Community detection for correlation matrices

A challenging problem in the study of complex systems is that of resolving, without prior information, the emergent, mesoscopic organization determined by groups of units whose dynamical activity is more strongly correlated internally than with the rest of the system. The existing techniques to filter correlations are not explicitly oriented towards identifying such modules and can suffer from an unavoidable information loss. A promising alternative is that of employing community detection techniques developed in network theory. Unfortunately, this approach has focused predominantly on replacing network data with correlation matrices, a procedure that tends to be intrinsically biased due to its inconsistency with the null hypotheses underlying the existing algorithms. Here we introduce, via a consistent redefinition of null models based on random matrix theory, the appropriate correlation-based counterparts of the most popular community detection techniques. Our methods can filter out both unit-specific noise and system-wide dependencies, and the resulting communities are internally correlated and mutually anti-correlated. We also implement multiresolution and multifrequency approaches revealing hierarchically nested sub-communities with `hard' cores and `soft' peripheries. We apply our techniques to several financial time series and identify mesoscopic groups of stocks which are irreducible to a standard, sectorial taxonomy, detect `soft stocks' that alternate between communities, and discuss implications for portfolio optimization and risk management.Comment: Final version, accepted for publication on PR

Can earnings forecasts be improved by taking into account the forecast bias?

The recent period has highlighted a well-known phenomenon, namely the existence of a positive bias in experts' anticipations. Literature on this subject underlines optimism in the financial analyst community. In this work, our significant contributions are twofold: we provide explanatory bias prediction models which will subsequently allow the calculation of earnings adjusted forecasts, for horizons from 1 to 24 months. We explain the bias using macroeconomic as well as sector and firm specific variables. We obtain some important results. In particular, the macroeconomic variables are statistically significant and their signs are coherent with the intuition. However, we conclude that the microeconomic variables are the main explanatory variables. From the forecast evaluation statistics viewpoints, the adjusted forecasts make it possible quasi-systematically to improve the forecasts of the analysts.Analysts Forecasts

Will the US Economy Recover in 2010? A Minimal Spanning Tree Study

We calculated the cross correlations between the half-hourly times series of the ten Dow Jones US economic sectors over the period February 2000 to August 2008, the two-year intervals 2002--2003, 2004--2005, 2008--2009, and also over 11 segments within the present financial crisis, to construct minimal spanning trees (MSTs) of the US economy at the sector level. In all MSTs, a core-fringe structure is found, with consumer goods, consumer services, and the industrials consistently making up the core, and basic materials, oil and gas, healthcare, telecommunications, and utilities residing predominantly on the fringe. More importantly, we find that the MSTs can be classified into two distinct, statistically robust, topologies: (i) star-like, with the industrials at the center, associated with low-volatility economic growth; and (ii) chain-like, associated with high-volatility economic crisis. Finally, we present statistical evidence, based on the emergence of a star-like MST in Sep 2009, and the MST staying robustly star-like throughout the Greek Debt Crisis, that the US economy is on track to a recovery.Comment: elsarticle class, includes amsmath.sty, graphicx.sty and url.sty. 68 pages, 16 figures, 8 tables. Abridged version of the manuscript presented at the Econophysics Colloquim 2010, incorporating reviewer comment

Corporate Dollar Debt and Depreciations: Much Ado About Nothing?

Much has been written recently about the problems for emerging markets that might result from a mismatch between foreign-currency denominated liabilities and assets (or income flows) denominated in local currency. In particular, several models, developed in the aftermath of financial crises of the late 1990s, suggest that the expansion in the "peso" value of "dollar" liabilities resulting from a devaluation could, via a net worth effect, offset the expansionary competitiveness effect. Assessing which effect dominates is ultimately an empirical matter. In this vein, this paper constructs a new database with accounting information (including the currency composition of liabilities) for over 450 non-financial firms in five Latin American countries. The authors estimate, at the firm level, the reduced-form effect on investment of holding foreign-currency-denominated debt during an exchange-rate realignment. It is consistently found that, contrary to the predicted sign of the net-worth effect, firms holding more dollar debt do not invest less than their counterparts in the aftermath of a depreciation. The paper shows that this result is due to firms matching the currency denomination of their liabilities with the exchange-rate sensitivity of their profits. Because of this matching, the negative balance-sheet effects of a depreciation on firms holding dollar debt are offset by the larger competitiveness gains of these firms.

Forecasting Tourist Arrivals and Supply and Demand Gap Analysis for Hotel Sector in Addis Ababa, Ethiopia

This paper aims to forecast the long term behavior of tourist arrivals and analyze the gap between supply and demand for the hotel/accommodation sector of the city of Addis Ababa. It also intends to provide vital information in regards to the sparse knowledge in the subject of forecasting tourist arrivals in Ethiopia. The research is largely conducted based on the secondary data obtained from the Ministry of Culture and Tourism (MOCT) in the tenth edition of the Policy, Planning, Evaluation and Monitoring Directorate’s bulletin publication on Tourism Statistics (2009-2012),(MOCT, 2013). Theoretical assessments of the requirement of a forecasting process and a critical analysis of available forecasting methods have been carried out to fit the profile of long term tourist arrivals. Based on the assessments and analysis, the Box-Jenkins process was selected. Furthermore, the gap analysis is done using the Funneling Technique. The method has also determined that the annual tourist arrivals for the country in the year 2015 will be 798,157 and for the year of 2020, it is expected to be 1,130,971 and finally in 2025, the annual tourist arrivals are expected to climb to 1,463,743. The use of the Funneling Technique in combination with the Stepped Function Intervention Model establishes a different case scenario (positive, negative and starched intervention) which has then been studied to foresee the relationship between supply and demand of the accommodation sector under different circumstances.Keywords: Forecasting, tourist arrival, X-12-ARIMA, Supply, demand

Are disaggregate data useful for factor analysis in forecasting French GDP?

This paper compares the GDP forecasting performance of alternative factor models based on monthly time series for the French economy. These models are based on static and dynamic principal components. The dynamic principal components are obtained using time and frequency domain methods. The forecasting accuracy is evaluated in two ways for GDP growth. First, we question whether it is more appropriate to use aggregate or disaggregate data (with three disaggregating levels) to extract the factors. Second, we focus on the determination of the number of factors obtained either from various criteria or from a fixed choice.GDP forecasting ; Factor models ; Data aggregation.