Sebastian Heiden’s research while affiliated with University of Augsburg and other places

Zusammenfassung Langfrist-Prognosen sind typischerweise problematischer als Kurzfrist-Prognosen. Im Kapitalmarktkontext ist die Sachlage jedoch umgekehrt, da verlässliche Kurzfrist-Prognosen durch Arbitrageure sofort zunichte gemacht würden. Ex ante ist der Performance-Index am Prognosehorizont eine extrem rechtsschief verteilte Zufallsvariable. Prognosen, die auf dessen Modalwert abzielen, sind daher viel zu pessimistisch. Prognosen, die auf den Erwartungswert abzielen, sind dagegen zu optimistisch. Von den drei prominenten Lagemaßen ist nur der Median in der Lage, als Basis für eine verlässliche Prognose zu dienen. Es werden einige Praktiker-Verfahren untereinander und mit einem neuen Prognoseverfahren verglichen, welches auf der erwartungstreuen Schätzung des Medians beruht. Zur Illustration der Verfahren und der resultierenden Prognosen werden Daten des DAX bis 2022 verwendet. Es zeigt sich unter anderem, dass der erwartungstreue Median-Schätzer bessere Prognosen als das beste ‚Praktiker-Verfahren‘ liefert.

Employing a nonlinear multi-criteria optimization approach, sentiment-based disagreement is incorporated into portfolio optimization as additional risk factor. A multi-criteria trading strategy outperforms several benchmarks regarding various performance measures. Applying the strategy over a long time period including downturns and upswings, disagreement proves itself especially valuable in bear markets as it is an indicator for future volatility.

Based on a dataset of positioning data from one of the largest forex trading platforms in the world, we study the trading behaviour of retail investors on a daily frequency for 14 currency pairs. We examine, whether retail investors could benefit from positioning data. Using a quantile regression framework we find that a representative investor shows a combination of short term contrarian behaviour and long term trend following. Employing positioning data in a naive trading strategy, we observe strong signs of the disposition effect. Overall, investors approach to market timing paired with an asymmetry in realizing gains and losses leads to systematic underperformance based on a strategy mimicking the trading behaviour of investors average positioning.

The information contained in investor sentiment has up to now hardly been used for portfolio optimization, although theoretical works demonstrate that it should not be neglected and it has already been shown to contain exploitable information on future returns and volatility. Employing the approach of Copula Opinion Pooling, we explore how sentiment information regarding international stock markets can be directly incorporated into the portfolio optimization procedure. We subsequently show that sentiment information can be exploited by a trading strategy that takes into account a medium-term reversal effect of sentiment on returns. This sentiment-based strategy outperforms several benchmark strategies in terms of different performance and downside risk measures. More importantly, the results remain robust to changes in the parameter specification.

During the last decades, many empirical studies have indicated a significant influence of noneconomic factors on asset pricing. More recently, it seems to be acknowledged that international soccer match results significantly affect subsequent daily returns of national stock markets via investor sentiment. In this article, we provide evidence that such observations should be treated with caution. Resuming a current debate on the link between the performance of England's national soccer team and FTSE 100 returns, we validate findings made by Ashton et al. (2011). Our results raise doubts on their conclusions and emphasize the importance of thoroughly validating empirical results.

Using a new variable to measure investor sentiment we show that the sentiment of German and European investors matters for return volatility in local stock markets. A flexible empirical similarity (ES) approach is used to emulate the dynamics of the volatility process by a time-varying parameter that is created via the similarity of realized volatility and investor sentiment. Out-of-sample results show that the ES model produces significantly better volatility forecasts than various benchmark models for DAX and EUROSTOXX. Regarding other international markets no significant difference between the forecasts can be observed.

The model of Mehra and Prescott (1985, J. Econometrics, 22, 145–161) implies that reasonable coefficients of risk-aversion of economic agents cannot explain the equity risk premium generated by financial markets. This discrepancy is hitherto regarded as a major financial puzzle. We propose an alternative model to explain the equity premium. For normally distributed returns and for returns far away from normality (but still light tailed), realistic equity risk premia do not imply puzzlingly high risk aversions. Following our approach, the ‘equity premium puzzle’ does not exist. We also consider fat-tailed return distributions and show that Pareto tails are incompatible with constant relative risk aversion.

This paper examines the intraday behavior of 5-min DAX futures return volatility, volume and transactions, employing data from between January 1999 and September 2011, thus covering major market up and down trends. We focus on the interplay of the above variables finding a W-shape due to US macroeconomic news releases and the opening of US markets. By carefully modeling regular but infrequent events, we show that the last trading days of the FDAX and ODAX have significant impact on volatility and alter the intraday patterns. Additionally, we pay special attention to interactions between the futures and cash market caused by different trading hours at the Eurex. Thereby, we discover a uW-shape lending support for Daigler’s (J Futures Markets 17:45–74, 1997) extended market closure theory. Focusing on possible changes in the interplay of volume and volatility, we empirically analyze the implications of different volume–volatility theories. Finally, we model simultaneously the main volatility components—intraday calendar effects, macroeconomic announcement effects and interday volatility clustering—employing the framework of Andersen and Bollerslev (J Finance 53:219–265, 1998) to quantify and compare the impact of macroeconomic news announcements during contractions and expansions and focus on the economic impact of the crisis 2007/2008 on intraday volatility.