Forecasting Inflation Rates Using Daily Data: A Nonparametric MIDAS Approach

In this paper a nonparametric approach for estimating mixed‐frequency forecast equations is proposed. In contrast to the popular MIDAS approach that employs an (exponential) Almon or Beta lag distribution, we adopt a penalized least‐squares estimator that imposes some degree of smoothness to the lag distribution. This estimator is related to nonparametric estimation procedures based on cubic splines and resembles the popular Hodrick–Prescott filtering technique for estimating a smooth trend function. Monte Carlo experiments suggest that the nonparametric estimator may provide more reliable and flexible approximations to the actual lag distribution than the conventional parametric MIDAS approach based on exponential lag polynomials. Parametric and nonparametric methods are applied to assess the predictive power of various daily indicators for forecasting monthly inflation rates. It turns out that the commodity price index is a useful predictor for inflations rates 20–30 days ahead with a hump‐shaped lag distribution. Copyright © 2015 John Wiley & Sons, Ltd.     

A Robust Data‐Mining Approach to Bankruptcy Prediction

In this study, new variants of genetic programming (GP), namely gene expression programming (GEP) and multi‐expression programming (MEP), are utilized to build models for bankruptcy prediction. Generalized relationships are obtained to classify samples of 136 bankrupt and non‐bankrupt Iranian corporations based on their financial ratios. An important contribution of this paper is to identify the effective predictive financial ratios on the basis of an extensive bankruptcy prediction literature review and upon a sequential feature selection analysis. The predictive performance of the GEP and MEP forecasting methods is compared with the performance of traditional statistical methods and a generalized regression neural network. The proposed GEP and MEP models are effectively capable of classifying bankrupt and non‐bankrupt firms and outperform the models developed using other methods. Copyright © 2011 John Wiley & Sons, Ltd.     

Nonparametric density forecast based on time‐ and state‐domain

We propose a new nonparametric density forecast based on time‐ and state‐domain smoothing. We analyze some of its asymptotic properties and provide an empirical illustration. Copyright © 2010 John Wiley & Sons, Ltd.     

A Meta‐learning Framework for Bankruptcy Prediction

The implication of corporate bankruptcy prediction is important to financial institutions when making lending decisions. In related studies, many bankruptcy prediction models have been developed based on some machine‐learning techniques. This paper presents a meta‐learning framework, which is composed of two‐level classifiers for bankruptcy prediction. The first‐level multiple classifiers perform the data reduction task by filtering out unrepresentative training data. Then, the outputs of the first‐level classifiers are utilized to create the second‐level single (meta) classifier. The experiments are based on five related datasets and the results show that the proposed meta‐learning framework provides higher prediction accuracy rates and lower type I/II errors when compared with the stacked generalization classifier and other three widely developed baselines, such as neural networks, decision trees, and logistic regression. Copyright © 2011 John Wiley & Sons, Ltd.     

Forecasting Performance of Nonlinear Models for Intraday Stock Returns

We studied the predictability of intraday stock market returns using both linear and nonlinear time series models. For the S&P 500 index we compared simple autoregressive and random walk linear models with a range of nonlinear models, including smooth transition, Markov switching, artificial neural network, nonparametric kernel regression and support vector machine models for horizons of 5, 10, 20, 30 and 60 minutes. The empirical results indicate that nonlinear models outperformed linear models on the basis of both statistical and economic criteria. Specifically, although return serial correlation receded by around 10 minutes, return predictability still persisted for up to 60 minutes according to nonlinear models, even though profitability decreases as time elapses. More flexible nonlinear models such as support vector machines and artificial neural network did not clearly outperform other nonlinear models. Copyright © 2011 John Wiley & Sons, Ltd.     

A semi‐parametric time series approach in modeling hourly electricity loads

In this paper we develop a semi‐parametric approach to model nonlinear relationships in serially correlated data. To illustrate the usefulness of this approach, we apply it to a set of hourly electricity load data. This approach takes into consideration the effect of temperature combined with those of time‐of‐day and type‐of‐day via nonparametric estimation. In addition, an ARIMA model is used to model the serial correlation in the data. An iterative backfitting algorithm is used to estimate the model. Post‐sample forecasting performance is evaluated and comparative results are presented. Copyright © 2006 John Wiley & Sons, Ltd.     

Modeling and Forecasting the Yield Curve by an Extended Nelson‐Siegel Class of Models: A Quantile Autoregression Approach

This paper compares the in‐sample fitting and the out‐of‐sample forecasting performances of four distinct Nelson–Siegel class models: Nelson–Siegel, Bliss, Svensson, and a five‐factor model we propose in order to enhance the fitting flexibility. The introduction of the fifth factor resulted in superior adjustment to the data. For the forecasting exercise the paper contrasts the performances of the term structure models in association with the following econometric methods: quantile autoregression evaluated at the median, VAR, AR, and a random walk. As a pattern, the quantile procedure delivered the best results for longer forecasting horizons. Copyright © 2011 John Wiley & Sons, Ltd.     

Direction‐of‐Change Financial Time Series Forecasting using a Similarity‐Based Classification Model

A similarity‐based classification model is proposed whereby densities of positive and negative returns in a delay‐embedded input space are estimated from a graphical representation of the data using an eigenvector centrality measure, and subsequently combined under Bayes' theorem to predict the probability of upward/downward movements. Application to directional forecasting of the daily close price of the Dow Jones Industrial Average over a 20‐year out‐of‐sample period yields performance superior to random walk and logistic regression models, and on a par with that of multilayer perceptrons. A feature of the classifier is that it is parameter free, parameters entering the model only via the measure used to determine pairwise similarity between data points. This allows intuitions about the nature of time series to be elegantly integrated into the model. The recursive nature of eigenvector centrality makes it better able to deal with sparsely populated input spaces than conventional approaches based on density estimation. Copyright © 2013 John Wiley & Sons, Ltd.     

Using CAViaR Models with Implied Volatility for Value‐at‐Risk Estimation

This paper proposes value‐at risk (VaR) estimation methods that are a synthesis of conditional autoregressive value at risk (CAViaR) time series models and implied volatility. The appeal of this proposal is that it merges information from the historical time series and the different information supplied by the market's expectation of risk. Forecast‐combining methods, with weights estimated using quantile regression, are considered. We also investigate plugging implied volatility into the CAViaR models—a procedure that has not been considered in the VaR area so far. Results for daily index returns indicate that the newly proposed methods are comparable or superior to individual methods, such as the standard CAViaR models and quantiles constructed from implied volatility and the empirical distribution of standardised residuals. We find that the implied volatility has more explanatory power as the focus moves further out into the left tail of the conditional distribution of S&P 500 daily returns. Copyright © 2012 John Wiley & Sons, Ltd.     

Using accounting‐based information on young firms to predict bankruptcy

This study analyzes the nonlinear relationships between accounting‐based key performance indicators and the probability that the firm in question will become bankrupt or not. The analysis focuses particularly on young firms and examines whether these nonlinear relationships are affected by a firm's age. The analysis of nonlinear relationships between various predictors of bankruptcy and their interaction effects is based on a structured additive regression model and on a comprehensive data set on German firms. The results of this analysis provide empirical evidence that a firm's age has a considerable effect on how accounting‐based key performance indicators can be used to predict the likelihood that a firm will go bankrupt. More specifically, the results show that there are differences between older firms and young firms with respect to the nonlinear effects of the equity ratio, the return on assets, and the sales growth on their probability of bankruptcy.     

Forecasting Simultaneously High‐Dimensional Time Series: A Robust Model‐Based Clustering Approach

This paper considers the problem of forecasting high‐dimensional time series. It employs a robust clustering approach to perform classification of the component series. Each series within a cluster is assumed to follow the same model and the data are then pooled for estimation. The classification is model‐based and robust to outlier contamination. The robustness is achieved by using the intrinsic mode functions of the Hilbert–Huang transform at lower frequencies. These functions are found to be robust to outlier contamination. The paper also compares out‐of‐sample forecast performance of the proposed method with several methods available in the literature. The other forecasting methods considered include vector autoregressive models with ∕ without LASSO, group LASSO, principal component regression, and partial least squares. The proposed method is found to perform well in out‐of‐sample forecasting of the monthly unemployment rates of 50 US states. Copyright © 2013 John Wiley & Sons, Ltd.     

Semi‐Structural Forecasting of UK Inflation Based on the Hybrid New Keynesian Phillips Curve

We develop a semi‐structural model for forecasting inflation in the UK in which the New Keynesian Phillips curve (NKPC) is augmented with a time series model for marginal cost. By combining structural and time series elements we hope to reap the benefits of both approaches, namely the relatively better forecasting performance of time series models in the short run and a theory‐consistent economic interpretation of the forecast coming from the structural model. In our model we consider the hybrid version of the NKPC and use an open‐economy measure of marginal cost. The results suggest that our semi‐structural model performs better than a random‐walk forecast and most of the competing models (conventional time series models and strictly structural models) only in the short run (one quarter ahead) but it is outperformed by some of the competing models at medium and long forecast horizons (four and eight quarters ahead). In addition, the open‐economy specification of our semi‐structural model delivers more accurate forecasts than its closed‐economy alternative at all horizons. Copyright © 2014 John Wiley & Sons, Ltd.     

Forecasting real‐time data allowing for data revisions

A modeling approach to real‐time forecasting that allows for data revisions is shown. In this approach, an observed time series is decomposed into stochastic trend, data revision, and observation noise in real time. It is assumed that the stochastic trend is defined such that its first difference is specified as an AR model, and that the data revision, obtained only for the latest part of the time series, is also specified as an AR model. The proposed method is applicable to the data set with one vintage. Empirical applications to real‐time forecasting of quarterly time series of US real GDP and its eight components are shown to illustrate the usefulness of the proposed approach. Copyright © 2007 John Wiley & Sons, Ltd.     

LASSO‐Type Penalties for Covariate Selection and Forecasting in Time Series

This paper studies some forms of LASSO‐type penalties in time series to reduce the dimensionality of the parameter space as well as to improve out‐of‐sample forecasting performance. In particular, we propose a method that we call WLadaLASSO (weighted lag adaptive LASSO), which assigns not only different weights to each coefficient but also further penalizes coefficients of higher‐lagged covariates. In our Monte Carlo implementation, the WLadaLASSO is superior in terms of covariate selection, parameter estimation precision and forecasting, when compared to both LASSO and adaLASSO, especially for a higher number of candidate lags and a stronger linear dependence between predictors. Empirical studies illustrate our approach for US risk premium and US inflation forecasting with good results. Copyright © 2016 John Wiley & Sons, Ltd.     

Accounting for Word‐of‐Mouth Effects in Preference‐Based Market Forecasts

Consumers differ in their involvement in new product purchase decisions. Opinion leaders usually show a higher involvement in their purchase decisions than other consumers. This leads to a higher stability in their answers when being asked about their preferences. An important question that previous research has not analyzed yet is whether and how to capture this finding in preference‐based market forecasts. The authors study these aspects for a representative sample of 364 consumers in the mobile phone market of a large European country. They find that assigning higher weights to the preferences of opinion leaders in aggregate market forecasts results in estimates that are more consistent with observed market shares than forecasts in which all consumers are given equal weights. The authors further test different measures of opinion leadership and find that sociometric indicators outperform psychographic constructs to account for the outcome of opinion leadership in preference‐based market forecasts. Copyright © 2013 John Wiley & Sons, Ltd.     

Adaptive Evolutionary Neural Networks for Forecasting and Trading without a Data‐Snooping Bias

In this paper, we present two neural‐network‐based techniques: an adaptive evolutionary multilayer perceptron (aDEMLP) and an adaptive evolutionary wavelet neural network (aDEWNN). The two models are applied to the task of forecasting and trading the SPDR Dow Jones Industrial Average (DIA), the iShares NYSE Composite Index Fund (NYC) and the SPDR S&P 500 (SPY) exchange‐traded funds (ETFs). We benchmark their performance against two traditional MLP and WNN architectures, a smooth transition autoregressive model (STAR), a moving average convergence/divergence model (MACD) and a random walk model. We show that the proposed architectures present superior forecasting and trading performance compared to the benchmarks and are free from the limitations of the traditional neural networks such as the data‐snooping bias and the time‐consuming and biased processes involved in optimizing their parameters. Copyright © 2015 John Wiley & Sons, Ltd.     

Exponentially Smoothing the Skewed Laplace Distribution for Value‐at‐Risk Forecasting

Value‐at‐risk (VaR) is a standard measure of market risk in financial markets. This paper proposes a novel, adaptive and efficient method to forecast both volatility and VaR. Extending existing exponential smoothing as well as GARCH formulations, the method is motivated from an asymmetric Laplace distribution, where skewness and heavy tails in return distributions, and their potentially time‐varying nature, are taken into account. The proposed volatility equation also involves novel time‐varying dynamics. Back‐testing results illustrate that the proposed method offers a viable, and more accurate, though conservative, improvement in forecasting VaR compared to a range of popular alternatives. Copyright © 2013 John Wiley & Sons, Ltd.     

A Neuro‐wavelet Model for the Short‐Term Forecasting of High‐Frequency Time Series of Stock Returns

We propose a wavelet neural network (neuro‐wavelet) model for the short‐term forecast of stock returns from high‐frequency financial data. The proposed hybrid model combines the capability of wavelets and neural networks to capture non‐stationary nonlinear attributes embedded in financial time series. A comparison study was performed on the predictive power of two econometric models and four recurrent neural network topologies. Several statistical measures were applied to the predictions and standard errors to evaluate the performance of all models. A Jordan net that used as input the coefficients resulting from a non‐decimated wavelet‐based multi‐resolution decomposition of an exogenous signal showed a consistent superior forecasting performance. Reasonable forecasting accuracy for the one‐, three‐ and five step‐ahead horizons was achieved by the proposed model. The procedure used to build the neuro‐wavelet model is reusable and can be applied to any high‐frequency financial series to specify the model characteristics associated with that particular series. Copyright © 2013 John Wiley & Sons, Ltd.     

The role of age‐structured education data for economic growth forecasts

This paper utilizes for the first time age‐structured human capital data for economic growth forecasting. We concentrate on pooled cross‐country data of 65 countries over six 5‐year periods (1970–2000) and consider specifications chosen by model selection criteria, Bayesian model averaging methodologies based on in‐sample and out‐of‐sample goodness of fit and on adaptive regression by mixing. The results indicate that forecast averaging and exploiting the demographic dimension of education data improve economic growth forecasts systematically. In particular, the results are very promising for improving economic growth predictions in developing countries. Copyright © 2009 John Wiley & Sons, Ltd.     

Comparison of two non‐parametric models for daily traffic forecasting in Hong Kong

The most up‐to‐date annual average daily traffic (AADT) is always required for transport model development and calibration. However, the current‐year AADT data are not always available. The short‐term traffic flow forecasting models can be used to predict the traffic flows for the current year. In this paper, two non‐parametric models, non‐parametric regression (NPR) and Gaussian maximum likelihood (GML), are chosen for short‐term traffic forecasting based on historical data collected for the annual traffic census (ATC) in Hong Kong. These models are adapted as they are more flexible and efficient in forecasting the daily vehicular flows in the Hong Kong ATC core stations (in total of 87 stations). The daily vehicular flows predicted by these models are then used to calculate the AADT of the current year, 1999. The overall prediction and comparison results show that the NPR model produces better forecasts than the GML model using the ATC data in Hong Kong. Copyright © 2006 John Wiley _ Sons, Ltd.