Home

The Scientific Way to Long-Term Capital Appreciation.

Schlossberg Technologies is a quantitative investment management company trading in global financial markets, dedicated to producing exceptional returns for its investors by combining the most sophisticated scientific methods of quantitative finance, machine learning and behavioural finance.

Products

Technology

We build the best performing, mathematically sound, long-term oriented, liquid, multi-asset, multi-strategy investment approach.

Years of research and a strong team of financial experts, physicists and mathematicians from University of St.Gallen (HSG), ETH Zurich and ETH Lausanne have led to break-through innovations in volatility forecasting and time-series predictions. Our proprietary algorithms are constantly evolving through machine learning and work in different market regimes.

Schlossberg Technologies is at the nexus of economics, data and technology. Our evolution has been a continuous exploration of what drives financial markets and how it can be scientifically applied for long-term capital appreciation.

Our seven quantitative sub-models have been live since 2018, with each sub-model having its own asset universe and a low correlation to the other sub-models. We have developed investable products by using these sub-models. Two of our flagship products, AIM10 (DD) and AIM40 (DD), aim to maximize returns by dynamically allocating to the seven sub-models while targeting maximum drawdown levels of 10% and 40%, respectively. Additionally, our AIM3 (VOL) and AIM20 (VOL) products allocate funds equally across the seven sub-models, with targets for volatility levels of 3% and 20%, respectively. For those interested in accessing our DA sub-model, we offer the AIMDA (SUB) product. In the near future, we plan to release additional sub-models as single products.

Technology
 
Return
Volatility
Sharpe Ratio
Max.
Drawdown
Equity Corr.
Bond Corr.
1m
-0.35%
N/A
N/A
N/A
N/A
N/A
N/A
3m
-0.51%
2.02%
-0.25
N/A
N/A
N/A
N/A
6m
-1.05%
2.80%
-0.38
N/A
N/A
N/A
N/A
YTD
0.19%
2.23%
0.08
N/A
N/A
N/A
N/A
1yr (p.a.)
-2.70%
2.25%
-1.20
-9.60%
Dec-2022
29.81%
3.88%
3yr (p.a.)
14.24%
10.07%
1.41
-9.60%
Dec-2022
-3.28%
-19.58%
5yr (p.a.)
15.25%
10.42%
1.46
-9.60%
Dec-2022
-3.86%
-17.05%
Live (p.a.)
16.81%
10.50%
1.60
-9.60%
Dec-2022
24.85%
0.57%
10yr (p.a.)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
15yr (p.a.)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
20yr (p.a.)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
30yr (p.a.)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
50yr (p.a.)
N/A
N/A
N/A
N/A
N/A
N/A
N/A

Details

NAV

921.90

ISIN

CH0596276250

Valor

59627625

Bloomberg

ID CH0596276250

Currency

CHF

Subscription

Daily

Min. Subscription

1 Certificate

Maturity

Open-End

Management Fee

0.50% p.a.

Performance Fee

20% (HWM)

Strategy live date

January 1st, 2018

Product live date

March 19th, 2021

Paying Agent

ISP Securities AG

Clearing

SIX SIS

Calculation Agent

ISP Securities AG

Administration Fee

0.35% p.a.

Subscription Fee

0.30% - 0.70%

Redemption Fee

0.00%

Product

Goal: Maximize the return while aiming for a long-term max. Drawdown target of 10%.
How it works: The strategy invests in a global, broadly diversified portfolio of equities, bonds, commodities, currencies, digital assets and cash. The dynamic allocations are derived from a combination of different quantitative models with a proven multi-year track record.
Use Case: This product can be used as a stand-alone low-medium max. Drawdown product in any given portfolio. In addition it can be combined with our AIM40 product which has a long-term max. Drawdown target of 40%. By weighting these products, one can tailor their respective max. Drawdown target to their liking.

Core Investment Team

David Bühlmann worked at Deutsche Bank, Julius Bär and HSBC in Zurich, Singapore and Hong Kong. He is an expert in derivatives and quantitative finance with degrees from University of St.Gallen (HSG) and Bayes Business School in London.
Prof. Dr. Semyon Malamud has a PhD from ETH Zurich and is a Professor at ETH Lausanne (EPFL). He is a Swiss Finance Institute Senior Chair and a Research Fellow at the BIS and the ECB.
Boris Kuznetsov holds an M.Sc. in Mathematics and Fin. Eng. from ETH Lausanne (EPFL) and a B.Sc. in Physics from Saint-Petersburg State University. He is an expert in Machine Learning & AI.
Andrea Xu Teng holds an M.Sc. in Computer Science with in-depth studies in Algorithm Design, Big Data Computing and Distributed Systems amongst others. He is currently finishing his Ph.D. at ETH Lausanne (EPFL).

 
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Total
2023
0.70%
-0.64%
0.48%
-0.35%
-
-
-
-
-
-
-
-
0.19%
2022
-0.16%
-0.18%
-0.32%
0.32%
-1.01%
-0.86%
-0.02%
0.10%
-0.05%
0.17%
0.23%
-1.46%
-3.21%
2021
6.78%
7.13%
5.69%
0.36%
-0.83%
2.54%
-2.81%
-2.04%
-0.19%
-1.35%
-0.22%
-0.15%
15.24%
2020
2.76%
3.70%
8.99%
7.84%
4.19%
2.49%
4.67%
3.85%
-2.72%
3.32%
9.42%
4.53%
67.12%
2019
1.17%
2.37%
1.03%
3.29%
4.65%
3.97%
-2.28%
-3.42%
-1.06%
-0.43%
2.60%
-0.19%
11.96%
2018
6.16%
-1.57%
1.27%
5.00%
-1.09%
0.95%
0.13%
-1.49%
0.75%
-2.46%
3.81%
-3.19%
8.08%

Annual Returns (Live)

Histogram (Live)

SCHLOSSTECH DM

Is a dual-momentum based investment strategy with a vigorous crash protection and a fast momentum filter. Dual momentum combines absolute (trendfollowing) and relative (strength) momentum. Compared to the traditional dual momentum approaches, we have replaced the usual crash protection through trendfollowing on the asset level by our breadth momentum on the universe level instead.

SCHLOSSTECH DM

Is based on both relative as well as absolute momentum. PM has done extremely well managing drawdowns by using a “crash protection” asset to protect the portfolio from excessive loss. PM also considers correlation. The strategy is more likely to choose assets that are less positively correlated to other assets in the universe, which produces a more diversified portfolio.

SCHLOSSTECH RP

Is allocating to major US asset classes based on current risk premium valuations relative to historical norms. RP takes an entirely unrelated approach to momentum; it’s a value strategy. As an asset’s price increases, the asset tends to become less attractive. By combining with our momentum sub-models, we gain an additional degree of diversification and benefit from negative correlations.

SCHLOSSTECH DOW

Uses an algorithm to choose the top four Dow 30 stocks based on risk-adjusted momentum avoiding the old fashioned underperforming members of the Dow 30 index. With a variable allocation to treasuries or gold it smoothes the equity curve and provides crash protection in bear markets. The strategy combines well with our broader momentum and value strategies to form a well balanced portfolio.

SCHLOSSTECH NAS

Uses an algorithm to choose the top four Nasdaq 100 stocks based on risk-adjusted momentum riding the extraordinary momentum of the index. With a variable allocation to treasuries or gold it smoothes the equity curve and provides crash protection in bear markets. The strategy combines well with our broader momentum and value strategies to form a well balanced portfolio.

SCHLOSSTECH GLD

Takes advantage of the historically negative correlation between gold and the U.S. dollar. It switches between the two assets based on their recent risk-adjusted performance enabling the strategy to provide protection against severe gold corrections due to dollar strength. It is an excellent addition to existing equity or bond portfolios as it holds very little correlation to either.

SCHLOSSTECH DA

Invests in the top performers across a selection of digital assets, equity, treasury and precious metal assets with similar volatility characteristics. On a regular basis the strategy ranks these assets using our Modified Sharpe Ratio and invests 50% of the DA portfolio in each of the top two performers. In a prolonged digital assets bear market it can be invested 100% in traditional asset classes.

Live since Jan-2018.
Performance data quoted represent past performance. Past performance does not guarantee future results and current performance may be lower or higher than the data quoted. All returns shown are total returns that assume reinvestment of dividends and capital gains. Investment returns and principal will fluctuate with market and economic conditions and you may have a gain or loss when you sell shares. Benchmarks: SPX: S&P500 Index in CHF; IEF: iShares 7-10 Year Treasury Bond ETF in CHF; 60/40: 60% SPX and 40% IEF in CHF; 60/40_3: 60/40 scaled to 3% Volatility in CHF; 60/40_10: 60/40 scaled to 10% Volatility in CHF; 60/40_20: 60/40 scaled to 20% Volatility in CHF

 
Return
Volatility
Sharpe Ratio
Max.
Drawdown
Equity Corr.
Bond Corr.
1m
-0.79%
N/A
N/A
N/A
N/A
N/A
N/A
3m
-1.65%
4.52%
-0.36
N/A
N/A
N/A
N/A
6m
-2.83%
8.14%
-0.35
N/A
N/A
N/A
N/A
YTD
0.63%
N/A
N/A
N/A
N/A
N/A
N/A
1yr (p.a.)
-5.59%
6.44%
-0.87
-20.07%
Dec-2022
26.77%
2.80%
3yr (p.a.)
33.61%
24.87%
1.35
-20.07%
Dec-2022
2.41%
-14.40%
5yr (p.a.)
35.36%
26.85%
1.32
-20.07%
Dec-2022
0.43%
-11.49%
Live (p.a.)
39.24%
26.78%
1.47
-20.07%
Dec-2022
26.69%
-2.01%
10yr (p.a.)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
15yr (p.a.)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
20yr (p.a.)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
30yr (p.a.)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
50yr (p.a.)
N/A
N/A
N/A
N/A
N/A
N/A
N/A

Details

NAV

845.54

ISIN

CH0596276268

Valor

59627626

Bloomberg

ID CH0596276268

Currency

CHF

Subscription

Daily

Min. Subscription

1 Certificate

Maturity

Open-End

Management Fee

1.50% p.a.

Performance Fee

20% (HWM)

Strategy live date

January 1st, 2018

Product live date

March 19th, 2021

Paying Agent

ISP Securities AG

Clearing

SIX SIS

Calculation Agent

ISP Securities AG

Administration Fee

0.35% p.a.

Subscription Fee

0.30% - 0.70%

Redemption Fee

0.00%

Product

Goal: Maximize the return while aiming for a long-term max. Drawdown target of 40%.
How it works: The strategy invests in a global, broadly diversified portfolio of equities, bonds, commodities, currencies, digital assets and cash. The dynamic allocations are derived from a combination of different quantitative models with a proven multi-year track record.
Use Case: This product can be used as a stand-alone high max. Drawdown product in any given portfolio. In addition it can be combined with our AIM10 product which has a long-term max. Drawdown target of 10%. By weighting these products, one can tailor their respective max. Drawdown target to their liking.

Core Investment Team

David Bühlmann worked at Deutsche Bank, Julius Bär and HSBC in Zurich, Singapore and Hong Kong. He is an expert in derivatives and quantitative finance with degrees from University of St.Gallen (HSG) and Bayes Business School in London.
Prof. Dr. Semyon Malamud has a PhD from ETH Zurich and is a Professor at ETH Lausanne (EPFL). He is a Swiss Finance Institute Senior Chair and a Research Fellow at the BIS and the ECB.
Boris Kuznetsov holds an M.Sc. in Mathematics and Fin. Eng. from ETH Lausanne (EPFL) and a B.Sc. in Physics from Saint-Petersburg State University. He is an expert in Machine Learning & AI.
Andrea Xu Teng holds an M.Sc. in Computer Science with in-depth studies in Algorithm Design, Big Data Computing and Distributed Systems amongst others. He is currently finishing his Ph.D. at ETH Lausanne (EPFL).

 
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Total
2023
2.31%
-1.71%
0.86%
-0.79%
-
-
-
-
-
-
-
-
0.63%
2022
-0.26%
-0.26%
-0.65%
1.03%
-2.46%
-2.15%
0.11%
0.43%
0.42%
0.83%
0.89%
-4.29%
-6.32%
2021
12.68%
13.14%
10.81%
-0.83%
0.21%
6.07%
-7.07%
-4.34%
-0.05%
-3.50%
-0.57%
0.08%
27.06%
2020
8.96%
9.99%
23.35%
18.15%
10.50%
6.91%
13.30%
11.90%
-9.80%
4.56%
27.19%
10.83%
247.78%
2019
4.42%
6.41%
0.52%
7.97%
8.10%
11.34%
-7.28%
-11.93%
0.30%
-1.37%
6.20%
-4.44%
19.00%
2018
14.63%
-4.26%
5.41%
7.94%
-1.74%
1.46%
1.02%
-2.63%
0.34%
-9.48%
12.66%
-8.56%
14.57%

Annual Returns (Live)

Histogram (Live)

SCHLOSSTECH DM

Is a dual-momentum based investment strategy with a vigorous crash protection and a fast momentum filter. Dual momentum combines absolute (trendfollowing) and relative (strength) momentum. Compared to the traditional dual momentum approaches, we have replaced the usual crash protection through trendfollowing on the asset level by our breadth momentum on the universe level instead.

SCHLOSSTECH DM

Is based on both relative as well as absolute momentum. PM has done extremely well managing drawdowns by using a “crash protection” asset to protect the portfolio from excessive loss. PM also considers correlation. The strategy is more likely to choose assets that are less positively correlated to other assets in the universe, which produces a more diversified portfolio.

SCHLOSSTECH RP

Is allocating to major US asset classes based on current risk premium valuations relative to historical norms. RP takes an entirely unrelated approach to momentum; it’s a value strategy. As an asset’s price increases, the asset tends to become less attractive. By combining with our momentum sub-models, we gain an additional degree of diversification and benefit from negative correlations.

SCHLOSSTECH DOW

Uses an algorithm to choose the top four Dow 30 stocks based on risk-adjusted momentum avoiding the old fashioned underperforming members of the Dow 30 index. With a variable allocation to treasuries or gold it smoothes the equity curve and provides crash protection in bear markets. The strategy combines well with our broader momentum and value strategies to form a well balanced portfolio.

SCHLOSSTECH NAS

Uses an algorithm to choose the top four Nasdaq 100 stocks based on risk-adjusted momentum riding the extraordinary momentum of the index. With a variable allocation to treasuries or gold it smoothes the equity curve and provides crash protection in bear markets. The strategy combines well with our broader momentum and value strategies to form a well balanced portfolio.

SCHLOSSTECH GLD

Takes advantage of the historically negative correlation between gold and the U.S. dollar. It switches between the two assets based on their recent risk-adjusted performance enabling the strategy to provide protection against severe gold corrections due to dollar strength. It is an excellent addition to existing equity or bond portfolios as it holds very little correlation to either.

SCHLOSSTECH DA

Invests in the top performers across a selection of digital assets, equity, treasury and precious metal assets with similar volatility characteristics. On a regular basis the strategy ranks these assets using our Modified Sharpe Ratio and invests 50% of the DA portfolio in each of the top two performers. In a prolonged digital assets bear market it can be invested 100% in traditional asset classes.

Live since Jan-2018.
Performance data quoted represent past performance. Past performance does not guarantee future results and current performance may be lower or higher than the data quoted. All returns shown are total returns that assume reinvestment of dividends and capital gains. Investment returns and principal will fluctuate with market and economic conditions and you may have a gain or loss when you sell shares. Benchmarks: SPX: S&P500 Index in CHF; IEF: iShares 7-10 Year Treasury Bond ETF in CHF; 60/40: 60% SPX and 40% IEF in CHF; 60/40_3: 60/40 scaled to 3% Volatility in CHF; 60/40_10: 60/40 scaled to 10% Volatility in CHF; 60/40_20: 60/40 scaled to 20% Volatility in CHF

 
Return
Volatility
Sharpe Ratio
Max.
Drawdown
Equity Corr.
Bond Corr.
1m
-0.20%
N/A
N/A
N/A
N/A
N/A
N/A
3m
-0.09%
1.40%
-0.06
N/A
N/A
N/A
N/A
6m
-0.37%
1.50%
-0.25
N/A
N/A
N/A
N/A
YTD
0.25%
1.30%
0.19
N/A
N/A
N/A
N/A
1yr (p.a.)
-2.11%
1.49%
-1.41
-2.97%
Dec-2022
78.21%
70.06%
3yr (p.a.)
2.06%
2.63%
0.78
-2.97%
Dec-2022
64.80%
-14.15%
5yr (p.a.)
1.73%
2.52%
0.68
-2.97%
Dec-2022
68.10%
-20.43%
Live (p.a.)
1.67%
2.58%
0.65
-2.97%
Dec-2022
66.30%
-27.92%
10yr (p.a.)
2.34%
2.29%
1.02
-2.97%
Dec-2022
49.92%
-14.18%
15yr (p.a.)
3.00%
2.33%
1.28
-3.29%
Jun-2008
36.77%
-10.55%
20yr (p.a.)
2.90%
2.46%
1.18
-3.29%
Jun-2008
34.99%
-12.75%
30yr (p.a.)
2.63%
2.40%
1.10
-3.29%
Jun-2008
32.51%
3.30%
50yr (p.a.)
2.72%
2.60%
1.05
-5.13%
Jul-1981
22.96%
6.18%

Details

NAV

994.04

ISIN

CH1108674842

Valor

110867484

Bloomberg

ID CH1108674842

Currency

CHF

Subscription

Daily

Min. Subscription

1 Certificate

Maturity

Open-End

Management Fee

0.50% p.a.

Performance Fee

20% (HWM)

Strategy live date

January 1st, 2018

Product live date

September 5th, 2022

Paying Agent

ISP Securities AG

Clearing

SIX SIS

Calculation Agent

ISP Securities AG

Administration Fee

0.35% p.a.

Subscription Fee

0.30% - 0.70%

Redemption Fee

0.00%

Product

Goal: Maximize the return while aiming for a long-term volatility of 3%.
How it works: The strategy invests in a global, broadly diversified portfolio of equities, bonds, commodities, currencies, digital assets and cash. The dynamic allocations are derived from a combination of different quantitative models with a proven multi-year track record.
Use Case: This product can be used as a stand-alone ultra-low volatility product in any given portfolio. In addition it can be combined with our AIM20 product, which has a long-term target volatility of 20%. By weighting these products, one can tailor their respective target volatility to their liking. For instance one could target an index similar to the S&P Global Dev. Sov. Bond Index with a 10yr volatility of 5.44% by investing 85.6% in AIM3 and 14.4% in AIM20. The resulting 10yr Sharpe Ratio of the synthetic portfolio is 1.07 instead of -0.18 for the stand-alone investment in the index.

Core Investment Team

David Bühlmann worked at Deutsche Bank, Julius Bär and HSBC in Zurich, Singapore and Hong Kong. He is an expert in derivatives and quantitative finance with degrees from University of St.Gallen (HSG) and Bayes Business School in London.
Prof. Dr. Semyon Malamud has a PhD from ETH Zurich and is a Professor at ETH Lausanne (EPFL). He is a Swiss Finance Institute Senior Chair and a Research Fellow at the BIS and the ECB.
Boris Kuznetsov holds an M.Sc. in Mathematics and Fin. Eng. from ETH Lausanne (EPFL) and a B.Sc. in Physics from Saint-Petersburg State University. He is an expert in Machine Learning & AI.
Andrea Xu Teng holds an M.Sc. in Computer Science with in-depth studies in Algorithm Design, Big Data Computing and Distributed Systems amongst others. He is currently finishing his Ph.D. at ETH Lausanne (EPFL).

 
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Total
2023
0.33%
-0.32%
0.43%
-0.20%
-
-
-
-
-
-
-
-
0.25%
2022
-0.86%
-0.06%
0.87%
-0.20%
-0.24%
-0.75%
0.36%
-0.70%
-0.47%
0.05%
0.09%
-0.71%
-2.59%
2021
0.53%
0.49%
1.17%
0.52%
0.18%
-0.29%
0.73%
0.30%
-0.79%
1.47%
-0.68%
0.30%
3.99%
2020
-0.25%
-0.96%
-1.09%
1.52%
0.86%
0.44%
1.99%
0.79%
-1.33%
-0.66%
1.54%
1.01%
3.86%
2019
0.90%
-0.01%
0.58%
0.67%
-0.91%
0.97%
-0.09%
0.92%
-0.54%
0.23%
0.37%
0.45%
3.59%
2018
1.47%
-1.14%
-0.31%
0.10%
0.37%
-0.26%
0.32%
0.66%
-0.26%
-1.00%
-0.04%
-0.06%
-0.17%
2017
0.48%
0.90%
0.61%
0.49%
1.40%
-0.11%
0.47%
0.56%
-0.13%
0.75%
0.60%
0.80%
7.03%
2016
1.00%
1.36%
0.73%
0.05%
-0.06%
1.27%
0.46%
-0.62%
0.12%
-0.60%
0.54%
0.47%
4.80%
2015
0.97%
-0.08%
-0.49%
-0.38%
0.09%
-0.74%
0.12%
-0.64%
-0.13%
1.22%
-0.06%
-0.54%
-0.68%
2014
0.25%
0.56%
-0.33%
0.12%
0.31%
0.35%
-0.22%
1.03%
-0.74%
0.47%
0.66%
0.02%
2.50%
2013
1.03%
0.05%
0.59%
0.30%
-0.18%
-0.10%
0.78%
-0.30%
0.07%
0.54%
0.29%
0.11%
3.22%
2012
0.59%
1.10%
0.31%
-0.46%
0.69%
0.09%
0.34%
0.14%
0.31%
-0.58%
0.36%
-0.05%
2.87%
2011
-0.24%
0.86%
0.00%
1.47%
-0.22%
-0.77%
0.74%
1.19%
0.55%
0.85%
-0.09%
0.31%
4.73%
2010
-0.24%
0.68%
0.78%
0.90%
-0.28%
0.25%
0.20%
0.07%
0.69%
1.01%
0.14%
0.40%
4.69%
2009
-0.78%
-1.24%
0.65%
-0.73%
2.17%
-0.07%
1.32%
0.02%
0.90%
0.06%
1.93%
-0.64%
3.58%
2008
-0.61%
-0.06%
-0.75%
-0.34%
0.36%
-0.56%
0.65%
0.23%
-0.09%
-0.15%
1.70%
1.55%
1.89%
2007
0.61%
-0.86%
-0.15%
0.44%
0.58%
-0.19%
-0.62%
0.34%
1.25%
1.87%
-1.47%
0.12%
1.91%
2006
1.99%
-0.56%
0.73%
-0.03%
-0.68%
-0.57%
0.16%
0.35%
-0.21%
0.48%
0.21%
0.07%
1.93%
2005
-0.79%
1.13%
-1.44%
-0.56%
0.38%
0.01%
0.95%
-0.26%
0.88%
-0.74%
0.97%
-0.05%
0.42%
2004
0.36%
0.71%
0.36%
-2.08%
0.04%
0.16%
-0.06%
0.90%
0.80%
0.67%
1.48%
0.78%
4.15%
2003
-0.15%
-0.33%
-0.23%
0.98%
1.74%
-0.02%
0.18%
0.54%
0.78%
1.34%
0.29%
1.49%
6.80%
2002
-0.17%
-0.10%
-0.89%
0.51%
0.11%
-0.31%
-0.47%
0.80%
0.43%
-0.20%
1.22%
0.14%
1.04%
2001
0.36%
0.06%
0.07%
-0.45%
0.08%
1.19%
0.52%
0.33%
0.10%
0.41%
-0.75%
-0.19%
1.73%
2000
-0.43%
0.30%
-0.09%
-1.50%
-0.08%
0.64%
0.15%
0.51%
0.11%
-0.25%
0.61%
0.64%
0.58%
1999
0.67%
-1.69%
0.29%
1.57%
-1.01%
0.25%
-0.11%
-0.14%
0.08%
0.12%
0.16%
2.12%
2.24%
1998
0.35%
0.57%
0.49%
0.07%
-0.49%
0.06%
-0.07%
0.70%
1.48%
-0.60%
-0.25%
0.29%
2.62%
1997
0.64%
-0.39%
-0.90%
-0.17%
0.33%
0.69%
1.48%
-1.22%
0.17%
-0.18%
0.43%
0.03%
0.87%
1996
0.51%
-0.80%
0.42%
0.80%
0.30%
-0.09%
-1.79%
0.24%
1.06%
0.71%
1.42%
-0.60%
2.17%
1995
0.53%
0.76%
0.31%
0.63%
1.20%
0.23%
0.06%
0.13%
0.61%
0.19%
0.75%
0.28%
5.81%
1994
-0.13%
-0.50%
-0.97%
-0.21%
0.18%
-0.22%
0.43%
1.10%
-0.37%
-0.28%
-0.17%
0.29%
-0.86%
1993
0.88%
0.93%
0.13%
1.52%
0.00%
0.17%
0.62%
1.54%
0.27%
1.38%
0.18%
0.60%
8.54%
1992
1.03%
0.20%
-0.31%
0.11%
0.86%
-1.26%
1.42%
0.41%
0.56%
-0.88%
0.13%
0.89%
3.19%
1991
0.26%
0.20%
0.57%
0.35%
1.00%
-1.08%
0.38%
0.82%
0.93%
0.25%
-0.11%
1.81%
5.50%
1990
-0.01%
0.06%
-0.33%
-0.35%
0.77%
0.26%
1.08%
-2.58%
0.17%
0.36%
0.36%
0.58%
0.32%
1989
0.91%
-0.80%
1.66%
3.10%
0.50%
0.11%
1.16%
0.34%
0.40%
-0.23%
0.26%
0.64%
8.29%
1988
1.20%
0.11%
-0.07%
-0.32%
0.04%
0.37%
-0.95%
-1.30%
0.63%
0.31%
0.82%
0.40%
1.22%
1987
0.68%
0.14%
1.22%
1.31%
-0.27%
0.07%
0.68%
1.56%
-1.71%
0.44%
-0.19%
0.25%
4.22%
1986
0.03%
2.77%
2.82%
1.08%
-1.37%
0.62%
0.88%
0.90%
-0.28%
-0.43%
0.37%
-0.07%
7.47%
1985
0.04%
-1.61%
0.47%
0.65%
2.54%
0.33%
-0.08%
0.72%
1.35%
0.96%
0.73%
1.06%
7.33%
1984
1.89%
-0.89%
-0.19%
-0.40%
-0.89%
0.25%
0.81%
0.49%
0.22%
1.59%
0.31%
0.46%
3.66%
1983
-0.83%
-0.07%
0.41%
1.64%
-0.86%
0.33%
-1.83%
-0.38%
0.75%
-0.81%
0.54%
0.72%
-0.42%
1982
-0.14%
0.13%
-0.12%
0.23%
0.15%
-0.55%
0.44%
1.63%
1.63%
1.97%
0.18%
0.45%
6.13%
1981
-1.14%
-0.47%
0.23%
-0.56%
-0.05%
-0.40%
-0.64%
0.05%
0.16%
0.97%
1.60%
-1.41%
-1.68%
1980
0.96%
-0.41%
-0.73%
0.45%
1.46%
1.81%
-0.78%
0.21%
-0.36%
-0.35%
-0.51%
-0.44%
1.26%
1979
0.11%
-0.49%
-0.21%
0.25%
-0.36%
0.06%
0.25%
1.66%
1.58%
-1.05%
0.07%
0.94%
2.80%
1978
0.00%
0.45%
0.37%
-0.50%
0.66%
-0.29%
0.71%
0.32%
0.46%
1.48%
-0.13%
-0.10%
3.48%
1977
-0.38%
0.34%
0.31%
0.13%
-0.30%
0.44%
0.13%
0.23%
0.14%
-0.18%
-0.02%
1.03%
1.88%
1976
0.68%
-0.39%
0.53%
-0.57%
-0.56%
0.27%
-0.75%
0.63%
0.35%
0.05%
0.88%
0.74%
1.84%
1975
-0.21%
3.04%
-0.62%
0.80%
0.12%
0.76%
-1.99%
-0.61%
-0.51%
-0.21%
-0.36%
0.18%
0.31%
1974
0.16%
0.00%
-0.02%
0.06%
-0.03%
0.05%
0.06%
0.09%
-0.05%
-0.06%
0.11%
-0.12%
0.24%
1973
2.06%
5.65%
0.03%
-0.06%
0.05%
-0.10%
-0.10%
-0.09%
0.43%
-0.80%
-1.48%
-0.72%
4.79%
1972
1.95%
1.94%
0.86%
0.51%
0.28%
-1.30%
1.80%
0.12%
-1.62%
1.57%
1.16%
2.00%
9.61%
1971
1.38%
0.18%
1.01%
2.89%
-0.94%
4.93%
-0.34%
-0.06%
-0.06%
-1.42%
-0.06%
0.62%
8.28%
1970
0.10%
-0.02%
-0.50%
0.06%
0.06%
0.02%
0.73%
-0.02%
1.05%
-0.58%
-0.06%
-0.06%
0.79%

Annual Returns (Live & Simulated)

Histogram (Live & Simulated)

SCHLOSSTECH DM

Is a dual-momentum based investment strategy with a vigorous crash protection and a fast momentum filter. Dual momentum combines absolute (trendfollowing) and relative (strength) momentum. Compared to the traditional dual momentum approaches, we have replaced the usual crash protection through trendfollowing on the asset level by our breadth momentum on the universe level instead.

SCHLOSSTECH DM

Is based on both relative as well as absolute momentum. PM has done extremely well managing drawdowns by using a “crash protection” asset to protect the portfolio from excessive loss. PM also considers correlation. The strategy is more likely to choose assets that are less positively correlated to other assets in the universe, which produces a more diversified portfolio.

SCHLOSSTECH RP

Is allocating to major US asset classes based on current risk premium valuations relative to historical norms. RP takes an entirely unrelated approach to momentum; it’s a value strategy. As an asset’s price increases, the asset tends to become less attractive. By combining with our momentum sub-models, we gain an additional degree of diversification and benefit from negative correlations.

SCHLOSSTECH DOW

Uses an algorithm to choose the top four Dow 30 stocks based on risk-adjusted momentum avoiding the old fashioned underperforming members of the Dow 30 index. With a variable allocation to treasuries or gold it smoothes the equity curve and provides crash protection in bear markets. The strategy combines well with our broader momentum and value strategies to form a well balanced portfolio.

SCHLOSSTECH NAS

Uses an algorithm to choose the top four Nasdaq 100 stocks based on risk-adjusted momentum riding the extraordinary momentum of the index. With a variable allocation to treasuries or gold it smoothes the equity curve and provides crash protection in bear markets. The strategy combines well with our broader momentum and value strategies to form a well balanced portfolio.

SCHLOSSTECH GLD

Takes advantage of the historically negative correlation between gold and the U.S. dollar. It switches between the two assets based on their recent risk-adjusted performance enabling the strategy to provide protection against severe gold corrections due to dollar strength. It is an excellent addition to existing equity or bond portfolios as it holds very little correlation to either.

SCHLOSSTECH DA

Invests in the top performers across a selection of digital assets, equity, treasury and precious metal assets with similar volatility characteristics. On a regular basis the strategy ranks these assets using our Modified Sharpe Ratio and invests 50% of the DA portfolio in each of the top two performers. In a prolonged digital assets bear market it can be invested 100% in traditional asset classes.

Live since Jan-2018 / Simulated since Jan-1970.
Performance data quoted represent past performance. Past performance does not guarantee future results and current performance may be lower or higher than the data quoted. All returns shown are total returns that assume reinvestment of dividends and capital gains. Investment returns and principal will fluctuate with market and economic conditions and you may have a gain or loss when you sell shares. Benchmarks: SPX: S&P500 Index in CHF; IEF: iShares 7-10 Year Treasury Bond ETF in CHF; 60/40: 60% SPX and 40% IEF in CHF; 60/40_3: 60/40 scaled to 3% Volatility in CHF; 60/40_10: 60/40 scaled to 10% Volatility in CHF; 60/40_20: 60/40 scaled to 20% Volatility in CHF

 
Return
Volatility
Sharpe Ratio
Max.
Drawdown
Equity Corr.
Bond Corr.
1m
-0.76%
N/A
N/A
N/A
N/A
N/A
N/A
3m
-1.64%
4.65%
-0.35
N/A
N/A
N/A
N/A
6m
-2.73%
8.18%
-0.33
N/A
N/A
N/A
N/A
YTD
0.67%
6.30%
0.11
N/A
N/A
N/A
N/A
1yr (p.a.)
-11.72%
8.95%
-1.31
-14.72%
Dec-2022
84.48%
68.87%
3yr (p.a.)
16.18%
17.24%
0.94
-14.72%
Dec-2022
64.96%
-15.40%
5yr (p.a.)
13.88%
16.46%
0.84
-14.72%
Dec-2022
68.15%
-21.27%
Live (p.a.)
13.49%
16.85%
0.80
-14.72%
Dec-2022
66.29%
-28.78%
10yr (p.a.)
18.76%
14.95%
1.25
-14.72%
Dec-2022
49.95%
-14.69%
15yr (p.a.)
23.77%
15.25%
1.56
-18.27%
Jun-2008
36.89%
-10.67%
20yr (p.a.)
22.94%
16.11%
1.42
-18.27%
Jun-2008
34.97%
-12.90%
30yr (p.a.)
20.97%
15.67%
1.34
-18.27%
Jun-2008
32.59%
3.25%
50yr (p.a.)
21.48%
17.03%
1.26
-26.86%
Jul-1981
22.96%
6.21%

Details

NAV

978.13

ISIN

CH1108674859

Valor

110867485

Bloomberg

ID CH1108674859

Currency

CHF

Subscription

Daily

Min. Subscription

1 Certificate

Maturity

Open-End

Management Fee

2.00% p.a.

Performance Fee

20% (HWM)

Strategy live date

January 1st, 2018

Product live date

September 5th, 2022

Paying Agent

ISP Securities AG

Clearing

SIX SIS

Calculation Agent

ISP Securities AG

Administration Fee

0.35% p.a.

Subscription Fee

0.30% - 0.70%

Redemption Fee

0.00%

Product

Goal: Maximize the return while aiming for a long-term volatility of 20%.
How it works: The strategy invests in a global, broadly diversified portfolio of equities, bonds, commodities, currencies, digital assets and cash. The dynamic allocations are derived from a combination of different quantitative models with a proven multi-year track record.
Use Case: This product can be used as a stand-alone high volatility product in any given portfolio. In addition it can be combined with our AIM3 product, which has a long-term target volatility of 3%. By weighting these products, one can tailor their respective target volatility to their liking. For instance one could target an index similar to the S&P 500 Index with a 10yr volatility of 13.68% by investing 37.2% in AIM3 and 62.8% in AIM20. The resulting 10yr Sharpe Ratio of the synthetic portfolio is 1.31 instead of 0.79 for the stand-alone investment in the index.

Core Investment Team

David Bühlmann worked at Deutsche Bank, Julius Bär and HSBC in Zurich, Singapore and Hong Kong. He is an expert in derivatives and quantitative finance with degrees from University of St.Gallen (HSG) and Bayes Business School in London.
Prof. Dr. Semyon Malamud has a PhD from ETH Zurich and is a Professor at ETH Lausanne (EPFL). He is a Swiss Finance Institute Senior Chair and a Research Fellow at the BIS and the ECB.
Boris Kuznetsov holds an M.Sc. in Mathematics and Fin. Eng. from ETH Lausanne (EPFL) and a B.Sc. in Physics from Saint-Petersburg State University. He is an expert in Machine Learning & AI.
Andrea Xu Teng holds an M.Sc. in Computer Science with in-depth studies in Algorithm Design, Big Data Computing and Distributed Systems amongst others. He is currently finishing his Ph.D. at ETH Lausanne (EPFL).

 
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Total
2023
2.35%
-1.77%
0.89%
-0.76%
-
-
-
-
-
-
-
-
0.67%
2022
-5.32%
-0.09%
6.00%
-1.04%
-1.27%
-4.60%
2.63%
-4.27%
-2.79%
0.89%
0.92%
-4.26%
-12.99%
2021
3.71%
3.46%
8.02%
3.70%
1.43%
-1.60%
5.00%
2.23%
-4.85%
9.91%
-4.16%
2.27%
31.98%
2020
-1.33%
-5.99%
-6.85%
10.24%
5.79%
3.13%
13.66%
5.50%
-8.39%
-4.05%
10.36%
6.88%
29.22%
2019
6.16%
0.22%
3.68%
4.46%
-5.64%
6.60%
-0.30%
6.28%
-3.25%
1.80%
2.55%
3.23%
28.03%
2018
10.06%
-7.19%
-1.71%
0.97%
2.69%
-1.41%
2.41%
4.62%
-1.40%
-6.24%
0.01%
-0.09%
1.57%
2017
3.25%
6.24%
4.28%
3.47%
9.56%
-0.41%
3.28%
3.91%
-0.55%
5.15%
4.18%
5.54%
59.36%
2016
6.83%
8.72%
5.06%
0.57%
-0.10%
8.63%
3.27%
-3.75%
1.09%
-3.66%
3.85%
3.24%
38.23%
2015
6.69%
-0.26%
-2.89%
-2.22%
0.91%
-4.52%
1.05%
-3.89%
-0.55%
8.28%
-0.12%
-3.26%
-1.62%
2014
1.87%
3.93%
-1.86%
1.10%
2.16%
2.54%
-1.17%
7.03%
-4.55%
3.34%
4.52%
0.39%
20.47%
2013
6.94%
0.55%
4.11%
2.21%
-0.88%
-0.35%
5.26%
-1.70%
0.78%
3.68%
2.13%
0.98%
26.01%
2012
4.13%
7.55%
2.29%
-2.72%
4.72%
0.84%
2.45%
1.17%
2.28%
-3.51%
2.63%
-0.05%
23.50%
2011
-1.27%
5.87%
0.25%
10.04%
-1.16%
-4.73%
5.15%
8.01%
3.85%
5.89%
-0.30%
2.22%
38.19%
2010
-1.31%
4.75%
5.22%
6.20%
-1.54%
1.85%
1.53%
0.69%
4.81%
6.96%
1.14%
2.88%
38.15%
2009
-4.78%
-7.83%
4.57%
-4.50%
14.43%
-0.19%
8.99%
0.38%
6.22%
0.60%
13.20%
-3.86%
27.35%
2008
-3.70%
-0.13%
-4.62%
-1.93%
2.63%
-3.39%
4.53%
1.77%
-0.32%
-0.66%
11.38%
9.99%
15.09%
2007
3.98%
-5.32%
-0.66%
3.16%
3.90%
-0.93%
-3.77%
2.54%
8.38%
12.83%
-9.35%
1.09%
14.83%
2006
13.60%
-3.34%
4.96%
0.08%
-4.19%
-3.43%
1.37%
2.55%
-1.10%
3.44%
1.66%
0.60%
16.08%
2005
-4.90%
7.62%
-9.16%
-3.38%
2.77%
0.35%
6.49%
-1.43%
5.89%
-4.55%
6.51%
-0.06%
4.61%
2004
2.59%
4.93%
2.58%
-13.32%
0.54%
1.36%
-0.12%
6.20%
5.51%
4.55%
10.13%
5.41%
32.49%
2003
-0.70%
-1.85%
-1.20%
6.55%
11.93%
0.15%
1.39%
3.78%
5.43%
9.15%
2.12%
10.22%
56.78%
2002
-0.82%
-0.39%
-5.52%
3.60%
0.99%
-1.77%
-2.80%
5.55%
3.10%
-1.00%
7.71%
1.06%
9.35%
2001
2.58%
0.63%
0.69%
-2.63%
0.80%
8.01%
3.66%
2.43%
0.86%
2.96%
-4.62%
-0.96%
14.77%
2000
-2.53%
2.26%
-0.32%
-9.49%
-0.25%
4.44%
1.28%
3.61%
0.98%
-1.32%
3.84%
4.37%
6.18%
1999
4.49%
-10.78%
2.16%
10.55%
-6.34%
1.93%
-0.44%
-0.64%
0.79%
1.04%
1.31%
14.03%
16.98%
1998
2.55%
3.72%
3.47%
0.67%
-2.92%
0.67%
-0.17%
4.69%
10.14%
-3.62%
-1.32%
2.19%
21.14%
1997
4.38%
-2.28%
-5.57%
-0.82%
2.43%
4.81%
9.87%
-7.72%
1.39%
-0.91%
3.08%
0.49%
8.21%
1996
3.62%
-4.91%
3.06%
5.41%
2.18%
-0.33%
-11.40%
1.89%
7.24%
4.80%
9.75%
-3.62%
17.00%
1995
3.54%
4.78%
2.30%
4.39%
8.21%
1.75%
0.62%
1.09%
4.26%
1.45%
5.23%
2.09%
47.43%
1994
-0.55%
-3.00%
-6.06%
-1.11%
1.44%
-1.16%
3.13%
7.46%
-2.10%
-1.56%
-0.79%
2.20%
-2.69%
1993
6.11%
6.39%
1.08%
10.41%
0.24%
1.37%
4.36%
10.54%
2.01%
9.47%
1.42%
4.21%
74.33%
1992
7.08%
1.57%
-1.77%
1.04%
5.80%
-7.95%
9.59%
2.94%
3.96%
-5.45%
1.17%
6.01%
25.03%
1991
2.02%
1.61%
3.98%
2.38%
6.89%
-6.75%
2.80%
5.54%
6.41%
1.86%
-0.40%
12.35%
44.73%
1990
0.16%
0.59%
-1.85%
-2.01%
5.16%
1.94%
7.41%
-16.57%
1.41%
2.65%
2.62%
4.11%
3.52%
1989
6.09%
-4.96%
11.23%
21.36%
3.54%
0.98%
7.95%
2.46%
2.85%
-1.22%
1.91%
4.47%
70.22%
1988
8.08%
0.94%
-0.17%
-1.78%
0.58%
2.47%
-5.94%
-8.20%
4.39%
2.32%
5.65%
2.83%
10.47%
1987
4.49%
1.13%
8.37%
8.96%
-1.47%
0.73%
4.63%
10.70%
-10.92%
3.19%
-0.92%
1.94%
33.19%
1986
0.40%
19.02%
19.39%
7.39%
-8.69%
4.37%
5.99%
6.23%
-1.54%
-2.54%
2.69%
-0.14%
61.78%
1985
0.47%
-10.20%
3.36%
4.55%
17.26%
2.41%
-0.25%
4.93%
9.23%
6.64%
5.05%
7.26%
60.81%
1984
12.49%
-5.52%
-0.98%
-2.33%
-5.53%
1.93%
5.60%
3.47%
1.71%
10.46%
2.30%
3.24%
28.31%
1983
-5.12%
-0.17%
2.96%
11.03%
-5.30%
2.48%
-11.67%
-2.18%
5.21%
-4.99%
3.84%
4.97%
-1.07%
1982
-0.64%
1.13%
-0.47%
1.79%
1.25%
-3.33%
3.16%
10.94%
10.68%
12.75%
1.44%
3.19%
48.94%
1981
-7.15%
-2.77%
1.81%
-3.39%
-0.03%
-2.33%
-3.87%
0.64%
1.33%
6.61%
10.74%
-8.94%
-8.63%
1980
6.52%
-2.41%
-4.50%
3.21%
9.74%
12.40%
-4.80%
1.68%
-2.07%
-1.97%
-3.04%
-2.60%
10.91%
1979
0.97%
-2.89%
-1.09%
1.90%
-2.09%
0.71%
1.92%
10.83%
10.79%
-6.56%
0.73%
6.39%
22.13%
1978
0.23%
3.18%
2.70%
-2.95%
4.54%
-1.63%
4.88%
2.33%
3.28%
10.15%
-0.56%
-0.37%
28.21%
1977
-2.23%
2.54%
2.31%
1.17%
-1.65%
3.14%
1.13%
1.72%
0.96%
-0.90%
0.15%
6.05%
15.07%
1976
4.74%
-2.27%
3.75%
-3.44%
-3.39%
2.04%
-4.64%
4.43%
2.57%
0.62%
6.04%
5.12%
15.81%
1975
-1.09%
19.59%
-3.74%
5.47%
1.01%
5.26%
-12.74%
-3.68%
-3.04%
-1.11%
-2.09%
1.47%
2.24%
1974
1.36%
0.26%
0.14%
0.69%
0.10%
0.59%
0.71%
0.86%
-0.07%
-0.08%
1.00%
-0.51%
5.15%
1973
14.11%
39.44%
0.45%
-0.09%
0.51%
-0.33%
-0.38%
-0.31%
2.89%
-4.96%
-9.38%
-4.40%
34.58%
1972
12.90%
13.32%
5.94%
3.58%
2.10%
-8.19%
12.16%
1.05%
-10.33%
10.57%
8.04%
13.73%
81.71%
1971
9.18%
1.40%
6.97%
19.88%
-5.84%
33.94%
-1.92%
-0.09%
-0.09%
-8.98%
-0.09%
4.36%
66.35%
1970
0.96%
0.14%
-2.97%
0.70%
0.70%
0.43%
4.82%
0.14%
7.19%
-3.49%
-0.09%
-0.09%
8.28%

Annual Returns (Live & Simulated)

Histogram (Live & Simulated)

SCHLOSSTECH DM

Is a dual-momentum based investment strategy with a vigorous crash protection and a fast momentum filter. Dual momentum combines absolute (trendfollowing) and relative (strength) momentum. Compared to the traditional dual momentum approaches, we have replaced the usual crash protection through trendfollowing on the asset level by our breadth momentum on the universe level instead.

SCHLOSSTECH PM

Is based on both relative as well as absolute momentum. PM has done extremely well managing drawdowns by using a “crash protection” asset to protect the portfolio from excessive loss. PM also considers correlation. The strategy is more likely to choose assets that are less positively correlated to other assets in the universe, which produces a more diversified portfolio.

SCHLOSSTECH RP

Is allocating to major US asset classes based on current risk premium valuations relative to historical norms. RP takes an entirely unrelated approach to momentum; it’s a value strategy. As an asset’s price increases, the asset tends to become less attractive. By combining with our momentum sub-models, we gain an additional degree of diversification and benefit from negative correlations.

SCHLOSSTECH DOW

Uses an algorithm to choose the top four Dow 30 stocks based on risk-adjusted momentum avoiding the old fashioned underperforming members of the Dow 30 index. With a variable allocation to treasuries or gold it smoothes the equity curve and provides crash protection in bear markets. The strategy combines well with our broader momentum and value strategies to form a well balanced portfolio.

SCHLOSSTECH NAS

Uses an algorithm to choose the top four Nasdaq 100 stocks based on risk-adjusted momentum riding the extraordinary momentum of the index. With a variable allocation to treasuries or gold it smoothes the equity curve and provides crash protection in bear markets. The strategy combines well with our broader momentum and value strategies to form a well balanced portfolio.

SCHLOSSTECH GLD

Takes advantage of the historically negative correlation between gold and the U.S. dollar. It switches between the two assets based on their recent risk-adjusted performance enabling the strategy to provide protection against severe gold corrections due to dollar strength. It is an excellent addition to existing equity or bond portfolios as it holds very little correlation to either.

SCHLOSSTECH DA

Invests in the top performers across a selection of digital assets, equity, treasury and precious metal assets with similar volatility characteristics. On a regular basis the strategy ranks these assets using our Modified Sharpe Ratio and invests 50% of the DA portfolio in each of the top two performers. In a prolonged digital assets bear market it can be invested 100% in traditional asset classes.

Live since Jan-2018 / Simulated since Jan-1970.
Performance data quoted represent past performance. Past performance does not guarantee future results and current performance may be lower or higher than the data quoted. All returns shown are total returns that assume reinvestment of dividends and capital gains. Investment returns and principal will fluctuate with market and economic conditions and you may have a gain or loss when you sell shares. Benchmarks: SPX: S&P500 Index in CHF; IEF: iShares 7-10 Year Treasury Bond ETF in CHF; 60/40: 60% SPX and 40% IEF in CHF; 60/40_3: 60/40 scaled to 3% Volatility in CHF; 60/40_10: 60/40 scaled to 10% Volatility in CHF; 60/40_20: 60/40 scaled to 20% Volatility in CHF

 
Return
Volatility
Sharpe Ratio
Max.
Drawdown
Equity Corr.
Bond Corr.
1m
-3.15%
N/A
N/A
N/A
N/A
N/A
N/A
3m
2.53%
17.23%
0.15
N/A
N/A
N/A
N/A
6m
8.24%
19.34%
0.43
N/A
N/A
N/A
N/A
YTD
13.12%
N/A
N/A
N/A
N/A
N/A
N/A
1yr (p.a.)
-14.67%
24.18%
-0.61
-47.93%
Dec-2022
23.48%
-33.47%
3yr (p.a.)
40.46%
40.63%
1.00
-47.93%
Dec-2022
5.36%
-13.93%
5yr (p.a.)
39.18%
47.45%
0.83
-47.93%
Dec-2022
-5.79%
-19.61%
Live (p.a.)
43.99%
48.49%
0.91
-47.93%
Dec-2022
16.76%
0.78%
10yr (p.a.)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
15yr (p.a.)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
20yr (p.a.)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
30yr (p.a.)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
50yr (p.a.)
N/A
N/A
N/A
N/A
N/A
N/A
N/A

Details

NAV

600.70

ISIN

CH0596276243

Valor

59627624

Bloomberg

ID CH0596276243

Currency

CHF

Subscription

Daily

Min. Subscription

1 Certificate

Maturity

Open-End

Management Fee

2.00% p.a.

Performance Fee

20% (HWM)

Strategy live date

January 1st, 2018

Product live date

March 19th, 2021

Paying Agent

ISP Securities AG

Clearing

SIX SIS

Calculation Agent

ISP Securities AG

Administration Fee

0.35% p.a.

Subscription Fee

0.30% - 0.70%

Redemption Fee

0.00%

Product

Goal: Maximize the return with a max. Drawdown target of 50%.
How it works: The strategy invests primarily in digital assets. However it can diversify into equities, bonds, commodities, currencies and cash when it sees fit. The dynamic allocations are derived from a combination of different quantitative models with a proven multi-year track record.
Use Case: Digital Assets are extremely volatile. Use this product as a digital assets substitute in a given portfolio. We constantly manage the risk of volatility and interest rate shocks with the help of our proprietary investment algorithms.

Core Investment Team

David Bühlmann worked at Deutsche Bank, Julius Bär and HSBC in Zurich, Singapore and Hong Kong. He is an expert in derivatives and quantitative finance with degrees from University of St.Gallen (HSG) and Bayes Business School in London.
Prof. Dr. Semyon Malamud has a PhD from ETH Zurich and is a Professor at ETH Lausanne (EPFL). He is a Swiss Finance Institute Senior Chair and a Research Fellow at the BIS and the ECB.
Boris Kuznetsov holds an M.Sc. in Mathematics and Fin. Eng. from ETH Lausanne (EPFL) and a B.Sc. in Physics from Saint-Petersburg State University. He is an expert in Machine Learning & AI.
Andrea Xu Teng holds an M.Sc. in Computer Science with in-depth studies in Algorithm Design, Big Data Computing and Distributed Systems amongst others. He is currently finishing his Ph.D. at ETH Lausanne (EPFL).

 
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Total
2023
10.33%
-0.55%
6.46%
-3.15%
-
-
-
-
-
-
-
-
13.12%
2022
-0.32%
-0.27%
-1.01%
-5.15%
-10.67%
-12.18%
8.23%
-7.18%
0.40%
-0.37%
-1.75%
-2.61%
-29.59%
2021
17.30%
23.64%
19.74%
0.54%
-2.62%
5.26%
-11.33%
-6.23%
-1.73%
-8.71%
-1.11%
0.25%
32.34%
2020
24.05%
16.44%
13.53%
16.20%
7.30%
-0.69%
23.70%
3.31%
-6.33%
16.74%
36.52%
29.32%
400.90%
2019
-5.65%
2.99%
2.96%
23.83%
51.93%
5.92%
-19.63%
-17.51%
2.59%
-2.02%
3.52%
-7.63%
27.05%
2018
28.45%
-19.12%
8.78%
14.66%
-3.95%
6.78%
-1.92%
-8.39%
-4.86%
-12.49%
23.62%
-17.86%
0.96%

Annual Returns (Live)

Histogram (Live)

SCHLOSSTECH DM

Is a dual-momentum based investment strategy with a vigorous crash protection and a fast momentum filter. Dual momentum combines absolute (trendfollowing) and relative (strength) momentum. Compared to the traditional dual momentum approaches, we have replaced the usual crash protection through trendfollowing on the asset level by our breadth momentum on the universe level instead.

SCHLOSSTECH DM

Is based on both relative as well as absolute momentum. PM has done extremely well managing drawdowns by using a “crash protection” asset to protect the portfolio from excessive loss. PM also considers correlation. The strategy is more likely to choose assets that are less positively correlated to other assets in the universe, which produces a more diversified portfolio.

SCHLOSSTECH RP

Is allocating to major US asset classes based on current risk premium valuations relative to historical norms. RP takes an entirely unrelated approach to momentum; it’s a value strategy. As an asset’s price increases, the asset tends to become less attractive. By combining with our momentum sub-models, we gain an additional degree of diversification and benefit from negative correlations.

SCHLOSSTECH DOW

Uses an algorithm to choose the top four Dow 30 stocks based on risk-adjusted momentum avoiding the old fashioned underperforming members of the Dow 30 index. With a variable allocation to treasuries or gold it smoothes the equity curve and provides crash protection in bear markets. The strategy combines well with our broader momentum and value strategies to form a well balanced portfolio.

SCHLOSSTECH NAS

Uses an algorithm to choose the top four Nasdaq 100 stocks based on risk-adjusted momentum riding the extraordinary momentum of the index. With a variable allocation to treasuries or gold it smoothes the equity curve and provides crash protection in bear markets. The strategy combines well with our broader momentum and value strategies to form a well balanced portfolio.

SCHLOSSTECH GLD

Takes advantage of the historically negative correlation between gold and the U.S. dollar. It switches between the two assets based on their recent risk-adjusted performance enabling the strategy to provide protection against severe gold corrections due to dollar strength. It is an excellent addition to existing equity or bond portfolios as it holds very little correlation to either.

SCHLOSSTECH DA

Invests in the top performers across a selection of digital assets, equity, treasury and precious metal assets with similar volatility characteristics. On a regular basis the strategy ranks these assets using our Modified Sharpe Ratio and invests 50% of the DA portfolio in each of the top two performers. In a prolonged digital assets bear market it can be invested 100% in traditional asset classes.

Live since Jan-2018.
Performance data quoted represent past performance. Past performance does not guarantee future results and current performance may be lower or higher than the data quoted. All returns shown are total returns that assume reinvestment of dividends and capital gains. Investment returns and principal will fluctuate with market and economic conditions and you may have a gain or loss when you sell shares. Benchmarks: SPX: S&P500 Index in CHF; IEF: iShares 7-10 Year Treasury Bond ETF in CHF; 60/40: 60% SPX and 40% IEF in CHF; 60/40_3: 60/40 scaled to 3% Volatility in CHF; 60/40_10: 60/40 scaled to 10% Volatility in CHF; 60/40_20: 60/40 scaled to 20% Volatility in CHF

Thank you for your interest in our products. Unfortunately, due to legal restrictions, we are unable to offer our services to individuals who are not resident in our approved list of countries or who do not meet our minimum investor status requirements.

We apologize for any inconvenience this may cause and we appreciate your understanding in this matter. If you have any questions or concerns, please feel free to contact us.

Team

Diversified Leadership Team

Schlossberg Technologies is backed by a strong team of financial experts, physicists and mathematicians from University of St.Gallen (HSG), ETH Zurich and ETH Lausanne. The team has prior work experience at UBS, Julius Bär, Deutsche Bank, BlackRock or the European Central Bank.

name

David Bühlmann

Chief Executive

David Bühlmann

Chief Executive

David holds an M.Sc. in Finance from Cass Business School in London and a B.A. degree in Business from University of Saint Gallen. David worked in different capacities at Deutsche Bank, Julius Bär and HSBC in Zürich, Singapore and Hong Kong before founding Schlossberg Technologies.

name

Boris Kuznetsov

Chief Quant

Boris Kuznetsov

Chief Quant

Boris holds an M.Sc. in Mathematics and Financial Engineering from ETH Lausanne (EPFL) and a B.Sc. in Physics from Saint-Petersburg State University. He is an expert in Machine Learning and Neural Networks.

name

Prof. Dr. Semyon Malamud

Investment Counselor

Prof. Dr. Semyon Malamud

Investment Counselor

Semyon is Professor of Finance at ETH Lausanne (EPFL) and Senior Chair of the Swiss Finance Institute. He is a Research Fellow at the Bank for International Settlements (BIS) and the Centre for Economic Policy Research (CEPR), and a Lamfalussy Research Fellow at the European Central Bank (ECB).

name

Lars Fasel

Investor Relations

Lars Fasel

Investor Relations

Lars holds a B.A. of Business Administration in Banking and Finance from the Geneva School of Business. Lars worked as a portfolio manager in a real estate foundation and as investor relations at a hedge fund before joining Schlossberg Technologies as a business development professional.

name

Michael Schemitsch

Account Executive

Michael Schemitsch

Account Executive

Michael holds a B.A. degree in Business from University of Saint Gallen. He held various positions in sales and supply chain management in the past before joining Schlossberg Technologies as a business development professional.

name

Andrea Xu Teng

Quant Engineer

Andrea Xu Teng

Quant Engineer

Andrea Xu holds an M.Sc. in Computer Science with in-depth studies in Algorithm Design, Big Data Computing and Distributed Systems amongst others. He is currently finishing his Ph.D. at ETH Lausanne (EPFL).

name

Marc Egger

Data Scientist

Marc Egger

Data Scientist

Marc Egger holds a B.A. in Economics and Finance from Bocconi University and has deep knowledge in algorithm design and data analytics. Previously, he worked on Blackrock's Aladdin platform on optimizing their portfolio metrics engine.

name

Mark Milne

Senior Account Executive

Mark Milne

Senior Account Executive

Mark has an M.A. in Analytic Philosophy and has built a long career in business development for hedge funds and other investments from some of the largest firms in the industry, with an emphasis on quantitatively managed investment strategies.

name

Andy Heilmann

Chief Finance

Andy Heilmann

Chief Finance

Andy is a certified federal fiduciary expert with extensive work experience. He was a partner at Swisspartners Investment Network for more than six years. At Schlossberg Technologies Andy is an invaluable asset for our clients when it comes to structuring complex wealth.

show the whole team

Culture

Culture Is Key

At Schlossberg Technologies, we attribute our unique success to our culture, which values complete openness, ambitious goal-setting, and a dedication to truth and excellence. Unlike many other organizations that focus solely on “How” and “What,” we also prioritize the crucial question of “Why” in all of our endeavors.

By constantly challenging the status quo and seeking to identify our purpose, we are able to build meaningful work and relationships with our clients, partners, and colleagues. Our mission is to empower our clients to benefit from the greatest wealth transfer in human history, and we achieve this through the application of sophisticated mathematical algorithms.

At Schlossberg Technologies, we believe that our unique culture and unwavering commitment to our mission enable us to deliver outstanding results for our clients and make a positive impact on the world.

Work With Us
Technology

Want to hear the latest about Schlossberg Technologies?

    Insights

    Latest Insights

    All

    Research

    In the News

    Webinar

    National Currencies’ Tragic Race to the Bottom

    Money—the magical power it has over people is almost universal. But whether we earn it, spend it, or save it, ...
    Read More
    / In the News

    The Result of “Too Much Money”: Asset Price Inflation and Inequality

    In the eyes of many, covid-19 has truly accelerated things. Tech aficionados have been rejoicing as virtual meetings, Zoom calls, ...
    Read More
    / In the News

    Central Banks and the Problem with Playing God

    Today’s Western institutions have long been deemed to be sacrosanct. As a matter of fact, though, nation-states are increasingly met ...
    Read More
    / In the News

    Debt Cycles. Long and short.

    The economy consists of “a few simple parts” and transactions repeatedly being used throughout. Human nature drives these transactions, and ...
    Read More
    / Research

    Clash of the Protocols — War of the Digital Age

    The reality is that, as is the case in all financial markets, the cryptocurrency market burgeoned, over-extended, and then corrected ...
    Read More
    / Research

    Scalability, Centralization and Other Challenges of the Bitcoin Blockchain

    New Technology needs time to develop - The Internet exists since the late 1960's. The technology underlying Bitcoin, and most ...
    Read More
    / Research
    No posts found.
    No posts found.

    Contacts

    Cookies policy

    We use cookies to ensure you get the best experience on our site. More details

    declineAccept
    Close