Contributions published at Financial Engineering (Markus Leippold)

This paper examines momentum strategies on three types of governmental bond spreads namely term-/ cross-country-/ and cross-country-term spread. The strategies take strategic long and short positions based on signals derived from past performance. It is found that significant returns can be achieved especially on the term spread (different maturity in the same country). This return is even greater when combining a long-term and a short- term governmental bond future. This relationship is also evident between different maturities of cross-country government bonds. The results remain stable over multiple holding periods and when transaction costs are included.

Die vorliegende Bachelorarbeit belegt die Existenz der Low-Beta Anomalie anhand der Aktientitel des S&P 500. Unter Verwendung des Capital Asset Pricing Model (CAPM) werden über einen 20-jährigen Betrachtungszeitraum die Aktientitel mittels der historischen Indexzusammensetzung und der Datenverfügbarkeit selektiert. Die Resultate zeigen, dass (i) das Jensen’s Alpha bei den Portfolios mit einem Beta kleiner als 1 positiv und für die Portfolios mit einem Beta grösser als 1 negativ ist und dass (ii) die tatsächliche Security Market Line (SML) flacher als die des CAPM verläuft.

There is a large amount of untapped alternative data that can be used for sustainable investments. We discuss this and other topics with our guest Markus Leippold, Professor of Financial Engineering at the University of Zurich and Swiss Finance Institute.

Many portfolio managers need new ideas to create long-term portfolios which are also robust during volatile times. Especially in current times, a portfolio with minimal variance helps investors keep their head when the markets go crazy. While supervised learning has already been applied in various setups in finance, direct optimisation through reinforcement learning and letting an agent directly interact with the market is a relatively new approach for financial applications. I analyse of a deep reinforcement learning model to create a minimum variance portfolio. The central part of this thesis is to test the performance in terms of the standard deviation of a global minimum variance portfolio created by covariance shrinkage methods which yield state-of-the-art performance, against the portfolio generated by a deep reinforcement learning algorithm. For the deep reinforcement learning setup, I follow Cong et al. (2021) who originally suggested this direct optimisation approach. Two benchmarks are generated. The first is the equal-weighted portfolio, 1/N. The second uses covariance shrinkage to create a global minimum variance portfolio. Quadratic inverse shrinkage, an approach introduced by Leidoit and Wolf (2022) is used to get stateof- the-art performance regarding the performance measure minimal standard deviation. The empirical analysis is based on daily return data from CRSP and the firm characteristics from Gu et al. (2020) of the biggest US stocks starting from January 1, 2005, until December 31, 2021. The portfolios are generated from January 1, 2010, until December 31, 2021, and are monthly rebalanced. I compare the portfolios in terms of their standard deviation, average return, maximum drawdown, information ratio, Sharpe ratio, turnover and gross leverage. However, the most relevant performance measure is the standard deviation, as this is the main objective of the deep reinforcement learner. I train the reinforcement learning algorithm on the data from January 1, 2005, until December 31, 2009. I use a wide variety of di↵erent parameter values to achieve the maximal reward. However, the training of a deep reinforcement learning algorithm is computationally very intensive and, therefore, also very time-consuming. After generating the portfolios, I find that the 1/N portfolio with a standard deviation of 17.15% is outperformed by a relatively large margin by the deep reinforcement learning and the covariance II III shrinkage portfolio with a standard deviation of 13.98% and 12.44%, respectively. But as we can see, the deep reinforcement learning model could not outperform the global minimum variance portfolio with quadratic inverse shrinkage. Thus, I could not beat the leading benchmark with the new direct optimisation approach by Cong et al. (2021). However, with the correct parameters, it should be possible to achieve better performance with the deep reinforcement learning architecture proposed by Cong et al. (2021) than the global minimum variance portfolio generated with covariance shrinkage methods. Thus, further empirical analysis on this topic would be necessary. Jonas

Der Anteil an passiven Anlagen nimmt am Gesamtmarkt ein immer grösserer Anteil ein. Dies führt dazu, dass den Indizes eine immer grösser werdende Bedeutung zukommen, da nun viele Anleger mittels ETFs und Indexfonds indirekt in unterschiedlichste Indizes investieren. In dieser Arbeit wurde untersucht, welchen Einfluss eine Indexanpassung im S&P 500 auf die neu aufgenommen Unternehmen hat. Dabei konnte gezeigt werden, dass durch die Indexanpassung der Aktienkurs der neu aufgenommen Unternehmen signifikant zunahm. Auf das Kurs-Buchwert-Verhältnis und das Kurs-Gewinn-Verhältnis konnte jedoch kein signifikanter Effekt festgestellt werden.

Diese Arbeit reflektiert die Übernahme der Sunrise Communications Group AG durch die UPC Schweiz GmbH im Jahr 2020 und stützt sich dabei insbesondere auf das in diesem Zu-sammenhang entwickelte Fairness-Gutachten der Beratungsfirma ValueTrust Financial Advi-sors Switzerland AG. Konkret wird dieses Praxisbeispiel in eine Fallstudie für Lehrzwecke überführt und ein dazugehöriger Lösungsvorschlag präsentiert. Die auf Basis der Analyse ver-gleichbarer börsennotierter Unternehmen, der Analyse vergleichbarer vergangener Transak-tionen und der Discounted Cashflow-Methode ermittelten Unternehmenswerte für die Sun-rise-Aktien werden den Ergebnissen der Fairness Opinion gegenübergestellt. Die Wertband-breiten für den Wert pro Sunrise-Aktie sind im Wesentlichen ähnlich und führen zum gleich-lautenden Fazit, dass der Kaufpreis gegenüber dem Sunrise-Aktionariat finanziell angemes-sen ist.

In dieser Arbeit werden Momentum-Effekte im Schweizer Aktienmarkt anhand von Kursdaten kleiner und mittlerer Unternehmen analysiert. Dazu werden einfache Momentum-Strategien implementiert, die sich am Vorbild ähnlicher Analysen orientieren. Die Ergebnisse aus zwei verschiedenen Zeitabschnitten zeigen auf, dass Momentum-Effekte existieren und eine positive Überrendite generieren. Diese Überendite bleibt auch nach Risikoanpassung signifikant und robust gegenüber einiger getesteter Spezifikationsänderungen. Die Einführung hoher Transaktionskosten zeigt jedoch, dass positive Momentum-Effekte schnell verschwinden, wenn für illiquide Titel grosse Bid-Ask-Spreads herrschen. Momentum bleibt indes ein Phänomen, dass nicht vollständig erklärt werden kann.

Diese Arbeit untersucht, ob eine risikobereinigte Überrendite gegenüber einer Buy-and- Hold Strategie (BHS) im S&P 500 Total Return Index (SPTR) erzielt werden kann, indem ein eigens konstruierter Fear & Greed Index, der auf Daten des S&P 500 Index (SPX) basiert, als technischer Indikator verwendet wird. Die Ergebnisse zeigen, dass es vier von sechs Anlagestrategien gelungen ist, im Anlagezeitraum vom 27. Februar 1998 bis zum 04. März 2022, eine höhere geometrische Durchschnittsrendite (p.a.) als die Benchmark (BHS) zu generieren (nach Kosten). Darüber hinaus konnten drei Strategien, basierend auf der Sharpe Ratio und dem Jensen’s Alpha, eine risikobereinigte Überrendite gegenüber der BHS erzielen.

Diese Arbeit versucht eine Vorgehensweise aufzustellen, wodurch ein unabhängiger Vermögensverwalter die Nachhaltigkeit eines Unternehmens Analysieren und in die Unternehmensbewertung integrieren kann. Obwohl aktuell sehr viel über Nachhaltigkeit gesprochen wird, tun sich viele schwer den Begriff genau einzuordnen. Deshalb wird ein grosser Fokus auf die Definition des Begriffs und die einhergehenden ESG-Kriterien gelegt. Danach werden verschiedene Strategien aufgezeigt, welche das Ziel verfolgen, Nachhaltigkeit erfolgreich in der Titelwahl zu integrieren, um dadurch langfristige Übergewinne verzeichnen zu können. Schlussendlich wird ein Prozedere aufgestellt, wodurch ein unabhängiger Vermögensverwalter Nachhaltigkeit in der Titelwahl integrieren und die Risiken bezüglich Nachhaltigkeit im Portfolio minimieren kann.

This paper examines the different explanations describing the GameStop short squeeze in January 2021. Firstly, this thesis characterizes textbook-likes short squeezes and gives a detailed overview of the events leading up to the short squeeze including financial datapoints based on the SEC’s staff report. Next academic research is outlined and grouped in the following baskets: (1) retail investor sentiments and coordination and (2) market contagion and regulatory implications. An analysis yields that researchers mostly agree on the uniqueness of this particular squeeze including zero-commission platforms, social media engagement. They however disagree on its impact on financial markets and regulatory implications.

Multi-Asset Barrier Reverse Convertible (MBRC) are one of the most sold structured products in the world, especially in Switzerland where 200 billion CHF of assets are being sold per year. Some structured products can be evaluated with a close-end solution, which does not exist for MBRCs. There exist multiple methods to price MBRC and this thesis will focus on the implementation of the pricing algorithm of MBRC suggested by Lindauer and Seiz (2008). In this thesis also ceteris paribus effects of influencing parameters are measured and described. It is observed that volatility, maturity, type of the product and a barrier level have a big impact on the final price, whereas the dividend yield and risk-free rate are influencing each other in an unknown way.

We add to the emerging literature on empirical asset pricing in the Chinese stock market by building and analyzing a comprehensive set of factors with 1,160 signals for return prediction. Using various machine learning algorithms, we investigate which signals dominate in the Chinese market, a market characterized by a large proportion of retail investors with speculative motives, state-owned firms, and short-sales restrictions. Contrary to studies for the U.S. market, liquidity and fundamental factors emerge as the most important predictors, while price trend signals are less significant. We find that retail investors' dominating presence positively affects short-term predictability, particularly for small stocks. Another feature that distinguishes the Chinese from the U.S. market is the high predictability of large stocks and state-owned enterprises over longer horizons. Our portfolio analysis shows that this overall increased predictability leads to significantly higher out-of-sample performance than in other markets, which remains economically significant after transaction costs.

We study the impact of economic policy uncertainty on the term structure of nominal interest rates. In a general equilibrium model populated by an uncertainty-averse agent, we show that political uncertainty not only affects the yield curve and the corresponding volatility term structure but also bond risk premia carry a premium for political uncertainty. Our model simultaneously captures both the shape of the yield curve and the hump shape of yield volatilities, a stylized feature that is hard to match with a theoretical model. Our model gives rise to a set of testable predictions for which we find strong support in the data: Higher policy uncertainty leads to a significant decline in yield levels and increases bond yield volatilities. Moreover, policy uncertainty predicts future short rates and has an ambiguous effect on term premia. Finally, short (long) maturity bond risk premia respond negatively (positively) to increases in policy uncertainty.

Many researchers seek factors that predict the cross-section of stock returns. In finance, the key is to replicate anomalies by long–short portfolios based on their firm characteristics, with microcap biases alleviated via New York Stock Exchange (NYSE) breakpoints and value-weighted returns. In econometrics, the key is to include a covariance matrix estimator of stock returns for (mimicking) the portfolio construction. This paper marries these two strands of literature in order to test the zoo of cross-sectional anomalies by injecting size controls, basically NYSE breakpoints and value-weighted returns, into efficient sorting. We propose to use a covariance matrix estimator for ultra-high dimensions (up to 5,000) taking into account large, small and microcap stocks. We demonstrate that using a nonlinear shrinkage estimator of the covariance matrix substantially enhances the power of tests for cross-sectional anomalies: On average, -statistics more than double. Furthermore, the proposed revisited efficient sorting method computes even highly significant factor portfolios net of transaction costs. Keywords: Anomalies, cross-section of returns, efficient sorting, large dimensions, Markowitz portfolio selection, nonlinear shrinkage

This study aims to test the predictive power of the United States and Swiss equity markets based on the term structure of the yield curve. For this purpose, the principal components of the yield curve were applied in a probit regression model to establish a market timing strategy from December 1994 to April 2022. The results suggest that the yield curve has some explanatory power for both countries in the six to twelve-month prediction horizon. The developed market timing strategy based on the model could not generate excess returns in an out-of-sample backtest.

The goal of this thesis was to examine the viability of Statistical Arbitrage in Government Bond Futures. This was achieved by applying well known methods of Pairs Trading to data from the World Bond Futures page on Bloomberg. Data includes historical data on Futures for diverse currencies and maturities for the period of 2002 to 2022. The examined strategies rely on identifying relative mispricings between a pair of securities instead of determining their fundamental values. Whenever a pair is identified, the dependence structure between the assets can be quantified using statistical methods to generate trading signals. There exist many ways of identifying pairs, and this thesis applies the Sum of Squared Deviations between normalized price time series to match assets into pairs. Whichever pair has moved together the closest in the past will exhibit the smallest Sum of Squared Deviations and will therefore be matched in a pair. Three different strategies were tested to generate returns based off the identified pairs. The first strategy is based on the distance approach, which uses estimates for the standard deviation of normalized price spreads between a pair to generate trading signals. Whenever the spread exceeds two historical standard deviations, a long-short position is initiated based on the direction in which the spread has diverged. Positions are unwound whenever normalized prices converge again. The second and third strategies are based on the Copula approach to Pairs Trading, which relies on modeling the dependence structure of a pair’s returns by using Copulas. Copulas combine the best fitting marginal distributions of returns into a multidimensional joint distribution function. They can then be used to compute conditional probabilities, also called the Mispricing Indices, which are then used to interpret the co-movement of assets. This allows the arbitrageur to make statements about whether an II asset within the pair has overperformed, underperformed, or moved fairly, relative to its partner on any given day. Two different rules of generating trading signals based on the values of the Mispricing Indices are tested. The first rule is called the Simple Threshold Rule, which generates trading signals based on large divergences that happen on a single day. Whenever the conditional probability of an asset exceeds a Mispricing Index of 0.95 or is smaller than 0.05, a long-short position is initiated based on the direction of divergence and thereafter unwound whenever the prices behave as if they reached equilibrium again. The second rule is called the Cumulative Flag Rule, which cumulatively adds up the daily divergences of the Mispricing Indices from a fair value of 0.5 until the sum reaches an arbitrary threshold. The cumulative sums are called Flags in this approach. Whichever asset has overperformed during this period is shorted, whereas the underperforming asset is bought. Positions are unwound when the Flags reach 0 again. To prevent a pair from continually diverging, a stop-loss parameter can be implemented by setting a maximum value for the Flags. The strategies were tested in the following way. During a formation period, the top three pairs out of all possible pairs with the smallest Sum of Squared Deviations are identified as pairs and thereafter traded for a predefined amount of time during the trading period. Each month, a new portfolio is opened and ran for a predefined amount of time. Therefore, the methodology concurrently runs different portfolios in an overlapping way. Previous research has relied on a one year formation period and a six months trading period. Three different period length configurations were employed in this thesis, namely one year and six months, six months and three months, and two years and one year for the formation and trading periods. The Results identify the Copula approach using the Cumulative Flag Rule as the best strategy out of the three. In its standard form, the strategy would have yielded an average annualized return of 1.90% over the past 20 years, with a Sharpe ratio of 1.15 and 87.66% of III portfolios yielding positive returns. The shorter period length configuration exhibits better performance, with an annualized return of 1.95%, a Sharpe Ratio of 1.23 and 84.43% of portfolios yielding positive returns. In addition, removing the universal stop-loss parameter improves the strategy’s performance, and it can be further optimized by tuning thresholds. The second best performance is achieved by the distance approach. In its standard form, the strategy would have yielded an average annualized return of 0.65%, with a Sharpe ratio of 0.48 and 65.53% of portfolios yielding positive returns. The third strategy, the Copula approach using the Simple Threshold Rule, can be disqualified as a viable strategy. It is excessively sensitive, leading to an unreasonable amount of necessary roundtrip trades, and fails to break even in most cases. The only period length configuration which generates positive returns is the 6m/3m configuration, with an average annualized return of 0.07%. Even after optimization, results remain unpromising. This thesis also contains rough recommendations on how to set the threshold values of the Flags when applying the strategies to Government Bond Futures. Threshold values in the literature are recommended based on stock market data, and only for the 1y/6m period lengths. Interestingly, the standard recommendations for thresholds are very close to optimal even for Government Bond Futures, but only for the 1y/6m period length configuration. In other configurations, the optimal ranges of the thresholds can differ widely. For the Cumulative Flag Rule, setting the threshold value of flags d to 0.69 and 0.95 for the shorter and longer period length configurations, respectively, would have notably improved performance. To conclude, the results identify the Cumulative Flag Rule as a promising strategy. The Copulas are effective in estimating the dependence structure between Futures and are able to reliably generate returns from it. Performance could be improved upon by experimenting with different conditions and rules of generating trading signals in the future.

U.S. and European companies vulnerable to the clean energy transition have seen their stock prices go very different ways since Russia invaded Ukraine.

null

In this master’s thesis, I examined 50 key performance indicators to establish, whether those are valuable independent factors, and to which group they belong to. Therefore, I formulated the following research questions: Are the key performance indicators factors? To which factor group do they belong, or are they independent factors?