Momentum strategies of German mutual funds

The existence of the momentum effect in stock returns has been documented for the US (e.g., Jegadeesh and Titman in J. Finance 48(1), 65–91, 1993) and many other national equity markets worldwide (e.g., Griffin et al. in J. Finance 58(6), 2515–2547, 2003). However, little is known about the active employment of momentum strategies among institutional investors outside the US. This paper provides first evidence of momentum behavior among German mutual funds. We find the fund trades to follow stock returns on an aggregated institutional level. Moreover, we detect significant momentum behavior among funds with a European and global equity focus, as well as among funds predominantly investing in Asia. In contrast, German funds do not seem to engage in momentum strategies when trading domestic stocks. While only half the funds in our sample trade in accordance with past returns, 66 % of the funds within the largest size quintile follow momentum strategies. Finally, we do not find momentum trading funds to outperform the other funds.     

GP algorithm versus hybrid and mixed neural networks

In the current paper, we present an integrated genetic programming (GP) environment called java GP modelling. The java GP modelling environment is an implementation of the steady-state GP algorithm. This algorithm evolves tree-based structures that represent models of inputs and outputs. The motivation of this paper is to compare the GP algorithm with neural network (NN) architectures when applied to the task of forecasting and trading the ASE 20 Greek Index (using autoregressive terms as inputs). This is done by benchmarking the forecasting performance of the GP algorithm and six different autoregressive moving average model (ARMA) NN combination designs representing a Hybrid, Mixed Higher Order Neural Network (HONN), a Hybrid, Mixed Recurrent Neural Network (RNN), a Hybrid, Mixed classic Multilayer Perceptron with some traditional techniques, either statistical such as a an ARMA or technical such as a moving average convergence/divergence model, and a naïve trading strategy. More specifically, the trading performance of all models is investigated in a forecast and trading simulation on ASE 20 time-series closing prices over the period 2001–2008, using the last one and a half years for out-of-sample testing. We use the ASE 20 daily series as many financial institutions are ready to trade at this level, and it is therefore possible to leave orders with a bank for business to be transacted on that basis. As it turns out, the GP model does remarkably well and outperforms all other models in a simple trading simulation exercise. This is also the case when more sophisticated trading strategies using confirmation filters and leverage are applied, as the GP model still produces better results and outperforms all other NN and traditional statistical models in terms of annualized return.     

Home-field advantage or a matter of ambiguity aversion? Local bias among German individual investors

This paper investigates whether familiarity induced by ambiguity aversion can help explaining the local bias phenomenon among individual investors. Using geographic closeness as a proxy for investor familiarity, we find that investors pull out of (unfamiliar) remote stocks and pour into (familiar) local stocks during times of increased market uncertainty. Moreover, the magnitude of this ‘flight to familiarity’ increases in the spread of an investor's ambiguity (about expected returns) between local and remote stocks. Our results prove robust to a number of alternative explanations of local bias. Specifically, we rule out a ‘home-field advantage’, where investors are able to translate information advantages about nearby companies into excess returns on their local stockholdings. We conclude that individual investors’ local bias is induced by ambiguity aversion in the portfolio selection process rather than a trading strategy based on superior information about local companies.     

Value creation in the relationship life cycle: A quasi-longitudinal analysis

Among the growing literature on value creation in collaborative buyer-seller relationships, most researchers examine relationship value at a single point in time. In the present research, we explore whether different stages of the relationship life cycle moderate the relative importance of value-creating dimensions. To shed light on the dynamic nature of value in B2B relationships, we present the results of a survey among purchasing managers using a quasi-longitudinal research design. Our findings confirm the moderating role of the relationship life cycle in value creation. More precisely, our results indicate that a key supplier's potential for value creation in customer's operations increases in relative importance as relationships move through the life cycle. In turn, supplier's capabilities to create superior value at the level of the customer's sourcing process display a decreasing role over the life cycle of a business relationship. No significant link was found in the present study between value creation through a supplier's core offering and different stages of a buyer-seller relationship.     

Prediction markets for foresight

To navigate turbulent business environments, organizations have to develop foresight capacities that enable them to anticipate probable futures, respond rapidly to emerging changes, and support future oriented action. However, there are remaining barriers that impede a wider implementation of foresight. In particular, the necessities to deal with the future, anticipate change, enhance participation and reduce costs and complexity call for new methods to improve current foresight activities. In this paper, we introduce prediction markets to the field of foresight. Prediction markets are a structured approach to collect and aggregate information from groups and have recently gained attention in forecasting. Prediction markets go beyond simple forecasting and can contribute to foresight by providing advantages in terms of continuous and real-time information aggregation, motivation of participation and information revelation as well as cost-efficiency and scalability. We suggest four promising fields of application for prediction markets to enhance foresight: (1) continuous forecasting and environmental scanning, (2) combining with deliberative approaches, (3) continuous idea generation and (4) expert identification. We conclude by considering prediction markets as a nascent and promising method for foresight and advocate for further research.     

Merger policy and tax competition: the role of foreign firm ownership

In many situations, governments have sector-specific tax and regulation policies at their disposal to influence the market outcome after a national or an international merger has taken place. In this paper we study the implications for merger policy when countries non-cooperatively deploy production-based taxes and firms may be partly owned by foreigners. We find that when foreign firm ownership is low in the pre-merger situation, non-cooperative tax policies are more efficient after a national merger, and smaller synergy effects are needed for this type of merger to be proposed and cleared. In contrast, cross-border mergers dominate when the degree of foreign firm ownership is high initially. These results suggest a link between increasing international portfolio diversification and the rising share of cross-border mergers.     

Private information in currency markets

Using daily abnormal currency returns for the universe of countries with flexible exchange rates, we show local currency depreciations ahead of unscheduled, public sovereign debt downgrade announcements. Consistent with the private information hypothesis, the effect is stronger in lower institutional quality countries and holds after we control for concurrent public information and for publicly available rumors about the forthcoming downgrades. Our results persist when abnormal currency returns are adjusted for global carry and dollar risk factors, world equity and bond returns, as well as local stock market returns. Finally, the currency depreciations are permanent, providing evidence for a link between fundamentals and currency markets.     

Development costs capitalization and debt financing

This study investigates debt market effects of research and development (R&D) costs capitalization, using a global sample of public bonds and private syndicated loans issued by public non‐financial firms. Firstly, we show that firms capitalize larger amounts of R&D in a year when they exhibit a propensity for issuing bonds, rather than borrowing funds privately from the syndicated loan market, in the subsequent year. Secondly, we provide evidence that capitalized R&D investments reduce the cost of debt. We infer that debt market participants are able to identify firms’ motives for R&D capitalization, as we find a reduction in the cost of debt only for those firms that do not show indications of employing R&D capitalization for earnings management reasons. Indeed, only for this sub‐sample of firms, the amount of capitalized R&D contributes positively to future earnings. We confirm that R&D capitalization is positively associated with audit fees and thus can be deemed to be a signaling device. Lastly, we find that it is the amount of R&D a firm is expected to capitalize and not the discretionary counterparts, which facilitates a firm's access to public debt markets, reduces bond and syndicated loan prices, and contributes to future benefits.