nep-for New Economics Papers
on Forecasting
Issue of 2014‒07‒05
eight papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting with DSGE models with financial frictions By Kolasa, Marcin; Rubaszek, Michał
  2. Do Media Data Help to Predict German Industrial Production? By Konstantin A. Kholodilin; Tobias Thomas; Dirk Ulbricht
  3. Forecasting In a Non-Linear DSGE Model By Sergey Ivashchenko
  4. Forecasting inflation at the Central Bank of Malta� By Gatt, William
  5. Forecasting with a mismatch-enhanced labor market matching function By Hutter, Christian; Weber, Enzo
  6. Predictability of Volatility Homogenised Financial Time Series By Pawe{\l} Fiedor; Odd Magnus Trondrud
  7. Stock Market Predictability: Global Evidence and an Explanation By Cheolbeom Park; Dong-hun Shin
  8. Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization By Felix Ming Fai Wong; Zhenming Liu; Mung Chiang

  1. By: Kolasa, Marcin; Rubaszek, Michał
    Abstract: This paper compares the quality of forecasts from DSGE models with and without financial frictions. We find that accounting for financial market imperfections does not result in a uniform improvement in the accuracy of point forecasts during non-crisis times while the average quality of density forecast even deteriorates. In contrast, adding frictions in the housing market proves very helpful during the times of financial turmoil, overperforming both the frictionless benchmark and the alternative that incorporates financial frictions in the corporate sector. Moreover, we detect complementarities among the analyzed setups that can be exploited in the forecasting process.
    Keywords: DSGE models; Financial frictions; Housing market
    JEL: C11 C53 E44
    Date: 2014–06
  2. By: Konstantin A. Kholodilin; Tobias Thomas; Dirk Ulbricht
    Abstract: Expectations form the basis of economic decisions of market participants in an uncertain world. Sentiment indicators reflect those expectations and thus have a proven track record for predicting economic variables. However, respondents of surveys perceive the world to a large extent with the help of media. So far, mainly very crude media information, such as word-count indices, has been used in the prediction of macroeconomic and financial variables. In this paper, we employ a rich data set provided by Media Tenor International, based on the sentiment analysis of all relevant media information in Germany from 2001 to 2014, whose results are transformed into several monthly indices. German industrial production is predicted in a real-time out-of-sample forecasting experiment using more than 17,000 models formed of all possible combinations with a maximum of 3 out of 48 macroeconomic, survey, and media indicators. It is demonstrated that media data are indispensable when it comes to the prediction of German industrial production both for individual models and as a part of combined forecasts. They increase reliability by improving accuracy and reducing instability of the forecasts, particularly during the recent global financial crisis.
    Keywords: Forecast combination, media data, German industrial production, reliability index, R-word
    JEL: C10 C52 C53 E32
    Date: 2014
  3. By: Sergey Ivashchenko
    Abstract: A medium-scale nonlinear dynamic stochastic general equilibrium (DSGE) model is estimated (54 variables, 29 state variables, 7 observed variables). The model includes a observed variable for stock market returns. The root-mean square error (RMSE) of the in-sample and out-of-sample forecasts is calculated. The nonlinear DSGE model with measurement errors outperforms AR (1), VAR (1) and the linearized DSGE in terms of the quality of the out-of-sample forecasts. The nonlinear DSGE model without measurement errors is actually of a quality equal to that of the linearized DSGE model.
    Keywords: nonlinear DSGE, Quadratic Kalman Filter, QKF, out-of-sample forecasts
    JEL: E32 E37 E44 E47
    Date: 2014–05–17
  4. By: Gatt, William
    Abstract: A short, non-technical description of how inflation forecasts are conducted at the Central Bank of Malta
    Keywords: HICP inflation, ARIMA, judgement
    JEL: C32 E37 E58
    Date: 2013–03
  5. By: Hutter, Christian (Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany]); Weber, Enzo (Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany])
    Abstract: "This paper investigates the role of mismatch between job seekers and job openings for the forecasting performance of a labor market matching function. In theory, higher mismatch lowers matching efficiency which increases the risk that the vacancies cannot be filled within the usual period of time. We investigate whether and to what extent forecasts of German job findings can be improved by a mismatch-enhanced labor market matching function. For this purpose, we construct so-called mismatch indicators that reflect regional, occupational and qualification-related mismatch on a monthly basis. In pseudo out-of-sample tests that account for the nested model environment, we find that forecasting models enhanced by the mismatch indicator significantly outperform their benchmark counterparts for all forecast horizons ranging between one month and a year. This is especially pronounced in the aftermath of the Great Recession where a low level of mismatch improved the possibility of unemployed to find a job again." (Author's abstract, IAB-Doku) ((en))
    Keywords: mismatch, offene Stellen, Arbeitslose, Qualifikationsanforderungen, Qualifikationsmerkmale, Prognoseverfahren, Stellenbesetzung - Dauer, Indikatorenbildung
    JEL: C22 C52 C53 C78 E24 E27
    Date: 2014–06–25
  6. By: Pawe{\l} Fiedor; Odd Magnus Trondrud
    Abstract: Modelling financial time series as a time change of a simpler process has been proposed in various forms over the years. One of such recent approaches is called volatility homogenisation decomposition, and has been designed specifically to aid the forecasting of price changes on financial markets. The authors of this method have attempted to prove the its usefulness by applying a specific forecasting procedure and determining the effectiveness of this procedure on the decomposed time series, as compared with the original time series. This is problematic in at least two ways. First, the choice of the forecasting procedure obviously has an effect on the results, rendering them non-exhaustive. Second, the results obtained were not completely convincing, with some values falling under 50% guessing rate. Additionally, only nine Australian stocks were being investigated, which further limits the scope of this proof. In this study we propose to find the usefulness of volatility homogenisation by calculating the predictability of the decomposed time series and comparing it to the predictability of the original time series. We are applying information-theoretic notion of entropy rate to quantify predictability, which guarantees the result is not tied to a specific method of prediction, and additionally we base our calculations on a large number of stocks from the Warsaw Stock Exchange.
    Date: 2014–06
  7. By: Cheolbeom Park (Department of Economics, Korea University, Seoul, Republic of Korea); Dong-hun Shin (Department of Economics, Korea University, Seoul, Republic of Korea)
    Abstract: Using a comprehensive dataset covering 34 countries from Datastream, we find that dividend-price ratio has a broad spectrum of forecasting abilities internationally. In some countries, such as the US, the dividend-price ratio is a powerful predictor of exclusively stock returns, whereas in others it is a powerful predictor of exclusively dividend growth rates. For many countries, however, the dividend-price ratio has some predictive power for both stock returns and dividend growth rates, although the relative degree of predictive power differs. We have provided an explanation for these differences in stock market predictabilities between countries. When a firm with a dominant shareholder is publicly traded, then the dominant shareholder determines cash-flow policy but the stochastic discount factor contained in the stock price may reflect the minority shareholders¡¯ stochastic discount factor. For this reason, the correlation between cash-flow and stochastic discount factor approaches zero as the disparity between voting rights and cash-flow rights increases. As a result, the stock price becomes more dependent on expected dividends than expected stock returns, and, therefore, the dividend-price ratio has a stronger predictive power for dividend growth. Consistent with our explanation, we find a strong positive relation between dividend growth predictability and disparity, but a significantly negative one between stock return predictability and disparity. These relations are found to be consistent across various robust tests.
    Keywords: Dividend-price ratio, Stock returns, Dividend growth, Predictability, Disparity
    JEL: G12 G32 E44
    Date: 2014
  8. By: Felix Ming Fai Wong; Zhenming Liu; Mung Chiang
    Abstract: We revisit the problem of predicting directional movements of stock prices based on news articles: here our algorithm uses daily articles from The Wall Street Journal to predict the closing stock prices on the same day. We propose a unified latent space model to characterize the "co-movements" between stock prices and news articles. Unlike many existing approaches, our new model is able to simultaneously leverage the correlations: (a) among stock prices, (b) among news articles, and (c) between stock prices and news articles. Thus, our model is able to make daily predictions on more than 500 stocks (most of which are not even mentioned in any news article) while having low complexity. We carry out extensive backtesting on trading strategies based on our algorithm. The result shows that our model has substantially better accuracy rate (55.7%) compared to many widely used algorithms. The return (56%) and Sharpe ratio due to a trading strategy based on our model are also much higher than baseline indices.
    Date: 2014–06

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