1,720,984 research outputs found
COVID-19 firms’ fast innovation reaction analyzed through dynamic capabilities
No full textDuring the COVID-19 emergency, several companies have been able to rapidly reconfigure their innovation and production processes to help support health and other services to cope with the shortage of needed supplies. Using the dynamic capability perspective, this work aims to understand which capabilities enable companies to have fast innovation reactions when they are not pursuing a competitive advantage but they are responding to a societal requirement. A multiple case study approach was used and results reveal that the use of internal and external sources is fundamental. In particular, the Italian companies with a fast innovation reaction to COVID-19 are not the ones that possess all the competencies internally but are rather those able to orchestrate internal and external resources by means of ‘fast’ and flat management. Internal commitment and a culture of continual renewal are essential to rapidly reach a performing product
Information measure for long-range correlated time series: Quantifying horizon dependence in financial markets
No full textMarket dynamics is quantified via the cluster entropy S(τ,n)=∑jPj(τ,n)logPj(τ,n), an information measure with Pjτ,n the probability for the clusters, defined by the intersection between the price series and its moving average with window n, to occur with duration τ. The cluster entropy S(τ,n) is estimated over a broad range of temporal horizons M, for raw and sampled highest-frequency data of US markets. A systematic dependence of S(τ,n) on M emerges in agreement with price dynamics and correlation involving short and long range horizon dependence over multiple temporal scales. A comparison with the price dynamics based on Kullback–Leibler entropy simulations with different representative agent models is also reported
A measure of innovation performance: the Innovation Patent Index
No full textPurpose: The measure of companies' Innovation Performance is fundamental for enhancing the value and decision-making processes of firms. The purpose of this paper is to present a new measure of Innovation Performance, called Innovation Patent Index (IPI), which makes it possible to quantitatively summarize different aspects of firms' innovation. Design/methodology/approach: In order to define the IPI, a secondary source, i.e. patent data, has been used. The five dimensions of IPI, i.e. efficiency, time, diversification, quality and internationalization have been defined both analyzing the literature and applying three different machine learning algorithms (regularized least squares, deep neural networks and decision trees), considering patent forward citations as a proxy of the innovation performance. Findings: Results show that the IPI index is a very useful tool, simple to use and very promptly. In fact, it is possible to get important results without making time consuming analysis with primary sources. It is a tool that can be used by managers, businessmen, policymakers, organizations, patent experts and financiers to evaluate and plan future activities, to enhance the innovation capability, to find financing and to support and improve innovation. Research limitations/implications: Patent data are not widely used in all the sectors. Moreover, the pure number of forward citations is not the only forward looking indicator suggested by the literature. Originality/value: The demand for a useable Innovation Performance tool, as well as the lack of tools able to grasp different aspects of the innovation, highlight the need to develop new instruments. In fact, although previous studies provide several measures of Innovation Performance, these are often difficult for managers to use, do not appreciate different aspects of the innovation and are not forward looking
Inferring multi-period optimal portfolios via detrending moving average cluster entropy
No full textDespite half a century of research, there is still no general agreement about the optimal approach to build a robust multi-period portfolio. We address this question by proposing the detrended cluster entropy approach to estimate the weights of a portfolio of high-frequency market indices. The information measure gathered from the markets produces reliable estimates of the weights at varying temporal horizons. The portfolio exhibits a high level of diversity, robustness and stability as not affected by the drawbacks of traditional mean-variance approaches
Information-based multi-assets artificial stock market with heterogeneous agents
No full textIn this paper, an artificial stock market characterized by heterogeneous and informed
agents is presented. The heterogeneous agents are seen as nodes of sparsely connected
graphs. The agents trade risky assets and are characterized by sentiments, amount of cash
and stocks owned. Agents share information and sentiments by means of interactions
determined by graphs. A central market maker (clearing house mechanism) determines
the price processes for each stock at the intersection of the demand and supply curves.
In this framework, the statistical properties of the univariate and multivariate process
of prices and returns are studied. Importantly, concerning univariate price processes, the
proposed model is able to reproduce unit root, volatility cluster and fat tails of returns. The
multivariate price process exhibits both static and dynamic stylized facts, in particular the
presence of static factors and common trends. Static factors are studied making reference to
the cross-correlation between returns of different stocks, whereas the common trends are
investigated considering the variance–covariance matrix of prices. The proposed approach
allows to endogenously reproduce the multivariate stylized facts
Heterogeneous information-based artificial stock market
No full textIn this paper, an information-based artificial stock market is
considered. The market is populated by heterogeneous agents that are seen as nodes of a sparsely connected graph. Agents trade a risky asset in exchange for cash. Besides the amount of cash and assets owned, each agent is characterized by a sentiment. Moreover, agents share their sentiments by means of interactions that are identified by the graph. Interactions are nidirectional and are supplied with heterogeneous weights. The agent’s trading decision is based on sentiment and, consequently, the stock price process depends on the propagation of information among the interacting agents, on budget constraints and on market feedback. A central market maker (clearing house mechanism) determines the price process at the intersection of the demand and supply curves. Both closed and open-market conditions are considered. The results point out the validity of the proposed model of information exchange among agents and are helpful for understanding the role of information in real markets. Under closed market conditions, the interaction among agents’ sentiments yields a price process that reproduces the main stylized facts of real markets, e.g. the fat tails of the returns
distributions and the clustering of volatility. Within open-market conditions, i.e. with an external cash inflow that results in asset price inflation, also the unitary root stylized fact is reproduced by the artificial stock market. Finally, the effects of model parameters on the properties of the artificial stock market are also
addressed
Information measure for financial time series: quantifying short-term market heterogeneity
Full text linkA well-interpretable measure of information has been recently proposed based on a partition obtained by intersecting a random sequence with its moving average. The partition yields disjoint sets of the sequence, which are then ranked according to their size to form a probability distribution function and finally fed in the expression of the Shannon entropy. In this work, such entropy measure is implemented on the time series of prices and volatilities of six financial markets. The analysis has been performed, on tick-by-tick data sampled every minute for six years of data from 1999 to 2004, for a broad range of moving average windows and volatility horizons. The study shows that the entropy of the volatility series depends on the individual market, while the entropy of the price series is practically invariant for the six markets. Finally, a cumulative information measure - the Market Heterogeneity Index - derived from the integral of the entropy measure, is introduced for obtaining the weights of an Efficient Portfolio. A comparison with the weights obtained by using the Sharpe ratio - a traditional risk diversity measure - is also reported
The role of monetary incentives: Bonus and/or stimulus
No full textIn this paper, the role of the monetary incentives in the employee performance is investigated in the context of Public Administration (PA). In particular, the distribution of monetary incentives among the employees based on the position held, is compared with a merit approach which tends to recognize and reward individual contributions. Starting from a questionnaire, the informal network, which ignores the vertical relation among supervisor and employees, is created and a Centrality Index, based on the employee connections, has been defined and used to proxy the performance of employees. The main goals of the paper are to understand if the two mechanisms of monetary incentive distribution affect the employee performance, to analyze the variables that influence the employee performance, and therefore to identify the role of monetary incentives. The linear regression methodology has been chosen as a tool of analysis. Results show that the distribution of monetary incentives according to merit criteria rewards the employee performance and has positive effects on the employee performance in the short term
Kullback-Leibler cluster entropy to quantify volatility correlation and risk diversity
No full textThe Kullback-Leibler cluster entropy DC[PHQ] is evaluated for the empirical and model probability distributions P and Q of the clusters formed in the realized volatility time series of five assets (S&P500, NASDAQ, DJIA, DAX, and FTSEMIB). The Kullback-Leibler functional DC[PHQ] provides complementary perspectives about the stochastic volatility process compared to the Shannon functional S-C[P]. While D-C[PHQ] is maximum at the short timescales, S-C[P] is maximum at the large timescales leading to complementary optimization criteria tracing back respectively to the maximum and minimum relative entropy evolution principles. The realized volatility is modelled as a time-dependent fractional stochastic process characterized by power-law decaying distributions with positive correlation (H > 1/2). As a case study, we build a multiperiod portfolio on diversity indexes derived from the Kullback-Leibler entropy measure of the realized volatility. The portfolio is robust and exhibits better performances over the horizon periods. A comparison with the portfolio built either according to the uniform distribution or in the framework of the Markowitz theory is also reported
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