Neural networks principal component analysis for estimating the generative multifactor model of returns under a statistical approach to the arbitrage pricing theory: Evidence from the mexican stock exchange

A nonlinear principal component analysis (NLPCA) represents an extension of the standard principal component analysis (PCA) that overcomes the limitation of the PCA’s assumption about the linearity of the model. The NLPCA belongs to the family of nonlinear versions of dimension reduction or the extraction techniques of underlying features, including nonlinear factor analysis and nonlinear independent component analysis, where the principal components are generalized from straight lines to curves. The NLPCA can be achieved via an artificial neural network specification where the PCA classic model is generalized to a nonlinear mode, namely, Neural Networks Principal Component Analysis (NNPCA). In order to extract a set of nonlinear underlying systematic risk factors, we estimate the generative multifactor model of returns in a statistical version of the Arbitrage Pricing Theory (APT), in the context of the Mexican Stock Exchange. We used an auto-associative multilayer perceptron neural network or autoencoder, where the ‘bottleneck’ layer represented the nonlinear principal components, or in our context, the scores of the underlying factors of systematic risk. This neural network represents a powerful technique capable of performing a nonlinear transformation of the observed variables into the nonlinear principal components, and to execute a nonlinear mapping that reproduces the original variables. We propose a network architecture capable of generating a loading matrix that enables us to make a first approach to the interpretation of the extracted latent risk factors. In addition, we used a two stage methodology for the econometric contrast of the APT involving first, a simultaneous estimation of the system of equations via Seemingly Unrelated Regression (SUR), and secondly, a cross-section estimation via Ordinary Least Squared corrected by heteroskedasticity and autocorrelation by means of the Newey-West heteroskedasticity and autocorrelation consistent covariances estimates (HEC). The evidence found shows that the reproductions of the observed returns using the estimated components via NNPCA are suitable in almost all cases; nevertheless, the results in an econometric contrast lead us to a partial acceptance of the APT in the samples and periods studied.Peer ReviewedPostprint (published version

Neural Networks Principal Component Analysis for Estimating the Generative Multifactor Model of Returns under a Statistical Approach to the Arbitrage Pricing Theory: Evidence from the Mexican Stock Exchange

A nonlinear principal component analysis (NLPCA) represents an extension of the standard principal component analysis (PCA) that overcomes the limitation of the PCA's assumption about the linearity of the model. The NLPCA belongs to the family of nonlinear versions of dimension reduction or the extraction techniques of underlying features, including nonlinear factor analysis and nonlinear independent component analysis, where the principal components are generalized from straight lines to curves. The NLPCA can be achieved via an artificial neural network specification where the PCA classic model is generalized to a nonlinear mode, namely, Neural Networks Principal Component Analysis (NNPCA). In order to extract a set of nonlinear underlying systematic risk factors, we estimate the generative multifactor model of returns in a statistical version of the Arbitrage Pricing Theory (APT), in the context of the Mexican Stock Exchange. We used an auto-associative multilayer perceptron neural network or autoencoder, where the 'bottleneck' layer represented the nonlinear principal components, or in our context, the scores of the underlying factors of systematic risk. This neural network represents a powerful technique capable of performing a nonlinear transformation of the observed variables into the nonlinear principal components, and to execute a nonlinear mapping that reproduces the original variables. We propose a network architecture capable of generating a loading matrix that enables us to make a first approach to the interpretation of the extracted latent risk factors. In addition, we used a two stage methodology for the econometric contrast of the APT involving first, a simultaneous estimation of the system of equations via Seemingly Unrelated Regression (SUR), and secondly, a cross-section estimation via Ordinary Least Squared corrected by heteroskedasticity and autocorrelation by means of the Newey-West heteroskedasticity and autocorrelation consistent covariances estimates (HEC). The evidence found shows that the reproductions of the observed returns using the estimated components via NNPCA are suitable in almost all cases; nevertheless, the results in an econometric contrast lead us to a partial acceptance of the APT in the samples and periods studied

Comparison of Statistical Underlying Systematic Risk Factors and Betas Driving Returns on Equities

The objective of this paper is to compare four dimension reduction techniques used for extracting the underlying systematic risk factors driving returns on equities of the Mexican Market. The methodology used compares the results of estimation produced by Principal Component Analysis (PCA), Factor Analysis (FA), Independent Component Analysis (ICA), and Neural Networks Principal Component Analysis (NNPCA) under three different perspectives. The results showed that in general: PCA, FA, and ICA produced similar systematic risk factors and betas; NNPCA and ICA produced the greatest number of fully accepted models in the econometric contrast; and, the interpretation of systematic risk factors across the four techniques was not constant. Additional research testing alternative extraction techniques, econometric contrast, and interpretation methodologies are recommended, considering the limitations derived from the scope of this work. The originality and main contribution of this paper lie in the comparison of these four techniques in both the financial and Mexican contexts. The main conclusion is that depending on the purpose of the analysis, one technique will be more suitable than another

Extraction of the underlying structure of systematic risk from non-Gaussian multivariate financial time series using independent component analysis: Evidence from the Mexican stock exchange

Regarding the problems related to multivariate non-Gaussianity of financial time series, i.e., unreliable results in extraction of underlying risk factors -via Principal Component Analysis or Factor Analysis-, we use Independent Component Analysis (ICA) to estimate the pervasive risk factors that explain the returns on stocks in the Mexican Stock Exchange. The extracted systematic risk factors are considered within a statistical definition of the Arbitrage Pricing Theory (APT), which is tested by means of a two-stage econometric methodology. Using the extracted factors, we find evidence of a suitable estimation via ICA and some results in favor of the APT.Peer ReviewedPostprint (published version

Extraction of the underlying structure of systematic risk from Non-Gaussian multivariate financial time series using Independent Component Analysis. Evidence from the Mexican Stock Exchange

Regarding the problems related to multivariate non-Gaussianity of financial time series, i.e.,unreliable results in extraction of underlying risk factors - via Principal Component Analysis or Factor Analysis-, we use Independent Component Analysis (ICA) to estimate the pervasive risk factors that explain the returns on stocks in the Mexican Stock Exchange. The extracted systematic risk factors are considered within a statistical definition of the Arbitrage Pricing Theory (APT), which is tested by means of a two-stage econometric methodology. Using the extracted factors, we find evidence of a suitable estimation via ICA and some results in favor of the APT

Mining Text and Time Series Data with Applications in Finance

Finance is a field extremely rich in data, and has great need of methods for summarizing and understanding these data. Existing methods of multivariate analysis allow the discovery of structure in time series data but can be difficult to interpret. Often there exists a wealth of text data directly related to the time series. In this thesis it is shown that this text can be exploited to aid interpretation of, and even to improve, the structure uncovered. To this end, two approaches are described and tested. Both serve to uncover structure in the relationship between text and time series data, but do so in very different ways. The first model comes from the field of topic modelling. A novel topic model is developed, closely related to an existing topic model for mixed data. Improved held-out likelihood is demonstrated for this model on a corpus of UK equity market data and the discovered structure is qualitatively examined. To the authors’ knowledge this is the first attempt to combine text and time series data in a single generative topic model. The second method is a simpler, discriminative method based on a low-rank decomposition of time series data with constraints determined by word frequencies in the text data. This is compared to topic modelling using both the equity data and a second corpus comprising foreign exchange rates time series and text describing global macroeconomic sentiments, showing further improvements in held-out likelihood. One example of an application for the inferred structure is also demonstrated: construction of carry trade portfolios. The superior results using this second method serve as a reminder that methodological complexity does not guarantee performance gains

Flight-to-Quality phenomenon as a source of financial instability

Doutoramento em EconomiaA general theoretical framework is proposed to analyse Flight-to-Quality events, defined as a mass investment migration from risky to safe assets. The model consists of only two asset classes, risky and safe. The framework is applied to Flights-to-Quality from emerging market public debt to U.S. treasuries, in the period 1998-2010. An alarm signal system is designed to warn of upcoming Flights-to-Quality and their terminations, and is applied: (i) to delimiting hypothetical Flights-to-Quality on an ex-ante basis, which are compared with historically observed episodes, to test the quality of the alarm signals; (ii) to elaborate dynamic interest rate risk hedge strategies, characterized by higher returns and lower volatility in comparison with statically hedged investments. The proposed framework potentially allows for improving the timeliness of financial policies, which can be triggered by the alarm signals. It can also be a useful tool for defining adequate policies to be implemented acting either on an insufficient supply of the safe assets or on a decreasing demand for the risky investments, thus contributing to a more stable economic environment.Propõe-se uma abordagem teórica para análise de eventos Flight-to-Quality, definidos como a migração em massa de investimentos em, activos com risco para investimentos em activos sem risco. O modelo considera apenas dois tipos de activos, com e sem risco. A abordagem é aplicada a eventos Flight-to-Quality da dívida pública de mercados emergentes para dívida pública norte-americana, no período 1998-2010. É desenhado um sistema de sinais de alerta para emitir sinais de aviso relativos ao início e ao término dos eventos Flight-to-Quality, o qual é utilizado para: (i) a identificação ex-ante (hipotética) dos eventos, os quais são comparados com os eventos históricos observados, para testar a qualidade dos sinais gerados; (ii) para elaborar estratégias dinâmicas de cobertura de risco da taxa de juro, que asseguram rendimentos mais elevados e menor volatilidade que estratégias de cobertura de risco estáticas. A abordagem proposta permite melhorar o tempo de resposta das políticas financeiras, as quais podem ser despoletadas pelos sinais de alarme. E pode também ser um instrumento útil para a definição de políticas, seja para correcção de uma oferta insuficiente de activos sem risco ou de uma procura insuficiente pelos activos com risco, contribuindo assim para um ambiente económico mais estável

CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research

Malaysian bilateral trade relations and economic growth

This paper examines the structure and trends of Malaysian bilateral exports and imports and then investigates whether these bilateral exports and imports have caused Malaysian economic growth. Although the structure of Malaysia’s trade has changed quite significantly over the last three decades, the direction of Malaysia’s trade remains generally the same. Broadly, ASEAN, the EU, East Asia, the US and Japan continue to be the Malaysia’s major trading partners. The Granger causality tests have shown that it is the bilateral imports that have caused economic growth in Malaysia rather than the bilateral exports

Exchange rate misalignments in ASEAN-5 countries

The purpose of this paper is to estimate the exchange rate misalignments for Indonesia, Malaysia, Philippines, Singapore and Thailand before the currency crisis. By employing the sticky-price monetary exchange rate model in the environment of vector error-correction, the results indicate that the Indonesia rupiah, Malaysian ringgit, Philippines peso and Singapore dollar were overvalued before the currency crisis while Thai baht was undervalued on the eve of the crisis. However, they suffered modest misalignment. Therefore, little evidence of exchange misalignment is found to exist in 1997:2. In particular, Indonesia rupiah, Malaysia ringgit, Philippines peso and Singapore dollar were only overvalued about 1 to 4 percent against US dollar while the Thai baht was only 2 percent undervalued against US dollar