Narrow Results

It is a common belief that the behavior of shareholders depends upon the direction of price fluctuations: if prices increase they buy, if prices decrease they sell. That belief, however, is more based on "common sense" than on facts. In this paper, we present evidence for a specific class of shareholders which shows that the actual behavior of shareholders can be markedly different. For instance, they may continue to buy despite a prolonged fall in prices or they may sell even though prices climb. A closer analysis shows that a substantial proportion of investors are more influenced by the "general social climate" than by actual price changes. The percentage of speculative investors who optimize their portfolio on a monthly basis can be estimated and turns out to be about 5 to 10%. The results presented in this paper can be of usefulness in order to test the assumptions or the results of market simulations and models.

The price multiplier effect provides precious insight into the behavior of investors during episodes of speculative trading. It tells us that the higher the price of an asset (within a set of similar assets), the more its price is likely to increase during the upgoing phase of a speculative price peak. In short, instead of being risk averse, as is often assumed, investors rather seem to be "risk prone". While this effect is known to hold for several sorts of assets, it has not yet been possible to test it for stocks because the price of one share has no intrinsic significance, which means that one cannot say that stock A is more expensive than stock B on the basis of its price. In this paper we show that the price-dividend ratio gives a good basis for assessing the price of stocks in an intrinsic way. When this alternative measure is used instead, it turns out that the price multiplier effect also holds for stocks, at least if one concentrates on samples of companies which are sufficiently homogeneous.

A new stochastic stock price model of stock markets based on the contact process of the statistical physics systems is presented in this paper, where the contact model is a continuous time Markov process, one interpretation of this model is as a model for the spread of an infection. Through this model, the statistical properties of Shanghai Stock Exchange (SSE) and Shenzhen Stock Exchange (SZSE) are studied. In the present paper, the data of SSE Composite Index and the data of SZSE Component Index are analyzed, and the corresponding simulation is made by the computer computation. Further, we investigate the statistical properties, fat-tail phenomena, the power-law distributions, and the long memory of returns for these indices. The techniques of skewness–kurtosis test, Kolmogorov–Smirnov test, and R/S analysis are applied to study the fluctuation characters of the stock price returns.

This article examines the relationship between exchange rates and stock prices in eight Asian countries. We test for cointegration and Granger causality for both individual countries using the Gregory and Hansen cointegration test that accommodates a structural break in the cointegrating vector, and for a panel using the Westerlund panel Lagrange multiplier (LM) cointegration test that allows for multiple structural breaks in the level of the individual cointegrating equations. Our results for individual countries suggest that the only country for which exchange rates and stock prices are cointegrated over the entire period is Korea where there is a weak long-run unidirectional Granger causality running from exchange rates to stock prices. Employing the panel LM cointegration test with multiple structural breaks, we find that exchange rates and stock prices are not cointegrated. We conclude that for the eight Asian countries, exchange rates and stock prices primarily have only a contemporaneous effect on each other that is reflected in the short-run intertemporal comovements between these financial variables.

This study empirically investigates the dynamic effects of Japan’s quantitative easing (QE) policy on industry-specific business activity using a time-varying parameter model and monthly data spanning 2001–2006. This model yields more reliable and precise results than earlier fixed effects models using quarterly data. The first major finding is that the effect of QE on yen–dollar exchange rates varied during the sampled period and is most evident in the final phases, whereas its effect on stock prices persisted almost continuously. Second, QE’s effect on Japan’s real economy — i.e., on industrial production — varies by industry and over time. Most notably, QE raised production via yen–dollar depreciation in the machinery sector (e.g., general and transport machinery) and the sector including chemicals, non-ferrous metals and iron and steel during its latter phases. This study is the first to investigate how unconventional monetary policy influences Japan’s real economy by analyzing the real exchange rate during the second half of QE implementation in Japan.

This study offers further evidence of the holiday effect on excess stock returns and volatility, and additional insights into its impact on the amusement park and attractions industry in the United States. The generalized auto-regressive conditional heteroscedasticity (GARCH) model and dummy variables are adopted to investigate pre- and post-holiday excess returns and volatility. Empirical results suggest the following: (1) both excess returns and excess volatility happen more frequently in pre-holidays than post-holidays; (2) the frequency of excess volatility is higher than excess returns for the holiday effect; and (3) nearly all significant excess volatilities are negative. We, therefore, conclude that the holiday effect exists in publicly traded amusement and theme parks.

The effect of outliers on estimation of the fractal dimension of experimental chaotic and stock market stochastic data series is investigated. The results indicate that influential observations of a magnitude of mean ±5 standard deviations can lead to a distortion of fractal dimension estimations by as much as 40% for short (e.g. 500 observations) time series data. Moreover, the box dimension estimation method is more sensitive to outliers than information and correlation dimension estimation methods and the effect of outliers decreases as the number of observations increases. Application of outlier adjustment to the stock prices of 60 companies of the Dow Jones Industrial Index reveals that the effect of outliers is critical in estimating the fractal dimension. The fractal dimension has applications in risk analysis for financial markets that can be affected by outliers.

We perform a phenomenological study of stock price fluctuations of individual companies. We systematically analyze two different databases covering securities from the three major US stock markets. We consider (i) the trades and quotes (TAQ) database, for which we analyze 40 million records for 1000 US companies for the 2-year period 1994–95, and (ii) the Center for Research and Security Prices (CRSP) database, for which we analyze 35 million daily records for approximately 16,000 companies in the 35-year period 1962–96. We study the probability distribution of returns over varying time scales — from 5 min up to 4 years. For time scales from 5 min up to approximately 16 days, we find that the tails of the distributions can be well described by a power-law decay, characterized by an exponent α ≈ 3 — well outside the stable Lévy regime 0 < α < 2. For time scales greater than 16 days, we observe results consistent with a slow convergence to Gaussian behavior.

We propose a class of Markovian agent based models for the time evolution of a share price in an interactive market. The models rely on a microscopic description of a market of buyers and sellers who change their opinion about the stock value in a stochastic way. The actual price is determined in realistic way by matching (clearing) offers until no further transactions can be performed. Some analytic results for simple special cases are presented. We also propose basic interaction mechanisms and show in simulations that these already reproduce certain particular features of prices in real stock markets.

This paper studies the dynamic relationship between stock prices and exchange rates in the Brazilian economy. We use recently developed unit root and cointegration tests, which allow endogenous breaks, to test for a long run relationship between these variables. We performed linear, and nonlinear causality tests after considering both volatility and linear dependence. We found that there is no long run relationship, but there is linear Granger causality from stock prices to exchange rates, in line with the portfolio approach: stock prices lead exchange rates with a negative correlation. Furthermore, we found evidence of nonlinear Granger causality from exchange rates to stock prices, in line with the traditional approach: exchange rates lead stock prices. We believe these findings have practical applications for international investors and in the design of exchange rate policies.

This paper applies univariate and panel Lagrange Multiplier (LM) unit root tests with one and two structural breaks to examine the random walk hypothesis for stock prices in eight Asian countries. The results from the univariate LM unit root tests and panel LM unit root test with one structural break suggest that stock prices in each country is characterized by a random walk, but the findings from the panel LM unit root test with two structural breaks suggest that stock prices in the eight countries are mean reverting.

The main purpose of this paper is to provide evidence on some of the standard models of accounting earnings and returns relations mainly collected through the literature. Standard models such as earnings level and earnings changes, among others, have been investigated in this study. Models that correspond better to the data drawn from the Athens Stock Exchange have been selected. Models I, II, V, VII and IX have statistically significant coefficients of explanatory variables. In addition, model II with the MSE (minimum value of squared residuals) loss function in ARIMAX (2,0,2) is prevalent. Models that include prior earnings in various forms using levels, changes in price and changes in earnings, change in price to beginning price, lagged parameters and differentiated price models have statistically significant explanatory power.

In this paper, we develop a new constrained artificial-neural-network (ANN) formulation and the associated learning algorithm for predicting stock prices, a difficult time-series prediction problem. We characterize daily stock prices as a noisy non-stationary time series and identify its predictable low-frequency components. Using a recurrent finite-impulse-response ANN, we formulate the learning problem as a constrained optimization problem, develop constraints for incorporating cross validations, and solve the learning problem using algorithms based on the theory of extended saddle points for nonlinear constrained optimization. Finally, we illustrate our prediction results on ten stock-price time series. Our main contributions in this paper are the channel-specific low-pass filtering of noisy time series obtained by wavelet decomposition, the transformation of the low-pass signals to improve their stationarity, and the incorporation of constraints on cross validation that can improve the accuracy of predictions. Our experimental results demonstrate good prediction accuracy and annual returns.

We examine the impact of pandemics on equilibrium in an integrated epidemic-economy model with production. Two types of technologies are considered: a neo-classical technology and one capturing the notion of time-to-produce. The impact of a shelter-in-place policy with and without layoffs is studied. The paper documents adjustments in interest rate, market price of risk, stock market and real wage as the epidemic propagates. It shows the qualitative effects of a shelter-in-place policy in the model are consistent with the patterns displayed by the stock market and real wage during the COVID-19 outbreak. Puzzles emerging from the analysis are outlined.

The potential cointegrating relationship between stock prices and economic activity suggested by financial and economic theory is examined. It is found that the commonly employed tests of Engle and Granger (1987) and Johansen (1988) fail to detect cointegration between stock prices and industrial production for a long span of US data. In recognition of factors which may result in a failure to detect a genuine cointegrating relationship, the analysis is extended to consider higher-powered cointegration tests, tests which allow for structural change in the cointegrating relationship and tests of asymmetric cointegration. However, despite considering a range of tests, no evidence of cointegration is detected. The results therefore do not support the predictions of financial and economic theory.

This paper uses an artificial neural network (ANN) model to forecast broad dividends, and computes fundamental stock prices with a stochastic discount factor (SDF). Broad dividends are used because they measure payouts to shareholders more accurately. Since nonlinearity is found in broad dividends, an ANN process is fit to these. Empirical results show that the consumption-based broad dividends model with ANN forecasting procedure predicts fundamental prices better, compared with models using linear dividends process, narrow dividends, or a constant discount factor. Nonetheless, actual stock prices remain largely detached from fundamental prices. Deviations between actual and fundamental prices, positive or negative, are found to coincide with business cycles, a result not consistent with alternative models considered in the paper.

Let $p$, $p(I)$, $\varrho$ and $p(M)$ denote, respectively, the current stock price, the future stock price that is conditioned on information, the minimum stock market tick size and the realized future stock price. Formal theoretical proofs in this study show modeling of stock returns in continuous time induces stock returns that have parameterization as gambles over lotteries. Stock returns have parameterization as gambles because in the presence of fairness of formation of $p<[p+\varrho ]<p(I)$, regardless arrival of liquidity and speculative trades feasibly induces $p(M)<p<p(I)$. Evolution of ‘(conditional) trade imbalances’ as random walks is shown to be a necessary and sufficient condition for parameterization of stock returns as gambles over lotteries. Suppose, on the contrary, a resort to modeling of stock returns in discrete time. The formal theory arrives at two dichotomous sufficiency conditions, which predict directionality and sizes of price changes, and facilitate evolution of stock returns as random walks. In presence of the two dichotomous conditions, fairness of formation of $p<[p+\varrho ]<p(I)$ necessarily induces, regardless of arrival of liquidity and speculative trades, $p(M)\ge [p+\varrho ]$. Risk is parameterized by $p_t(M)>p_t(I)>[p_t+\varrho ]$, because all else constant, an inversion of the perturbing conditionally positive trade imbalance induces $p_{t+1}(M)<p_t(M)$. Whereas then, $p_{t+1}(M)<p_t(M)$ has parameterization as ‘materialization of risk’, always, it is $p_t(M)>p_t(I)$ that is statistic for risk; risk, as such is well parameterized, that is, does not coincide with its materialization (note that whereas volatility is statistic for risk, it is not a statistic for materialization of risk; a statistic for risk necessarily is robust to non-materialization of risk). Given modeling in continuous time does not facilitate either of the two sufficiency conditions, always, risk has parameterization as the probability that $p_{t+1}(M)<p_t(M)$. Since risk, as such coincides qualitatively with its materialization, it is not well parameterized. Given study findings parameterize general equilibrium, formal theoretical predictions have characterization as axiomatic statements, as opposed to propositional (parameter-dependent) statements.

This study investigates the dynamic relationship between exchange rates, oil prices, and stock prices in the Indian market, considering bearish, bullish, and neutral market states. By utilizing the Quantile ARDL method, we explore long- and short-run relationships in differing market states. The variations in sensitivities of oil prices and exchange rates with stock prices across diverse quantiles reflect significant variations in the Indian equity markets. Importantly, our study provides actionable insights for policymakers and stakeholders within India, empowering them with specific strategies for managing currency, stock prices, and oil prices under diverse market states. These insights are not just theoretical but directly applicable to real-world economic and financial decision-making, enhancing the ability of policymakers and stakeholders to navigate the complex Indian market.

This paper examines short-run determinants of the Indonesian rupiah/USD exchange rate based on a simultaneous-equation model. Based on a reduced form equation and the EGARCH method, the paper finds that the IDR/USD exchange rate is positively associated with the real 10-year U.S. government bond yield, real GDP in Indonesia, the stock price in the U.S. and the expected exchange rate and negatively influenced by the real deposit rate in Indonesia, real GDP in the U.S., and the stock price in Indonesia. The Asian financial crisis has shifted the mean exchange rate by 4,900.857.

Based on a sample during 1978–2014, this paper finds that India’s real GDP has a positive relationship with real depreciation during 1978–2002, the government debt/GDP ratio, the real stock price, the growth rate of U.S. real GDP, and a negative relationship with real depreciation during 2003–2014, the real lending rate and the expected inflation rate. Therefore, the stage of economic development may play an important role in deciding whether real depreciation or real appreciation may promote economic growth.