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This paper aims at analyzing the unexpected influence of Financial economics on Physics. The rise of Econophysics, a fundamentally new approach in finance, suggests that the influence between the two disciplines becomes less unilateral than in the past. Methodological debates emerging in Econophysics led physicists to acknowledge that dealing with financial complex systems contributed to a wider modelling of their field. The approach of econophysicists suggests that physicists might try to conceptualize physical phenomena by integrating elements they faced with in Financial economics, and more generally in Economics. Surprisingly, many of econophysicists’ argumentations have some methodological similarities with practices used in Financial economics. This paper analyzes the influence of Financial economics on Physics by discussing three examples: (i) out of equilibrium processes, (ii) signal detection and information filtering, and (iii) the role of information in complex systems. It investigates and illustrates what are the methodological changes generated by Econophysics that explain this new influence of finance on Physics. This paper sheds new light on the way finance and economics can improve physics modelling. With this purpose, this article is going one step further in the dialogue between econophysics and economics. Indeed, by investigating the reciprocal influence between the two fields, this methodological paper identifies some areas for a better cross-fertilisation between the fields.

The persistence analysis of short- and long-term interaction and causality in the international financial markets is a key issue for policy makers and portfolio investors. This paper assesses the dynamic evolution of short-term correlation, long-term cointegration and Error Correction Model (hereafter referred to as ECM)-based long-term Granger causality between each pair of US, UK, and Eurozone stock markets over the period of 1980--2015 using the rolling-window technique. A comparative analysis of pairwise dynamic integration and causality of stock markets, measured in common and domestic currency terms, is conducted to evaluate comprehensively how exchange rate fluctuations affect the time-varying integration among the S&P 500, FTSE 100 and EURO STOXX 50 indices. The results obtained show that the dynamic correlation, cointegration and ECM-based Granger causality vary significantly over the whole sample period. The degree of dynamic cointegration and correlation between pairs of stock markets rises in periods of high volatility and uncertainty, especially under the influence of external and internal economic, financial and political shocks. Meanwhile, weaker and decreasing cointegration and correlation among the three developed stock markets are observed during the recovery periods. Interestingly, the most persistent and significant cointegration among the three developed stock markets exists during the 2007--09 global financial crisis. Finally, the exchange rate fluctuations, also influence the dynamic correlation, cointegration and ECM-based Granger casual relations between all pairs of stock indices, with that influence increasing as the local currency terms are used.

The degree of correlation among stock returns aects the possibility to diversify the risk of investment

According to the leading models in modern finance, the presence of intraday lead-lag relationships between financial assets is negligible in efficient markets. With the advance of technology, however, markets have become more sophisticated. To determine whether this has resulted in an improved market efficiency, we investigate whether statistically significant lagged correlation relationships exist in financial markets. We introduce a numerical method to statistically validate links in correlation-based networks, and employ our method to study lagged correlation networks of equity returns in financial markets. Crucially, our statistical validation of lead-lag relationships accounts for multiple hypothesis testing over all stock pairs. In an analysis of intraday transaction data from the periods 2002-2003 and 2011-2012, we find a striking growth in the networks as we increase the frequency with which we sample returns. We compute how the number of validated links and the magnitude of correlations change with increasing sampling frequency, and compare the results between the two data sets. Finally, we compare topological properties of the directed correlation-based networks from the two periods using the in-degree and out-degree distributions and an analysis of three-node motifs. Our analysis suggests a growth in both the efficiency and instability of financial markets over the past decade.

Interbank markets allow credit institutions to exchange capital for purposes of liquidity management. These markets are among the most liquid markets in the financial system. However, liquidity of interbank markets dropped during the 2007-2008 financial crisis, and such a lack of liquidity influenced the entire economic system. In this paper, we analyze transaction data from the e-MID market which is the only electronic interbank market in the Euro Area and US, over a period of eleven years (1999-2009). We adapt a method developed to detect statistically validated links in a network, in order to reveal preferential trading in a directed network. Preferential trading between banks is detected by comparing empirically observed trading relationships with a null hypothesis that assumes random trading among banks doing a heterogeneous number of transactions. Preferential trading patterns are revealed at time windows of 3-maintenance periods. We show that preferential trading is observed throughout the whole period of analysis and that the number of preferential trading links does not show any significant trend in time, in spite of a decreasing trend in the number of pairs of banks making transactions. We observe that preferential trading connections typically involve large trading volumes. During the crisis, we also observe that transactions occurring between banks with a preferential connection occur at larger interest rates than the complement set — an effect that is not observed before the crisis.

We investigate the trading behavior of a large set of single investors trading the highly liquid Nokia stock over the period 2003-2008 with the aim of determining the relative role of endogenous and exogenous factors that may affect their behavior. As endogenous factors we consider returns and volatility, whereas the exogenous factors we use are the total daily number of news and a semantic variable based on a sentiment analysis of news. Linear regression and partial correlation analysis of data show that different categories of investors are differently correlated to these factors. Governmental and non profit organizations are weakly sensitive to news and returns or volatility, and, typically, they are more correlated with the former than with the latter. Households and companies, on the contrary, are very sensitive to both endogenous and exogenous factors, and volatility and returns are, on average, much more relevant than the number of news and sentiment, respectively. Finally, financial institutions and foreign organizations are intermediate between these two cases, in terms of both the total explanatory power of these factors and their relative importance.

We use statistically validated networks, a recently introduced method to validate links in a bipartite system, to identify clusters of investors trading in a financial market. Specifically, we investigate a special database allowing to track the trading activity of individual investors of the stock Nokia. We find that many statistically detected clusters of investors show a very high degree of synchronization in the time when they decide to trade and in the trading action taken. We investigate the composition of these clusters and we find that several of them show an over-expression of specific categories of investors.

We use a self-averaging measure called Kullback-Leibler divergence to evaluate the performance of four different correlation estimators: Fourier, Pearson, Maximum Likelihood and Hayashi-Yoshida estimator. The study uses simulated transaction prices for a large number of stocks and different data generating mechanisms, including synchronous and non-synchronous transactions, homogeneous and heterogeneous inter-transaction time. Different distributions of stock returns, i.e. multivariate Normal and multivariate Student's t-distribution, are also considered. We show that Fourier and Pearson estimators are equivalent proxies of the `true' correlation matrix within all the settings under analysis, and that both methods are outperformed by the Maximum Likelihood estimator when prices are synchronously sampled and price fluctuations follow a multivariate Student's t-distribution. Finally, we suggest to solve the singularity problem affecting the Hayashi-Yoshida estimator by shrinking the correlation matrix towards either Pearson or Fourier matrices, and provide evidence that the resulting combination leads to an improved estimator with respect to its single components.

We investigate the daily correlation present among market indices of stock exchanges located all over the world in the time period Jan 1996 - Jul 2009. We discover that the correlation among market indices presents both a fast and a slow dynamics. The slow dynamics reflects the development and consolidation of globalization. The fast dynamics is associated with critical events that originate in a specific country or region of the world and rapidly affect the global system. We provide evidence that the short term timescale of correlation among market indices is less than 3 trading months (about 60 trading days). The average values of the non diagonal elements of the correlation matrix, correlation based graphs and the spectral properties of the largest eigenvalues and eigenvectors of the correlation matrix are carrying information about the fast and slow dynamics of correlation of market indices. We introduce a measure of mutual information based on link co-occurrence in networks, in order to detect the fast dynamics of successive changes of correlation based graphs in a quantitative way.

We propose a hypothesis testing procedure to investigate whether the same growth rate distribution is shared by all the firms in a balanced panel or, more generally, whether they share the same functional form for this distribution, without necessarily sharing the same parameters. We apply the test to panels of US and European Union publicly quoted manufacturing firms, both at the sectoral and at the subsectoral NAICS levels. We consider the following null hypotheses about the growth rate distribution of the individual firms: i) an unknown shape common to all firms, with all the firms sharing also the same parameters, or with the firm variance related to its firm size through a scaling relationship, and ii) several functional shapes described the Subbotin family of distributions. Our empirical results indicate that firms do not have a common shape of the growth rate distribution at the sectorial NAICS level, whereas firms may typically be described by the same shape of the distribution at the subsectorial level, even if the specific shape may not be the same for different subsectors.

The use of improved covariance matrix estimators as an alternative to the sample estimator is considered an important approach for enhancing portfolio optimization. Here we empirically compare the performance of 9 improved covariance estimation procedures by using daily returns of 90 highly capitalized US stocks for the period 1997-2007. We find that the usefulness of covariance matrix estimators strongly depends on the ratio between estimation period T and number of stocks N, on the presence or absence of short selling, and on the performance metric considered. When short selling is allowed, several estimation methods achieve a realized risk that is significantly smaller than the one obtained with the sample covariance method. This is particularly true when T/N is close to one. Moreover many estimators reduce the fraction of negative portfolio weights, while little improvement is achieved in the degree of diversification. On the contrary when short selling is not allowed and T>N, the considered methods are unable to outperform the sample covariance in terms of realized risk but can give much more diversified portfolios than the one obtained with the sample covariance. When T

We present a study of the order book data of the London Stock Exchange for five highly liquid stocks traded during the calendar year 2002. Specifically, we study the first passage time of order book prices needed to observe a prescribed price change Delta, the time to fill (TTF) for executed limit orders and the time to cancel (TTC) for canceled ones. We find that the distribution of the first passage time decays asymptotically in time as a power law with an exponent L_FPT ~ 1.5. The median of the same quantity scales as Delta^1.6, which is different from the Delta^2 behavior expected for Brownian motion. The quantities TTF, and TTC are also asymptotically power law distributed with exponents L_TTF = 1.8-2.2 and L_TTC = 1.9-2.4, respectively. For the medians of the time to fill we observe a scaling proportional to Delta^1.4. We outline a simple model, which assumes that prices are characterized by the empirically observed distribution of the first passage time and orders are canceled randomly with lifetimes that are asymptotically power law distributed with a power L_LT. The model predicts L_TTF = L_TTC and we estimate from empirical data L_LT ~ 1.6. We verify that the observed results are not crucially dependent on simplifying assumptions.