Timing Success Explained

Can technical trading systems outsmart their benchmark? In our analysis we take a look on what drives trading performance of SMA rules. The working paper is pending but our results have been presented on many conferences, e.g. EURO 2015 in Glasgow, MFS Annual Meetings 2016 in Lemesos and Stockholm, World Finance Conference 2016 in New York, and Quant 2017 in Venice.

QuantInvest 2016 in Munich

Directlimagey after the VI. HSBA Finance Conference, I travelled to Munich to participate in QuantInvest 2016. My co-author Ursula presented the results of our joint work „Avoiding pitfalls when measuring quantitative strategies: A new perspective“. We met some very interesting people there and made some great contacts. Thanks to the organizers for inviting us — it has been a great experience! If you are interested in our paper, you will find it here.

MFS Spring Conference 2016: Timing Success Explained — The Fallacy of Beating Efficient Markets

From April 22nd until 24th, the MFS Spring Conference took place at Cyprus University of Technology in Lemesos. I was invited to present my new findings on timing strategies.

The conference was a great event: well organized, balanced programm, fantastic talks, interesting people, and awesome receiption, dinner as well as tour! Last but not least: Lemesos is a place to be — absolutely worth the travel.

A big THANK YOU to all the people which made that event possible and organized it!


EURO2015: Timing Success Explained — The Fallacy of Beating Efficient Markets

From July 12th until 15th, the EURO2015 takes place at the University of Strathclyde in Glasgow. I am invited to present my new findings on timing strategies.


According to the efficient market hypothesis (EFM), technical trading rules should not have prediction power. However, a significant number of academic studies confirm at least slight excess returns. By applying parametric and historical simulation techniques, we show the connection between timing success and statistical properties of the underlying. Therefore, we check the time series data of prior studies with respect to their statistical properties in order to explain their findings. As long as drift, volatility and autocorrelation of a time series are unpredictable, there seems to be no benefit from technical trading rules.

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Managing Position Size Depending On Asset Price Characteristics


The application of a technical trading rule requires investors to determine a position size of the trades selected. In order to find an optimal position size, the Kelly criterion is widely suggested, which bets relative fractions from the remaining trading budget. Therefore, the general impact of position sizing on timing strategies and the relation to the Kelly criterion have been analyzed. The introduction of relative position sizing has a major impact on trading results. In contrast to a standard Kelly framework, however, an optimal position size does not exist.


Peter Scholz (2014): Managing Position Size Depending On Asset Price Characteristics. Journal of Applied Operational Research 6(4), 189-206.

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Performance Analysis of Investment Strategies – Pitfalls and Surprises


Active investment strategies are a subject of endless debates. Myriads of studies have been conducted to proof performance potential or to reject previous studies due to flaws or misinterpretations. The presentation will address three specific aspects which often are disregarded when performance is measured. Firstly, we will discuss the role of backtests and show that this instrument — even when used carefully and skilled — may lead to biased and misleading results. Secondly, we give an example that the concepts of performance and forecast power must be strictly distinguished. Finally, we demonstrate that implementation details, while largely neglected, may strongly impact and bias a strategy’s performance.


Peter Scholz & Ursula Walther (2013): Performance Analysis of Investment Strategies — Pitfalls and Surprises. Lecture Notes in Management Science Vol. 5, 176-180.

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