A novel time-series model based on empirical mode decomposition for forecasting TAIEX

Stock price prediction is regarded as a challenging task of the financial time series prediction process. Time series models have successfully solved prediction problems in many domains, including the stock market. Unfortunately, there are two major drawbacks in stock market by time-series model: (1) some models cannot be applied to the datasets that do not follow the statistical assumptions; and (2) most time-series models which use stock data with many noises involutedly (caused by changes in market conditions and environments) would reduce the forecasting performance. For solving the above problems and promoting the forecasting performance of time-series models, this paper proposes a hybrid time-series support vector regression (SVR) model based on empirical mode decomposition (EMD) to forecast stock price for Taiwan stock exchange capitalization weighted stock index (TAIEX). In order to evaluate the forecasting performances, the proposed model is compared with autoregressive (AR) model and SVR model. The experimental results show that the proposed model is superior to the listing models in terms of root mean squared error (RMSE). And the more fluctuation year (2000–2001) occurs, the better accuracy of proposed model will be obtained.     

Does the vector error correction model perform better than others in forecasting stock price? An application of residual income valuation theory

This paper employs a multi-equation model approach to consider three statistic problems (heteroskedasticity, endogeneity and persistency), which are sources of bias and inefficiency in the predictive regression models. This paper applied the residual income valuation model (RIM) proposed by Ohlson (1995) to forecast stock prices for Taiwan three sectors. We compare relative forecasting accuracy of vector error correction model (VECM) with the vector autoregressive model (VAR) as well as OLS and RW models used in the prior studies. We conduct out-of-sample forecasting and employ two instruments to assess forecasting performance. Our empirical results suggest that the VECM statistically outperforms other three models in forecasting stock prices. When forecasting horizons extend longer, VECM produces smaller forecasting errors and performs substantially better than VAR, suggesting that the ability of VECM to improve VAR forecast accuracy is stronger with longer horizons. These findings imply that an error correction term (ECT) of the VECM contributes to improving forecast accuracy of stock prices. Our economic significance analyses and robustness tests for different data frequency are in favor of the superiority of VECM estimator.     

Why is it so difficult to outperform the random walk? An application of the Meese–Rogoff puzzle to stock prices

Some economists suggest that the Meese–Rogoff puzzle is equally applicable to the stock market, in the sense that no model of stock prices can outperform the random walk in out-of-sample forecasting. We argue that this is not a puzzle and that we should expect nothing, but this result if forecasting accuracy is measured by the root mean square error (RMSE) and similar metrics that take into account the magnitude of the forecasting error only. We demonstrate by using two models for dividend-paying and nondividend-paying stocks that as price volatility rises, the RMSE of the random walk rises, but the RMSE of the model rises even more rapidly, making it unlikely for the model to outperform the random walk.     

Forecasting electricity spot prices using time-series models with a double temporal segmentation

The French wholesale market is set to expand in the next few years under European pressure and national decisions. In this article, we assess the forecasting ability of several classes of time-series models for electricity wholesale spot prices at a day-ahead horizon in France. Electricity spot prices display a strong seasonal pattern, particularly in France, given the high share of electric heating in housing during winter time. To deal with this pattern, we implement a double temporal segmentation of the data. For each trading period and season, we use a large number of specifications based on market fundamentals: linear regressions, Markov-switching (MS) models and threshold models with a smooth transition. An extensive evaluation on French data shows that modelling each season independently leads to better results. Among nonlinear models, MS models designed to capture the sudden and fast-reverting spikes in the price dynamics yield more accurate forecasts. Finally, pooling forecasts give more reliable results.     

The out-of-sample forecasting performance of nonlinear models of regional housing prices in the US

This article provides out-of-sample forecasts of linear and nonlinear models of US and four Census subregions’ housing prices. The forecasts include the traditional point forecasts, but also include interval and density forecasts, of the housing price distributions. The nonlinear smooth-transition autoregressive model outperforms the linear autoregressive model in point forecasts at longer horizons, but the linear autoregressive and nonlinear smooth-transition autoregressive models perform equally at short horizons. In addition, we generally do not find major differences in performance for the interval and density forecasts between the linear and nonlinear models. Finally, in a dynamic 25-step ex-ante and interval forecasting design, we, once again, do not find major differences between the linear and nonlinear models. In sum, we conclude that when forecasting regional housing prices in the United States, generally the additional costs associated with nonlinear forecasts outweigh the benefits for forecasts only a few months into the future.     

Forecasting US consumer price index: does nonlinearity matter?

The objective of this article is to predict, both in sample and out of sample, the consumer price index (CPI) of the US economy based on monthly data covering the period of 1980:1–2013:12, using a variety of linear (random walk (RW), autoregressive (AR) and seasonal autoregressive integrated moving average (SARIMA)) and nonlinear (artificial neural network (ANN) and genetic programming (GP)) univariate models. Our results show that, while the SARIMA model is superior relative to other linear and nonlinear models, as it tends to produce smaller forecast errors; statistically, these forecasting gains are not significant relative to higher-order AR and nonlinear models, though simple benchmarks like the RW and AR(1) models are statistically outperformed. Overall, we show that in terms of forecasting the US CPI, accounting for nonlinearity does not necessarily provide us with any statistical gains.     

Forecasting the US real house price index: Structural and non-structural models with and without fundamentals

We employ a 10-variable dynamic structural general equilibrium model to forecast the US real house price index as well as its downturn in 2006:Q2. We also examine various Bayesian and classical time-series models in our forecasting exercise to compare to the dynamic stochastic general equilibrium model, estimated using Bayesian methods. In addition to standard vector-autoregressive and Bayesian vector autoregressive models, we also include the information content of either 10 or 120 quarterly series in some models to capture the influence of fundamentals. We consider two approaches for including information from large data sets — extracting common factors (principle components) in factor-augmented vector autoregressive or Bayesian factor-augmented vector autoregressive models as well as Bayesian shrinkage in a large-scale Bayesian vector autoregressive model. We compare the out-of-sample forecast performance of the alternative models, using the average root mean squared error for the forecasts. We find that the small-scale Bayesian-shrinkage model (10 variables) outperforms the other models, including the large-scale Bayesian-shrinkage model (120 variables). In addition, when we use simple average forecast combinations, the combination forecast using the 10 best atheoretical models produces the minimum RMSEs compared to each of the individual models, followed closely by the combination forecast using the 10 atheoretical models and the DSGE model. Finally, we use each model to forecast the downturn point in 2006:Q2, using the estimated model through 2005:Q2. Only the dynamic stochastic general equilibrium model actually forecasts a downturn with any accuracy, suggesting that forward-looking microfounded dynamic stochastic general equilibrium models of the housing market may prove crucial in forecasting turning points.     

Forecasting the price of gold

This article seeks to evaluate the appropriateness of a variety of existing forecasting techniques (17 methods) at providing accurate and statistically significant forecasts for gold price. We report the results from the nine most competitive techniques. Special consideration is given to the ability of these techniques to provide forecasts which outperforms the random walk (RW) as we noticed that certain multivariate models (which included prices of silver, platinum, palladium and rhodium, besides gold) were also unable to outperform the RW in this case. Interestingly, the results show that none of the forecasting techniques are able to outperform the RW at horizons of 1 and 9 steps ahead, and on average, the exponential smoothing model is seen providing the best forecasts in terms of the lowest root mean squared error over the 24-month forecasting horizons. Moreover, we find that the univariate models used in this article are able to outperform the Bayesian autoregression and Bayesian vector autoregressive models, with exponential smoothing reporting statistically significant results in comparison with the former models, and classical autoregressive and the vector autoregressive models in most cases.     

Modeling the Taiwan Stock Market and International Linkages

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. Third, the misspecification test is carried out on various kinds of asymmetric ARCH factors. A substantial volatility spillover effect is found from the US stock market to the Taiwan stock market, especially for the model using close-to-open returns. There is also evidence supporting a spillover effect in price changes. The findings can be explained by the recent gradual opening of the Taiwan stock market to foreign investors.     

Nerlovian area response as an error correction model: An application to Western Canadian agriculture

The relationship between Nerlovian partial adjustment models and error correction models is explored. Unit root tests are employed to test stationarity of price, area and stock data of crops in the Canadian province of Saskatchewan. The data are found to be consistent with unit root non-stationarity. Evidence in favour of cointegrating relationships among area price and stock data is found. However, evidence in favour of the error correction form of the Nerlovian partial adjustment model is weak, indicating that more investigation of richer theoretical and empirical models of the short run dynamics of area response in Saskatchewan is needed.     

Time series analysis of monthly beef cattle prices with nonlinear autoregressive models

This paper analyses the dynamics underlying a time series of the monthly average beef cattle price received by producers in the State of São Paulo (Brazil). The time series under study records monthly prices since 1954. An exploratory analysis suggested that after a period of intense government intervention in the cattle and beef markets, the underlying dynamics seem to be settling to a pattern similar to the one observed prior to that period. In order to try to verify if the underlying dynamics after the interventionist phase are similar to those in former times, a forecasting procedure has been used based on nonlinear autoregressive models. This tye of models were used after the BDS test showed significant results which can be interpreted as nonlinearities in the data. The results discussed in the paper seem to suggest that after a period of intense interventions that lasted over two decades, the current underlying dynamics are close (from a forecasting point of view) to those observed more than thirty years ago.     

Are the Australian and New Zealand stock prices nonlinear with a unit root?

Whether or not stock prices are characterized by a unit root has important implications for policy. For instance, by applying unit root tests one can deduce whether stock returns can be predicted from previous changes in prices. A finding of a unit root implies that stock returns cannot be predicted. This paper investigates whether or not stock prices for Australia and New Zealand can be characterized by a unit root process. An unrestricted two-regime threshold autoregressive model is used with an autoregressive unit root. Among the main results, it is found that the stock prices of both countries are nonlinear processes that are characterized by a unit root process, consistent with the efficient market hypothesis.     

Assessing Market Integration in ASEAN with Retail Price Data

We assess the extent of market integration the Association of Southeast Asian Nations (ASEAN) using a comprehensive data set that contains actual local retail prices for 131 goods and services in ASEAN countries (except Laos and Myanmar) over the period of 1990–2013. We conduct two different, but complementary, approaches: analyzing price dispersion and testing for convergence to the law of one price via panel unit root tests. The 1997 Asian crisis and, to a lesser extent, the 2008 global crisis appear to have caused a considerable disruption in the process of market integration. Despite significant tariff reduction under the ASEAN Free Trade Area commitments in the past two decades, the level of price dispersion across ASEAN is higher in 2013 than in 1990. Panel unit root tests accounting for cross‐section dependence show that convergence to the law of one price holds for only a minority of retail prices, including those of traded goods, in the ASEAN markets. We also consider a nonlinear exponential smooth transition autoregressive approach and a structural break as alternative adjustment dynamics in the panel unit root tests. Overall, our results suggest that there is much to be done in ASEAN to achieve a meaningful ASEAN economic community.     

Forecasting house prices for the four census regions and the aggregate US economy in a data-rich environment

This article considers the ability of large-scale (involving 145 fundamental variables) time-series models, estimated by dynamic factor analysis and Bayesian shrinkage, to forecast real house price growth rates of the four US census regions and the aggregate US economy. Besides the standard Minnesota prior, we also use additional priors that constrain the sum of coefficients of the VAR models. We compare 1- to 24-months-ahead forecasts of the large-scale models over an out-of-sample horizon of 1995:01–2009:03, based on an in-sample of 1968:02–1994:12, relative to a random walk model, a small-scale VAR model comprising just the five real house price growth rates and a medium-scale VAR model containing 36 of the 145 fundamental variables besides the five real house price growth rates. In addition to the forecast comparison exercise across small-, medium- and large-scale models, we also look at the ability of the ‘optimal’ model (i.e. the model that produces the minimum average mean squared forecast error) for a specific region in predicting ex ante real house prices (in levels) over the period of 2009:04 till 2012:02. Factor-based models (classical or Bayesian) perform the best for the North East, Mid-West, West census regions and the aggregate US economy and equally well to a small-scale VAR for the South region. The ‘optimal’ factor models also tend to predict the downward trend in the data when we conduct an ex ante forecasting exercise. Our results highlight the importance of information content in large number of fundamentals in predicting house prices accurately.     

Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach

Crude oil price behaviour has fluctuated wildly since 1973 which has a major impact on key macroeconomic variables. Although the relationship between stock market returns and oil price changes has been scrutinized excessively in the literature, the possibility of predicting future stock market returns using oil prices has attracted less attention. This paper investigates the ability of oil prices to predict S&P 500 price index returns with the use of other macroeconomic and financial variables. Including all the potential variables in a forecasting model may result in an over-fitted model. So instead, dynamic model averaging (DMA) and dynamic model selection (DMS) are applied to utilize their ability of allowing the best forecasting model to change over time while parameters are also allowed to change. The empirical evidence shows that applying the DMA/DMS approach leads to significant improvements in forecasting performance in comparison to other forecasting methodologies and the performance of these models are better when oil prices are included within predictors.     

Revisiting the oil price and stock market nexus: A nonlinear Panel ARDL approach

In this paper, we re-examine the relationship between oil price and stock prices in oil exporting and oil importing countries in the following distinct ways. First, we account for possible nonlinearities in the relationship in order to quantify the asymmetric response of stock prices of these two categories to positive and negative oil price changes. Secondly, in order to capture within group differences, we allow for heterogeneity effect in the cross-sections by formulating a nonlinear Panel ARDL model which is the panel data representation of the Shin et al. (2014) model and also analogous to the non-stationary heterogenous panel data model. Thirdly, we evaluate the relative predictability of the linear (symmetric) and nonlinear (asymmetric) Panel ARDL models using the Campbell and Thompson (2008) test. Our results depict that stock prices of both oil exporting and oil importing groups respond asymmetrically to changes in oil price although the response is stronger in the latter than the former. This finding is further corroborated by the out-of-sample forecast results suggesting that the inclusion of positive and negative oil price changes in the predictive model for stock prices will produce better forecast results only for the oil importing countries. Our results are robust to different oil price proxies, lag structure and in-sample periods. Overall, the dichotomy between oil exporting and oil importing countries has implications on oil price-stock nexus.     

Price transmission in the international skim milk powder markets

This article investigates the strength and the pattern of spatial price linkages in skimmed milk powder markets using monthly wholesale price data from three major producers and exporters (the U.S.A., the E.U., and Oceania) and the nonlinear autoregressive distributed lag model. The results suggest that prices in the three regions considered are linked with stable long-run relationships. The law of one price, however, does not hold. The dominant pattern of transmission in the long run is asymmetric involving positive price stocks to be transmitted with higher intensity compared to negative prices shocks; asymmetries in price transmission exist in the short run as well.     

The price relationship between organic and non-organic vegetables in the U.S.: evidence from Nielsen scanner data

This study investigates price relationships between organic and conventional carrots, tomatoes, and lettuce in the U.S. utilizing Nielsen scanner data from 2006–2015. We employ a threshold vector error correction model (TVECM), threshold vector autoregressive model (TVAR), and threshold cointegration test to test whether market integration exists between organic and conventional vegetables as well as the existence of asymmetric price transmission. The results find positive long-run relationships between organic and conventional prices of carrots and tomatoes and show the existence of asymmetric price transmission in price pairs of lettuce and tomatoes. Our findings suggest that the price relationship between organic and conventional vegetables varies by characteristics, such as shelf life, volatility in the price premium, and substitutability.     

The Accuracy of OECD Forecasts for Japan

A variety of accuracy measures, error diagnostics and rationality tests are applied to the OECD's macroeconomic forecasts for Japan of aggregate demand and output, inflation and the balance of payments. It is found that the OECD forecasts are superior to naive no-change predictions and forecasts generated by simple autoregressive time-series models. Most forecasting error is nonsystematic. As predictors of direction the OECD's six-month ahead forecasts should be considered valuable; this cannot be said for forecasts which look ahead a year and 18 months. Many forecasts fail bias, efficiency and consistency tests so that the rational expectations hypothesis is not generally supported.     

The comovement between exchange rates and stock prices in the Asian emerging markets

This study investigates the comovement between exchange rates and stock prices in the Asian emerging markets. The sample covers major institutional changes, such as market liberalization and financial crises, so as to examine how the short-term and long-term relations change after such events. The autoregressive distributed lag (ARDL) model proposed by Pesaran et al. (2001) is adopted, which allows us to deal with structural breaks easily, and to handle data that have integrals of different orders. Interest rates and foreign reserves are also included in the analysis to reduce potential omitted variable bias. My empirical results suggest that the comovement between exchange rates and stock prices becomes stronger during crisis periods, consistent with contagion or spillover between asset prices, when compared with tranquil periods. Furthermore, most of the spillovers during crisis periods can be attributed to the channel running from stock price shocks to the exchange rate, suggesting that governments should stimulate economic growth and stock markets to attract capital inflow, thereby preventing a currency crisis. However, the industry causality analysis shows the comovement is not stronger for export-oriented industries for all periods, such as industrials and technology industries, thus implying that comovement between exchange rates and stock prices in the Asian emerging markets is generally driven by capital account balance rather than that of trade.