We create a simulated financial market and examine the effect of different levels of active and passive investment on fundamental market efficiency. In our simulated market, active, passive, and random investors interact with each other through issuing orders. Active and passive investors select their portfolio weights by optimizing Markowitz-based utility functions. We find that higher fractions of active investment within a market lead to an increased fundamental market efficiency. The marginal increase in fundamental market efficiency per additional active investor is lower in markets with higher levels of active investment. Furthermore, we find that a large fraction of passive investors within a market may facilitate technical price bubbles, resulting in market failure. By examining the effect of specific parameters on market outcomes, we find that that lower transaction costs, lower individual forecasting errors of active investors, and less restrictive portfolio constraints tend to increase fundamental market efficiency in the market.

Today, energy system models are becoming increasingly powerful and detailed regarding techno-economic parameters. However, current models rarely include social and political factors, although these factors constitute important determinants for the design of energy systems. In Task Area 2 of the nfdi4energy research project, we therefore explore social and political drivers and constraints of the energy transition, generate and link the relevant data, and prepare it for incorporation on a data sharing platform. The aim of this task area is to co-design a scientific energy data and research sharing platform that can feed into new or existing energy models to help inform the public and political decision makers to determine socially acceptable energy pathways of the future. In addition, we will involve citizens during the project lifetime in the development of a platform that enables and incentivizes the active participation of public stakeholders in energy system research.Consequentially, the intention of this abstract within the “Linking RDM Track” is to provide an overview of the platform engagement design process for society and policy.

The creation of synthetic heat pump load profiles is essential for energy system modeling and simulations. This paper proposes a methodology to create synthetic heat pump load profiles based on the k-means algorithm and a data set from water-to-water heat pumps from Hamelin, Germany. The quality of the generated load profiles is shown according to load factors, load distribution curves and the Pearson correlation coefficient, and is also applied on two exemplary geographies in Germany. We publish our work open-source and provide a web-based heat pump load profile generator.

The study examines the predictability of S\&P 500 stock movements during the COVID-19 pandemic. It presents a comparative analysis of random forest and logistic regression models and evaluates the importance of different COVID-19-related features and control variables for the prediction task. The results show that all examined models significantly outperform a random classifier at a significance level of 1\%. The random forest and logistic regression forecasts increase significantly in accuracy when a feature set including COVID-19-related data is utilized compared to a benchmark feature set without COVID-19-related variables. The random forest model trained on the full features set yields the statistically most accurate market forecasts. A feature importance analysis of the most accurate model reveals that the predictive power is not concentrated on a single COVID-19-related feature type but spreads over multiple different features. Due to its empirical nature, this study represents a snapshot between July 2020 and December 2021. Hence, future research may examine its application in future market environments. The paper introduces a machine learning-based market prediction framework during the COVID-19 pandemic. The presented results suggest that COVID-19-related features improve market forecasts during the COVID-19 pandemic and should be included in contemporary asset pricing models. This paper presents a machine learning-based market prediction framework and sheds light on market predictability in the changing market environment of the COVID-19 pandemic.

The integration of renewable energy sources, the decentralization of the energy system, and the increasing digitization of energy-related processes require the integration of a wide range of energy-related data. In this context, a data sharing platform can serve as a hub for exchanging energy-related data and developing innovative solutions to improve the efficiency and sustainability of the energy system. However, especially because of the involvement of the energy-related industry in such a platform poses several challenges related to data protection, intellectual property, and business interests. This paper presents a framework for ensuring transparency and involvement of the energy-related industry in a data sharing platform, based on the FAIR data principles and a co-creation approach involving industry partners.

We employ and analyze various machine learning models for daily cryptocurrency market prediction and trading. We train the models to predict binary relative daily market movements of the 100 largest cryptocurrencies. Our results show that all employed models make statistically viable predictions, whereby the average accuracy values calculated on all cryptocurrencies range from 52.9% to 54.1%. These accuracy values increase to a range from 57.5% to 59.5% when calculated on the subset of predictions with the 10% highest model confidences per class and day. We find that a long-short portfolio strategy based on the predictions of the employed LSTM and GRU ensemble models yields an annualized out-of-sample Sharpe ratio after transaction costs of 3.23 and 3.12, respectively. In comparison, the buy-and-hold benchmark market portfolio strategy only yields a Sharpe ratio of 1.33. These results indicate a challenge to weak form cryptocurrency market efficiency, albeit the influence of certain limits to arbitrage cannot be entirely ruled out.

As levelized costs of electricity for many renewable generation sources are continuing to fall and as feed-in tariffs are consequently being phased out, financial risk hedging for intermittent renewable generators takes a central stage. Battery storage as complementary capacity can support renewable generators regarding a more stable supply of electricity. In this study, we take first steps in modelling battery storage options as service products that are provided by battery storage operators to renewable generation operators. We model the situation theoretically, develop corresponding hedging strategies and apply the models to a fictional solar PV plant. The results show that battery storage options can reduce the risk for intermittent renewable generators and that the options can be financially beneficial for both the battery storage and the renewable capacity operator.

The 6th European Retail Investment Conference was hosted by the Stuttgart Stock Exchange from May 12th to 14th, 2021. Due to the persisting global pandemic, this year’s conference was held exclusively via online platforms. The conference chairs invited academics and practitioners to participate and discuss empirical and theoretical research focusing on retail investor products and services, new trends in the behavior of private customers, investors’ decision-making, investor protection schemes and market microstructure. The keynote about “Climate Finance” was held by Prof. Dr. Zacharias Sautner, Professor of Finance at Frankfurt School of Finance & Management.

We analyze the predictability of the bitcoin market across prediction horizons ranging from 1 to 60 min. In doing so, we test various machine learning models and find that, while all models outperform a random classifier, recurrent neural networks and gradient boosting classifiers are especially well-suited for the examined prediction tasks. We use a comprehensive feature set, including technical, blockchain-based, sentiment-/interest-based, and asset-based features. Our results show that technical features remain most relevant for most methods, followed by selected blockchain-based and sentiment-/interest-based features. Additionally, we find that predictability increases for longer prediction horizons. Although a quantile-based long-short trading strategy generates monthly returns of up to 39% before transaction costs, it leads to negative returns after taking transaction costs into account due to the particularly short holding periods.

We analyze the predictability of the bitcoin market across prediction horizons ranging from 1 to 60 min. In doing so, we test various machine learning models and find that, while all models outperform a random classifier, recurrent neural networks and gradient boosting classifiers are especially well-suited for the examined prediction tasks. We use a comprehensive feature set, including technical, blockchain-based, sentiment-/interest-based, and asset-based features. Our results show that technical features remain most relevant for most methods, followed by selected blockchain-based and interest-based features. Additionally, we find that predictability increases for longer prediction horizons. Although a quantile-based long-short trading strategy generates monthly returns of up to 31

Bitcoin, as the most popular cryptocurrency, has received increasing attention from both investors and researchers over recent years. One emerging branch of the research on bitcoin focuses on empirical bitcoin pricing. Machine learning methods are well suited for predictive problems, and researchers frequently apply these methods to predict bitcoin prices and returns. In this study, we analyze the existing body of literature on empirical bitcoin pricing via machine learning and structure it according to four different concepts. We show that research on this topic is highly diverse and that the results of several studies can only be compared to a limited extent. We further derive guidelines for future publications in the field to ensure a sufficient level of transparency and reproducibility.

The 5th European Retail Investment Conference was hosted at Börse Stuttgart, Germany, from April 10th to 12th 2019. The conference chairs invited academics and practitioners to participate and discuss empirical and theoretical research focusing on retail investor products and services, the impact of technology on retail investors, investors’ decision-making, investor protection schemes, and market microstructure. Albert Menkveld, Professor of Finance at Vrije Universiteit Amsterdam and Fellow at the Tinbergen Institute, held the keynote about the fundamental value of bitcoin.