Showing papers by "Blake LeBaron published in 1996"

A test for independence based on the correlation dimension

Abstract: This paper presents a test of independence that can be applied to the estimated residuals of any time series model that can be transformed into a model driven by independent and identically distributed errors. The first order asymptotic distribution of the test statistic is independent of estimation error provided that the parameters of the model under test can be estimated -consistently. Because of this, our method can be used as a model selection tool and as a specification test. Widely used software1 written by Dechert and LeBaron can be used to implement the test. Also, this software is fast enough that the null distribution of our test statistic can be estimated with bootstrap methods. Our method can be viewed as a nonlinear analog of the Box-Pierce Q statistic used in ARIMA analysis.

Asset Pricing Under Endogenous Expectations in an Artificial Stock Market

Abstract: We propose a theory of asset pricing based on heterogeneous agents who continually adapt their expectations to the market that these expectations aggregatively create. And we explore the implications of this theory computationally using our Santa Fe artificial stock market.

A dynamic structural model for stock return volatility and trading volume

Abstract: An examination is made of an adaptive beliefs model that is able to roughly reproduce the following features seen in the data: 1. The autocorrelation functions of the volatility of returns and trading volume are positive with slowly decaying tails. 2. The cross-correlation function of volatility is approximately zero for squared returns with past and future volumes and is positive for squared returns with current volumes. 3. Abrupt changes in prices and returns occur that are hard to attach to "news." The last feature is obtained because the Law of Large Numbers can fail in the large economy limit.

Technical Trading Rule Profitability and Foreing Exchange Intervention

Abstract: There is reliable evidence that simple rules used by traders have some predictive value over the future momoment of foreign exchange prices. This paper will review some of this evidence and discuss the economic magnitude of this predictabiliy.

A Bootstrap Evaluation of the Effect of Data Splitting on Financial Time Series

Abstract: This article exposes problems of the commonly used technique of splitting the available data into training, validation, and test sets that are held fixed, warns about drawing too strong conclusions from such static splits, and shows potential pitfalls of ignoring variability across splits. Using a bootstrap or resampling method, we compare the uncertainty in the solution stemming from the data splitting with neural network specific uncertainties (parameter initialization, choice of number of hidden units, etc.). We present two results on data from the New York Stock Exchange. First, the variation due to different resamplings is significantly larger than the variation due to different network conditions. This result implies that it is important to not over-interpret a model (or an ensemble of models) estimated on one specific split of the data. Second, on each split, the neural network solution with early stopping is very close to a linear model; no significant nonlinearities are extracted.

Asset Pricing Under Endogenous Expectation in an Artificial Stock Market

Abstract: We propose a theory of asset pricing based on heterogeneous agents who continually adapt their expectations to the market that these expectations aggregatively create And we explore the implications of this theory computationally using our Santa Fe artificial stock market Asset markets, we argue, have a recursive nature in that agents' expectations are formed on the basis of their anticipations of other agents' expectations, which precludes expectations being formed by deductive means Instead traders continually hypothesize---continually explore---expectational models, buy or sell on the basis of those that perform best, and confirm or discard these according to their performance Thus individual beliefs or expectations become endogenous to the market, and constantly compete within an ecology of others' beliefs or expectations The ecology of beliefs coevolves over time Computer experiments with this endogenous-expectations market explain one of the more striking puzzles in finance: that market traders often believe in such concepts as technical trading, "market psychology," and bandwagon effects, while academic theorists believe in market efficiency and a lack of speculative opportunities Both views, we show, are correct, but within different regimes Within a regime where investors explore alternative expectational models at a low rate, the market settles into the rational-expectations equilibrium of the efficient-market literature Within a regime where the rate of exploration of alternative expectations is higher, the market self-organizes into a complex pattern It acquires a rich psychology, technical trading emerges, temporary bubbles and crashes occur, and asset prices and trading volume show statistical features---in particular, GARCH behavior---characteristic of actual market data

Technical Trading Rule Profitability and Foreign Exchange Intervention

Abstract: There is reliable evidence that simple rules used by traders have some predictive value over the future movement of foreign exchange prices. This paper will review some of this evidence and discuss the economic magnitude of this predictability. The profitability of these trading rules will then be analyzed in connection with central bank activity using intervention data from the Federal Reserve. The objective is to find out to what extent foreign exchange predictability can be confined to periods of central bank activity in the foreign exchange market. The results indicate that after removing periods in which the Federal Reserve is active, exchange rate predictability is dramatically reduced.