nep-for New Economics Papers
on Forecasting
Issue of 2019‒09‒02
ten papers chosen by
Rob J Hyndman
Monash University

  1. Forecast uncertainty, disagreement, and the linear pool By Knüppel, Malte; Krüger, Fabian
  2. How forecast accuracy depends on conditioning assumptions By Engelke, Carola; Heinisch, Katja; Schult, Christoph
  3. An artificial neural network augmented GARCH model for Islamic stock market volatility: Do asymmetry and long memory matter? By Manel Hamdi; Walid Chkili
  4. Machine Learning vs Traditional Forecasting Methods: An Application to South African GDP By Lisa-Cheree Martin
  5. Monthly Forecasting of GDP with Mixed Frequency Multivariate Singular Spectrum Analysis By António Rua; Hossein Hassani; Emmanuel Sirimal Silva; Dimitrios Thomakos
  6. Quantitative portfolio selection: using density forecasting to find consistent portfolios By N. Meade; J. E. Beasley; C. J. Adcock
  7. Conditional variance forecasts for long-term stock returns By Enno Mammen; Jens Perch Nielsen; Michael Scholz; Stefan Sperlich
  8. Wheat Futures Trading Volume Forecasting and the Value of Extended Trading Hours By Janzen, Joseph; Legrand, Nicolas
  9. Intra-day Equity Price Prediction using Deep Learning as a Measure of Market Efficiency By David Byrd; Tucker Hybinette Balch
  10. Forecasting e-scooter competition with direct and access trips by mode and distance in New York City By Mina Lee; Joseph Y. J. Chow; Gyugeun Yoon; Brian Yueshuai He

  1. By: Knüppel, Malte; Krüger, Fabian
    Abstract: The linear pool is the most popular method for combining density forecasts. We analyze the linear pool's implications concerning forecast uncertainty in a new theoretical framework that focuses on the mean and variance of each density forecast to be combined. Our results show that, if the variance predictions of the individual forecasts are unbiased, the well-known 'disagreement' component of the linear pool exacerbates the upward bias of the linear pool's variance prediction. Moreover, we find that disagreement has no predictive content for ex-post forecast uncertainty under conditions which can be empirically relevant. These findings suggest the removal of the disagreement component from the linear pool. The resulting centered linear pool outperforms the linear pool in simulations and in empirical applications to inflation and stock returns.
    JEL: C32 C53
    Date: 2019
  2. By: Engelke, Carola; Heinisch, Katja; Schult, Christoph
    Abstract: This paper examines the extent to which errors in economic forecasts are driven by initial assumptions that prove to be incorrect ex post. Therefore, we construct a new data set comprising an unbalanced panel of annual forecasts from different institutions forecasting German GDP and the underlying assumptions. We explicitly control for different forecast horizons to proxy the information available at the release date. Over 75% of squared errors of the GDP forecast comove with the squared errors in their underlying assumptions. The root mean squared forecast error for GDP in our regression sample of 1.52% could be reduced to 1.13% by setting all assumption errors to zero. This implies that the accuracy of the assumptions is of great importance and that forecasters should reveal the framework of their assumptions in order to obtain useful policy recommendations based on economic forecasts.
    Keywords: forecasts,accuracy,forecast errors,external assumptions,forecast efficiency,forecast horizon
    JEL: C53 E02 E32
    Date: 2019
  3. By: Manel Hamdi (International Financial Group-Tunisia, Faculty of Economics and Management of Tunis, University of Tunis); Walid Chkili (International Financial Group-Tunisia, Faculty of Economics and Management of Tunis, University of Tunis)
    Abstract: The aim of this paper is to study the volatility and forecast accuracy of the Islamic stock market. For this purpose, we construct a new hybrid GARCH-type models based on artificial neural network (ANN). This model is applied to daily prices for DW Islamic markets during the period June 1999-December 2016. Our in-sample results show that volatility of Islamic stock market can be better described by the FIAPARCH approach that take into account asymmetry and long memory features. Considering the out of sample analysis, we have applied a hybrid forecasting model, which combines the FIAPARCH approach and the artificial neural network (ANN). Empirical results show that the proposed hybrid model (FIAPARCH-ANN) outperforms all other single models such as GARCH, FIGARCH, FIAPARCH in terms of all performance criteria used in our study.
    Date: 2019–08–21
  4. By: Lisa-Cheree Martin (Department of Economics, Stellenbosch University)
    Abstract: This study employs traditional autoregressive and vector autoregressive forecasting models, as well as machine learning methods of forecasting, in order to compare the performance of each of these techniques. Each technique is used to forecast the percentage change of quarterly South African Gross Domestic Product, quarter-on-quarter. It is found that machine learning methods outperform traditional methods according to the chosen criteria of minimising root mean squared error and maximising correlation with the actual trend of the data. Overall, the outcomes suggest that machine learning methods are a viable option for policy-makers to use, in order to aid their decision-making process regarding trends in macroeconomic data. As this study is limited by data availability, it is recommended that policy-makers consider further exploration of these techniques.
    Keywords: Machine learning, Forecasting, Elastic-net, Random Forests, Support Vector Machines, Recurrent Neural Networks
    JEL: C32 C45 C53 C88
    Date: 2019
  5. By: António Rua; Hossein Hassani; Emmanuel Sirimal Silva; Dimitrios Thomakos
    Abstract: The literature on mixed-frequency models is relatively recent and has found applications across economics and finance. The standard application in economics considers the use of (usually) monthly variables (e.g. industrial production) in predicting/fitting quarterly variables (e.g. real GDP). In this paper we propose a Multivariate Singular Spectrum Analysis (MSSA) based method for mixed frequency interpolation and forecasting, which can be used for any mixed frequency combination. The novelty of the proposed approach rests on the grounds of simplicity within the MSSA framework. We present our method using a combination of monthly and quarterly series and apply MSSA decomposition and reconstruction to obtain monthly estimates and forecasts for the quarterly series. Our empirical application shows that the suggested approach works well, as it offers forecasting improvements on a dataset of eleven developed countries over the last 50 years. The implications for mixed frequency modelling and forecasting, and useful extensions of this method, are also discussed.
    JEL: C1 C53 E1
    Date: 2019
  6. By: N. Meade; J. E. Beasley; C. J. Adcock
    Abstract: In the knowledge that the ex-post performance of Markowitz efficient portfolios is inferior to that implied ex-ante, we make two contributions to the portfolio selection literature. Firstly, we propose a methodology to identify the region of risk-expected return space where ex-post performance matches ex-ante estimates. Secondly, we extend ex-post efficient set mathematics to overcome the biases in the estimation of the ex-ante efficient frontier. A density forecasting approach is used to measure the accuracy of ex-ante estimates using the Berkowitz statistic, we develop this statistic to increase its sensitivity to changes in the data generating process. The area of risk-expected return space where the density forecasts are accurate, where ex-post performance matches ex-ante estimates, is termed the consistency region. Under the 'laboratory' conditions of a simulated multivariate normal data set, we compute the consistency region and the estimated ex-post frontier. Over different sample sizes used for estimation, the behaviour of the consistency region is shown to be both intuitively reasonable and to enclose the estimated ex-post frontier. Using actual data from the constituents of the US Dow Jones 30 index, we show that the size of the consistency region is time dependent and, in volatile conditions, may disappear. Using our development of the Berkowitz statistic, we demonstrate the superior performance of an investment strategy based on consistent rather than efficient portfolios.
    Date: 2019–08
  7. By: Enno Mammen (University of Heidelberg, Germany); Jens Perch Nielsen (Cass Business School, City, University of London, UK); Michael Scholz (University of Graz, Austria); Stefan Sperlich (Universite de Geneve, Switzerland)
    Abstract: In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, including the short-term interest rate, long-term interest rate, earnings-by-price ratio, and inflation. In particular, we apply and implement in a two-step procedure a fully nonparametric smoother with the covariates and the smoothing parameters chosen via cross-validation. We find that volatility forecastability is much less important at longer horizons regardless of the chosen model and that the homoscedastic historical average of the squared return prediction errors gives an adequate approximation of the unobserved realized conditional variance for both the one-year and five-year horizon.
    Keywords: Benchmark; Cross-validation; Prediction; Stock return volatility; Long-term forecasts; Overlapping returns; Autocorrelation
    JEL: C14 C53 C58 G17 G22
    Date: 2019–08
  8. By: Janzen, Joseph; Legrand, Nicolas
    Keywords: Demand and Price Analysis
    Date: 2019–06–25
  9. By: David Byrd; Tucker Hybinette Balch
    Abstract: In finance, the weak form of the Efficient Market Hypothesis asserts that historic stock price and volume data cannot inform predictions of future prices. In this paper we show that, to the contrary, future intra-day stock prices could be predicted effectively until 2009. We demonstrate this using two different profitable machine learning-based trading strategies. However, the effectiveness of both approaches diminish over time, and neither of them are profitable after 2009. We present our implementation and results in detail for the period 2003-2017 and propose a novel idea: the use of such flexible machine learning methods as an objective measure of relative market efficiency. We conclude with a candidate explanation, comparing our returns over time with high-frequency trading volume, and suggest concrete steps for further investigation.
    Date: 2019–08
  10. By: Mina Lee; Joseph Y. J. Chow; Gyugeun Yoon; Brian Yueshuai He
    Abstract: Given the lack of demand forecasting models for e-scooter sharing systems, we address this research gap using data from Portland, OR, and New York City. A log-log regression model is estimated for e-scooter trips based on user age, income, labor force participation, and health insurance coverage, with an adjusted R squared value of 0.663. When applied to the Manhattan market, the model predicts 66K daily e-scooter trips, which would translate to 67 million USD in annual revenue (based on average 12-minute trips and historical fare pricing models). We propose a novel nonlinear, multifactor model to break down the number of daily trips by the alternate modes of transportation that they would likely substitute. The final model parameters reveal a relationship with taxi trips as well as access/egress trips with public transit in Manhattan. Our model estimates that e-scooters would replace at most 1% of taxi trips; the model can explain $800,000 of the annual revenue from this competition. The distance structure of revenue from access/egress trips is found to differ significantly from that of substituted taxi trips.
    Date: 2019–08

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