PurposeThe purpose of this paper is to analyze the market response of the aerospace and defense industry and the airline industry to the ongoing conflict between Ukraine and Russia based on the ...sentiments from war-related news articles over the period from October 2021 to June 2022.Design/methodology/approachThe study uses the news article database of Global Database of Events, Languages and Tone (GDELT) to create a new set of variables that reflect the news sentiment regarding war and conflict. By investigating the newly created sentiment variables in combination with traditional event study methodology, the authors seek to find out whether sentiment indicators can be helpful to rationalize the evolution of the different stock markets before and after the conflict.FindingsThe authors' results point out a significant negative impact of the war on the airline market and a positive impact on the defense market. The authors' study also introduces a new set of war-related news-based sentiment variables that is significant to explain the evolution of the two markets before and after the war. The relationships between this study's new set of variables and the performance of the two markets are also proven to be significantly impacted by the invasion.Originality/valueTo the best of the authors' knowledge, this is the first research to use the news sentiment related to the topic of war and conflict to explain the market movement of different industries during the Ukraine invasion.
•Italy’s political development had implications for sovereign credit and redenomination risk.•Italy’s political development has affected the relationship between bank funding costs in Germany and ...Italy.•One explanation could be implied changes to redenomination risk due to fears about Italy leaving the Euro.
The political situation in Italy had and still has implications for sovereign credit and redenomination risk. The current political environment is discussed from an economic and legal perspective focusing strongly on the funding situation of Italian banks. Some empirical evidence is reported. The findings depicted here are compatible with the point of view that the political development in Rome has affected the relationship between bank funding costs in Germany and Italy. In fact, there is clear evidence for the relevance of nonlinearities. Given the time period examined, changes to redenomination risk due to fears about Italy leaving the Euro could be one crucial explanation for the findings reported here.
Decision Support for the Automotive Industry Gleue, Christoph; Eilers, Dennis; von Mettenheim, Hans-Jörg ...
Business & information systems engineering,
08/2019, Letnik:
61, Številka:
4
Journal Article
Recenzirano
In the automotive industry, it is very common for new vehicles to be leased rather than sold. This implies forecasting an accurate residual value for the vehicles, which is a major factor for ...determining monthly leasing rates. Either a systematic overestimation or underestimation of future residual values can incur large potential losses in resale value or, respectively, competitive disadvantages. For the purpose of facilitating residual value related management decisions, an operative decision support system is introduced with emphasis on its forecasting capabilities. In the paper, the use of artificial neural networks for this application is demonstrated in a case study based on more than 250,000 data sets of leasing contracts from a major German car manufacturer, completed between 2011 and 2017. The importance of determining price factors and the effect of different time horizons on forecasting accuracy are investigated and practical implications are discussed. In addition, the authors neither found a significant explanatory nor predictive power of external economic factors, which underlines the importance of collecting and taking advantage of vehicle-specific data or, in more general terms, the exclusive data of corporations, which is often only available internally.
Leasing is a popular channel for marketing new cars. However, the pricing of leases is complicated because the leasing rate must embody an expectation of the car’s residual value after contract ...expiration. This paper develops resale price forecasting models in order to aid pricing decisions. One feature of the leasing business is that different forecast errors entail different costs. The primary objective of this paper is to identify effective ways of addressing cost asymmetry. Specifically, this paper contributes to the literature by (i) consolidating prior work in forecasting on asymmetric functions of the cost of errors; (ii) systematically evaluating previous approaches and comparing them to a new approach; and (iii) demonstrating that forecasting using asymmetric cost of error functions improves the quality of decision support in car leasing. For example, if the costs of overestimating resale prices are twice those of underestimating them, incorporating cost asymmetry into forecast model development reduces costs by about 8%.
A large number of modern practices in financial forecasting rely on technical analysis, which involves several heuristics techniques of price charts visual pattern recognition as well as other ...technical indicators. In this study, we aim to investigate the potential use of those technical information (candlestick information as well as technical indicators) as inputs for machine learning models, especially the state-of-the-art deep learning algorithms, to generate trading signals. To properly address this problem, empirical research is conducted which applies several machine learning methods to 5 years of Bitcoin hourly data from 2017 to 2022. From the result of our study, we confirm the potential of trading strategies using machine learning approaches. We also find that among several machine learning models, deep learning models, specifically the recurrent neural networks, tend to outperform the others in time-series prediction.
•Bitcoin price history might contain some predictive information.•Machine learning models can create profits that are better than the classic Buy-and-Hold strategy and provide much precision.•The neural networks did the best compared to other popular machine learning models.•The recurrent neural network shows superior performance with the GRU architectures compared to the CNN models.
The low oil price recently has caused fears about the sustainability of public finances in some oil producing countries. We examine the relationship between oil prices and sovereign credit risk ...examining the CDS market. Analysing data from nine countries (Brazil, Malaysia, Norway, Qatar, Russia, Saudi Arabia, the United Kingdom, the United States of America and Venezuela) we have estimated bivariate VAR-GARCH-in-mean models. The results of our empirical investigations generally speaking do suggest that positive oil price shocks lead to lower sovereign CDS spreads. Thus, our findings support the hypothesis that higher oil prices improve the fiscal stability of oil producing countries.
The Vietnamese stock market provides an interesting and enriching test field for the application of trading expert systems as its economy is opening up, has high growth rate and may offer risk ...diversification opportunities. This paper examines the question of whether this frontier emerging market offers possibilities for statistical arbitrage through a financial expert system. Based on a sample of the most liquid stocks in the VN30 benchmark index, our results indicate that the index itself and some of its components offer moderate opportunities for statistical arbitrage even after considering transaction costs. It is also found that the purely momentum-based models already work satisfactorily for specific stocks, while the long-short strategies do not work more robustly than the long-only strategies. Overall, our findings hint into the direction of some exploitable inefficiencies, but the magnitude of the tradable volume is such that only comparatively small amounts can be traded.
•Examine whether Vietnamese market offers possibilities for statistical arbitrage.•The benchmark index and some of its components offer moderate arbitrage opportunities.•Statistical arbitrage holds even after considering transaction costs.•Momentum-based models already work satisfactorily for specific stocks.•Long-short strategies do not work more robustly than the long-only strategies.
PDF
