Adaptive trading rules and dynamic market disequilibrium

Kongrithi, Weerasak; Isvilanonda, Somporn

doi:10.1080/00036848400000001

Cited by 10 publications

(3 citation statements)

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“…Murphy [30] suggests that adaptive control processes be used in economic processes that are in stationary or nonstationary disequilibrium. Studies by Martell and Philippatos [27] with commodity price data and by Nawrocki [32] with stock market price data found that adaptive trading rules outperform nonadaptive trading rules, thus supporting the stationary disequilibrium model. However, neither adaptive trading rule could outperform a buy-and-hold consistently.…”

Section: Implications For Future Researchmentioning

confidence: 97%

Entropy, Bifurcation and Dynamic Market Disequilibrium

Nawrocki

1984

The Financial Review

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The study of security market behavior in recent years has focused on the dynamic process of disseminating information throughout the marketplace. Lags in information dissemination and the subsequent market disequilibrium are stressed by many studies in the finance literature [12,3,5,9,10,19,20,29]. While these studies are exciting because of their stress on disequilibrium processes, they are constrained by a standard assumption of stationary stochastic processes [19, footnote 2].The relaxation of this assumption should help to enrich our understanding of disequilibrium processes. Work with chemical processes by Nicolis and Prigogine [33] has relaxed the stationary stochastic process assumption, thereby increasing our knowledge of the evolution of dynamic systems. The major tools used by Nicolis and Prigogine are entropy theory and bifurcation theory. Bifurcation theory is concerned with the restructuring of systems in order to ensure their survival. A bifurcation point is the threshold where the system is restructured. Through the use of bifurcation theory, entropy models can model dynamic processes that change their information structure or their physical structure in response to stress created by disequilibrium conditions, thereby providing a description of nonstationary processes.The application of evolutionary theory to economic processes is strongly defended by Boulding [6,7]. In addition, Majthay [26] provides a strong case for using bifurcation theory instead of the currently popular catastrophe theory [23,38,39]. However, while the use of entropy theory and bifurcation theory is known in the finance and economics literature, [6,12,17,24,26,30,36], these theories have not been used to describe security market disequilibrium.The purposes of this paper are to survey the work in financial market disequilibrium and to describe an application of entropy and bifurcation theories to financial market disequilibrium.The structure of the paper is as follows. First, current models of financial market disequilibrium are described. Next, the work of Nicolis and Prigogine [33] with entropy arid bifurcation models is discussed, along with the application of this work to financial market disequilibrium. Finally, some implications of the entropy model are explored.

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Section: Implications For Future Researchmentioning

confidence: 97%

Entropy, Bifurcation and Dynamic Market Disequilibrium

Nawrocki

1984

The Financial Review

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“…However, over a longer time period, markets may slowly adjust to new information. Price trends may be the result of the adjustment process over time, caused by the information shocks, which in turn makes trading returns profitable (Beja and Goldman, 1980;Nawrocki, 1984).…”

Section: Trading Return Levelsmentioning

confidence: 99%

Commodity futures trading performance using neural network models versus ARIMA models

Ntungo

1998

J. Fut. Mark.

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“…Black (1976) shows that an uneven flow of information impedes market reaction, thus creating the potential for profitable trading. Nawrocki (1984) argues that the existence of noncontinuous information flows in combination with institutional impediments, such as transaction costs and information acquisition costs, cause disequilibrium in prices and, thus, opportunities for profitable trading.…”

Section: Efficient Market Hypothesismentioning

confidence: 99%

The soybean complex spread: An examination of market efficiency from the viewpoint of a production process

Johnson

Zulauf

Irwin

et al. 1991

Journal of Futures Markets

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arket efficiency is an important topic of research in the academic community.M Futures market efficiency means that futures prices accurately incorporate all currently known information. Therefore, current futures prices are unbiased forecasts of subsequent cash and/or futures prices. Futhermore, traders cannot earn abnormal returns (see Fama (1970)). Previous studies have examined the efficiency of futures markets from the viewpoint of whether futures prices follow a random walk; whether futures prices are efficient forecasts of subsequent prices; and whether profits can be generated from trading rules.This research tests for trading profits by applying a profit margin trading rule to the intercommodity spread of soybeans, soyoil, and soymeal (i.e., the soy complex). The trading rule applied in this study has been tested extensively in hedging studies. The rule calls for initiating trades when a pre-specified level of implied profit exists. Implied profit is derived from currently-quoted futures prices for the output of the production process and estimated production costs.One approach used to estimate production costs employs cash prices, historical prices, and/or imputed opportunity costs for variable and/or fixed inputs. Hedging studies employing this approach usually hedge only the output price (for examples of crop studies see Frank et ul. (1989); soybeans;and Wood, et al., (1989); cotton; for examples of livestock studies see Holland, et ul. (1972); fed cattle; Leuthold and Pe-

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Adaptive trading rules and dynamic market disequilibrium

Cited by 10 publications

References 25 publications

Entropy, Bifurcation and Dynamic Market Disequilibrium

Entropy, Bifurcation and Dynamic Market Disequilibrium

Commodity futures trading performance using neural network models versus ARIMA models

The soybean complex spread: An examination of market efficiency from the viewpoint of a production process

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