As the narratives about the future of banks seem to lack of theoretical rigour, the paper undertakes a very fundamental analysis of what economic theories can tell us about the future of banks. The New Institutional Economics portrays new challengers as perfecting the market and thereby sidelining the more passive banks, which could lead to their demise. This gloomy narrative is critically discussed and contrasted. By re-evaluating concepts such as uncertainty, trust, and power, a more nuanced perspective emerges. Banks may have a greater agency and potential for success than previously thought, challenging the pessimism about their future.
This study investigates the financing decisions within German SMEs, examining firm-specific, macroeconomic, and news-related determinants. Utilizing a 10-year dataset encompassing 13,051 SMEs, we employ a dynamic panel data model with an unbiased Dynamic Panel Fractional (DPF) estimator to identify the key variables influencing the debt-to-equity ratio. The findings underscore the importance of factors such as the non-debt tax shield, firm size, interest rate spread, and the economic policy uncertainty index. The study's findings propose the following policy implications: 1) Policy initiatives targeting firm size and non-debt tax shields affect SME leverage; 2) Policies addressing the term spread and economic uncertainty influence debt levels across various German industries; 3) Industry-specific SME policies are advisable, due to the significant industry effects on German SME leverage; 4) SME policy incentives yield short-term effects on capital structures, as SMEs adjust leverage within 8 months.
This paper is concerned with the valuation and analysis of risky debt instruments with arbitrary interest and principal payments subject to default risk. For the valuation, we use a risk-neutral present value model with expected payments for risk-neutral investors and risk-free spot rates. The required risk-neutral default probabilities are derived from historically observable risk-averse migration matrices. Based on this debt valuation, we calculate various key figures for the analysis of risky debt from the point of view of risk-averse investors (e.g., promised and expected yields, yield spreads, Z-spreads, risk premia, risk-averse default probabilities, and risk-averse expected payments). Our approach is well-suited for practical applications, since the parameters required are easily available from observable data.
Domain-specific dictionaries have prevailed, when conducting the dictionary-based approach to measure the sentiment of textual data in finance. Through the contributions of Bannier et al. (2019a) and Pöferlein (2021), two versions of a dictionary suitable for analyzing German finance-related texts are available (BPW dictionary). This paper conducts and tests further improvements of the given word lists by calculating the sentiment of German-speaking annual reports to forecast future return on assets and future return on equity. This corrected and expanded version provides more significant results. Despite the broad usage of negations, this type of improvement in combination with the BPW dictionary has not yet been tested when conducting the dictionary-based approach. Therefore, this paper additionally tests different negation lists to show that implementing negations can improve results.
If banks' performance is to be be evaluated against the objectives they actually pursue, assessments of stakeholder-oriented banks should go beyond financial efficiency. Furthermore, also the environment these institutions operate in has to be kept in mind when interpreting levels of managerial inefficiency. For 401 Austrian regional banks, this study compares financial efficiency to a measure of social efficiency that considers several kinds of stakeholder benefits. Both efficiency scores are calculated by use of data envelopment analysis. In a second estimation stage, we use truncated regression to account for differences in efficiency due to the market environment. Our results show that efficiency rankings across Austrian savings banks and credit cooperatives change considerably when their double bottom line and local market factors are considered. Both issues thus are important for adequate and fair performance benchmarking.
As a response to the latest financial crisis, the Basel Committee published the Basel III accords which intensify micro- and introduce macro-prudential instruments to enhance the resilience of the financial market. One crucial aspect that the regulatory reforms do not address is the diversity of the banking sector. We introduce a heterogeneous agent-based model that develops a housing and a capital market to assess the ability of Basel III rules to mitigate mutual feedback effects and dampen instability. Computational experiments reveal that the most stable markets are achieved if the financial market is diversified and consists of different types of financial intermediaries that need to comply with type-specific capital adequacy requirements. The results point out that capital adequacy requirements are, in principle, effective in stabilizing the banking sector. However, the stability of housing and share prices and the solidity of the banking sector can be increased if capital adequacy requirements are aligned to the individual business models of financial intermediaries and their institutional frameworks. These findings advocate in favor of a diversified banking sector and heterogeneous capital adequacy requirements.
We empirically study the perception of political uncertainty by UK’s financial markets, covering the entire Brexit period from January 2013 to March 2020. We find that indices dominated by the largest capitalized companies anticipate negative events already prior to the actual event, whereas positively events only effect them on the event day or following. In contrast, the FTSE 250, composed of smaller companies, tends to move prior to positively perceived events. Furthermore, we introduce a metric based on Google Trends as a proxy for the perception of Brexit. Our results show that this significantly affects all major UK indices.
The objective of this paper is to analyze the suitability of the Total Market Return approach within the requirements of the capital asset pricing model, and for the purpose of business valuation, particularly in light of its endorsement by the institute of German auditors (IDW). First, we question the use of the total market return approach on a theoretical basis. Then, we analyze whether total market returns influence the institute's recommendation for the market risk premium in a meaningful way and show the implications of a rigorous application for a large sample of valuation reports authored by German auditors. Our results reject the suitability of the Total Market Return approach for the purpose of business valuation on theoretical grounds, show that its rigorous application would have led to much lower company valuations, and highlight the necessity of revising the reasoning behind the recommended bandwidth of market risk premia.
In the academic literature there are different indices to quantify the independence of Central Banks. The main indices to measure Central Bank Independence (CBI) were published between 1970 and 1990. This paper applies CBI indices to a sample of 15 industrial countries and the European Central Bank (ECB) to the current legal conditions. Hence, we are able to study the development of CBI and how it is related with change of inflation. Overall, we find that CBI has increased over the last decades. However, the original approaches to determine CBI do not consider the unconventional monetary policy (Quantitative Easing (QE)) that has been practised by Central Banks in recent years. Considering QE leads to lower CBI scores which are still higher than the original scores that were determined in the 1970’s–1990’s.
Marketplace lending has fundamentally changed the relationship between borrowers and lenders in financial markets. As with many other financial products that have emerged in recent years, internet-based investors may be inexperienced in marketplace lending, highlighting the importance of forecasting default rates and evaluating default features such as the loan amount, interest rates, and FICO score. Potential borrowers on marketplace lending platforms may already have been rejected by banks as too risky to lend to, which amplifies the problem of asymmetric information. This paper proposes a holistic data processing flow for the loan status classification of marketplace lending multivariate time series data by using the Bidirectional Long Short-Term Memory model (BiLSTM) to predict “non-default,” “distressed,” and “default” loan status, which outperforms conventional techniques. We adopt the SHapely Additive exPlanations (SHAP) and a four-step ahead model, allowing us to extract the most significant features for default risk assessment. Using our approach, lenders and regulators can identify the most relevant features to enhance the default risk assessment method over time in addition to early risk prediction.
We contribute to the literature on the valuation of risky debt by providing three nested multivariate extensions of the standard Merton model. First, we lay forth an approach to pricing risky debt irrespective of its interest payment structure and the specified redemption agreement. Second, we propose a technique for valuing multiple debt instruments within the same firm. Third, we provide an approach for pricing one or more debt instruments with continuous dividend payments.
