Stock Price Evolution Research Articles

Bubble, Critical Zone and the Crash of Royal Ahold

Our analysis of financial data, in terms of super-exponential growth, suggests that the seed of the 2002/03 crisis of the Dutch supermarket giant AHOLD was planted in 1996. It became quite visible in 1999 when the post-bubble destabilization regime was well-developed and acted as the precursor of an inevitable collapse fueled by raising expectations of investors to maintain strong herding pressures. We have adapted Weidlich's theory of opinion formation to describe the formation of buy or sell decisions among investors, based on a competition between the mechanisms of herding and of personal opinion opposing the herd. Among four typical patterns of stock price evolution, we have identified a in the model characterized by a strong sensitivity of the price trajectory on the herding and personal inclination parameters. The critical zone describes the maturation of a systemic instability forewarning of an inevitable crash. Classification and recognition of the spontaneous emergence of patterns of stock market evolution based on Weidlich's theory of complex systems, and in particular our discovery of the post-bubble destabilization regime which acts as a precursor to a subsequent crash or antibubble, not only presents the possibility of developing early warning signals but also suggests to top management ways of dealing with the coming crisis.

Stock Prices and Employment

:In this article, effects of stock price developments on employment are examined. For this purpose, the reduced-form equation of the Keynes-Weintraub-Davidson model for employment is augmented with stock prices. Estimation of the reduced-form equation, employing cointegration methodology and quarterly data for the 1991: 1–2000:4 period, reveals that while stock prices have a positive effect on employment, this effect is marginal compared to that of autonomous expenditures in wage units in determining the path of employment.

The High‐Volume Return Premium

The idea that extreme trading activity contains information about the future evolution of stock prices is investigated. We find that stocks experiencing unusually high (low) trading volume over a day or a week tend to appreciate (depreciate) over the course of the following month. We argue that this high‐volume return premium is consistent with the idea that shocks in the trading activity of a stock affect its visibility, and in turn the subsequent demand and price for that stock. Return autocorrelations, firm announcements, market risk, and liquidity do not seem to explain our results.

On the structure of proper Black-Scholes formulae

The thinking behind the original Black-Scholes formula is criticised on the grounds that it holds out the quite unrealistic prospect of risk-free operation, that it can sacrifice asset maximisation to exact meeting of the contract, and that it restricts investment to those stocks on which an option is being sold. An alternative approach is given, based on a model of risk-averse asset maximisation, which, while meeting these criticisms, gives the option price in the familiar form of a discounted and weighted conditional expectation of the seller's liability at maturity. This evaluation is extended to a completely general stochastic model of stock price evolution; consideration is also given to the possibility of seller's ruin.

On the structure of proper Black-Scholes formulae

The thinking behind the original Black-Scholes formula is criticised on the grounds that it holds out the quite unrealistic prospect of risk-free operation, that it can sacrifice asset maximisation to exact meeting of the contract, and that it restricts investment to those stocks on which an option is being sold. An alternative approach is given, based on a model of risk-averse asset maximisation, which, while meeting these criticisms, gives the option price in the familiar form of a discounted and weighted conditional expectation of the seller's liability at maturity. This evaluation is extended to a completely general stochastic model of stock price evolution; consideration is also given to the possibility of seller's ruin.

Effect of trading momentum and price resistance on stock market dynamics: a Glauber Monte Carlo simulation

A Monte Carlo computer simulation model is presented to study the evolution of stock price and the distribution of price fluctuation. The resistance is described by an elastic energy E e = e· x 2 resulting from the price deviation x from an initial value and the momentum trading by the potential energy E p =− b· y in a price gradient y field. The distribution of price fluctuation ( P( y)) is symmetric and shows a long time tail compatible over some range with a power-law, P( y)∼ y − μ with μ≃4 at e=1.0, b=5 . The volatility auto-correlation function ( c( τ)) is positive for several iterations.

Derivation of one-dimensional hydrodynamic model for stock price evolution

It was proved that balance equations for systems with corpuscular structure can be derived if a kinematic description by piece-wise analytic functions is available (Vamoş et al., Physica A 227 (1996) 81). This article presents a rigorous derivation of an one-dimensional hydrodynamic model for the stock price evolution. The kinematic description is given by a set of time functions describing the evolution of the stock price.

Some aspects of modeling and statistical inference for financial models

Modeling the stock price development as a geometric Brownian motion or, more generally, as a stochastic exponential of a diffusion, requires the use of specific statistical methods. For instance, the observations seldom reach us in the form of a continuous record and we are led to infer about diffusion coefficients from discrete time data. Next, often the classical assumption that the volatility is constant has to be dropped. Instead, a range of various stochastic volatility models is formed by the limiting transition from known volatility models in discrete time towards their continuous time counterparts. These are the main topics of the present survey. It is closed by a quick look beyond the usual Gaussian world in continuous time modeling by allowing a Levy process to be the driving process.

Stochastic modelling for evolution of stock prices by means of functional principal component analysis

The objective of this paper is to apply functional principal component analysis to model and forecast financial prices of the banking in Madrid Stock Market from weekly observations of a random sample of banks. It is well known that direct statistical analysis of stock prices is difficult, therefore principal component prediction models for weekly returns are performed to give appropriate forecasts for prices. Copyright © 1999 John Wiley & Sons, Ltd.

A Genetic Learning Algorithm Applied to Financial Forecasting

An introduction is made about financial theory and several financial forecasting techniques, putting some emphasis in the new role of non-linear techniques. A supervised inductive machine learning approach to the problem of representing, identifying and classifying patterns in the evolution of stock prices is presented. A genetic learning algorithm is described to deal with such a machine learning problem. Preliminary results allowing conclusions about the system's good learning and generalisation capabilities and consequent forecasting performances are shown.

A note on modified lattice approaches to option pricing

where r is the constant risk-free rate of interest, and a is the standard deviation of the underlying stock return. A logarithmic transformation simplifies the above process to: </ log 5, = {r - a^l2)dt ^ adz (2) A binomial approximation for this stochastic process may be developed by assuming that during a short time interval, At, stock prices move from an initial value S, to one of two new values Su or Sd. The probability of moving to Su is assumed to be p, so that the probability of moving to Sd is / - p. These parameters uniquely determine the evolution of stock prices, which in turn determine a unique value of the option on the stock. The parameters p, u, and d cannot be chosen arbitrarily. They must give correct values according to the continuous-time process for the mean and variance of the change in the stock price during the time interval At. This imposes the following two conditions on p, u, and d.

Order characteristics and stock price evolution An application to program trading

This paper is an econometric analysis of the information content of automated orders arriving at the NYSE. The model captures the joint behavior of automated orders and also the return on the stock index future and the futures-spot basis. The results indicate that orders contain information useful in predicting stock returns beyond the information contained in the reported trades. Furthermore, program and index-arbitrage orders contain information beyond that available from the futures return and basis, suggesting that these orders are not merely passive conveyors of common-factor information. Nonprogram, program, and index-arbitrage orders have roughly similar price impacts.

Foreign dependence of individual stock prices: The role of aggregate product market developments

In this paper, we analyze the role of aggregate variables in the transmission from international stock price developments to individual domestic stock prices in a small open stock market. In particular, a theoretical and econometric model is used to determine whether international aggregate product market developments explain observed differences in foreign dependence among individual Belgian stocks. The results suggest that, except for the stocks of some internationally oriented companies, expected international production is not the most important explanatory variable and that an estimation model of aggregate fundamentals explains only part of individual stock price adjustments.