Ensayos sobre la E ciencia Informativa del Mercado de Capitales

La Hipòtesi del Mercat Eficient (HME) és un dels pilars de l'economia financera. Diem que un mercat financer és informativament eficient si els preus reflecteixen tota la informació disponible en un determinat moment. Tot i les diverses dècades d'investigació sobre la HME, encara queden aspectes sobre els quals no s'ha arribat a un consens en la literatura. Per això, abordem aquest estudi des d'una perspectiva nova en tres aspectes. En primer lloc, assumint el caràcter dinàmic de l'eficiència informativa, la estudiem mitjançant finestres mòbils per veure la seva evolució en el temps. En segon lloc, introduïm tècniques estadístiques no utilitzades habitualment en economia financera. En tercer lloc, relacionem els nivells d'eficiència informativa amb determinades variables econòmiques, amb l'objecte de veure la seva interacció. El capítol 1 proveeix una introducció al tema i detalla l'estructura de la tesi. En el capítol 2 s'estableix el marc teòric i es realitza una detallada descripció de l'evolució i els tests empírics duts a terme sobre la HME. El capítol 3 es compon de 4 assaigs que estudien, mitjançant tècniques estadístiques avançades, diferents aspectes sobre la HME com són la memòria de llarg termini, el caràcter variable de l'eficiència informativa i la seva relació amb determinades variables econòmiques. Finalment, el capítol 4 proporciona les principals conclusions.La Hipótesis del Mercado Eficiente (HME) es uno de los pilares de la economía financiera. Decimos que un mercado financiero es informativamente eficiente si los precios reflejan toda la informacion disponible en un determinado momento. A pesar de las varias décadas de investigación sobre la HME, todavía quedan aspectos sobre los cuales no se ha llegado a un consenso en la literatura. Por ello, abordamos este estudio desde una perspectiva novedosa en tres aspectos. En primer lugar, asumiendo el carácter dinámico de la eficiencia informativa, estudiamos la misma mediante ventanas móviles para ver su evolución en el tiempo. En segundo lugar, introducimos técnicas estadísticas no utilizadas habitualmente en economía financiera. En tercer lugar, relacionamos los niveles de eficiencia informativa con determinadas variables económicas, con el objeto de ver su interacción. El capítulo 1 provee una introducción al tema y detalla la estructura de la tesis. En el capítulo 2 se establece el marco teórico y se realiza una pormenorizada descripcion de la evolución y los tests empíricos llevados a cabo sobre la HME. El capítulo 3 se compone de 4 ensayos que estudian mediante técnicas estadísticas avanzadas diferentes aspectos sobre la HME, como son la memoria de largo plazo, el carácter variable de la eficiencia informativa y su relación con determinadas variables economicas. Finalmente el capítulo 4 proporciona las principales conclusiones.The Efficient Market Hypothesis (EMH) is one of the pillars of the financial economy. We say that a financial market is informationally efficient if the prices reflect all available information at a given time. Despite several decades of research on EMH, there are still issues on which no consensus has been reached in the literature. Therefore, we approach this study from a new perspective in three respects. First, assuming the dynamic nature of information efficiency, we study it by sliding windows to observe their evolution in time. Secondly, we introduce statistical techniques not commonly used in financial economics. Third, we relate information efficiency levels with certain economic variables, in order to see their interaction. Chapter 1 provides an introduction to the topic and details the structure of the thesis. Chapter 2 provides the theoretical framework and a detailed description of the evolution and empirical tests carried out on the EMH is done. Chapter 3 consists of 4 essays which, using advanced statistical techniques different aspects of the EMH, such as long-term memory, the variable nature of the information efficiency and its relation to certain economic variables. Finally, Chapter 4 provides the main conclusions

An analysis of high-frequency cryptocurrencies prices dynamics using permutation-information-theory quantifiers

This paper discusses the dynamics of intraday prices of 12 cryptocurrencies during the past months´ boom and bust. The importance of this study lies in the extended coverage of the cryptoworld, accounting for more than 90% of the total daily turnover. By using the complexity-entropy causality plane, we could discriminate three different dynamics in the data set. Whereas most of the cryptocurrencies follow a similar pattern, there are two currencies (ETC and ETH) that exhibit a more persistent stochastic dynamics, and two other currencies (DASH and XEM) whose behavior is closer to a random walk. Consequently, similar financial assets, using blockchain technology, are differentiated by market participants.Fil: Fernández Bariviera, Aurelio. Universitat Rovira I Virgili; España. Universidad del Pacifico, Lima; PerúFil: Zunino, Luciano José. Universidad Nacional de La Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; ArgentinaFil: Rosso, Osvaldo Aníbal. Universidad de Los Andes, Santiago; Chile. Universidade Federal de Alagoas; Brasil. Instituto Universitario del Hospital Italiano de Buenos Aires; Argentin

An analysis of high-frequency cryptocurrencies prices dynamics using permutation-information-theory quantifiers

This paper discusses the dynamics of intraday prices of 12 cryptocurrencies during the past months´ boom and bust. The importance of this study lies in the extended coverage of the cryptoworld, accounting for more than 90% of the total daily turnover. By using the complexity-entropy causality plane, we could discriminate three different dynamics in the data set. Whereas most of the cryptocurrencies follow a similar pattern, there are two currencies (ETC and ETH) that exhibit a more persistent stochastic dynamics, and two other currencies (DASH and XEM) whose behavior is closer to a random walk. Consequently, similar financial assets, using blockchain technology, are differentiated by market participants.Centro de Investigaciones Óptica

Spurious Seasonality Detection: A Non-Parametric Test Proposal

This paper offers a general and comprehensive definition of the day-of-the-week effect. Using symbolic dynamics, we develop a unique test based on ordinal patterns in order to detect it. This test uncovers the fact that the so-called “day-of-the-week” effect is partly an artifact of the hidden correlation structure of the data. We present simulations based on artificial time series as well. While time series generated with long memory are prone to exhibit daily seasonality, pure white noise signals exhibit no pattern preference. Since ours is a non-parametric test, it requires no assumptions about the distribution of returns, so that it could be a practical alternative to conventional econometric tests. We also made an exhaustive application of the here-proposed technique to 83 stock indexes around the world. Finally, the paper highlights the relevance of symbolic analysis in economic time series studies.Instituto de Física La Plat

Data vs. information: using clustering techniques to enhance stock returns forecasting

This paper explores the use of clustering models of stocks to improve both (a) the prediction of stock prices and (b) the returns of trading algorithms. We cluster stocks using k-means and several alternative distance metrics, using as features quarterly financial ratios, prices and daily returns. Then, for each cluster, we train ARIMA and LSTM forecasting models to predict the daily price of each stock in the cluster. Finally, we employ the clustering-empowered forecasting models to analyze the returns of different trading algorithms. We obtain three key results: (i) LSTM models outperform ARIMA and benchmark models, obtaining positive investment returns in several scenarios; (ii) forecasting is improved by using the additional information provided by the clustering methods, therefore selecting relevant data is an important preprocessing task in the forecasting process; (iii) using information from the whole sample of stocks deteriorates the forecasting ability of LSTM models. These results have been validated using data of 240 companies of the Russell 3000 index spanning 2017 to 2022, training and testing with different subperiods.Instituto de Investigación en Informátic

Efficiency and credit ratings: A permutation-information-theory analysis

The role of credit rating agencies has been under severe scrutiny after the subprime crisis. In this paper we explore the relationship between credit ratings and informational efficiency of a sample of thirty nine corporate bonds of US oil and energy companies from April 2008 to November 2012. For this purpose we use a powerful statistical tool, relatively new in the financial literature: the complexity–entropy causality plane. This representation space allows us to graphically classify the different bonds according to their degree of informational efficiency. We find that this classification agrees with the credit ratings assigned by Moody's. In particular, we detect the formation of two clusters, which correspond to the global categories of investment and speculative grades. Regarding the latter cluster, two subgroups reflect distinct levels of efficiency. Additionally, we also find an intriguing absence of correlation between informational efficiency and firm characteristics. This allows us to conclude that the proposed permutation-information-theory approach provides an alternative practical way to justify bond classification.Fil: Fernández Bariviera, Aurelio. Universitat Rovira I Virgili; España; . Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Computación. Laboratorio de Sistemas Complejos; ArgentinaFil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - La Plata. Centro de Investigaciones Opticas (i); Argentina. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Ciencias Básicas; ArgentinaFil: Guercio, María Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Bahia Blanca. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina. Universidad Nacional del Sur; ArgentinaFil: Martinez, Lisana Belén. Universitat Rovira I Virgili; España; . Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Bahia Blanca. Instituto de Investigaciones Económicas y Sociales del Sur; ArgentinaFil: Rosso, Osvaldo Anibal. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Computación. Laboratorio de Sistemas Complejos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Bahia Blanca; Argentina. Universidade Federal de Alagoas; Brasil

On the efficiency of sovereign bond markets

The existence of memory in financial time series has been extensively studied for several stock markets around the world by means of different approaches. However, fixed income markets, i.e. those where corporate and sovereign bonds are traded, have been much less studied. We believe that, given the relevance of these markets, not only from the investors', but also from the issuers' point of view (government and firms), it is necessary to fill this gap in the literature. In this paper, we study the sovereign market efficiency of thirty bond indices of both developed and emerging countries, using an innovative statistical tool in the financial literature: the complexity-entropy causality plane. This representation space allows us to establish an efficiency ranking of different markets and distinguish different bond market dynamics. We conclude that the classification derived from the complexity-entropy causality plane is consistent with the qualifications assigned by major rating companies to the sovereign instruments. Additionally, we find a correlation between permutation entropy, economic development and market size that could be of interest for policy makers and investors.Centro de Investigaciones Óptica

Efficiency and credit ratings: a permutation-information-theory analysis

The role of credit rating agencies has been under severe scrutiny after the subprime crisis. In this paper we explore the relationship between credit ratings and informational efficiency of a sample of thirty nine corporate bonds of US oil and energy companies from April 2008 to November 2012. For this purpose we use a powerful statistical tool, relatively new in the financial literature: the complexity–entropy causality plane. This representation space allows us to graphically classify the different bonds according to their degree of informational efficiency. We find that this classification agrees with the credit ratings assigned by Moody's. In particular, we detect the formation of two clusters, which correspond to the global categories of investment and speculative grades. Regarding the latter cluster, two subgroups reflect distinct levels of efficiency. Additionally, we also find an intriguing absence of correlation between informational efficiency and firm characteristics. This allows us to conclude that the proposed permutation-information-theory approach provides an alternative practical way to justify bond classification.Centro de Investigaciones Óptica

Agrupamiento dinámico de trayectorias vehiculares

Vehicular traffic volume in large cities has increased in recent years, causing mobility problems; therefore, the analysis of vehicle flow data becomes a relevant research topic. Intelligent Transportation Systems monitor and control vehicular movements by collecting GPS trajectories, which provides the geographic location of vehicles in real time. Thus information is processed using clustering techniques to identify vehicular flow patterns. This work presents a methodology capable of analyzing the vehicular flow in a given area, identifying speed ranges and keeping an interactive map updated that facilitates the identification of possible traffic jam areas. The obtained results on three data sets from the cities of Guayaquil-Ecuador, Rome- Italy and Beijing-China are satisfactory and clearly represent the speed of movement of the vehicles, automatically identifying the most representative ranges in real time.El volumen de tráfico vehicular de las grandes ciudades se ha incrementado en los últimos años originando problemas de movilidad, por ello el análisis de los datos del flujo vehicular toma importancia para los investigadores. Los Sistemas Inteligentes de transportación realizan el monitoreo y control vehicular recolectando trayectorias GPS, información que brinda en tiempo real la ubicación geográfica de los vehículos. Su procesamiento por medio de técnicas de agrupamiento permite identificar patrones sobre el flujo vehicular. Este trabajo presenta una metodología capaz de analizar el flujo vehicular en un área dada, identificando los rangos de velocidades y manteniendo actualizado un mapa interactivo que facilita la identificación de zonas de posibles atascos. Los resultados obtenidos sobre tres conjuntos de datos de las ciudades de Guayaquil-Ecuador, Roma-Italia y Beijing-China son satisfactorios y representan claramente la velocidad de desplazamiento de los vehículos identificando de manera automática los rangos más representativos para cada instante de tiempo.Facultad de Informátic

COVID-19 Impact on Cryptocurrencies : Evidence from a Wavelet-Based Hurst Exponent

Cryptocurrency history begins in 2008 as a means of payment proposal. However, cryptocurrencies evolved into complex, high yield speculative assets. Contrary to traditional financial instruments, they are not (mostly) traded in organized, law-abiding venues, but on online platforms, where anonymity reigns. This paper examines the long term memory in return and volatility, using high frequency time series of eleven important coins. Our study covers the pre-COVID-19 and the subsequent pandemia period. We use a recently developed method, based on the wavelet transform, which provides more robust estimators of the Hurst exponent. We detect that, during the peak of COVID-19 pandemic (around March 2020), the long memory of returns was only mildly affected. However, volatility suffered a temporary impact in its long range correlation structure. Our results could be of interest for both academics and practitioners.Facultad de Ciencias ExactasFacultad de Ingenierí