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We examine the stability of market prices for 35 technology and 35 industrial stocks for the period December 31, 1993 to October 31, 2002. A phase portrait plot of the…
We examine the stability of market prices for 35 technology and 35 industrial stocks for the period December 31, 1993 to October 31, 2002. A phase portrait plot of the detrended log prices and de‐meaned returns of the two sectors shows a chaotic pattern in the stock prices indicating the presence of nonlinearity. However, when we compute the Lyapunov exponents, negative values are obtained. This shows that the price fluctuations for the 70 stocks result primarily from diffusion processes rather than from nonlinear dynamics. We evaluate forecast errors from a naïve model, a neural network, and ARMA models and find that the forecast errors are correlated with average changes in closed‐end fund discounts and other sentiment indexes. These results support an investor sentiment explanation for the closed‐end fund puzzle and behavioral theories of investor overreaction.
The purpose of this paper is to address the issue of optimal management of ecosystems by developing a dynamic model of strategic behavior by users/communities of an ecosystem such as a lake, which is subject to pollution resulting from the users. More specifically, it builds a model of two ecosystems that are spatially connected.
The paper uses the techniques of optimal control theory and game theory.
The paper uncovers sufficient conditions under which the analysis of the dynamic game can be converted to an optimal problem for a pseudo authority. It is shown that if the discount rate on the future is high enough relative to ecological self‐restoration parameters then multiple stable states appear. In this case, if the pollution level is high enough it is too costly in terms of what must be given up today to restore the damaged system. By using computational methods, the paper evaluates the relative strengths of lack of coordination, strength of ecosystem self‐cleaning forces, size of discount rates, etc.
The methodology as well as findings can help to devise an optimal management strategy over time for ecosystems.
This paper is focused on two particular issues related to the stochastic structure of stock prices: linear long‐memory and nonlinearity.
In this paper chaos is viewed as an alternative approach to modeling complex and random appearing behavior. The spatial (static) characteristics of weekly returns and…
In this paper chaos is viewed as an alternative approach to modeling complex and random appearing behavior. The spatial (static) characteristics of weekly returns and price levels for eleven International Indices are quantified. We find evidence that all countries exhibit similar static characteristics. Evidence presented supports the examination of price series instead of returns.
The equity premium of the S&P 500 index is explained in this paper by several variables that can be grouped into fundamental, behavioral, and macroeconomic factors. We…
The equity premium of the S&P 500 index is explained in this paper by several variables that can be grouped into fundamental, behavioral, and macroeconomic factors. We hypothesize that the statistical significance of these variables changes across economic regimes. The three regimes we consider are the low‐volatility, medium‐volatility, and high‐volatility regimes in contrast to previous studies that do not differentiate across economic regimes. By using the three‐state Markov switching regime econometric methodology, we confirm that the statistical significance of the independent variables representing fundamentals, macroeconomic conditions, and a behavioral variable changes across economic regimes. Our findings offer an improved understanding of what moves the equity premium across economic regimes than what we can learn from single‐equation estimation. Our results also confirm the significance of momentum as a behavioral variable across all economic regimes