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Johannes Stübinger
Department of Statistics and Econometrics, University of Erlangen-Nuremberg

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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.

Conference paper
Published: 08 July 2020 in CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics
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The efficient-market hypothesis states that it is impossible to beat the market, as the price reflects all available information. Applied to bookmaker odds for football games, there should not be a systematic way of winning money on the long run.However, we show that by using simple machine learning models we can systematically outperform the markets belief manifested through the bookmakers odds. The effect of this inefficiency is diminishing over time, which indicates that the knowledge that has been derived from and the pure amount of the data is also reflected in the odds in recent times.We give some insights how this effect differs across major football leagues in Europe, which algorithms are performing best and statistics on the ROI using machine learning in football betting. Additionally, we share how the simulation study has been designed in more detail.

ACS Style

Benedikt Mangold; Johannes Stübinger. Investigating inefficiencies of bookmaker odds in football using machine learning. CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics 2020, 1 .

AMA Style

Benedikt Mangold, Johannes Stübinger. Investigating inefficiencies of bookmaker odds in football using machine learning. CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics. 2020; ():1.

Chicago/Turabian Style

Benedikt Mangold; Johannes Stübinger. 2020. "Investigating inefficiencies of bookmaker odds in football using machine learning." CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics , no. : 1.

Journal article
Published: 16 April 2020 in Algorithms
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This paper develops the generalized causality algorithm and applies it to a multitude of data from the fields of economics and finance. Specifically, our parameter-free algorithm efficiently determines the optimal non-linear mapping and identifies varying lead–lag effects between two given time series. This procedure allows an elastic adjustment of the time axis to find similar but phase-shifted sequences—structural breaks in their relationship are also captured. A large-scale simulation study validates the outperformance in the vast majority of parameter constellations in terms of efficiency, robustness, and feasibility. Finally, the presented methodology is applied to real data from the areas of macroeconomics, finance, and metal. Highest similarity show the pairs of gross domestic product and consumer price index (macroeconomics), S&P 500 index and Deutscher Aktienindex (finance), as well as gold and silver (metal). In addition, the algorithm takes full use of its flexibility and identifies both various structural breaks and regime patterns over time, which are (partly) well documented in the literature.

ACS Style

Johannes Stübinger; Katharina Adler. How to Identify Varying Lead–Lag Effects in Time Series Data: Implementation, Validation, and Application of the Generalized Causality Algorithm. Algorithms 2020, 13, 95 .

AMA Style

Johannes Stübinger, Katharina Adler. How to Identify Varying Lead–Lag Effects in Time Series Data: Implementation, Validation, and Application of the Generalized Causality Algorithm. Algorithms. 2020; 13 (4):95.

Chicago/Turabian Style

Johannes Stübinger; Katharina Adler. 2020. "How to Identify Varying Lead–Lag Effects in Time Series Data: Implementation, Validation, and Application of the Generalized Causality Algorithm." Algorithms 13, no. 4: 95.

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: 19 December 2019 in Applied Sciences
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In recent times, football (soccer) has aroused an increasing amount of attention across continents and entered unexpected dimensions. In this course, the number of bookmakers, who offer the opportunity to bet on the outcome of football games, expanded enormously, which was further strengthened by the development of the world wide web. In this context, one could generate positive returns over time by betting based on a strategy which successfully identifies overvalued betting odds. Due to the large number of matches around the globe, football matches in particular have great potential for such a betting strategy. This paper utilizes machine learning to forecast the outcome of football games based on match and player attributes. A simulation study which includes all matches of the five greatest European football leagues and the corresponding second leagues between 2006 and 2018 revealed that an ensemble strategy achieves statistically and economically significant returns of 1.58% per match. Furthermore, the combination of different machine learning algorithms could neither be outperformed by the individual machine learning approaches nor by a linear regression model or naive betting strategies, such as always betting on the victory of the home team.

ACS Style

Johannes Stübinger; Benedikt Mangold; Julian Knoll. Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics. Applied Sciences 2019, 10, 46 .

AMA Style

Johannes Stübinger, Benedikt Mangold, Julian Knoll. Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics. Applied Sciences. 2019; 10 (1):46.

Chicago/Turabian Style

Johannes Stübinger; Benedikt Mangold; Julian Knoll. 2019. "Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics." Applied Sciences 10, no. 1: 46.

Technical contribution
Published: 12 July 2019 in KI - Künstliche Intelligenz
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Across countries and continents, football (soccer) has drawn increasingly more attention over the last decades and developed into a huge commercial complex. Consequently, the market of bookmakers providing the possibility to bet on the result of football matches grew rapidly, especially with the appearance of the internet. With a high number of games every week in multiple countries, football league matches hold enormous potential for generating profits over time with the use of advanced betting strategies. In this paper, we use machine learning for predicting the outcome of football league matches by exploiting data about match characteristics. Based on insights from the field of statistical arbitrage stock market trading, we show that one could generate meaningful profits over time by betting accordingly. A simulation study analyzing the matches of the five top European football leagues from season 2013/14 to 2017/18 presented economically and statistically significant returns achieved by exploiting large data sets with modern machine learning algorithms. In contrast to these modern algorithms, the break-even point could not be reached with an ordinary linear regression approach or simple betting strategies, e.g. always betting on the home team.

ACS Style

Julian Knoll; Johannes Stübinger. Machine-Learning-Based Statistical Arbitrage Football Betting. KI - Künstliche Intelligenz 2019, 34, 69 -80.

AMA Style

Julian Knoll, Johannes Stübinger. Machine-Learning-Based Statistical Arbitrage Football Betting. KI - Künstliche Intelligenz. 2019; 34 (1):69-80.

Chicago/Turabian Style

Julian Knoll; Johannes Stübinger. 2019. "Machine-Learning-Based Statistical Arbitrage Football Betting." KI - Künstliche Intelligenz 34, no. 1: 69-80.

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.

Research papers
Published: 14 November 2018 in Quantitative Finance
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This paper develops the optimal causal path algorithm and applies it within a fully-fledged statistical arbitrage framework to minute-by-minute data of the S&P 500 constituents from 1998 to 2015. Specifically, the algorithm efficiently determines the optimal non-linear mapping and the corresponding lead–lag structure between two time series. Afterwards, this study explores the use of optimal causal paths as a means for identifying promising stock pairs and for generating buy and sell signals. For this purpose, the established trading strategy exploits information about the leading stock to predict future returns of the following stock. The value-add of the proposed framework is assessed by benchmarking it with variants relying on classic similarity measures and a buy-and-hold investment in the S&P 500 index. In the empirical back-testing study, the trading algorithm generates statistically and economically significant returns of 54.98% p.a. and an annualized Sharpe ratio of 3.57 after transaction costs. Returns are well superior to the benchmark approaches and do not load on any common sources of systematic risk. The strategy outperforms in the context of cryptocurrencies even in recent times due to the fact that stock returns contain substantial information about the future bitcoin returns.

ACS Style

Johannes Stübinger. Statistical arbitrage with optimal causal paths on high-frequency data of the S&P 500. Quantitative Finance 2018, 19, 921 -935.

AMA Style

Johannes Stübinger. Statistical arbitrage with optimal causal paths on high-frequency data of the S&P 500. Quantitative Finance. 2018; 19 (6):921-935.

Chicago/Turabian Style

Johannes Stübinger. 2018. "Statistical arbitrage with optimal causal paths on high-frequency data of the S&P 500." Quantitative Finance 19, no. 6: 921-935.

Journal article
Published: 24 April 2018 in Quantitative Finance
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We develop a multivariate statistical arbitrage strategy based on vine copulas—a highly flexible instrument for linear and nonlinear multivariate dependence modeling. In an empirical application on the S&P 500, we find statistically and economically significant returns of 9.25% p.a. and a Sharpe ratio of 1.12 after transaction costs for the period from 1992 until 2015. Tail risk is limited, with maximum drawdown at 6.57%. The high returns can only partially be explained by common sources of systematic risk. We benchmark the vine copula strategy against other variants relying on the multivariate Gaussian and t-distribution and we find its results to be superior in terms of risk and return characteristics. The multivariate dependence structure of the vine copulas is time-varying, and we see that the share of copulas capable of modelling upper and lower tail dependences increases well over 90% at times of high market turmoil.

ACS Style

Johannes Stübinger; Benedikt Mangold; Christopher Krauss. Statistical arbitrage with vine copulas. Quantitative Finance 2018, 18, 1831 -1849.

AMA Style

Johannes Stübinger, Benedikt Mangold, Christopher Krauss. Statistical arbitrage with vine copulas. Quantitative Finance. 2018; 18 (11):1831-1849.

Chicago/Turabian Style

Johannes Stübinger; Benedikt Mangold; Christopher Krauss. 2018. "Statistical arbitrage with vine copulas." Quantitative Finance 18, no. 11: 1831-1849.

Research papers
Published: 22 February 2018 in Quantitative Finance
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This paper develops a pairs trading framework based on a mean-reverting jump–diffusion model and applies it to minute-by-minute data of the S&P 500 oil companies from 1998 to 2015. The established statistical arbitrage strategy enables us to perform intraday and overnight trading. Essentially, we conduct a three-step calibration procedure to the spreads of all pair combinations in a formation period. Top pairs are selected based on their spreads’ mean-reversion speed and jump behaviour. Afterwards, we trade the top pairs in an out-of-sample trading period with individualized entry and exit thresholds. In the back-testing study, the strategy produces statistically and economically significant returns of 60.61% p.a. and an annualized Sharpe ratio of 5.30, after transaction costs. We benchmark our pairs trading strategy against variants based on traditional distance and time-series approaches and find its performance to be superior relating to risk–return characteristics. The mean-reversion speed is a main driver of successful and fast termination of the pairs trading strategy.

ACS Style

Johannes Stübinger; Sylvia Endres. Pairs trading with a mean-reverting jump–diffusion model on high-frequency data. Quantitative Finance 2018, 18, 1735 -1751.

AMA Style

Johannes Stübinger, Sylvia Endres. Pairs trading with a mean-reverting jump–diffusion model on high-frequency data. Quantitative Finance. 2018; 18 (10):1735-1751.

Chicago/Turabian Style

Johannes Stübinger; Sylvia Endres. 2018. "Pairs trading with a mean-reverting jump–diffusion model on high-frequency data." Quantitative Finance 18, no. 10: 1735-1751.

Preprint
Published: 01 January 2017
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This paper develops a pairs trading framework based on a mean-reverting jump-diffusion model and applies it to minute-by-minute data of the S&P 500 oil companies from 1998 to 2015. The established statistical arbitrage strategy enables us to perform intraday and overnight trading. Essentially, we conduct a 3-step calibration procedure to the spreads of all pair combinations in a formation period. Top pairs are selected based on their spreads' meanreversion speed and jump behavior. Afterwards, we trade the top pairs in an out-of-sample trading period with individualized entry and exit thresholds. In the back-testing study, the strategy produces statistically and economically significant returns of 60.61 percent p.a. and an annualized Sharpe ratio of 5.30, after transaction costs. We benchmark our pairs trading strategy against variants based on traditional distance and time-series approaches and find its performance to be superior relating to risk-return characteristics. The mean-reversion speed is a main driver of successful and fast termination of the pairs trading strategy.

ACS Style

Johannes Stübinger; Sylvia Endres. Pairs trading with a mean-reverting jump-diffusion model on high-frequency data. 2017, 1 .

AMA Style

Johannes Stübinger, Sylvia Endres. Pairs trading with a mean-reverting jump-diffusion model on high-frequency data. . 2017; ():1.

Chicago/Turabian Style

Johannes Stübinger; Sylvia Endres. 2017. "Pairs trading with a mean-reverting jump-diffusion model on high-frequency data." , no. : 1.

Preprint
Published: 01 January 2017
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This study derives an optimal pairs trading strategy based on a Lévy-driven Ornstein-Uhlenbeck process and applies it to high-frequency data of the S&P 500 constituents from1998 to 2015. Our model provides optimal entry and exit signals by maximizing the expected return expressed in terms of the first-passage time of the spread process. An explicit representation of the strategy's objective function allows for direct optimization without Monte Carlo methods. Categorizing the data sample into 10 economic sectors, we depict both the performance of each sector and the efficiency of the strategy in general. Results from empirical back-testing show strong support for the profitability of the model with returns after transaction costs ranging from 31.90 percent p.a. for the sector \Consumer Staples" to 278.61 percent p.a. for the sector \Financials". We find that the remarkable returns across all economic sectors are strongly driven by model parameters and sector size. Jump intensity decreases over time with strong outliers in times of high market turmoils. The value-add of our Lévy-based model is demonstrated by benchmarking it with quantitative strategies based on Brownian motion-driven processes.

ACS Style

Sylvia Endres; Johannes Stübinger. Optimal trading strategies for Lévy-driven Ornstein-Uhlenbeck processes. 2017, 1 .

AMA Style

Sylvia Endres, Johannes Stübinger. Optimal trading strategies for Lévy-driven Ornstein-Uhlenbeck processes. . 2017; ():1.

Chicago/Turabian Style

Sylvia Endres; Johannes Stübinger. 2017. "Optimal trading strategies for Lévy-driven Ornstein-Uhlenbeck processes." , no. : 1.

Preprint
Published: 01 January 2014
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In Pleier und Mangold (2013) wurde ein Pilotprojekt zur Verbesserung des Lernverhaltens untersucht, welches im Wintersemester 2012/2013 durchgeführt wurde. Studierende sollten dabei semesterbegleitend an Online-Tests teilnehmen, um sich frühzeitig auf die abschließende Klausur vorzubereiten. Als Anreiz zur Teilnahme diente die Aussicht auf Bonuspunkte, welche für jene Klausur im Vorfeld erworben werden konnten. Nun, ein Jahr später, soll untersucht werden, ob sich die Ergebnisse auf das Wintersemester 2013/2014 übertragen lassen und welchen Einfl uss mögliche Manipulationen durch die Studierenden besitzen.

ACS Style

Benedikt Mangold; Thomas Pleier; Christoph Brug; Jan Nolzen; Johannes Stübinger. Verbesserung des Lernverhaltens durch Online-Tests: Ein Jahr später. 2014, 1 .

AMA Style

Benedikt Mangold, Thomas Pleier, Christoph Brug, Jan Nolzen, Johannes Stübinger. Verbesserung des Lernverhaltens durch Online-Tests: Ein Jahr später. . 2014; ():1.

Chicago/Turabian Style

Benedikt Mangold; Thomas Pleier; Christoph Brug; Jan Nolzen; Johannes Stübinger. 2014. "Verbesserung des Lernverhaltens durch Online-Tests: Ein Jahr später." , no. : 1.