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on Forecasting |
| By: | Yue-Jun Zhang (Business School, Hunan University, Changsha 410082, China; Center for Resource and Environmental Management, Hunan University, Changsha 410082, China); Han Zhang (Business School, Hunan University, Changsha 410082, China; Center for Resource and Environmental Management, Hunan University, Changsha 410082, China); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | Forecasting of the artificial intelligence index returns is of great significance for financial market stability and the development of artificial intelligence industry. To provide investors more reliable reference in terms of artificial intelligence index investment, this paper selects the Nasdaq CTA Artificial Intelligence and Robotics (AI) Index as the research target, and proposes novel hybrid methods to forecast the AI index returns by considering its nonlinear and time-varying characteristics. Specifically, this paper uses the ensemble empirical mode decomposition (EEMD) method to decompose the AI index returns, and combines the least square support vector machine approach together with the particle swarm optimization (PSO-LSSVM) method and the generalized autoregressive conditional heteroskedasticity (GARCH) model to construct novel hybrid forecasting methods. The empirical results indicate that: first, the decomposition and integration models usually produce superior forecasting accuracy than the single forecasting models, due to the complicated feature of the non-decomposed data. Second, the newly proposed hybrid forecasting method (i.e., the EEMD-PSO-LSSVM-GARCH model) which combines the advantage of traditional econometric models and machine learning techniques can yield the optimal forecasting performance for the AI index returns. |
| Keywords: | AI index return forecasting, PSO-LSSVM model, GARCH model, Decomposition and integration model, Combination model |
| JEL: | Q43 G15 E37 |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202182&r= |
| By: | Sattarhoff, Cristina; Lux, Thomas |
| Abstract: | We adapt the multifractal random walk model by Bacry et al. (2001) to realized volatilities (denoted RV-MRW) and take stock of recent theoretical insights on this model in Duchon et al. (2012) to derive forecasts of financial volatility. Moreover, we propose a new extension of the binomial Markov-switching multifractal (BMSM) model by Calvet and Fisher (2001) to the RV framework. We compare the predictive ability of the two against seven classical and multifractal volatility models. Forecasting performance is evaluated out-of-sample based on the empirical MSE and MAE as well as using model confidence sets following the methodology of Hansen et al. (2011). Overall, our empirical study for 14 international stock market indices has a clear message: The RV-MRW is throughout the best model for all forecast horizons under the MAE criterium as well as for large forecast horizons h=50 and 100 days under the MSE criterion. Moreover, the RV-MRW provides most accurate 20-day ahead forecasts in terms of MSE for the great majority of indices, followed by RV-ARFIMA, the latter dominating the competition at the 5-day-horizon. These results are very promising if we consider that this is the first empirical application of the RV-MRW. Moreover, whereas RV-ARFIMA forecasts are often a time consuming task, the RV-MRW stands out due to its fast execution and straightforward implementation. The new RV-BMSM appears to be specialized in short term forecasting, the model providing most accurate one-day ahead forecasts in terms of MSE for the same number of cases as RV-ARFIMA. |
| Keywords: | Realized volatility,multiplicative volatility models,multifractal random walk,longmemory,international volatility forecasting |
| JEL: | C20 G12 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cauewp:202102&r= |
| By: | Jeronymo Marcondes Pinto; Jennifer L. Castle |
| Abstract: | Forecasting economic indicators is an important task for analysts. However, many indicators suffer from structural breaks leading to forecast failure. Methods that are robust following a structural break have been proposed in the literature but they come at a cost: an increase in forecast error variance. We propose a method to select between a set of robust and non-robust forecasting models. Our method uses time-series clustering to identify possible structural breaks in a time series, and then switches between forecasting models depending on the series dynamics. We perform a rigorous empirical evaluation with 400 simulated series with an artificial structural break and with real data economic series: Industrial Production and Consumer Prices for all Western European countries available from the OECD database. Our results show that the proposed method statistically outperforms benchmarks in forecast accuracy for most case scenarios, particularly at short horizons. |
| Keywords: | Machine Learning, Forecasting, Structural Breaks, Model Selection, Cluster Analysis |
| Date: | 2021–10–13 |
| URL: | http://d.repec.org/n?u=RePEc:oxf:wpaper:950&r= |
| By: | Jaydip Sen; Saikat Mondal; Sidra Mehtab |
| Abstract: | Predictive model design for accurately predicting future stock prices has always been considered an interesting and challenging research problem. The task becomes complex due to the volatile and stochastic nature of the stock prices in the real world which is affected by numerous controllable and uncontrollable variables. This paper presents an optimized predictive model built on long-and-short-term memory (LSTM) architecture for automatically extracting past stock prices from the web over a specified time interval and predicting their future prices for a specified forecast horizon, and forecasts the future stock prices. The model is deployed for making buy and sell transactions based on its predicted results for 70 important stocks from seven different sectors listed in the National Stock Exchange (NSE) of India. The profitability of each sector is derived based on the total profit yielded by the stocks in that sector over a period from Jan 1, 2010 to Aug 26, 2021. The sectors are compared based on their profitability values. The prediction accuracy of the model is also evaluated for each sector. The results indicate that the model is highly accurate in predicting future stock prices. |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2111.04976&r= |
| By: | Metodij Hadzi-Vaskov; Rene Zamarripa; Mr. Luca A Ricci; Alejandro M. Werner |
| Abstract: | Do governments in Latin America tend to be optimistic when preparing budgetary projections? We address this question by constructing a novel dataset of the authorities’ fiscal forecasts in six Latin American economies using data from annual budget documents over the period 2000-2018. In turn, we compare such forecasts with the outturns reported in the corresponding budget documents of the following years to understand the evolution of fiscal forecast errors. Our findings suggest that: (i) for most countries, there is no general optimistic bias in the forecasts for the fiscal balance-to-GDP ratio (though there may be for the components); (ii) fiscal forecasts have improved for some countries over time, albeit they have worsened for others; (iii) in terms of drivers, we show that forecast errors for the fiscal balance-to-GDP ratio are positively correlated with GDP growth and terms of trade changes and negatively with GDP deflator surprises; (iv) forecast errors for public debt-to-GDP ratios are negatively associated with surprises to GDP growth; (v) lastly, budget balance rules seem to help contain the size of the fiscal forecast errors. |
| Keywords: | forecast bias; budget balance rule; GDP ratio; expenditure rule; revenue-to-GDP ratio; Fiscal stance; Budget planning and preparation; Fiscal rules; Caribbean |
| Date: | 2021–06–04 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2021/154&r= |
| By: | Saiz, Lorena; Ashwin, Julian; Kalamara, Eleni |
| Abstract: | This paper shows that newspaper articles contain timely economic signals that can materially improve nowcasts of real GDP growth for the euro area. Our text data is drawn from fifteen popular European newspapers, that collectively represent the four largest Euro area economies, and are machine translated into English. Daily sentiment metrics are created from these news articles and we assess their value for nowcasting. By comparing to competitive and rigorous benchmarks, we find that newspaper text is helpful in nowcasting GDP growth especially in the first half of the quarter when other lower-frequency soft indicators are not available. The choice of the sentiment measure matters when tracking economic shocks such as the Great Recession and the Great Lockdown. Non-linear machine learning models can help capture extreme movements in growth, but require sufficient training data in order to be effective so become more useful later in our sample. JEL Classification: C43, C45, C55, C82, E37 |
| Keywords: | business cycles, COVID-19, forecasting, machine learning, text analysis |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20212616&r= |
| By: | Reda Cherif; Karl Walentin; Brandon Buell; Carissa Chen; Jiawen Tang; Nils Wendt |
| Abstract: | The COVID-19 pandemic underscores the critical need for detailed, timely information on its evolving economic impacts, particularly for Sub-Saharan Africa (SSA) where data availability and lack of generalizable nowcasting methodologies limit efforts for coordinated policy responses. This paper presents a suite of high frequency and granular country-level indicator tools that can be used to nowcast GDP and track changes in economic activity for countries in SSA. We make two main contributions: (1) demonstration of the predictive power of alternative data variables such as Google search trends and mobile payments, and (2) implementation of two types of modelling methodologies, machine learning and parametric factor models, that have flexibility to incorporate mixed-frequency data variables. We present nowcast results for 2019Q4 and 2020Q1 GDP for Kenya, Nigeria, South Africa, Uganda, and Ghana, and argue that our factor model methodology can be generalized to nowcast and forecast GDP for other SSA countries with limited data availability and shorter timeframes. |
| Keywords: | model prediction; quantile plot; ML model; GDP YoY; data variable; YoY percent change; Factor models; Machine learning; Time series analysis; Spot exchange rates; Mobile banking; Africa; Sub-Saharan Africa |
| Date: | 2021–05–01 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2021/124&r= |