An Sing Chen

Application of neural networks to an emerging financial market: forecasting and trading the Taiwan stock index

In this study, we attempt to model and predict the direction of return on market index of the Taiwan Stock Exchange, one of the fastest growing financial exchanges in developing Asian countries. Our motivation is based on the notion that trading strategies guided by forecasts of the direction of price movement may be more effective and lead to higher profits. The probabilistic neural network (PNN) is used to forecast the direction of index return after it is trained by historical data. Statistical performance of the PNN forecasts are measured and compared with that of the generalized methods of moments (GMM) with Kalman filter. Moreover, the forecasts are applied to various index trading strategies, of which the performances are compared with those generated by the buy-and-hold strategy as well as the investment strategies guided by forecasts estimated by the random walk model and the parametric GMM models. Empirical results show that the PNN-based investment strategies obtain higher returns than other investment strategies examined in this study. Influences of length of investment horizon and commission rate are also considered.

Forecasting Stock Indices: A Comparison of Classification and Level Estimation Models

Despite abundant research which focuses on estimating the level of return on stock market index, there is a lack of studies examining the predictability of the direction/sign of stock index movement. Given the notion that a prediction with little forecast error does not necessarily translate into capital gain, we evaluate the efficacy of several multivariate classification techniques relative to a group of level estimation approaches. Specifically, we conduct time series comparisons between the two types of models on the basis of forecast performance and investment return. The tested classification models, which predict direction based on probability, include linear discriminant analysis, logit, probit, and probabilistic neural network. On the other hand, the level estimation counterparts, which forecast the level, are exponential smoothing, multivariate transfer function, vector autoregression with Kalman filter, and multilayered feedforward neural network. Our comparative study also measures the relative strength of these models with respect to the trading profit generated by their forecasts. To facilitate more effective trading, we develop a set of threshold trading rules driven by the probabilities estimated by the classification models. Empirical experimentation suggests that the classification models outperform the level estimation models in terms of predicting the direction of the stock market movement and maximizing returns from investment trading. Further, investment returns are enhanced by the adoption of the threshold trading rules.

Forecasting exchange rates using general regression neural networks

Predicting currency movements has always been a problematic task as most conventional econometric models are not able to forecast exchange rates with significantly higher accuracy than a naive random walk model. For large multinational firms which conduct substantial currency transfers in the course of business, being able to accurately forecast the movements of exchange rates can result in considerable improvement in the overall profitability of the firm. In this study, we apply the General Regression Neural Network (GRNN) to predict the monthly exchange rates of three currencies, British pound, Canadian dollar, and Japanese yen. Our empirical experiment shows that the performance of GRNN is better than other neural network and econometric techniques included in this study. The results demonstrate the predictive strength of GRNN and its potential for solving financial forecasting problems.

Regression neural network for error correction in foreign exchange forecasting and trading

Predicting exchange rates has long been a concern in international finance as most standard econometric methods are unable to produce significantly better forecasts than the random walk model. Recent studies provide some evidence for the ability of using multivariate time series models to generate better forecasts. At the same time, artificial neural networks have been emerging as alternatives to predict exchange rates. In this paper, we propose an adaptive forecasting approach which combines the strengths of neural networks and multivariate econometric models. This hybrid approach contains two forecasting stages. In the first stage, a time series model generates estimates of the exchange rates. In the second stage, General Regression Neural Network is used to correct the errors of the estimates. A number of tests and statistical measures are then applied to compare the performances of the two-stage models (with error-correction by neural network) with those of the single-stage models (without error-correction by neural network). Both empirical and trading simulation experiments suggest that the proposed hybrid approach not only produces better exchange rate forecasts but also results in higher investment returns than the single-stage models. The effect of risk aversion in currency trading is also considered.

Stock auction bidding behavior and information asymmetries: An empirical analysis using the discriminatory auction model framework

Abstract This paper empirically analyzes bidding behavior and information asymmetries of stock auctions using a discriminatory auction model framework. Analyzing stock auctions using auction theory is important because it provides a logical framework for explaining observable behaviors and a solid foundation for empirical testing. Because of limited availability of stock auction data, existing empirical research based on auction theory focus mainly on treasury auctions. Away from the treasury markets, however, there is a significant paucity of literature on this subject. In this study, we make use of a detailed stock auction data set from the Taiwan Stock Exchange to empirically examine the behaviors of the Taiwan stock auction market within the framework of the auction theory. Our results show that the level of competition, the dispersion of opinion among bidders, and the bidder’s risk aversion are significant in determining auction prices. Results also show that if the offering prices are set equal to the average offering prices, the first-day post-initial public offering abnormal return will be equal to zero. Additionally, we find institutional bidders possess superior bidding skills compared to small bidders and that underwriter’s characteristics influence the bidding results.

A Bayesian vector error correction model for forecasting exchange rates

This paper develops a new method called Bayesian Vector Error Correction Model (BVECM), which is applied to forecast 1 month ahead changes of currency exchange rates for three major Asia Pacific economies. The study also compares out-of-sample forecasting performance with those of the random walk model and the Bayesian Vector Autoregression (BVAR), which has been shown in recent studies to outperform a variety competing of econometric techniques in exchange rate forecasting. Our experimental results indicate that both BVECM and BVAR are able to forecast the changes in exchange rates better than the random walk model. In terms of conventional forecast evaluation statistics, BVECM outperforms BVAR for all three currencies examined. In addition, the bias tests find that BVECM produces systematically less biased and more efficient out-of-sample forecasts than BVAR. Although the results of market timing tests indicate that both BVAR and BVECM have an economically significant value in predicting the directional change in two of the three exchange rates, BVECM is shown to produce equally or more economically significant directional change forecasts than BVAR.

Application of polynomial projection ensembles to hedging crude oil commodity risk

Although the rapid expansion in derivative market in previous decades has drawn research in both theory and practice of hedging against commodity risk, recent volatile fluctuations in crude oil prices in world market have renewed profound interest in examination of existing and development of new hedging models and strategies. In this paper, we propose and develop a methodological framework for applying individual and ensembles of polynomial projection models to hedge against oil commodity price risk. The study also comparatively evaluates the hedging performances of these projection models and benchmarks them against naive hedging, VEC-GARCH model, and the case of no hedging. In addition, the empirical analysis considers a traders level of risk aversion in commodity hedging as well as the adoption of transaction cost. Our findings indicate promising out-of-sample hedging capability by polynomial projection models. Also, different forms of integrated ensembles of projections outperform individual polynomial projections, suggesting the usefulness of ensemble structure in enhancement of hedging in an uncertain environment.

A paradigm for Group Technology cellular layout planning in JIT facility

A commonly examined domain associated with Just-In-Time (JIT) manufacturing is the implementation of Group Technology (GT), which requires identification of part families and machine groups in order to exploit similarities and achieve economies in manufacturing. One of the issues is how to design an effective layout which promotes the benefits of JIT cells. We present a procedure to design the layout of manufacturing cells operating within a GT environment. The approach, which can be easily adopted by practitioners, translates information such as processing requirement of parts, demand volume, and machine capacity into a material flow matrix. With an initial layout plan generated by assigning machines on a space filling curve, the cellular layout problem is solved by CRAFT. Using this procedure, we can recursively create a number of layout designs. A ¿gauge¿ measure indicating the intensity of material flows is also provided to assist managers in selecting desirable floor plan.

Option straddle trading: Financial performance and economic significance of direct profit forecast and conventional strategies

The present study focuses on the trading of at-the-money straddles using options on foreign currency futures, namely British Pound, Canadian Dollar, and Japanese Yen. The financial performance and economic significance of a direct profit forecast trading strategy are examined. This strategy uses a linear projection to directly forecast the profit (net of transaction costs) of engaging in a straddle. The straddle is purchased when the forecast is positive and sold when negative. This differs from the conventional option trading strategy of basing trading decisions on a two-step procedure of first generating a volatility forecast and then inputting the volatility forecast into an appropriate option pricing model to price the straddle. The direct profit forecast trading strategy removes volatility forecasting and option pricing models from the straddle trading decision process altogether. This method has only one source of model risk, compared to the conventional two step method which has two sources of model risk. It is possible that the direct forecast trading strategy with only one source of model risk may outperform the conventional method of trading straddles. The experimental investigation confirms this notion and the out-of-sample results indicate that, for each of the currencies analysed, the direct forecasting strategy is more profitable than the conventional two-step method. Furthermore, the results are robust with respect to different transaction cost assumptions. Finally, tests of economic significance indicate consistent market timing value for the direct forecast method.

Stochastic properties and predictability of intraday Taiwan exchange rates

Abstract This study explores the time-series properties and predictability of intraday movements in the Taiwan exchange rates with respect to its most important trading partner, the United States, using the EGARCH-M model along with the generalized error distribution (GED), allows for variable kurtosis in the data. The study (1) examines whether the Martingale hypothesis of no predictability remains appropriate at the intraday level; (2) tests whether the more general GED, which can account for varying degrees of skewness and kurtosis, provides a better representation of the stochastic behavior of the intraday Taiwan exchange rate series than the normal distribution; (3) addresses the issue of whether knowledge of the trading hour provides incremental information that is useful for forecasting intraday movements of the exchange rate; (4) investigates whether intraday movements in the new Taiwan dollar (NT) are positively (or negatively) influenced by their conditional standard deviations; and (5) tests for the presence of asymmetric volatility in the intraday data. In our experiment, the Martingale hypothesis is rejected at the intraday level and the distribution of the intraday NT/U.S. dollar exchange rate is found to be highly leptokurtic relative to the normal distribution. The GED appears to provide a better characterization of the leptokurtic distribution than the normal distribution. Also, knowledge of the trading hour is shown to provide incremental information that is useful for forecasting intraday movements of the exchange rate. There is evidence to suggest that the conditional standard deviation negatively influences future intraday movements of the NT/U.S. dollar exchange rate. Only weak evidence for asymmetric volatility in the intraday data is found.