330 Wirtschaft
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This article studies the effects of reverse factoring in a supply chain when the buyer company facilitates its lower short-term borrowing rates to the supplier corporation in return for extended payment terms. We explore the role of interest rate changes, rating changes, and the business cycle position on the cost and benefit trade-off from a supplier perspective. We utilize a combined empirical approach consisting of an event study in Step 1 and a simulation model in Step 2. The event study identifies the quantitative magnitude of central bank decisions and rating changes on the interest rate differential. The simulation computes with a rolling-window methodology the daily cost and benefits of reverse factoring from 2010 to 2018 under the assumption of the efficient market hypothesis. Our major finding is that changes of crucial financial variables such as interest rates, ratings, or news alerts will turn former win-win into win-lose situations for the supplier contingent to the business cycle. Overall, our results exhibit sophisticated trade-offs under reverse factoring and consequently require a careful evaluation in managerial decisions.
This paper studies option pricing based on a reverse engineering (RE) approach. We utilize artificial intelligence in order to numerically compute the prices of options. The data consist of more than 5000 call- and put-options from the German stock market. First, we find that option pricing under reverse engineering obtains a smaller root mean square error to market prices. Second, we show that the reverse engineering model is reliant on training data. In general, the novel idea of reverse engineering is a rewarding direction for future research. It circumvents the limitations of finance theory, among others strong assumptions and numerical approximations under the Black–Scholes model.
This paper develops a new methodology in order to study the role of dynamic expectations. Neither reference-point theories nor feedback models are sufficient to describe human expectations in a dynamic market environment. We use an interdisciplinary approach and demonstrate that expectations of non-learning agents are time-invariant and isotropic. On the contrary, learning enhances expectations. We uncover the “yardstick of expectations” in order to assess the impact of market developments on expectations. For the first time in the literature, we reveal new insights about the motion of dynamic expectations. Finally, the model is suitable for an AI approach and has major implications on the behaviour of market participants.
This article investigates the fundamental value of digital platforms, such as Facebook and Google. Despite the transformative nature of digital technologies, it is challenging to value digital services, given that the usage is free of charge. Applying the methodology of discrete choice experiments, we estimated the value of digital free goods. For the first time in the literature, we obtained data for the willingness-to-pay and willingness-to-accept, together with socio-economic variables. The customer´s valuation of free digital services is on average, for Google, 121 € per week and Facebook, 28 €.
This paper analyzes different government debt relief programs in the European Monetary Union. I build a model and study different options ranging from debt relief to the European Stability Mechanism (ESM). The analysis reveals the following: First, patient countries repay debt, while impatient countries more likely consume and default. Second, without ESM loans, indebted countries default anyway. Third, if the probability to be an impatient government is high, then the supply of loans is constrained. In general, sustainable and unsustainable governments should be incentivized differently especially in a supranational monetary union. Finally, I develop policy recommendations for the ongoing debate in the Eurozone.