Editorial

3 pages, 167 KiB

Open AccessEditorial

Editorial for Special Issue “New Frontiers in Forecasting the Business Cycle and Financial Markets”

by Alessia Paccagnini

Forecasting 2021, 3(3), 498-500; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3030030 - 05 Jul 2021

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The global financial crisis of 2007–2009 and the COVID-19 pandemic have heightened uncertainty in financial markets and the business cycle [...] Full article

(This article belongs to the Special Issue New Frontiers in Forecasting the Business Cycle and Financial Markets)

Research

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16 pages, 860 KiB

Open AccessArticle

The Yield Curve as a Leading Indicator: Accuracy and Timing of a Parsimonious Forecasting Model

by Knut Lehre Seip and Dan Zhang

Forecasting 2021, 3(2), 421-436; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3020025 - 28 May 2021

Cited by 1 | Viewed by 2838

Abstract

Previous studies have shown that the treasury yield curve, T, forecasts upcoming recessions when it obtains a negative value. In this paper, we try to improve the yield curve model while keeping its parsimony. First, we show that adding the federal funds rate, [...] Read more.

Previous studies have shown that the treasury yield curve, T, forecasts upcoming recessions when it obtains a negative value. In this paper, we try to improve the yield curve model while keeping its parsimony. First, we show that adding the federal funds rate, FF, to the model, GDP = f(T, FF), gives seven months vs. five months warning time, and it gives a higher prediction skill for the recessions in the out-of-sample test set. Second, we find that including the quadratic term of the yield curve and the federal funds rate improves the prediction of the 1990 recession, but not the other recessions in the period 1977 to 2019. Third, the T caused a pronounced false peak in GDP for the test set. Restricting the learning set to periods where T and FF were leading the GDP in the learning set did not improve the forecast. In general, recessions are predicted better than the general movement in the economy. A “horse race” between GDP = f(T, FF) and the Michigan consumer sentiment index suggests that the first beats the latter by being a leading index for the observed GDP for more months (50% vs. 6%) during the first test year. Full article

(This article belongs to the Special Issue New Frontiers in Forecasting the Business Cycle and Financial Markets)

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15 pages, 1547 KiB

Open AccessArticle

Dynamic Pricing Recognition on E-Commerce Platforms with VAR Processes

by Alexander Faehnle and Mariangela Guidolin

Forecasting 2021, 3(1), 166-180; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3010011 - 05 Mar 2021

Cited by 5 | Viewed by 3347

Abstract

In an environment such as e-commerce, characterized by the presence of numerous agents, competition based on product characteristics is a very important aspect. This paper proposes a model based on vector autoregressive processes (VAR) and Lasso penalization to detect and examine the dynamics [...] Read more.

In an environment such as e-commerce, characterized by the presence of numerous agents, competition based on product characteristics is a very important aspect. This paper proposes a model based on vector autoregressive processes (VAR) and Lasso penalization to detect and examine the dynamics that govern real-time price competition in electronic marketplaces. Employing this model, an empirical study was performed on the price trends of smartphone models on the major electronic sales platforms of the Italian market. The proposed model detects real-time price variations in single vendors, based on the variations of their direct competitors. The statistical method adopted in this analysis may be useful for e-commerce companies that conduct market analyses of competitors’ pricing strategies. Full article

(This article belongs to the Special Issue New Frontiers in Forecasting the Business Cycle and Financial Markets)

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28 pages, 671 KiB

Open AccessArticle

Forecasting Principles from Experience with Forecasting Competitions

by Jennifer L. Castle, Jurgen A. Doornik and David F. Hendry

Forecasting 2021, 3(1), 138-165; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3010010 - 23 Feb 2021

Cited by 11 | Viewed by 5056

Abstract

Economic forecasting is difficult, largely because of the many sources of nonstationarity influencing observational time series. Forecasting competitions aim to improve the practice of economic forecasting by providing very large data sets on which the efficacy of forecasting methods can be evaluated. We [...] Read more.

Economic forecasting is difficult, largely because of the many sources of nonstationarity influencing observational time series. Forecasting competitions aim to improve the practice of economic forecasting by providing very large data sets on which the efficacy of forecasting methods can be evaluated. We consider the general principles that seem to be the foundation for successful forecasting, and show how these are relevant for methods that did well in the M4 competition. We establish some general properties of the M4 data set, which we use to improve the basic benchmark methods, as well as the Card method that we created for our submission to that competition. A data generation process is proposed that captures the salient features of the annual data in M4. Full article

(This article belongs to the Special Issue New Frontiers in Forecasting the Business Cycle and Financial Markets)

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35 pages, 2606 KiB

Open AccessArticle

Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data

by Monica Defend, Aleksey Min, Lorenzo Portelli, Franz Ramsauer, Francesco Sandrini and Rudi Zagst

Forecasting 2021, 3(1), 56-90; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3010005 - 08 Feb 2021

Cited by 3 | Viewed by 2330

Abstract

This article considers the estimation of Approximate Dynamic Factor Models with homoscedastic, cross-sectionally correlated errors for incomplete panel data. In contrast to existing estimation approaches, the presented estimation method comprises two expectation-maximization algorithms and uses conditional factor moments in closed form. To determine [...] Read more.

This article considers the estimation of Approximate Dynamic Factor Models with homoscedastic, cross-sectionally correlated errors for incomplete panel data. In contrast to existing estimation approaches, the presented estimation method comprises two expectation-maximization algorithms and uses conditional factor moments in closed form. To determine the unknown factor dimension and autoregressive order, we propose a two-step information-based model selection criterion. The performance of our estimation procedure and the model selection criterion is investigated within a Monte Carlo study. Finally, we apply the Approximate Dynamic Factor Model to real-economy vintage data to support investment decisions and risk management. For this purpose, an autoregressive model with the estimated factor span of the mixed-frequency data as exogenous variables maps the behavior of weekly S&P500 log-returns. We detect the main drivers of the index development and define two dynamic trading strategies resulting from prediction intervals for the subsequent returns. Full article

(This article belongs to the Special Issue New Frontiers in Forecasting the Business Cycle and Financial Markets)

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17 pages, 326 KiB

Open AccessArticle

Valuation Models Applied to Value-Based Management—Application to the Case of UK Companies with Problems

by Marcel Ausloos

Forecasting 2020, 2(4), 549-565; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast2040029 - 11 Dec 2020

Cited by 2 | Viewed by 5193

Abstract

Many still rightly wonder whether accounting numbers affect business value. Basic questions are “why?” and “how?” We aim at promoting an objective choice on how optimizing the most suitable valuation methods under a “value-based management” framework through some performance measurement systems. First, we [...] Read more.

Many still rightly wonder whether accounting numbers affect business value. Basic questions are “why?” and “how?” We aim at promoting an objective choice on how optimizing the most suitable valuation methods under a “value-based management” framework through some performance measurement systems. First, we present a comprehensive review of valuation methods. Three valuations methods, (i) Free Cash Flow Valuation Model (FCFVM), (ii) Residual Earning Valuation Model (REVM) and (iii) Abnormal Earning Growth Model (AEGM), are presented. We point out advantages and limitations. As applications, the proofs of our findings are illustrated on three study cases: Marks & Spencer’s (M&S’s) business pattern (size and growth prospect), which had a recently advertised valuation “problem”, and two comparable companies, Tesco and Sainsbury’s, all three chosen for multiple-based valuation. For the purpose, two value drivers are chosen, EnV/EBIT (entity value/earnings before interests and taxes) and the corresponding EnV/Sales. Thus, the question whether accounting numbers through models based on mathematical economics truly affect business value has an answer: “Maybe, yes”. Full article

(This article belongs to the Special Issue New Frontiers in Forecasting the Business Cycle and Financial Markets)

21 pages, 1061 KiB

Open AccessArticle

Bankruptcy Prediction: The Case of the Greek Market

by Angeliki Papana and Anastasia Spyridou

Forecasting 2020, 2(4), 505-525; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast2040027 - 03 Dec 2020

Cited by 15 | Viewed by 3478

Abstract

Financial bankruptcy prediction is an essential issue in emerging economies taking into consideration the economic upheaval that can be caused by business failures. The research on bankruptcy prediction is of the utmost importance as it aims to build statistical models that can distinguish [...] Read more.

Financial bankruptcy prediction is an essential issue in emerging economies taking into consideration the economic upheaval that can be caused by business failures. The research on bankruptcy prediction is of the utmost importance as it aims to build statistical models that can distinguish healthy firms from financially distressed ones. This paper explores the applicability of the four most used approaches to predict financial bankruptcy using data concerning the case of Greece. A comparison of linear discriminant analysis, logit, decision trees and neural networks is performed. The results show that discriminant analysis is slightly superior to the other methods. Full article

(This article belongs to the Special Issue New Frontiers in Forecasting the Business Cycle and Financial Markets)

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23 pages, 850 KiB

Open AccessArticle

Are Issuer Margins Fairly Stated? Evidence from the Issuer Estimated Value for Retail Structured Products

by Janis Bauer, Holger Fink and Eva Stoller

Forecasting 2020, 2(4), 387-409; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast2040021 - 29 Sep 2020

Cited by 2 | Viewed by 2700

Abstract

From 2014 to 2018, issuers of retail structured products in Germany established and calculated the Issuer Estimated Value (IEV), a fair value designed to offer more transparency for retail investors. By reporting the IEV in the product information sheet, banks implicitly make a [...] Read more.

From 2014 to 2018, issuers of retail structured products in Germany established and calculated the Issuer Estimated Value (IEV), a fair value designed to offer more transparency for retail investors. By reporting the IEV in the product information sheet, banks implicitly make a statement on their expected gross margin and, as one of the first papers, we provide an empirical study of the fairness of these disclosed figures. On a sample of discount and capped bonus certificates, we find that reported issuer margins can be verified using standard option pricing models and we illustrate that hedging costs take on an important role for structured product valuation. Consequently, the answer to the raised question in the title seems to be an (initial) ‘yes’ for our chosen product sample. Even though in 2018 the IEV calculations have been replaced by similar margin and cost statements due to the newly introduced Packaged Retail and Insurance-based Investment Products Regulation, this finding might still be a good guide for future research. Full article

(This article belongs to the Special Issue New Frontiers in Forecasting the Business Cycle and Financial Markets)

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28 pages, 2997 KiB

Open AccessArticle

Revisiting the Dynamic Linkages of Treasury Bond Yields for the BRICS: A Forecasting Analysis

by Stelios Bekiros and Christos Avdoulas

Forecasting 2020, 2(2), 102-129; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast2020006 - 16 May 2020

Cited by 4 | Viewed by 2448

Abstract

We examined the dynamic linkages among money market interest rates in the so-called “BRICS” countries (Brazil, Russia, India, China, and South Africa) by using weekly data of the overnight, one-, three-, and six- months, as well as of one year, Treasury bills rates [...] Read more.

We examined the dynamic linkages among money market interest rates in the so-called “BRICS” countries (Brazil, Russia, India, China, and South Africa) by using weekly data of the overnight, one-, three-, and six- months, as well as of one year, Treasury bills rates covering the period from January 2005 to August 2019. A long-run relationship among interest rates was established by employing the Vector Error Correction modeling (VECM), which revealed the validation of the Expectation Hypothesis Theory (EH) of the term structure of interest rates, taking into account long-run deviations from equilibrium and inherent nonlinearities. We unveiled short-run dynamic adjustments for the term structure of the BRICS, subject to regime switches. We then used Markov Switching Vector Error Correction models (MS-VECM) to forecast them dynamically during an out-of-sample period of May 2016 through August 2019. The MSIH-VECM forecasts were found to be superior to the VECM approaches. The novelty of our paper is mainly due to the exploration of the possibility of parameter instability as a crucial factor, which might explain the rejection of the restricted version of the cointegration space, and on the dynamic out-of-sample forecasts of the term structure over a more recent time span in order to assess further the usefulness of our nonlinear MS-VECM characterization of the term structure, capturing the effects of the global and domestic financial crisis. Full article

(This article belongs to the Special Issue New Frontiers in Forecasting the Business Cycle and Financial Markets)

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