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Lucas Schneider
Department of Statistics and Econometrics, University of Erlangen-Nürnberg, Lange Gasse 20, 90403 Nürnberg, Germany

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Journal article
Published: 14 October 2020 in Sustainability
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This paper systematically reviews the top 200 Google Scholar publications in the area of smart city with the aid of data-driven methods from the fields natural language processing and time series forecasting. Specifically, our algorithm crawls the textual information of the considered articles and uses the created ad-hoc database to identify the most relevant streams “smart infrastructure”, “smart economy & policy”, “smart technology”, “smart sustainability”, and “smart health”. Next, we automatically assign each manuscript into these subject areas by dint of several interdisciplinary scientific methods. Each stream is evaluated in a deep-dive analysis by (i) creating a word cloud to find the most important keywords, (ii) examining the main contributions, and (iii) applying time series methodologies to determine the past and future relevance. Due to our large-scaled literature, an in-depth evaluation of each stream is possible, which ultimately reveals strengths and weaknesses. We hereby acknowledge that smart sustainability will come to the fore in the next years—this fact confirms the current trend, as minimizing the required input of energy, water, food, waste, heat output and air pollution is becoming increasingly important.

ACS Style

Johannes Stübinger; Lucas Schneider. Understanding Smart City—A Data-Driven Literature Review. Sustainability 2020, 12, 8460 .

AMA Style

Johannes Stübinger, Lucas Schneider. Understanding Smart City—A Data-Driven Literature Review. Sustainability. 2020; 12 (20):8460.

Chicago/Turabian Style

Johannes Stübinger; Lucas Schneider. 2020. "Understanding Smart City—A Data-Driven Literature Review." Sustainability 12, no. 20: 8460.

Journal article
Published: 20 September 2020 in Mathematics
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This paper develops a dispersion trading strategy based on a statistical index subsetting procedure and applies it to the S&P 500 constituents from January 2000 to December 2017. In particular, our selection process determines appropriate subset weights by exploiting a principal component analysis to specify the individual index explanatory power of each stock. In the following out-of-sample trading period, we trade the most suitable stocks using a hedged and unhedged approach. Within the large-scale back-testing study, the trading frameworks achieve statistically and economically significant returns of 14.52 and 26.51 percent p.a. after transaction costs, as well as a Sharpe ratio of 0.40 and 0.34, respectively. Furthermore, the trading performance is robust across varying market conditions. By benchmarking our strategies against a naive subsetting scheme and a buy-and-hold approach, we find that our statistical trading systems possess superior risk-return characteristics. Finally, a deep dive analysis shows synchronous developments between the chosen number of principal components and the S&P 500 index.

ACS Style

Lucas Schneider; Johannes Stübinger. Dispersion Trading Based on the Explanatory Power of S&P 500 Stock Returns. Mathematics 2020, 8, 1627 .

AMA Style

Lucas Schneider, Johannes Stübinger. Dispersion Trading Based on the Explanatory Power of S&P 500 Stock Returns. Mathematics. 2020; 8 (9):1627.

Chicago/Turabian Style

Lucas Schneider; Johannes Stübinger. 2020. "Dispersion Trading Based on the Explanatory Power of S&P 500 Stock Returns." Mathematics 8, no. 9: 1627.

Journal article
Published: 15 April 2020 in Healthcare
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This paper forecasts the future spread of COVID-19 by exploiting the identified lead-lag effects between different countries. Specifically, we first determine the past relation among nations with the aid of dynamic time warping. This procedure allows an elastic adjustment of the time axis to find similar but phase-shifted sequences. Afterwards, the established framework utilizes information about the leading country to predict the Coronavirus spread of the following nation. The presented methodology is applied to confirmed Coronavirus cases from 1 January 2020 to 28 March 2020. Our results show that China leads all other countries in the range of 29 days for South Korea and 44 days for the United States. Finally, we predict a future collapse of the healthcare systems of the United Kingdom and Switzerland in case of our explosion scenario.

ACS Style

Johannes Stübinger; Lucas Schneider. Epidemiology of Coronavirus COVID-19: Forecasting the Future Incidence in Different Countries. Healthcare 2020, 8, 99 .

AMA Style

Johannes Stübinger, Lucas Schneider. Epidemiology of Coronavirus COVID-19: Forecasting the Future Incidence in Different Countries. Healthcare. 2020; 8 (2):99.

Chicago/Turabian Style

Johannes Stübinger; Lucas Schneider. 2020. "Epidemiology of Coronavirus COVID-19: Forecasting the Future Incidence in Different Countries." Healthcare 8, no. 2: 99.

Journal article
Published: 01 April 2019 in Journal of Risk and Financial Management
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This paper develops a fully-fledged statistical arbitrage strategy based on a mean-reverting jump–diffusion model and applies it to high-frequency data of the S&P 500 constituents from January 1998–December 2015. In particular, the established stock selection and trading framework identifies overnight price gaps based on an advanced jump test procedure and exploits temporary market anomalies during the first minutes of a trading day. The existence of the assumed mean-reverting property is confirmed by a preliminary analysis of the S&P 500 index; this characteristic is particularly significant 120 min after market opening. In the empirical back-testing study, the strategy delivers statistically- and economically-significant returns of 51.47 percent p.a.and an annualized Sharpe ratio of 2.38 after transaction costs. We benchmarked our trading algorithm against existing quantitative strategies from the same research area and found its performance superior in a multitude of risk-return characteristics. Finally, a deep dive analysis shows that our results are consistently profitable and robust against drawdowns, even in recent years.

ACS Style

Johannes Stübinger; Lucas Schneider. Statistical Arbitrage with Mean-Reverting Overnight Price Gaps on High-Frequency Data of the S&P 500. Journal of Risk and Financial Management 2019, 12, 51 .

AMA Style

Johannes Stübinger, Lucas Schneider. Statistical Arbitrage with Mean-Reverting Overnight Price Gaps on High-Frequency Data of the S&P 500. Journal of Risk and Financial Management. 2019; 12 (2):51.

Chicago/Turabian Style

Johannes Stübinger; Lucas Schneider. 2019. "Statistical Arbitrage with Mean-Reverting Overnight Price Gaps on High-Frequency Data of the S&P 500." Journal of Risk and Financial Management 12, no. 2: 51.