NEWSLETTER of July 25, 2025

The following content has been added at finexpert:

We updated the capital market data

(Multiples, Betas and Returns) as to July 15, 2025 >more

Invesco
GLOBAL SOVEREIGN ASSET MANAGEMENT STUDY
In 2025, sovereign investors are reassessing their portfolio frameworks  as traditional models are being challenged, and adopting targeted approaches to emerging markets, with renewed interest in China. Additionally, these investors are exploring digital assets, deepening their commitment to active management, and building larger, more diversified reserves. >more

M&G Investments
GLOBAL REAL ESTATE OUTLOOK MID-YEAR 2025: OPPORTUNITY AMIDST UNCERTAINTY
With real estate generally viewed as fair value in Europe, the UK and APAC after ‘the big price reset’, we believe the recovery in real estate values is unlikely to be derailed, even if it struggles to gain as much traction in the near term. Robust underlying fundamentals continue to support income growth prospects and look set to persist, given a lack of new development. Questions over US policy and the economic outlook mean investors are showing greater interest in opportunities in markets such as Europe, the UK and APAC. >more

Deutsche Bank Research
DEUTSCHLAND-MONITOR BAUFINANZIERUNG Q3/2025
The economy is gaining momentum, with expansionary fiscal policy likely to stimulate growth, particularly in 2026 and 2027. This is likely to result in an increase in long-term interest rates. A further recovery is expected in the construction sector. Despite the severe and growing shortage of skilled workers, 250,000 residential units are expected to be completed in 2025, similar to the previous year. This will be driven by factors such as social housing construction, income-based subsidies, and the Growth Opportunities Act. In addition, completions are likely to increase due to the construction backlog. >more

Allianz Research
THE MARKET ALONE WON’T FIX IT: THE DILEMMA OF CLIMATE-NEUTRAL REAL ESTATE
The residential real estate sector is a major yet often underestimated contributor to greenhouse gas emissions in Europe. Decarbonizing housing is crucial for achieving carbon neutrality but presents complex challenges due to the region’s outdated building stock, high renovation costs and fragmented ownership structures. In major European economies (Germany, France, Italy, Spain and the Netherlands), direct emissions from homes account from 7% (Spain) to 14% (Germany) of national totals, on par with or even exceeding industrial emissions. When indirect emissions are included, this share can double, underscoring the sector’s systemic climate impact. >more

Deutsche Bank Research
IN SEARCH OF NEW EXPORT MARKETS
While the German export model is seriously challenged, ongoing structural changes in global trade patterns also offer opportunities for Germany. We use a gravity model to show how a strategic reconfiguration of German trade could look in order to be most beneficial for future German export growth. We find untapped potential in the EU single market and the Global South. >more

WHY CARE ABOUT DEBT-TO-GDP?
Jonathan Berk, and Jules H. van Binsbergen
2025
We construct an international panel data set comprising three distinct yet plausible measures of government indebtedness: the debt-to-GDP, the interest-to-GDP, and the debt-to-equity ratios. Our analysis reveals that these measures yield differing conclusions about recent trends in government indebtedness. While the debt-to-GDP ratio has reached historically high levels, the other two indicators show either no clear trend or a declining pattern over recent decades. We argue for the development of stronger theoretical foundations for the measures employed in the literature, suggesting that, without such grounding, assertions about debt (un)sustainability may be premature.  >more

A TREND FACTOR FOR THE CROSS-SECTION OF CRYPTOCURRENCY RETURNS
Christian Fieberg, Gerrit Liedtke, Thorsten Poddig, Thomas Walker, and Adam Zaremba
2023
We propose CTREND, a new trend factor for cryptocurrency returns, which aggregates price and volume information across various time horizons. Using data on more than 3,000 coins, we apply machine learning methods to exploit information from various technical indicators. The resulting signal reliably predicts the cross-section of cryptocurrency returns. The effect cannot be subsumed by known factors and remains remarkably robust across different sub-periods, market conditions, and alternative research designs. Moreover, it survives the impact of transaction costs and persists for large and liquid coins, paving the way for an effective trading strategy. Finally, an asset pricing model that incorporates CTREND outperforms competing factor models, providing a superior explanation of cryptocurrency returns. >more