Ramazan Gençay

Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules

Abstract This paper investigates the predictability of spot foreign exchange rate returns from past buy-sell signals of the simple technical trading rules by using the nearest neighbors and the feedforward network regressions. The optimal choices for nearest neighbors, hidden units in a feedforward network and the training set are determined by the cross validation method which minimizes the mean square error. Although this method is computationally expensive the results indicate that it has the advantage of avoiding overfitting in noisy environments and indicate that simple technical rules provide significant forecast improvements for the current returns over the random walk model.

The predictability of security returns with simple technical trading rules

Abstract Technical traders base their analysis on the premise that the patterns in market prices are assumed to recur in the future, and thus, these patterns can be used for predictive purposes. This paper uses the daily Dow Jones Industrial Average Index from 1897 to 1988 to examine the linear and nonlinear predictability of stock market returns with simple technical trading rules. The nonlinear specification of returns are modelled by single layer feedforward networks. The results indicate strong evidence of nonlinear predictability in the stock market returns by using the past buy and sell signals of the moving average rules.

SEMIPARAMETRIC ESTIMATION OF A HEDONIC PRICE FUNCTION

Previous work on the preferred specification of hedonic price models usually recommended a Box-Cox model. In this paper we note that any parametric model involves implicit restrictions and they can be reduced by using a semiparametric model. We estimate a benchmark parametric model which passes several common specification tests, before showing that a semiparametric model outperforms it significantly. In addition to estimating the model, we compare the predictions of the models by deriving the distribution of the predicted log(price) and then calculating the associated prediction intervals. Our data show that the semiparametric model provides more accurate mean predictions than the benchmark parametric model. Copyright 1996 by John Wiley & Sons, Ltd.

Scaling properties of foreign exchange volatility

In this paper, we investigate the scaling properties of foreign exchange volatility. Our methodology is based on a wavelet multi-scaling approach which decomposes the variance of a time series and the covariance between two time series on a scale by scale basis through the application of a discrete wavelet transformation. It is shown that foreign exchange rate volatilities follow different scaling laws at different horizons. Particularly, there is a smaller degree of persistence in intra-day volatility as compared to volatility at one day and higher scales. Therefore, a common practice in the risk management industry to convert risk measures calculated at shorter horizons into longer horizons through a global scaling parameter may not be appropriate. This paper also demonstrates that correlation between the foreign exchange volatilities is the lowest at the intra-day scales but exhibits a gradual increase up to a daily scale. The correlation coefficient stabilizes at scales one day and higher. Therefore, the benefit of currency diversification is the greatest at the intra-day scales and diminishes gradually at higher scales (lower frequencies). The wavelet cross-correlation analysis also indicates that the association between two volatilities is stronger at lower frequencies.

An algorithm for the n Lyapunov exponents of an n -dimensional unknown dynamical system

Abstract An algorithm for estimating Lyapunov exponents of an unknown dynamical system is designed. The algorithm estimates not only the largest but all Lyapunov exponents of the unknown system. The estimation is carried out by a multivariate feedforward network estimation technique. We focus our attention on deterministic as well as noisy system estimation. The performance of the algorithm is very satisfactory in the presence of noise as well as with limited number of observations.

Differentiating intraday seasonalities through wavelet multi-scaling

It is well documented that strong intraday seasonalities may induce distortions in the estimation of volatility models. These seasonalities are also the dominant source for the underlying misspecifications of the various volatility models. Therefore, an obvious route is to filter out the underlying intraday seasonalities from the data. In this paper, we propose a simple method for intraday seasonality extraction that is free of model selection parameters which may affect other intraday seasonality filtering methods. Our methodology is based on a wavelet multi-scaling approach which decomposes the data into its low- and high-frequency components through the application of a non-decimated discrete wavelet transform. It is simple to calculate, does not depend on a particular model selection criterion or model-specific parameter choices. The proposed filtering method is translation invariant, has the ability to decompose an arbitrary length series without boundary adjustments, is associated with a zero-phase filter and is circular. Being circular helps to preserve the entire sample unlike other two-sided filters where data loss occurs from the beginning and the end of the studied sample.

Optimization of technical trading strategies and the profitability in security markets

Abstract The ultimate goal of any testing strategy is to measure profitability. This paper measures the profitability of simple technical trading rules based on nonparametric models which maximize the total returns of an investment strategy. The profitability of an investment strategy is evaluated against a simple buy-and-hold strategy on the security and its distance from the ideal net profit. The predictive performance is evaluated by the market timing tests of Henriksson-Merton and Pesaran-Timmermann to measure whether forecasts have economic value in practice. The results of an illustrative example indicate that nonparametric models with technical strategies provide significant profits when tested against buy-and-hold strategies. In addition, the sign predictions of these models are statistically significant.

Nonlinear modelling and prediction with feedforward and recurrent networks

In feedforward networks, signals flow in only one direction without feedback. Applications in forecasting, signal processing and control require explicit treatment of dynamics. Feedforward networks can accommodate dynamics by including past input and target values in an augmented set of inputs. A much richer dynamic representation results from also allowing for internal network feedbacks. These types of network models are called recurrent network models and are used by Jordan (1986) for controlling and learning smooth robot movements, and by Elman (1990) for learning and representing temporal structure in linguistics. In Jordans network, past values of network output feed back into hidden units; in Elmans network, past values of hidden units feed back into themselves. The main focus of this study is to investigate the relative forecast performance of the Elman type recurrent network models in comparison to feedforward networks with deterministic and noisy data. The salient property of the Elman type recurrent network architecture is that the hidden unit activation functions (internal states) are fed back at every time step to provide an additional input. This recurrence gives the network dynamical properties which make it possible for the network to have internal memory. Exactly how this memory is represented in the internal states is not determined in advance. Instead, the network must discover the underlying temporal structure of the task and learn to encode that structure internally. The simulation results of this paper indicate that recurrent networks filter noise more successfully than feedforward networks in small as well as large samples.

Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging

We study the effectiveness of cross validation, Bayesian regularization, early stopping, and bagging to mitigate overfitting and improving generalization for pricing and hedging derivative securities with daily S&P 500 index daily call options from January 1988 to December 1993. Our results indicate that Bayesian regularization can generate significantly smaller pricing and delta-hedging errors than the baseline neural-network (NN) model and the Black-Scholes model for some years. While early stopping does not affect the pricing errors, it significantly reduces the hedging error (HE) in four of the six years we investigated. Although computationally most demanding, bagging seems to provide the most accurate pricing and delta hedging. Furthermore, the standard deviation of the MSPE of bagging is far less than that of the baseline model in all six years, and the standard deviation of the average HE of bagging is far less than that of the baseline model in five out of six years. We conclude that they be used at least in cases when no appropriate hints are available.

Using genetic algorithms to select architecture of a feedforward artificial neural network

This paper proposes a model selection methodology for feedforward network models based on the genetic algorithms and makes a number of distinct but inter-related contributions to the model selection literature for the feedforward networks. First, we construct a genetic algorithm which can search for the global optimum of an arbitrary function as the output of a feedforward network model. Second, we allow the genetic algorithm to evolve the type of inputs, the number of hidden units and the connection structure between the inputs and the output layers. Third, we study how introduction of a local elitist procedure which we call the election operator affects the algorithms performance. We conduct a Monte Carlo simulation to study the sensitiveness of the global approximation properties of the studied genetic algorithm. Finally, we apply the proposed methodology to the daily foreign exchange returns.