Srinivas Raghavendra

Hollywood blockbusters and long-tailed distributions: An empirical study of the popularity of movies

Abstract.Numerical data for all movies released in theaters in the USA during the period 1997-2003 are examined for the distribution of their popularity in terms of (i) the number of weeks they spent in the Top 60 according to the weekend earnings, and (ii) the box-office gross during the opening week, as well as, the total duration for which they were shown in theaters. These distributions show long tails where the most popular movies are located. Like the study of Redner [S. Redner, Eur. Phys. J. B 4, 131 (1998)] on the distribution of citations to individual papers, our results appear to be consistent with a power-law dependence of the rank distribution of gross revenues for the most popular movies with a exponent close to -1/2.

Lack of critical slowing down suggests that financial meltdowns are not critical transitions, yet rising variability could signal systemic risk

Complex systems inspired analysis suggests a hypothesis that financial meltdowns are abrupt critical transitions that occur when the system reaches a tipping point. Theoretical and empirical studies on climatic and ecological dynamical systems have shown that approach to tipping points is preceded by a generic phenomenon called critical slowing down, i.e. an increasingly slow response of the system to perturbations. Therefore, it has been suggested that critical slowing down may be used as an early warning signal of imminent critical transitions. Whether financial markets exhibit critical slowing down prior to meltdowns remains unclear. Here, our analysis reveals that three major US (Dow Jones Index, S&P 500 and NASDAQ) and two European markets (DAX and FTSE) did not exhibit critical slowing down prior to major financial crashes over the last century. However, all markets showed strong trends of rising variability, quantified by time series variance and spectral function at low frequencies, prior to crashes. These results suggest that financial crashes are not critical transitions that occur in the vicinity of a tipping point. Using a simple model, we argue that financial crashes are likely to be stochastic transitions which can occur even when the system is far away from the tipping point. Specifically, we show that a gradually increasing strength of stochastic perturbations may have caused to abrupt transitions in the financial markets. Broadly, our results highlight the importance of stochastically driven abrupt transitions in real world scenarios. Our study offers rising variability as a precursor of financial meltdowns albeit with a limitation that they may signal false alarms.

On the Systemic Fragility of Finance‐Led Growth

The paper sets up a model of economic crisis by investigating the role played by movement in asset price as a driver of the dynamic interaction between the real and the financial sectors. Such movement influences income determination in the real economy in the short period through aggregate demand leading to the emergence of two macroeconomic regimes. A short period flow model, underpinned by the stock flow consistent accounting framework, is developed to formalize the dynamics of interaction between real and financial sectors mediated by movement in asset price, generates bistability, abrupt crashes, and systemic fragility in the macroeconomic regimes.

Testing for Nonlinear Dependence in the Credit Default Swap Market

The objective of this paper is to test for nonlinear dependence in the GARCH residuals of a number of asset classes using nonlinear dynamic tools. The equity and bond market samples appear to be independent once GARCH has been applied, but evidence of nonlinear dependence in the CDS GARCH residuals is found. The sensitivity of this result is analysed by changing the specifications of the GARCH model, and the robustness of the result is verified by applying additional tests of nonlinearity. Evidence of nonlinear dependence in the GARCH residuals of CDS contracts has implications for the accurate modeling of the marginal distribution of the CDS market, for pricing of CDS contracts, for estimating risk neutral default probabilities in the bond market, as well as for bond market hedging strategies.

A Linear and Nonlinear Review of the Arbitrage-Free Parity Theory for the CDS and Bond Markets

The arbitrage-free parity theory states that there is equivalence between credit default swap (CDS) spreads and bond market spreads in equilibrium. We show that the testing of this theory through the application of linear Gaussian bivariate modeling will lead to misleading results for CDS and bond spreads, and that linear stochastic modeling is not appropriate for CDS spreads. We propose the nonlinear and nonparametric dynamic tools of cross recurrence plots and cross recurrence plot measures to evaluate the arbitrage-free parity theory. We conclude that convergence is nonmean reverting and varying through time and across countries. This finding refutes the arbitrage-free parity theory. We also conclude that the probability to arbitrage will be affected by country and time-specific factors such as the expectation for country-specific government intervention. We propose that this methodology could be used by policy markets to supervise arbitrage activity and to influence policy making.

Algorithmic Trading on an Artificial Stock Market

This work introduces algorithmic trading on artificial stock markets and describes past and existing approaches. A proposed framework of the artificial stock market approach is presented, together with the used agent types. Then the simulation results’ analyses are discussed. Conclusions and future work directions are presented, showing where the MACD algorithm and some rules can be used. The human behavior influence over the market is highlighted.

Market Polarization in Presence of Individual Choice Volatility

Financial markets are subject to long periods of polarized behavior, such as bull-market or bear-market phases, in which the vast majority of market participants seem to almost exclusively choose one action (between buying or selling) over the other. From the point of view of conventional economic theory, such events are thought to reflect the arrival of “external news” that justifies the observed behavior. However, empirical observations of the events leading up to such market phases, as well events occurring during the lifetime of such a phase, have often failed to find significant correlation between news from outside the market and the behavior of the agents comprising the market. In this paper, we explore the alternative hypothesis that the occurrence of such market polarizations are due to interactions amongst the agents in the market, and not due to any influence external to it. In particular, we present a model where the market (i.e., the aggregate behavior of all the agents) is observed to become polarized even though individual agents regularly change their actions (buy or sell) on a time-scale much shorter than that of the market polarization phase.

The Macroeconomic Loss due to Violence Against Women: The Case of Vietnam

ABSTRACT Violence against women (VAW) is now acknowledged as a global problem with substantial economic costs. However, the current estimates of costs in the literature provide the aggregate loss of income, but not the macroeconomic loss in terms of output and demand insofar as they fail to consider the structural interlinkages of the economy. Focusing on Vietnam, this study proposes an approach based on the social accounting matrix (SAM) to estimate the macroeconomic loss due to violence. Using Vietnam’s 2011 SAM, the study estimates the income and multiplier loss due to VAW. From a policy point of view, the study argues that the macroeconomic loss due to VAW renders a permanent invisible leakage to the circular flow that can potentially destabilize, weaken, or neutralize the positive gains from government expenditure on welfare programs.

Stocks scanner evaluator for stocks or options

This paper introduces a stock scanner evaluator for stocks and options. In the presented work the scanner picks from thousands of stocks the most suitable stocks for an options or stocks investor. The proposed stocks scanner evaluator suggests the stocks that have the largest positive near future change (for purchasing stocks or calls) and the stocks that have the largest negative near future change (for purchasing puts). The scanner uses a neural network to rank the stocks and the neural network is trained using parallel genetic algorithm. Related work is provided as well as model framework, neural network and parallel genetic algorithm, results testing and evaluation together with future work.