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About 10,000 papers for “multi-factor models”

MULTI-FACTOR INDEX MODELS

А. Ф. Шило · SHILAP Revista de lepidopterología · 2011

The paper presents an innovation factor formula for analysis of factor characteristics’ influence on general result.As distinguished from a valuable positional factor formula the proposed one provides objective results of the analysis with high accuracy level.

Cited by 0Open access

Multi-factor term structure models

Darrell Duffie, Rui Kan · Philosophical Transactions of the Royal Society of London Series A Physical and Engineering Sciences · 1994

Abstract This is a survey of multi-factor structure models, concentrating on models in which the term structure has a finite-dimensional (Markov diffusion) state-space representation. The special ‘affine’ case is shown to be tractable.

Cited by 47

Multi-factor Models in Finance

Guyi Liang · Advances in Economics Management and Political Sciences · 2024

This literature review explores the significance of multi-factor models in finance, emphasizing their dynamic nature and pivotal role in understanding global market patterns. The multi-factor framework, adept at capturing the complexities of asset pricing, incorporates various variables, enabling a comprehensive analysis of relationships impacting market conditions. The Fama-French Three-Factor Model, an influential extension of traditional models, introduces size and value components, enriching the understanding of asset valuations. Assumptions underpinning multi-factor models, such as market efficiency and rational investor behavior, form essential foundations. Despite their advantages, these models face challenges, including the risk of overfitting and the complexity of incorporating numerous variables. The study underscores the need to recognize these limitations while appreciating the models' contributions to portfolio management and risk assessment, ultimately contributing to the ongoing evolution of financial analysis.

Cited by 1Open access

Implied Multi-Factor Models Valuing CDOs of Bespoke Portfolios with Implied Multi-Factor Models

Dan Rosen, David Saunders, Roger Stein et al. · 2006

This paper presents a robust and practical CDO valuation framework based on the application of multi-factor credit models in conjunction with weighted Monte Carlo techniques used in options pricing. The general factor framework produces arbitrage-free prices and can be used to value consistently CDOs of bespoke portfolios, CDO-squared and cash CDOs. The quoted prices of individual name CDSs as well as various credit portfolio instruments, such as CDO tranches, are used to imply the “risk-neutral” distributions for the underlying systematic risk factors, which drive joint obligor defaults. We solve numerically the inverse problem of implying the factors ’ joint distribution, defined over a discrete set of scenarios on the factors. Multi-factor models allow the inclusion of sector and geographical concentrations, deals which refer simultaneously to multiple indices and potentially other risk factors such as recoveries and prepayments. We describe various numerical techniques for effectively sampling factor scenarios and

Cited by 0

Esg integration in multi factor models

Cutolo, Christopher · Universidade Nova de Lisboa's Repository (Universidade Nova de Lisboa) · 2021

Over the last years ESG has become one of the major trends within the financial world. Investors are expressing more interest in environmental, social and governance issues and experts are routinely integrating sustainability considerations when building portfolios. Several studies have been conducted and papers have been published trying to analyze ESG’s integration into the asset management world. Results show lack of uniformity and coherence when it comes to understanding the impact of socially responsible investments. We construct factor mimicking portfolios based on E, S and G scores to study the relationship between sustainability and financial performance in the American market. The results show that long short portfolios constructed in this fashion display negative returns over the period that spans from July 2002 until June 2020. This is mainly because of the significant positive returns of stocks with low sustainability scores. Moreover, regression analyses are conducted using the classic Fama Mac Beth procedure (1973), implementing the Newey West correction to account for heteroskedasticity and autocorrelation. Incorporating both the Fama- French five factor model and other control variables, ambiguous and mainly insignificant results are obtained

Cited by 0Open access

MULTI-FACTOR HEAT EXCHANGER DESIGN MODELS

Клімов, Р.О., Луста, Є.О. · The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy · 2021

Compressed air is widely used in enterprises, and it is possible to reduce air consumption on pneumatic devices by heating. Most often, heating is carried out in shell-and-tube heat exchangers. To increase the area of heat exchange between the heating medium and the air, finned tubes are used, which can significantly reduce the volume occupied by the heater. The design of the heater is influenced by many factors, and the importance of the influence of each of them can differ significantly. It is advisable to use the overall characteristic in the form of a compactness factor, which shows the ratio of the heat exchange area to the volume of the heater. The work developed a method for determining the optimal design of heaters by such a parameter as the compactness factor. The obtained regression equations make it possible to determine the influence of such factors as the number of rows of tubes across the flow and the length of one tube on the volume occupied by the heat exchanger and the compactness factor. According to Fisher's criterion, the equations of the model are adequate to the true dependence with a confidence level of 95%. Most of all, the volume of the heat exchanger and the compactness are affected by the number of tubes transverse to the air flow. Changing the length of one tube does not fundamentally affect the obtained values of the output parameters. With an increase in the length of one tube and their number across the flow, it is possible to achieve the highest values of the compactness coefficient, the dependence of which on the main factors has a pronounced maximum. Using the developed technique, it is possible, in a fairly simple formulation, to analyze the value of the compactness factor for various combinations of the above factors and to optimize the design of the heater.

Cited by 0Open access

Comparison and analysis of multi–factor models.

Hongyuan Zhang (2358817), Yule Wan (21222126), Shuming Zhao (308548) · Figshare · 2025

<b>The influence mechanism of work</b>–<b>family conflict on employees’ proactive behavior: A moderated mediator model</b>

Cited by 0Open access

Localized factor models for multi-context recommendation

Deepak Agarwal, Bee-Chung Chen, Bo Long · 2011

Combining correlated information from multiple contexts can significantly improve predictive accuracy in recommender problems. Such information from multiple contexts is often available in the form of several incomplete matrices spanning a set of entities like users, items, features, and so on. Existing methods simultaneously factorize these matrices by sharing a single set of factors for entities across all contexts. We show that such a strategy may introduce significant bias in estimates and propose a new model that ameliorates this issue by positing local, context-specific factors for entities. To avoid over-fitting in contexts with sparse data, the local factors are connected through a shared global model. This sharing of parameters allows information to flow across contexts through multivariate regressions among local factors, instead of enforcing exactly the same factors for an entity, everywhere. Model fitting is done in an EM framework, we show that the E-step can be fitted through a fast multi-resolution Kalman filter algorithm that ensures scalability. Experiments on benchmark and real-world Yahoo! datasets clearly illustrate the usefulness of our approach. Our model significantly improves predictive accuracy, especially in cold-start scenarios.

Cited by 76

DEFAULTABLE DEBT PRICING IN MULTI-FACTOR MODELS

Kian Guan Lim, Shiwei Chang, Tsui Kai Chong · International Journal of Theoretical and Applied Finance · 2002

This paper employs the structural approach to study defaultable debt pricing under multi-factor models. We extend existing results under the structural approach in two directions. By incorporating multiple factors, we can capture the impact of other economic variables on the debt prices apart from the interest rate factor. In our approach, we provide credit spread pricing using both the real interest rate and inflation rate as state variables. We also extend the analyses to a more general default boundary. In our paper we assume that default happens when the firm value hits a given fraction of the corresponding risk-free debt for the first time. This is also the recovery rate of the defaulted debt. Analytical solutions for both the case of constant recovery rate and the extended case of recovery rate being a deterministic function of time in the multi-factor models are provided in our study. We also provide comparison of the performance of our model with other relevant credit spread models.

Cited by 1

Multi-factor approximation of rough volatility models

Eduardo Abi Jaber, Omar El Euch · arXiv (Cornell University) · 2018

Rough volatility models are very appealing because of their remarkable fit of\nboth historical and implied volatilities. However, due to the non-Markovian and\nnon-semimartingale nature of the volatility process, there is no simple way to\nsimulate efficiently such models, which makes risk management of derivatives an\nintricate task. In this paper, we design tractable multi-factor stochastic\nvolatility models approximating rough volatility models and enjoying a\nMarkovian structure. Furthermore, we apply our procedure to the specific case\nof the rough Heston model. This in turn enables us to derive a numerical method\nfor solving fractional Riccati equations appearing in the characteristic\nfunction of the log-price in this setting.\n

Cited by 4Open access

Multi-Factor Models and Gradient Boosting for Improved Factor Selection

Xingyou Li · 2024

Multi-factor models are widely applied for explaining and predicting financial asset returns. Conventionally, methods like the Fama-MacBeth regression are employed to test factor validity, yet they overlook the potential heterogeneity in factor impacts across different assets. This paper proposes an improved approach by leveraging CatBoost and LightGBM to classify differentiate scenarios where factors are effective or ineffective, aiming to enhance the accuracy of factor selection. Using data from macro funds spanning 2018 to 2023, we first compare the performance of investment portfolios constructed without factor screening against those subjected to the Fama-MacBeth test. The results reveal instability in the traditional method. Subsequently, we train CatBoost and LightGBM, and construct portfolios by retaining funds exhibiting valid factor influences as predicted by the models. The improved method yields higher average portfolio returns but also increased return volatility. Further analysis indicates that the performance of our method is closely tied to the prediction accuracy of the models and the degree of matching between the training and test data distributions. When these align, our improved method can achieve considerable returns; conversely, a mismatch leads to extreme portfolio return fluctuations. These findings highlight the importance of enhancing model generalization capabilities and optimizing portfolio construction approaches for future research.

Cited by 0

NONLINEAR MODELS FOR MULTI-FACTOR PLANT NUTRITION EXPERIMENTS

Reid D. Landes, Walter W. Stroup, Ellen T. Paparozzi et al. · Conference on Applied Statistics in Agriculture · 1999

Plant scientists are interested in measuring plant response to quantitative treatment factors, e.g. amount of nutrient applied. Response surface methods are often used for experiments with multiple quantitative factors. However, in many plant nutrition studies, second-order response surface models result in unacceptable lack of fit. This paper explores multi-factor nonlinear models as an alternative. We have developed multi-factor extensions of Mitscherlich and Gompertz models, and fit them to data from experiments conducted at the University of Nebraska-Lincoln Horticulture department. These data are typical of experiments for which conventional response surface models perform poorly. We propose design selection strategies to facilitate economical multi-factor experiments when second-order response surface models are unlikely to fit.

Cited by 3Open access