Looking into the black box of boosting: the case of Germany

This article looks into the ‘fine print’ of boosting for economic forecasting. By using German industrial production for the period from 1996 to 2014 and a data set consisting of 175 monthly indicators, we evaluate which indicators get selected by the boosting algorithm over time and four different forecasting horizons. It turns out that a number of hard indicators like turnovers, as well as a small number of survey results, get selected frequently by the algorithm and are therefore important to forecasting the performance of the German economy. However, there are indicators such as money supply that never get chosen by the boosting approach at all.     

Forecasting stock returns: Do less powerful predictors help?

This paper proposes a simple but efficient way to improve the predictability of stock returns. Instead of torturously constructing new powerful predictors, we readily select existing predictors that have low correlations and thus provide complementary information. Our forecasting strategy is to use the selected predictors based on a multivariate regression model. In our forecasting strategy, less powerful predictors are also useful for forecasting stock returns if they could provide complementary information. The empirical results show that our forecasting strategy outperforms not only the univariate regression models that use each predictor's information separately but also combination approaches that use all predictors jointly. We also document that our strategy extracts significantly more useful information from the complementary predictors than the competing models. In addition, from an asset allocation perspective, a mean-variance investor realizes substantial economic gains. Furthermore, the evidence based on Monte Carlo simulations supports the feasibility of our forecasting strategy.     

Out‐of‐Sample Exchange Rate Forecasting and Macroeconomic Fundamentals: The Case of Japan

This study explores the respective out‐of‐sample exchange rate forecasting abilities of five macroeconomic fundamental models in comparison to a naïve random walk model for Japan during the post‐Bretton Woods era. To assess the influence of major economic changes, we estimate both linear and nonlinear models for all the macroeconomic fundamentals. Overall, most structural exchange rate models outperform a naïve random walk model in terms of forecasting accuracy in the short horizon. When the fundamentals are only linearly modelled, the forecasting ability of the Taylor rule is generally superior to other fundamental models. When the fundamentals are nonlinearly specified, the predictability of some other models rises dramatically to match that of the Taylor rule models in short and/or long horizons. Of importance, we determine that the yen/dollar exchange rate forecasting performance effectively improves in several fundamental models when influential economic changes are incorporated.     

Forecasting realized volatility of crude oil futures with equity market uncertainty

This paper examines whether the equity market uncertainty (EMU) index contains incremental information for forecasting the realized volatility of crude oil futures. We use 5-min high-frequency transaction data for WTI crude oil futures and develop six heterogeneous autoregressive (HAR) models based on classical HAR-type models. The empirical results suggest that EMU contains more incremental information than the economic policy uncertainty (EPU) for forecasting the realized volatility of crude oil futures. More importantly, we argue that EMU is a non negligible additional predictive variable that can significantly improve the 1-day ahead predictive accuracy of all six HAR-type models, and improve the 1-week ahead forecasting performance of the HAR-RV, HAR-RV-J, HAR-RSV, HAR-RV-SJ models. These findings highlight a strong short-term and a weak mid-term predictive ability of EMU in the crude oil futures market.     

The UK consumption function and structural instability: improving forecasting performance using a time-varying parameter approach

Previous studies indicate that the poor forecasting performance of constant parameter UK consumption expenditure models is caused by structural instability in the underlying data generating process. Typically, this instability is removed by reparameterization within the constant parameter framework. An alternative modelling strategy is to allow some, or all, of the parameters to vary over time. A UK non-durable consumption expenditure model with time-varying parameters is developed, based on the permanent income hypothesis of Friedman (1957). This model takes into account temporal changes in the average and marginal propensities to consume. The variation in the parameter estimates is given an economic interpretation in terms of the influence of omitted variables, namely UK financial liberalization and expectational changes. The forecasting performance of this model is superior to that of two widely used constant parameter models. Further tests show that, even if these constant parameter models are respecified as time varying parameter models, the authors' model still retains a superior forecasting performance.     

Monetary policy and heterogeneous expectations

This paper studies the implications for monetary policy of heterogeneous expectations in a New Keynesian model. The assumption of rational expectations is replaced with parsimonious forecasting models where agents select between predictors that are underparameterized. In a Misspecification Equilibrium agents only select the best-performing statistical models. We demonstrate that, even when monetary policy rules satisfy the Taylor principle by adjusting nominal interest rates more than one for one with inflation, there may exist equilibria with Intrinsic Heterogeneity. Under certain conditions, there may exist multiple misspecification equilibria. We show that these findings have important implications for business cycle dynamics and for the design of monetary policy.     

Measures of underlying inflation in the euro area: assessment and role for informing monetary policy

The paper evaluates the 24-month-ahead inflation forecasting performance of various indicators of underlying inflation and structural models. Measures derived using the generalized dynamic factor model (GDFM) overperform other measures over the monetary policy horizon and are leading indicators of headline inflation. Trimmed means, although weaker than GDFM indicators, have good forecasting performance, while indicators by permanent exclusion underperform but provide useful information about short-term dynamics. The forecasting performance of theoretically-founded models that relate monetary aggregates, the output gap, and inflation improves with the time horizon but generally falls short of that of the GDFM. A composite measure of underlying inflation, derived by averaging the statistical indicators and the model-based estimates, improves forecast accuracy by eliminating bias and offers valuable insight about the distribution of risks.     

MONTHLY GDP FORECASTING USING BRIDGE MODELS: APPLICATION FOR THE FRENCH ECONOMY

This paper presents a model to predict French gross domestic product (GDP) quarterly growth rate. The model is designed to be used on a monthly basis by integrating monthly economic information through bridge models, for both supply and demand sides, allowing thus economic interpretations. For each GDP component, bridge equations are specified by using a general‐to‐specific approach implemented in an automated way by Hoover and Perez and improved by Krolzig and Hendry. This approach allows to select explanatory variables among a large data set of hard and soft data. A rolling forecast study is carried out to assess the forecasting performance in the prediction of aggregated GDP, by taking publication lags into account in order to run pseudo real‐time forecasts. It turns out that the model outperforms benchmark models. The results show that changing the set of equations over the quarter is superior to keeping the same equations over time. In addition, GDP growth seems to be more precisely predicted from a supply‐side approach rather than a demand‐side approach.     

Investor attention and currency performance: international evidence

This article investigates the relationship between investor attention measured by Google search volume index and the performance of several currencies. We find that currency performance is remarkably responsive to changes in investor attention. These impacts, generated rapidly, are present over the relatively long term, especially for emerging currencies, and are intensified during periods of high uncertainty. We also demonstrate that there is a prominent asymmetric effect for the impact of attention, as past currency performance also influences attention. Typically, past currency performance can determine the magnitude of the impact on current currency performance. Moreover, we confirm that investor attention has a predictive power for forecasting emerging currency performance in the out-of-sample analysis. Further, these forecasts generate substantial economic value in the framework of asset allocation. By contrast, statistical predictability and economic value do not exist in the currencies from developed markets. These results indicate that investor attention can alter currency performance and its predictability. More broadly, our study emphasizes the potential of employing investor attention for emerging currency performance forecasting applications.     

International sign predictability of stock returns: The role of the United States

We study the directional predictability of monthly excess stock market returns in the U.S. and ten other markets using univariate and bivariate binary response models. We introduce a new bivariate (two-equation) probit model that allows us to examine the benefits of predicting the signs of returns jointly, focusing on the predictive power originating from the U.S. to foreign markets. Our in-sample and out-of-sample forecasting results indicate superior predictive performance of the new model over competing univariate binary response models, and conventional predictive regressions, by statistical measures and market timing performance. This highlights the importance of predictive information from the U.S. to the other markets providing also practical improvement in investors' market timing decisions.     

On a family of data-based transformed models useful in forecasting technological substitutions

This paper examines, via real data, some well known models for technology substitution analysis. We propose a family of data-based transformed models that will include the models under examination as special cases. The basic thrust of the paper is the recognition that for technology substitution analysis, the observations are time series data and hence are not independent. Also, the functional form of the model should be determined by both theoretical considerations as well as the data on hand. This suggests that the traditional ordinary least squares procedure used in estimating the parameters and the resulting forecasting procedures are not adequate. The existing models examined here are Fisher–Pry, Gompertz, Weibull, and Normal. We stress the statistical aspects of the models and their relative merits in terms of predictive power. The criteria used for the purpose of comparison are the mean squared deviation and the mean absolute deviation of the predicted values compared with the actual observations.     

The information content of money in forecasting euro area inflation

This article contributes to the debate on the role of money in monetary policy by analysing the information content of money in forecasting euro-area inflation. We compare the predictive performance within and among various classes of structural and empirical models in a consistent framework using Bayesian and other estimation techniques. We find that money contains relevant information for inflation in some model classes. Money-based New Keynesian Dynamic Stochastic General Equilibrium (DSGE) models and Vector Autoregressions (VARs) incorporating money perform better than their cashless counterparts. But there are also indications that the contribution of money has its limits. The marginal contribution of money to forecasting accuracy is often small, money adds little to dynamic factor models, and it worsens forecasting accuracy of partial equilibrium models. Finally, nonmonetary models dominate monetary models in an all-out horserace.     

Forecasting the aggregate stock market volatility in a data-rich world

ABSTRACT

In this article, we utilize the basic lasso and elastic net models to revisit the predictive performance of aggregate stock market volatility in a data-rich world. Motivated by the existing literature, we determine several candidate predictors that have 22 technical indicators and 14 macroeconomic and financial variables. Our out-of-sample results reveal several noteworthy findings. First, few macroeconomic and financial variables and most of technical indicators have superior performance relative to the benchmark model. Second, combination forecasts are able to significantly beat the benchmark and some signal predictors Third, the lasso and elastic models with all predictors can generate more accurate forecasts than the benchmark and some other predictors in both the statistical and economic sense. Fourth, the lasso and elastic models exhibit higher forecast accuracy during periods of expansions and recessions. Finally, our findings are robust to several tests, such as different forecasting windows, forecasting models, and forecasting evaluations.     

A bottom-up approach for forecasting GDP in a data-rich environment

In an increasingly data-rich environment, the use of factor models for forecasting purposes has gained prominence in the literature and among practitioners. Herein, we assess the forecasting behaviour of factor models to predict several GDP components and investigate the performance of a bottom-up approach to forecast GDP growth in Portugal, which was one of the hardest hit economies during the latest economic and financial crisis. We find supporting evidence of the usefulness of factor models and noteworthy forecasting gains when conducting a bottom-approach drawing on the main aggregates of GDP.     

Does non-linearity help us understand,model and forecast UK stock and bond returns: evidence from the BEYR

The usefulness of non-linear models to provide accurate estimates and forecasts remains an open empirical debate. This paper examines the nature of the estimated relationships and forecasting power of smooth-transition models for UK stock and bond returns using a range of financial and macroeconomic variables as predictors. Notably, evidence of non-linearity is stronger when the bond-equity yield ratio is used as the transition variable. This ratio measures whether stocks are over (under)-valued relative to bonds and can act as a signal for portfolio managers. In-sample results reveal noticeable differences regarding the nature of relationships between the linear and non-linear setting, while results of a recursive forecasting exercise reveal both statistical and economic improvement over a linear model. Overall, these results support the view that non-linear estimates and forecasts can provide useful information for stock market traders, portfolio managers and policy-makers.     

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.     

美国财务预测信息披露与监管的经验及借鉴

美国已基本形成了较为完备的财务预测信息披露与监管体系,其主要内容和值得借鉴的经验包括:对财务预测信息内容的完整界定;前瞻性信息和预测性信息区分标准的确立;安全港规则的确立;建立了由财务分析师为主体的独立专家预测体系.目前我国上市公司财务预测信息的披露和监管尚存在不少问题,需要在以下几方面改进和完善:建立完善的财务预测信息披露、监管规范体系,提高财务预测信息供给的有效性;明确企业管理当局对财务预测信息的编制责任;建立适合中国证券市场现实的安全港规则;加强对财务预测信息披露的审核.     

Volatility and VaR forecasting in the Madrid Stock Exchange

This paper provides an empirical study to assess the forecasting performance of a wide range of models for predicting volatility and VaR in the Madrid Stock Exchange. The models performance was measured by using different loss functions and criteria. The results show that FIAPARCH processes capture and forecast more accurately the dynamics of IBEX-35 returns volatility. It is also observed that assuming a heavy-tailed distribution does not improve models ability for predicting volatility. However, when the aim is forecasting VaR, we find evidence of that the Student’s t FIAPARCH outperforms the models it nests the lower the target quantile.      

Semiconductor industry cycles: Explanatory factors and forecasting

This paper aims to suggest the best forecasting model for the semiconductor market. A wide range of alternative modern econometric modeling approaches have been implemented, and a large variety of criteria and tests have been employed to assess the out-of-sample forecasting accuracy at various horizons. The results suggest that if a VECM can be an interesting source of information, the Bayesian models are superior forecasting tools compared to univariate and unrestricted VAR models. However, for decision makers a spectral method could be a useful tool, which can be easily implemented. In addition, MS-AR models make it possible to obtain valuable forecasts on turning-points in order to adjust the programming of heavy capital and research investments.     

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.