Deep-learning initiatives have vastly changed the analysis of data. Complex networks became accessible to anyone in any research area. In this paper we are proposing a deep-learning long short-term memory network (LSTM) for automated stock trading. A mechanical trading system is used to evaluate its performance. The proposed solution is compared to traditional trading strategies, i.e., passive and rule-based trading strategies, as well as machine learning classifiers. We have discovered that the deep-learning long short-term memory network has outperformed other trading strategies for the German blue-chip stock, BMW, during the 2010-2018 period. system (MTS) to sequentially (day-by-day) execute trading signals (decisions), and thus trade with real stocks [66] in virtual time. Consequently, the quality and effectiveness of a trading system over a period of a few years can be evaluated in a matter of seconds.The MTS works by giving three common trading signals, i.e., buy, hold, and sell, for each of the stocks. It is given an initial amount of cash to buy stocks that can be held in the portfolio or be sold at a later date. Buying and selling stocks can generate profits if trading signals are given rationally or generate loss if they are not. Profits and losses can be monitored and reviewed at the end of the trading period to realize strengths and weaknesses of the trading decisions [47]. Trading decisions are typically given by trading strategies, i.e., automated algorithms that constantly monitor market behaviour and react accordingly. Multiple trading strategies are usually incorporated within the trading system, by each giving unique trading decisions. The latter can be evaluated quickly and inexpensively either in realworld time or using the MTS.In this paper we are proposing a concept of an automated, single stock, trading system using the MTS, examining its potential by five different trading strategies, realizing their strengths and weaknesses, and proving the correctness of an optimal strategy. Based on this, we encourage further analysis and the design of an automated portfolio trading system for many similarly treated stocks in parallel. Here we employ three different trading strategies: (1) passive, (2) rule-based -relative strength index (RSI) and moving average convergence/divergence (MACD) technical indicators, and (3) surrogate model trading strategies using machine learning classifiers (MLC) and long short-term memory network (LSTM). Each of them can provide three trading signals: buy, hold, or sell.Since deep learning (DL) [37] is primarily intended for engineering applications, such as image and sound processing, we have not found many examples of DL in the fields of finance, banking, or insurance. In line with this, we would like to apply the DL to the area of finance, particularly mechanical trading systems as an alternative, and thus test whether the DL can be successfully deployed into this area.The structure of the paper is as follows: Section 2 presents the literature review, MLC application...
PurposeWith high public debts and suffering economies after the COVID-19 pandemic, governments will look for ways to promote recovery. Literature substantially reports on the favorable macroeconomic impact of the healthcare sector.Design/methodology/approachThe authors use data on 19 European countries. Over 30 variables are analyzed to find factors that foster or suppress the economic impact of the healthcare sector. The economic impact is thereby expressed through five types of total multipliers, acting as dependent variables. The authors estimate multiple econometric models.FindingsThe results indicate factors that intensify or reduce the economic impact of the healthcare sector as they cause the value of one or more economic multipliers to augment or to diminish. Positive effects are expected from the growth of public funds' share in total healthcare expenditure leading to a higher output, income and value-added multipliers. The import multiplier diminishes when expenditure on healthcare as percent of GDP rises. On the other hand, rising expenditure on pharmaceuticals in the share of healthcare expenditure lowers the output multiplier. Rising GDP per capita and higher healthcare systems' technical efficiency cause the employment multiplier to lower.Originality/valuePolicymakers can strengthen the economic impact of the healthcare sector on the national economy. This could be achieved by stimulating factors, being identified in our study. Strengthening the economic impact of the healthcare sector is especially welcomed when fostering economic recovery is needed.
Governments around the world are looking for ways to manage economic consequences of COVID-19 and promote economic development. The aim of this study is to identify the areas where the application of economic policy measures would enhance the resilience of societies on epidemic risks. We use data on the COVID-19 pandemic outcome in a large number of countries. With the estimation of multiple econometric models, we identify areas being a reasonable choice for economic policy intervention. It was found that viable remediation actions worth taking can be identified either for long-, mid-, or short-term horizons, impacting the equality, healthcare sector, and national economy characteristics. We suggest encouraging research and development based on innovative technologies linked to industries in healthcare, pharmaceutical, and biotech, promoting transformation of healthcare systems based on new technologies, providing access to quality healthcare, promoting public healthcare providers, and investing in the development of regional healthcare infrastructure, as a tool of equal regional development based on economic assessment. Further, a central element of this study, i.e. the innovative identification matrix, could be populated as a unique policy framework, either for latest pandemic or any similar outbreaks in future.
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