News reports have become an imperative conduit of public information. Several recent studies have... more News reports have become an imperative conduit of public information. Several recent studies have used news data from public media to investigate the impact of news on stock market returns. This study analyses the usefulness of news for predicting stock returns in the Taiwan stock market. We employ text mining of economic news, transform documents using a keyword matrix, and then convert the results into news variables. Subsequently, together with other quantitative variables, we construct a fixed effect model to investigate the behaviours of stock market returns in 20 subsectors from January 2008 to December 2014. Empirical analysis reveals that the news variables provide useful information for predicting Taiwan stock market returns, although the out-sample performance is only marginally improved. We also discover an asymmetric effect of economic news for predicting stock market returns. The prediction accuracy is higher when the stock market is booming than when it is glooming.
Using quarterly Taiwan economic data, we demonstrate that deeper un-derstanding of relations betw... more Using quarterly Taiwan economic data, we demonstrate that deeper un-derstanding of relations between variables and substantial gains in forecasting can be obtained by applying econometric and statistical tools to the traditional macro-econometric models. The improvement in forecasting accuracy is illustrated by out-of-sample forecasts, and the models employed in the comparison include univariate time series models, macro-econometric models and combined models in which time series techniques are used to describe the dynamic structure of the residual series of econometric models. The paper also considers various issues related to forecasting such as aggregation and model misspecification.
This paper investigates multistep prediction errors for non-stationary autoregressive processes w... more This paper investigates multistep prediction errors for non-stationary autoregressive processes with both model order and true parameters unknown. We give asymptotic expressions for the multistep mean squared prediction errors and accumulated prediction errors of two important methods, plug-in and direct prediction. These expressions not only characterize how the prediction errors are influenced by the model orders, prediction methods, values of parameters and unit roots, but also inspire us to construct some new predictor selection criteria that can ultimately choose the best combination of the model order and prediction method with probability 1. Finally, simulation analysis confirms the satisfactory finite sample performance of the newly proposed criteria.
2011 2nd International Conference on Advancements in Nuclear Instrumentation, Measurement Methods and their Applications, 2011
The purpose of this study is to develop a patient-specific dosimetry estimation system in nuclear... more The purpose of this study is to develop a patient-specific dosimetry estimation system in nuclear medicine examination using a SimSET-based Monte Carlo code. We added a dose deposition routine to store the deposited energy of the photons during their flights in SimSET and developed a user-friendly interface for reading PET and CT images. Dose calculated on ORNL phantom was used
This paper analyzes the Taiwan stock market and examines its price and volatility linkages with t... more This paper analyzes the Taiwan stock market and examines its price and volatility linkages with those of the United States. In particular, it tests the hypothesis that the short‐term volatility and price changes spill over from the developed markets, mainly the United States, to the emerging Taiwan stock market. The model and the test are built upon Engle’s ARCH (autoregressive conditional heteroskedasticity) and Engle and Kroner’s M‐GARCH (multivariate generalized ARCH) models. The paper differs from previous studies on the Taiwan stock market in three respects. First, instead of using daily closing prices, it uses close‐to‐open and open‐to‐close returns to avoid the problem of overlapping samples. It carefully models the day‐of‐the‐week effect in daily data to avoid misspecification of the model. Second, to circumvent the generated regressor problem arising from the two‐step estimation procedure, it also employs the M‐GARCH model where all parameters are estimated simultaneously. ...
The definition of causation, discussed in Granger (1980) and elsewhere, has been widely applied i... more The definition of causation, discussed in Granger (1980) and elsewhere, has been widely applied in economics and in other disciplines. For this definition, a series yt is said to cause xt+l if it contains information about the forecastability for xt+l contained nowhere else in some large information set, which includes xt−j, j ≥ 0. However, it would be convenient to think of causality being different in extent or direction at seasonal or low frequencies, say, than at other frequencies. The fact that a stationary series is effectively the (uncountably infinite) sum of uncorrelated components, each of which is associated with a single frequency, or a narrow frequency band, introduces the possibility that the full causal relationship can be decomposed by frequency. This is known as the Wiener decomposition or the spectral decomposition of the series, as discussed by Hannan (1970). For any series generated by , where xt, and are both stationary, with finite variances and a(B) is a backw...
Does co-integration help long-term forecasts? In this paper, we use simulation, real data sets, a... more Does co-integration help long-term forecasts? In this paper, we use simulation, real data sets, and multi-step-ahead post-sample forecasts to study this question. Based on the square root of the trace of forecasting error-covariance matrix, we found that for simulated data imposing the 'correct' unit-root constraints implied by co-integration does improve the accuracy of forecasts. For real data sets, the answer
News reports have become an imperative conduit of public information. Several recent studies have... more News reports have become an imperative conduit of public information. Several recent studies have used news data from public media to investigate the impact of news on stock market returns. This study analyses the usefulness of news for predicting stock returns in the Taiwan stock market. We employ text mining of economic news, transform documents using a keyword matrix, and then convert the results into news variables. Subsequently, together with other quantitative variables, we construct a fixed effect model to investigate the behaviours of stock market returns in 20 subsectors from January 2008 to December 2014. Empirical analysis reveals that the news variables provide useful information for predicting Taiwan stock market returns, although the out-sample performance is only marginally improved. We also discover an asymmetric effect of economic news for predicting stock market returns. The prediction accuracy is higher when the stock market is booming than when it is glooming.
Using quarterly Taiwan economic data, we demonstrate that deeper un-derstanding of relations betw... more Using quarterly Taiwan economic data, we demonstrate that deeper un-derstanding of relations between variables and substantial gains in forecasting can be obtained by applying econometric and statistical tools to the traditional macro-econometric models. The improvement in forecasting accuracy is illustrated by out-of-sample forecasts, and the models employed in the comparison include univariate time series models, macro-econometric models and combined models in which time series techniques are used to describe the dynamic structure of the residual series of econometric models. The paper also considers various issues related to forecasting such as aggregation and model misspecification.
This paper investigates multistep prediction errors for non-stationary autoregressive processes w... more This paper investigates multistep prediction errors for non-stationary autoregressive processes with both model order and true parameters unknown. We give asymptotic expressions for the multistep mean squared prediction errors and accumulated prediction errors of two important methods, plug-in and direct prediction. These expressions not only characterize how the prediction errors are influenced by the model orders, prediction methods, values of parameters and unit roots, but also inspire us to construct some new predictor selection criteria that can ultimately choose the best combination of the model order and prediction method with probability 1. Finally, simulation analysis confirms the satisfactory finite sample performance of the newly proposed criteria.
2011 2nd International Conference on Advancements in Nuclear Instrumentation, Measurement Methods and their Applications, 2011
The purpose of this study is to develop a patient-specific dosimetry estimation system in nuclear... more The purpose of this study is to develop a patient-specific dosimetry estimation system in nuclear medicine examination using a SimSET-based Monte Carlo code. We added a dose deposition routine to store the deposited energy of the photons during their flights in SimSET and developed a user-friendly interface for reading PET and CT images. Dose calculated on ORNL phantom was used
This paper analyzes the Taiwan stock market and examines its price and volatility linkages with t... more This paper analyzes the Taiwan stock market and examines its price and volatility linkages with those of the United States. In particular, it tests the hypothesis that the short‐term volatility and price changes spill over from the developed markets, mainly the United States, to the emerging Taiwan stock market. The model and the test are built upon Engle’s ARCH (autoregressive conditional heteroskedasticity) and Engle and Kroner’s M‐GARCH (multivariate generalized ARCH) models. The paper differs from previous studies on the Taiwan stock market in three respects. First, instead of using daily closing prices, it uses close‐to‐open and open‐to‐close returns to avoid the problem of overlapping samples. It carefully models the day‐of‐the‐week effect in daily data to avoid misspecification of the model. Second, to circumvent the generated regressor problem arising from the two‐step estimation procedure, it also employs the M‐GARCH model where all parameters are estimated simultaneously. ...
The definition of causation, discussed in Granger (1980) and elsewhere, has been widely applied i... more The definition of causation, discussed in Granger (1980) and elsewhere, has been widely applied in economics and in other disciplines. For this definition, a series yt is said to cause xt+l if it contains information about the forecastability for xt+l contained nowhere else in some large information set, which includes xt−j, j ≥ 0. However, it would be convenient to think of causality being different in extent or direction at seasonal or low frequencies, say, than at other frequencies. The fact that a stationary series is effectively the (uncountably infinite) sum of uncorrelated components, each of which is associated with a single frequency, or a narrow frequency band, introduces the possibility that the full causal relationship can be decomposed by frequency. This is known as the Wiener decomposition or the spectral decomposition of the series, as discussed by Hannan (1970). For any series generated by , where xt, and are both stationary, with finite variances and a(B) is a backw...
Does co-integration help long-term forecasts? In this paper, we use simulation, real data sets, a... more Does co-integration help long-term forecasts? In this paper, we use simulation, real data sets, and multi-step-ahead post-sample forecasts to study this question. Based on the square root of the trace of forecasting error-covariance matrix, we found that for simulated data imposing the 'correct' unit-root constraints implied by co-integration does improve the accuracy of forecasts. For real data sets, the answer
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Papers by Henry Lin