Libor at crossroads: stochastic switching detection using information theory quantifiers

This paper studies the 28 time series of Libor rates, classified in seven maturities and four currencies), during the last 14 years. The analysis was performed using a novel technique in financial economics: the Complexity-Entropy Causality Plane. This planar representation allows the discrimination of different stochastic and chaotic regimes. Using a temporal analysis based on moving windows, this paper unveals an abnormal movement of Libor time series arround the period of the 2007 financial crisis. This alteration in the stochastic dynamics of Libor is contemporary of what press called "Libor scandal", i.e. the manipulation of interest rates carried out by several prime banks. We argue that our methodology is suitable as a market watch mechanism, as it makes visible the temporal redution in informational efficiency of the market.Comment: 17 pages, 9 figures. arXiv admin note: text overlap with arXiv:1508.04748, arXiv:1509.0021

A permutation Information Theory tour through different interest rate maturities: the Libor case

This paper analyzes Libor interest rates for seven different maturities and referred to operations in British Pounds, Euro, Swiss Francs and Japanese Yen, during the period years 2001 to 2015. The analysis is performed by means of two quantifiers derived from Information Theory: the permutation Shannon entropy and the permutation Fisher information measure. An anomalous behavior in the Libor is detected in all currencies except Euro during the years 2006--2012. The stochastic switch is more severe in 1, 2 and 3 months maturities. Given the special mechanism of Libor setting, we conjecture that the behavior could have been produced by the manipulation that was uncovered by financial authorities. We argue that our methodology is pertinent as a market overseeing instrument.Comment: arXiv admin note: text overlap with arXiv:1304.039

The (in)visible hand in the Libor market: an Information Theory approach

This paper analyzes several interest rates time series from the United Kingdom during the period 1999 to 2014. The analysis is carried out using a pioneering statistical tool in the financial literature: the complexity-entropy causality plane. This representation is able to classify different stochastic and chaotic regimes in time series. We use sliding temporal windows to assess changes in the intrinsic stochastic dynamics of the time series. Anomalous behavior in the Libor is detected, especially around the time of the last financial crisis, that could be consistent with data manipulation.Comment: PACS 89.65.Gh Econophysics; 74.40.De noise and chao

Revisiting the European sovereign bonds with a permutation-information-theory approach

In this paper we study the evolution of the informational efficiency in its weak form for seventeen European sovereign bonds time series. We aim to assess the impact of two specific economic situations in the hypothetical random behavior of these time series: the establishment of a common currency and a wide and deep financial crisis. In order to evaluate the informational efficiency we use permutation quantifiers derived from information theory. Specifically, time series are ranked according to two metrics that measure the intrinsic structure of their correlations: permutation entropy and permutation statistical complexity. These measures provide the rectangular coordinates of the complexity-entropy causality plane; the planar location of the time series in this representation space reveals the degree of informational efficiency. According to our results, the currency union contributed to homogenize the stochastic characteristics of the time series and produced synchronization in the random behavior of them. Additionally, the 2008 financial crisis uncovered differences within the apparently homogeneous European sovereign markets and revealed country-specific characteristics that were partially hidden during the monetary union heyday.Centro de Investigaciones Óptica

Limitations to bank financing of high-tech SMEs

El objetivo de este trabajo es analizar el efecto del grado de intensidad tecnológica asociado a las Pymes en el acceso al financiamiento. Por lo tanto, se estima un modelo Logit ordenado considerando un conjunto de variables independientes como el tama˜no de la empresa, la antigüedad, la capacidad exportadora y la intensidad tecnológica asociada al sector. Los resultados obtenidos muestran que la intensidad tecnológica de la empresa resulta ser la variable que más impacta en la probabilidad de financiarse en el sector bancario. Esta conclusión es relevante para Argentina, dado que, a diferencia de otros países emergentes, las empresas no cuentan con instrumentos alternativos al financiamiento bancario que se adapten a las características propias de las Pymes de alta tecnología.The aim of this paper is to analyze whether the technological intensity level associated with the company affects financial access. The methodology applied is an ordered logit model considering a set of variables such as company size, age, export capacity and technological intensity associated to the company’s sector. The results show that the technological intensity of the company turns out to be the variable that most affects the probability of financing itself in the banking sector. This conclusion is relevant to Argentina, given that, if compared to others emerging markets, SMEs do not have alternative instruments to bank financing suitable to the characteristics of high technological SMEs

Post-pandemic performance of micro, small and medium-sized enterprises: A Self-organizing Maps application

AbstractThis paper examines the post-pandemic performance of micro, small, and medium-sized firms using Self-Organizing Maps (SOMs), a type of Artificial Neural Network that groups patterns based on their similarities. The goal is to identify the key characteristics that enable firms to face market changes and overcome the effects of the global COVID-19 pandemic crisis. Considering business failure theory, a set of critical factors (including internal production processes, firm age, number of employees, resilience, financial resources, commercial strategies, management, and the impact of external factors) is used to assess the performance of Argentinian firms. The study categorizes these firms into four clusters based on their patterns. The results reveal a trade-off between a firm’s age and its number of employees, confirming that younger firms with fewer employees, limited financial resources, relatively weaker management, internal production process issues, and lower resilience tend to perform poorly, despite facing fewer impact of external factors. Consequently, the findings emphasize the significance of internal fundamentals and resilience in achieving success or avoiding failure. This highlights the effectiveness of SOM as a tool to visualize the characteristics that lead to successful paths and the survival of firms