverage rises following exogenous shocks to underpricing and predicts improvements in firms’ fundamental performance, suggesting that return predictability stems from analysts more heavily covering underpriced stocks. Our findings highlight the usefulness of analysts’ actions in expected return estimations, and a potential inference problem when coverage proxies are used to study information asymmetry and dissemination.

Uncovering Expected Returns: Information in Analyst Coverage Proxies

We show that analyst coverage proxies contain information about expected returns. We decompose analyst coverage into abnormal and expected components using a simple characteristic-based model and show that firms with abnormally high analyst coverage subsequently outperform firms with abnormally low coverage by approximately 80 basis points per month. We also show abnormal coverage rises following exogenous shocks to underpricing and predicts improvements in firms' fundamental performance, suggesting that return predictability stems from analysts more heavily covering underpriced stocks. Our findings highlight the usefulness of analysts' actions in expected return estimations, and a potential inference problem when coverage proxies are used to study information asymmetry and dissemination.

The Cross Section of Long-Term Expected Returns

Effective capital budgeting decisions require reliable estimates of long-term expected returns. I extract a predictor for long-term returns from the book-to-market ratio by controlling for its variation associated with long-term expected profitability. The resulting predictor reliably describes the cross section of returns out-of-sample for at least five years, even when focusing exclusively on large-cap stocks and controlling for expected-return estimates generated by implied cost of capital models, factor models, and characteristic-based models. My results have important implications for improving our cost of equity estimates.

On pricing kernels, information and risk

This paper compares out-of-sample, ex-ante risk and returns of arbitrage pricing theory (APT) risk-factor based, zero-cost portfolios with characteristic-based, zero-cost portfolios. In particular the Haugen and Baker characteristic-based model framework is used in a comparison with the capital asset pricing model (CAPM) (Haugen, R. A., & Baker, L. N. (1996). Commonality in the determinants of expected stocks returns. Journal of Financial Economics, 41, 401–439), and three-factor Fama and French APT model portfolios to analyse returns of stocks listed on the Johannesburg Stock Exchange (Fama, E., & French, K. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56). The finding that cross-sectional characteristic-based models have yielded portfolios with higher excess monthly returns but lower risk than their arbitrage pricing theory counterparts is discussed. Under the assumption of general no arbitrage conditions, it is argued that evidence in favour of characteristic-based pricing provides insight into the non-linear nature in which information is assimilated into pricing kernels for the market considered.

A dynamic linear economy with characteristic-based endogenous technical coefficients

We develop a characteristic-based model for the endogenous determination of technical coefficients in a linear economy and use it to describe the dynamics of the economy as driven by changes in knowledge. The use of the characteristic approach to determine technical coefficients makes our model suitable to be interpreted as a first attempt towards the formulation of a formal knowledge-based model of technology.

What Drives the Liquidity Premium: Factors or Characteristics?

We investigate whether the liquidity premium is better explained by the risk-based model or the characteristic-based model. Based on three widely-used liquidity measures that are supposed to reflect different aspects of liquidity, we find that liquidity as a characteristic carries a significant liquidity premium that is beyond the size and book-to-market effects. In addition, liquidity as a factor does not yield a significant risk premium beyond that provided by Fama and French’s (1993) three factors. Finally, in direct comparisons under a liquidity-augmented two-factor capital asset-pricing model, the liquidity premium is better characterized by a characteristic-based model rather than a risk-based model, especially for the Amihud (2002) liquidity measure and for the post-1964 period.

On the Transformer Core Dynamic Behavior During Electromagnetic Transients

This paper introduces the concepts of 1) dynamic inductance and 2) effective inductance based on the magnetic energy, and provides a new insight on the transformer core behavior during steady-state and transient regimes. Based on a newly developed core modeling approach, which is described in references one to four, this paper also: 1) addresses the intrinsic limitations of the single-valued magnetization characteristic-based core model; 2) uses the dynamic inductance as the key parameter to fully and accurately represent the core behavior; and 3) explains the reason for discrepancies among the reported core behavior based on the simulated and experimental results. In contrast to the widely accepted belief that a single-valued magnetization characteristic-based model can accurately represent the transformer core behavior, this paper demonstrates the necessity of a dynamic and physically correct hysteresis core representation. This paper also compares various core representations in terms of their accuracies for the ferroresonance simulation. This paper shows that: 1) the single-valued core model not only cannot accurately represent the core behavior under asymmetric excitation conditions, it may provide erroneous results even under symmetric excitation conditions and 2) the accuracy of specific transients highly depends on the precise representation of the hysteresis minor loops and the core dynamic inductance.

Firm-specific characteristics and the cross-section of Australian stock exchange returns

Recent investigations of the Capital Asset Pricing Model (CAPM) have identified numerous inconsistencies with the model's predictions. A number of variables have displayed evidence of the ability to explain the cross-sectional variation in share returns beyond that explained by beta. This study sets out to ascertain the identity of these firm-specific characteristics over the period June 1994–May 2004 for members of the Australian Stock Exchange (ASX) All Ordinaries stock index. A data set including 207 firm-specific attributes is created for stocks in the sample and, with over 4.85 million observations, this is the largest set yet assembled for a study on the ASX. Using the Fama and Macbeth (1973) cross-sectional regression approach, attributes are tested for the ability to explain the cross-sectional variation in ASX share returns beyond that explained by the CAPM and a principal components-derived APT model. Similar significant characteristics are found when unadjusted and both sets of risk-adjusted returns are examined. The set of significant characteristics derived from the unadjusted returns test is then simplified using correlation analysis and an agglomerative hierarchical clustering algorithm, resulting in a list of 27 variables that are not highly correlated with each other. The existence of anomalies found in prior Australian literature (size, price-per-share, M/B, cashflow-to-price, and short- to medium-term momentum) is confirmed. As these previously documented anomalies only comprise five of the final simplified list of 27 significant characteristics, this paper identifies 22 previously undocumented Australian anomalies. The 27 significant style characteristics are then used to construct a multifactor model that comprises a set of factors that are simultaneously statistically significant when cross-sectionally regressed on share returns. A five-factor characteristic-based model for the ASX is empirically derived, which comprises (1) prior 12-month return, (2) book-to-market value, (3) two-year percentage change in dividends paid, (4) cashflow-to-price, and (5) two-year percentage change in market-to-book value as explanatory variables.

Relative importance of hedge fund characteristics

This study compares the relative importance of thirteen attributes in explaining the cross-sectional variations in the returns of hedge funds, using a large sample from the TASS database covering the 1989–2005 period. We use a simple characteristic-based model of return decomposition, which has never before been applied to hedge funds. The characteristic factors are estimated using cross-sectional dummy-variable regressions. Their importance is found to be time-varying. We find that diversification across styles matters, but that it could be improved significantly by also diversifying across other characteristics, mainly including performance, volatility and fee structure.

Explaining the Cross-Section Returns in France: Characteristics or Covariances?

Size and book to market ratio are both highly correlated with the average returns of common stocks. Fama and French (1993) argue that these effects are proxies for factors of risk. In this study, we test the three factor model of Fama and French and the Characteristic Model of Daniel and Titman (1997) on The French Stock Market. The sample is from July 1976 to June 2001. Yearly, we rank, independently, all stocks by size and book to market ratio. Then, we make another classification based on ex-ante HML, SMB or Mkt loadings. To test the two models, we use Daniel and Titman methodology to construct our characteristic-balanced portfolios. The characteristic-based model predicts that these portfolios should have an average return of zero. The factor model says that these returns should be positive. Our results reject the factor model with characteristic balanced portfolios that load on the HML, SMB and MKt factors. Moreover, the three factor model predicts that the intercepts of regressions of the returns of these characteristic-balanced portfolios on the Fama and French factor portfolios are indistinguishable from zero. In contrast, the alternative hypothesis of the characteristic model says that these intercepts should be negative. Our results are consistent with the factor pricing model and inconsistent with the characteristic-based pricing model. Because the size and the value premiums are relatively small, our conclusions must be interpreted carefully. In contrast, market premium allows more powerful tests of the two models.

Characteristic-based models for the evolution of cooling flows

view Abstract Citations (12) References (23) Co-Reads Similar Papers Volume Content Graphics Metrics Export Citation NASA/ADS Characteristic-based Models for the Evolution of Cooling Flows Murray, Stephen D. ; Balbus, Steven A. Abstract X-ray observations over the past several years have established the existence of large amounts of hot gas within clusters of galaxies. In many cases, the cooling time of the gas is less than a Hubble time, implying that the gas must be flowing inward as a cooling flow, with inferred mass accretion rates exceeding 100 M_sun_ yr^-1^ in some cases. The fate of the cooling gas is uncertain. The observations that M decreases inward have generally been taken to imply that gas is "dropping out" of the flow to form stars. We examine this picture using numerical models of cooling flows. A basic premise of the models is that cluster ages are not much greater than their initial central cooling times. The models are made as simple as possible in order to best determine the roles played by thermal and dynamical processes as the flows evolve in time. Most important, we assume that mass is conserved. Unlike previous studies, the numerical method uses the theory of characteristics, which has several practical and conceptual advantages over standard finite difference techniques. It is found that the techniques to observationally obtain values of M are valid, despite the fact that they assume the flows to be in steady state, which the models find not to be the case. This is because the conservative energy equation used in the data reduction is highly insensitive to non-steady state effects. The lack of steady state evolution stems from the fact that the system evolves on the cooling time scale, which is a decreasing function of time. Observations of M increasing with r need not, therefore, directly imply mass dropout from the flow, but may also result from dynamical evolution. Because of the acceleration of the evolution with time, the total accreted mass is much smaller than would be inferred from the final value of the accretion rate, such that only ~10^11^ M_sun_ accrete within the inner 50 kpc. In other models, it is found that neither having a cluster potential which deepens with time, nor adding a central galactic potential delay the onset of core collapse, but, if anything, accelerate the evolution. Publication: The Astrophysical Journal Pub Date: August 1992 DOI: 10.1086/171633 Bibcode: 1992ApJ...395...99M Keywords: Astronomical Models; Cooling Flows (Astrophysics); Galactic Clusters; Intergalactic Media; X Ray Sources; High Temperature Gases; Hubble Constant; X Ray Astronomy; Astrophysics; GALAXIES: COOLING FLOWS; GALAXIES: CLUSTERING; GALAXIES: INTERGALACTIC MEDIUM; X-RAYS: GALAXIES full text sources ADS |