Earthquake Forecasting Using Neural Networks: Results and Future Work

In 1994, a new earthquake forecasting method was developed, that integrated in a neural network several forecasting tools that had been originally developed for financial analysis. This method was tested with the seismicity of the Azores, predicting the July, 1998, and the January, 2004, earthquakes, albeit within very wide time and location windows. Work is beginning to integrate physical precursors in the neural network, in order to narrow the forecasting windows     

Role of noise in a market model with stochastic volatility

We study a generalization of the Heston model, which consists of two coupled stochastic differential equations, one for the stock price and the other one for the volatility. We consider a cubic nonlinearity in the first equation and a correlation between the two Wiener processes, which model the two white noise sources. This model can be useful to describe the market dynamics characterized by different regimes corresponding to normal and extreme days. We analyze the effect of the noise on the statistical properties of the escape time with reference to the noise enhanced stability (NES) phenomenon, that is the noise induced enhancement of the lifetime of a metastable state. We observe NES effect in our model with stochastic volatility. We investigate the role of the correlation between the two noise sources on the NES effect.     

规范场理论和金融市场模型

文章介绍近年理论物理在金融学市场建模中的应用的一个新方向，与一般的数学建模不同，它是应用几何结构的模型，建立在规范场的物理思想和纤维丛的几何结构的基础上，文章介绍了规范场的物理概念思想原则，也介绍纤维丛数学概念和几何结构，然后说明规范场理论与纤维丛理论的相结合，成为与金融市场概念和运作相匹配的市场模型，举出这一模型成功引导出金融市场产品定价的Black—Scholes方程和公式。文章对象以物理学者为主，对于理论经济学、金融理论和系统科学的读者来说可略去数学推导。     

Asymptotic pricing in large financial markets

The problem of hedging and pricing sequences of contingent claims in large financial markets is studied. Connection between asymptotic arbitrage and behavior of the α-quantile price is shown. The large Black–Scholes model is carefully examined.      

Leptokurtic portfolio theory

The question of optimal portfolio is addressed. The conventional Markowitz portfolio optimisation is discussed and the shortcomings due to non-Gaussian security returns are outlined. A method is proposed to minimise the likelihood of extreme non-Gaussian drawdowns of the portfolio value. The theory is called Leptokurtic, because it minimises the effects from “fat tails” of returns. The leptokurtic portfolio theory provides an optimal portfolio for investors, who define their risk-aversion as unwillingness to experience sharp drawdowns in asset prices. Two types of risks in asset returns are defined: a fluctuation risk, that has Gaussian distribution, and a drawdown risk, that deals with distribution tails. These risks are quantitatively measured by defining the “noise kernel” — an ellipsoidal cloud of points in the space of asset returns. The size of the ellipse is controlled with the threshold parameter: the larger the threshold parameter, the larger return are accepted for investors as normal fluctuations. The return vectors falling into the kernel are used for calculation of fluctuation risk. Analogously, the data points falling outside the kernel are used for the calculation of drawdown risks. As a result the portfolio optimisation problem becomes three-dimensional: in addition to the return, there are two types of risks involved. Optimal portfolio for drawdown-averse investors is the portfolio minimising variance outside the noise kernel. The theory has been tested with MSCI North America, Europe and Pacific total return stock indices.     

上市公司远期财务危机预测研究

尽管各界对预测企业远期财务危机有着很大需求,该领域的研究一直被这个问题所困惑:究竟哪些指标含有预测企业远期财务危机的重要信息?本文利用财务报表时间序列数据和贝叶斯统计方法设计出了一个这样的指标,用该指标建立的上市公司财务危机预模型具有较高的远期预测正确率。     

新技术设备租赁中最佳租赁契约长度安排

随着现代技术发展的日新月异，设备的更新也就日益成为企业关注的问题。金融租赁这种现代金融创新的产物，以其灵活、快捷的优势成为了现代企业重要的投资方式。如何选择租赁契约长度也就成为了企业面临的问题。本试图通过一个简单模型推导出租赁契约的最佳长度，并导出最佳契约长度与各个相关变量的关系。     

Simultaneity and non‐linear variability in financial markets: simulation and forecasting

Non‐linear variability in financial markets can emerge from several mechanisms, including simultaneity and time‐varying coefficients. In simultaneous equation systems, the reduced‐form coefficients that determine the behaviour of jointly dependent variables are products and ratios of the original structural coefficients. If the coefficients are stochastic, the resulting multiplicative interactions will result in high degrees of non‐linearity. Processes generated in this way will scale as fractals: they will exhibit intermittent outliers and scaling symmetries, i.e. proportionality relationships between fluctuations at different separation distances. A model is specified in which both the exchange rate itself and the exchange rate residual exhibit simultaneity. The exchange rate depends on other exchange rates, while the residual depends on the other residuals. The model is then simulated using embedding noise from a t‐distribution. The simulations replicate the observed properties of exchange rates, heavy‐tailed distributions and long memory in the variance. A forecasting algorithm is specified in two stages. The first stage is a model for the actual process. In the second stage the residuals are modelled as a function of the predicted rate of change. The first and second stage models are then combined. This algorithm exploits the scaling symmetry: the residual is proportional to the predicted rate of change at separation distances corresponding to the forecast horizon. The procedure is tested empirically on three exchange rates. At a daily frequency and a 1‐day forecast horizon, two‐stage models reduce the forecast error by one fourth. At a 5‐day horizon, the improvement is 10–15 percent. At a weekly frequency, the improvement at the 1‐week horizon is on the order of 30–40 percent. Copyright © 2006 John Wiley & Sons, Ltd.     

An econophysics approach to analyse uncertainty in financial markets: an application to the Portuguese stock market

In recent years there has been a closer interrelationship between several scientific areas trying to obtain a more realistic and rich explanation of the natural and social phenomena. Among these it should be emphasized the increasing interrelationship between physics and financial theory. In this field the analysis of uncertainty, which is crucial in financial analysis, can be made using measures of physics statistics and information theory, namely the Shannon entropy. One advantage of this approach is that the entropy is a more general measure than the variance, since it accounts for higher order moments of a probability distribution function. An empirical application was made using data collected from the Portuguese Stock Market.     

财务危机预警中财务比率的选择研究

本文采用定性与定量相结合的方法对财务危机预警中财务比率的选择进行了研究 .首先根据初选原则对财务比率进行了初步选择 ,然后提出了一种基于神经网络的变量选择方法 ,对初选的财务比率进行定量筛选并进行了实证研究 ,结果表明 ,这是一种科学、通用的变量选择方法