nep-for New Economics Papers
on Forecasting
Issue of 2022‒08‒08
nine papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting macroeconomic indicators for Eurozone and Greece: How useful are the oil price assumptions? By George Filis; Stavros Degiannakis; Zacharias Bragoudakis
  2. Deep Multiple Instance Learning For Forecasting Stock Trends Using Financial News By Yiqi Deng; Siu Ming Yiu
  3. Dynamic Early Warning and Action Model By Mueller, H.; Rauh, C.; Ruggieri, A.;
  4. On the universality of the volatility formation process: when machine learning and rough volatility agree By Mathieu Rosenbaum; Jianfei Zhang
  5. Predicting Stock Price Movement after Disclosure of Corporate Annual Reports: A Case Study of 2021 China CSI 300 Stocks By Fengyu Han; Yue Wang
  6. Khyber Pakhtunkhwa Food Outlook Report (Rabi) 2022 - Rabi crops 2021-22 By Raja, Sehrish; Tauqir, Aisha; Qureshi, Tehseen; Rana, Abdul Wajid
  7. The Prediction of Diabetes By Massaro, Alessandro; Magaletti, Nicola; Cosoli, Gabriele; Giardinelli, Vito O. M.; Leogrande, Angelo
  8. Robust Knockoffs for Controlling False Discoveries With an Application to Bond Recovery Rates By Konstantin G\"orgen; Abdolreza Nazemi; Melanie Schienle
  9. How Accurately Can We Predict Repeat Teen Pregnancy Based on Social Ecological Factors? By Jessica Harding; Betsy Keating; Jennifer Walzer; Fei Xing; Susan Zief; Jessica Gao

  1. By: George Filis (University of Patras); Stavros Degiannakis (Bank of Greece); Zacharias Bragoudakis (Bank of Greece)
    Abstract: This study evaluates oil price forecasts based on their economic significance for macroeconomic predictions. More specifically, we first use the current state-of-the-art frameworks to forecast monthly oil prices and subsequently we use these forecasts, as oil price assumptions, to predict eurozone and Greek inflation rates and industrial production indices. The macroeconomic predictions are generated by means of regression-based models. We show that when we assess oil price forecasts, based on statistical loss functions, the MIDAS models, as well as the futures-based forecasts outperform those generated by the VAR and BVAR models. By contrast, in terms of their economic significance we show that none of the oil price forecasts are capable of providing predictive gains for the eurozone core inflation rate and the Greek industrial production index, whereas some gains are evident for the eurozone industrial production index and the Greek core inflation rate. However, in all cases the oil price forecasting models, including the random-walk, generate equal macroeconomic predictive accuracy. Thus, overall, we show that it is important to assess oil price forecasting frameworks based on the purpose that they are designed to serve, rather than based on their ability to predict oil prices per se.
    Keywords: Oil price forecasts; MIDAS; conditional forecasts; core inflation; industrial production
    JEL: C53 E27 E37 Q47
    Date: 2022–04
  2. By: Yiqi Deng; Siu Ming Yiu
    Abstract: A major source of information can be taken from financial news articles, which have some correlations about the fluctuation of stock trends. In this paper, we investigate the influences of financial news on the stock trends, from a multi-instance view. The intuition behind this is based on the news uncertainty of varying intervals of news occurrences and the lack of annotation in every single financial news. Under the scenario of Multiple Instance Learning (MIL) where training instances are arranged in bags, and a label is assigned for the entire bag instead of instances, we develop a flexible and adaptive multi-instance learning model and evaluate its ability in directional movement forecast of Standard & Poors 500 index on financial news dataset. Specifically, we treat each trading day as one bag, with certain amounts of news happening on each trading day as instances in each bag. Experiment results demonstrate that our proposed multi-instance-based framework gains outstanding results in terms of the accuracy of trend prediction, compared with other state-of-art approaches and baselines.
    Date: 2022–06
  3. By: Mueller, H.; Rauh, C.; Ruggieri, A.;
    Abstract: This document presents the outcome of two modules developed for the UK Foreign, Commonwealth Development Office (FCDO): 1) a forecast model which uses machine learning and text downloads to predict outbreaks and intensity of internal armed conflict. 2) A decision making module that embeds these forecasts into a model of preventing armed conflict damages. The outcome is a quantitative benchmark which should provide a testing ground for internal FCDO debates on both strategic levels (i.e. the process of deciding on country priorities) and operational levels (i.e. identifying critical periods by the country experts). Our method allows the FCDO to simulate policy interventions and changes in its strategic focus. We show, for example, that the FCDO should remain engaged in recently stabilized armed conflicts and re-think its development focus in countries with the highest risks. The total expected economic benefit of reinforced preventive efforts, as defined in this report, would bring monthly savings in expected costs of 26 billion USD with a monthly gain to the UK of 630 million USD.
    Keywords: dynamic optimisation, forecasting, internal armed conflict, prevention
    Date: 2022–06–14
  4. By: Mathieu Rosenbaum; Jianfei Zhang
    Abstract: We train an LSTM network based on a pooled dataset made of hundreds of liquid stocks aiming to forecast the next daily realized volatility for all stocks. Showing the consistent outperformance of this universal LSTM relative to other asset-specific parametric models, we uncover nonparametric evidences of a universal volatility formation mechanism across assets relating past market realizations, including daily returns and volatilities, to current volatilities. A parsimonious parametric forecasting device combining the rough fractional stochastic volatility and quadratic rough Heston models with fixed parameters results in the same level of performance as the universal LSTM, which confirms the universality of the volatility formation process from a parametric perspective.
    Date: 2022–06
  5. By: Fengyu Han; Yue Wang
    Abstract: In the current stock market, computer science and technology are more and more widely used to analyse stocks. Not same as most related machine learning stock price prediction work, this work study the predicting the tendency of the stock price on the second day right after the disclosure of the companies' annual reports. We use a variety of different models, including decision tree, logistic regression, random forest, neural network, prototypical networks. We use two sets of financial indicators (key and expanded) to conduct experiments, these financial indicators are obtained from the EastMoney website disclosed by companies, and finally we find that these models are not well behaved to predict the tendency. In addition, we also filter stocks with ROE greater than 0.15 and net cash ratio greater than 0.9. We conclude that according to the financial indicators based on the just-released annual report of the company, the predictability of the stock price movement on the second day after disclosure is weak, with maximum accuracy about 59.6% and maximum precision about 0.56 on our test set by the random forest classifier, and the stock filtering does not improve the performance. And random forests perform best in general among all these models which conforms to some work's findings.
    Date: 2022–06
  6. By: Raja, Sehrish; Tauqir, Aisha; Qureshi, Tehseen; Rana, Abdul Wajid
    Abstract: A Food Outlook report explains the crop situation with in specific geographical boundaries. It identifies the past trends in area, production, yield, and price and predict future trends based on past values, thereby helping the government entities to make well informed decisions and farm growers to maximize profitability. The purpose of Khyber Pakhtunkhwa Food Outlook Report is to understand the trends and forecasts relating to area, production, yield, and price of four major rabi crops of KP i.e., wheat, gram, garlic, and onion for 2021-22 cropping season. The report provides commodity balance sheet showing changes in crop stocks, utilization, and output. Moreover, the forecasts analysis may help in anticipating any upcoming shock related to food demand and supply. It also helps farmers to take advantage of emerging opportunities and prevent themselves from any anticipated losses.
    Keywords: PAKISTAN, SOUTH ASIA, ASIA, forecasting, yields, wheat, chickpea flour, garlic, onlions, commodities, agricultural products
    Date: 2022
  7. By: Massaro, Alessandro; Magaletti, Nicola; Cosoli, Gabriele; Giardinelli, Vito O. M.; Leogrande, Angelo
    Abstract: The following article presents an analysis of the determinants of diabetes using a dataset containing the surveys of 2000 patients from the Frankfurt Hospital in Germany. The data were analyzed using the following models, namely: Tobit, Probit, Logit, Multinomial Logit, OLS, WLS with heteroskedasticity. The results show that the presence of diabetes is positively associated with "Pregnancies", "Glucose", "BMI", "Diabetes Pedigree Function", "Age" and negatively associated with "Blood Pressure". A cluster analysis is realized using the fuzzy c-Means algorithm optimized with the Elbow method and three clusters were found. Finally a confrontation among eight different machine learning algorithms is realized to select the best performing algorithm to predict the probability of patients to develop diabetes.
    Keywords: Machine Learning, Clusterization, Elbow Method, Prediction, Correlation Matrix, Principal Component Analysis, Binary and non-Binary regression models.
    JEL: I10 I11 I12 I13 I14 I15 I18
    Date: 2022–06–13
  8. By: Konstantin G\"orgen; Abdolreza Nazemi; Melanie Schienle
    Abstract: We address challenges in variable selection with highly correlated data that are frequently present in finance, economics, but also in complex natural systems as e.g. weather. We develop a robustified version of the knockoff framework, which addresses challenges with high dependence among possibly many influencing factors and strong time correlation. In particular, the repeated subsampling strategy tackles the variability of the knockoffs and the dependency of factors. Simultaneously, we also control the proportion of false discoveries over a grid of all possible values, which mitigates variability of selected factors from ad-hoc choices of a specific false discovery level. In the application for corporate bond recovery rates, we identify new important groups of relevant factors on top of the known standard drivers. But we also show that out-of-sample, the resulting sparse model has similar predictive power to state-of-the-art machine learning models that use the entire set of predictors.
    Date: 2022–06
  9. By: Jessica Harding; Betsy Keating; Jennifer Walzer; Fei Xing; Susan Zief; Jessica Gao
    Abstract: This study examined theoretically selected predictors of repeat teen pregnancy at the individual, relationship, family, and community or environmental levels. In addition, it examined whether these factors can accurately predict whether teen mothers will have a repeat pregnancy.
    Keywords: Teen pregnancy, machine learning, predictive analytics, teen parents, repeat pregnancy

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