Multiscale oil-stocks dynamics: the case of Visegrad group and Russia

This paper tries to determine the strength of the interdependence between Brent oil market and the stock markets of oil importing Visegrad group countries and oil exporting Russia in different time-horizons. The paper uses several novel and elaborate methodologies – bivariate DCC-EGARCH model, wavelet correlations and phase difference. The results of DCC model show that all dynamic correlations between Brent oil and the selected stock indices are low at daily-frequency level. The magnitude of mutual correlations does not exceed 20% for Visegrad countries, while for Russia it goes little bit over 30%. Wavelet correlations in shortterm confirms DCC results, whereby this relatively weak connection is found up to 32 days. However, in midterm and long-term, wavelet correlations strengthen, and go above 50% in midterm and even beyond 80% in long-term for majority of the indices. Slovakian SAX index has stronger wavelet correlation in 32 days than in 64 days, and it goes around 23%. This means that SAX can be coupled with Brent oil for diversification purposes in both short-term and midterm portfolios. Besides, phase-difference methodology provides an evidence that SAX was in anti-phase position in two separate occasions, meaning that SAX can also serve well for hedging purposes

Testing the significance of calendar effects

This paper studies tests of calendar effects in equity returns. It is necessary to control for all possible calendar effects to avoid spurious results. The authors contribute to the calendar effects literature and its significance with a test for calendar-specific anomalies that conditions on the nuisance of possible calendar effects. Thus, their approach to test for calendar effects produces robust data-mining results. Unfortunately, attempts to control for a large number of possible calendar effects have the downside of diminishing the power of the test, making it more difficult to detect actual anomalies. The authors show that our test achieves good power properties because it exploits the correlation structure of (excess) returns specific to the calendar effect being studied. We implement the test with bootstrap methods and apply it to stock indices from Denmark, France, Germany, Hong Kong, Italy, Japan, Norway, Sweden, the United Kingdom, and the United States. Bootstrap p-values reveal that calendar effects are significant for returns in most of these equity markets, but end-of-the-year effects are predominant. It also appears that, beginning in the late 1980s, calendar effects have diminished except in small-cap stock indices.

Portfolio Selection via Topological Data Analysis

Portfolio management is an essential part of investment decision-making. However, traditional methods often fail to deliver reasonable performance. This problem stems from the inability of these methods to account for the unique characteristics of multivariate time series data from stock markets. We present a two-stage method for constructing an investment portfolio of common stocks. The method involves the generation of time series representations followed by their subsequent clustering. Our approach utilizes features based on Topological Data Analysis (TDA) for the generation of representations, allowing us to elucidate the topological structure within the data. Experimental results show that our proposed system outperforms other methods. This superior performance is consistent over different time frames, suggesting the viability of TDA as a powerful tool for portfolio selection

Socially responsible investment and market performance: the case of energy and resource companies.

Do financial markets reward the energy and resource companies for adopting socially responsible practices? In this study, we investigate the stock market performance of major international energy and resource firms, classified within the socially responsible investment (SRI) category, from 2005 to 2016. We simulate investments in the portfolios of the SRI energy and resource companies stocks during this 11-year period and we further assess their risk-adjusted performance. The returns of the energy and resource SRI portfolio as a whole were neither consistently superior nor inferior to those of the benchmark indices. However, there exist substantial differences across the individual sub-sectors. The overall results show that markets do not reward or penalize the energy and resource firms for their SRI attitudes. We also find that the crude oil price consistently had a significant influence on the stock returns of the SRI energy and resource companies

Machine Learning-Driven Decision Making based on Financial Time Series

L'abstract è presente nell'allegato / the abstract is in the attachmen

Learning and mining from personal digital archives

Given the explosion of new sensing technologies, data storage has become significantly cheaper and consequently, people increasingly rely on wearable devices to create personal digital archives. Lifelogging is the act of recording aspects of life in digital format for a variety of purposes such as aiding human memory, analysing human lifestyle and diet monitoring. In this dissertation we are concerned with Visual Lifelogging, a form of lifelogging based on the passive capture of photographs by a wearable camera. Cameras, such as Microsoft's SenseCam can record up to 4,000 images per day as well as logging data from several incorporated sensors. Considering the volume, complexity and heterogeneous nature of such data collections, it is a signifcant challenge to interpret and extract knowledge for the practical use of lifeloggers and others. In this dissertation, time series analysis methods have been used to identify and extract useful information from temporal lifelogging images data, without benefit of prior knowledge. We focus, in particular, on three fundamental topics: noise reduction, structure and characterization of the raw data; the detection of multi-scale patterns; and the mining of important, previously unknown repeated patterns in the time series of lifelog image data. Firstly, we show that Detrended Fluctuation Analysis (DFA) highlights the feature of very high correlation in lifelogging image collections. Secondly, we show that study of equal-time Cross-Correlation Matrix demonstrates atypical or non-stationary characteristics in these images. Next, noise reduction in the Cross-Correlation Matrix is addressed by Random Matrix Theory (RMT) before Wavelet multiscaling is used to characterize the `most important' or `unusual' events through analysis of the associated dynamics of the eigenspectrum. A motif discovery technique is explored for detection of recurring and recognizable episodes of an individual's image data. Finally, we apply these motif discovery techniques to two known lifelog data collections, All I Have Seen (AIHS) and NTCIR-12 Lifelog, in order to examine multivariate recurrent patterns of multiple-lifelogging users

A Research on Dimension Reduction Method of Time Series Based on Trend Division

The characteristics of high dimension, complexity and multi granularity of financial time series make it difficult to deal with effectively. In order to solve the problem that the commonly used dimensionality reduction methods cannot reduce the dimensionality of time series with different granularity at the same time, in this paper, a method for dimensionality reduction of time series based on trend division is proposed. This method extracts the extreme value points of time series, identifies the important points in time series quickly and accurately, and compresses them. Experimental results show that, compared with the discrete Fourier transform and wavelet transform, the proposed method can effectively process data of different granularity and different trends on the basis of fully preserving the original information of time series. Moreover, the time complexity is low, the operation is easy, and the proposed method can provide decision support for high-frequency stock trading at the actual level

Bank insolvencies : cross-country experience

Few areas of the world have escaped significant losses from episodes of bank insolvency. Bank insolvency is more costly in the developing world, where losses represent a greater share of income. The authors present data on bank insolvency episodes since the late 1970s. This new database can be used in conjunction with readily available data. Information and insights are presented in seven tables on: a) major bank insolvencies episodes and systemic banking crises; b) main characteristics of banking crises; c) trade terms in crisis countries; d) trade concentration prior to crises; e) restructuring characteristics; f) financial analysis of crisis countries; and g) restructuring outcome in crisis countries. In a companion paper the authors discuss possible preventatives and the tradeoff between safety and soundness versus efficiency. Meanwhile, this initial database suggests further avenues for research. There is a dearth of widely available indicators on bank performance. More attention should be focused on developing indicators that might predict bank insolvency for individual banks and systems as a whole. The authors devise criteria for assessing how governments deal with insolvency and find that countries handle it well.Financial Crisis Management&Restructuring,Banks&Banking Reform,Payment Systems&Infrastructure,Financial Intermediation,Decentralization,Banks&Banking Reform,Financial Crisis Management&Restructuring,Financial Intermediation,Banking Law,Municipal Financial Management