A neural‐network‐based decision‐making model in the peer‐to‐peer lending market

This study proposes an investment recommendation model for peer‐to‐peer (P2P) lending. P2P lenders usually are inexpert, so helping them to make the best decision for their investments is vital. In this study, while we aim to compare the performance of different artificial neural network (ANN) models, we evaluate loans from two perspectives: risk and return. The net present value (NPV) is considered as the return variable. To the best of our knowledge, NPV has been used in few studies in the P2P lending context. Considering the advantages of using NPV, we aim to improve decision‐making models in this market by the use of NPV and the integration of supervised learning and optimization algorithms that can be considered as one of our contributions. In order to predict NPV, three ANN models are compared concerning mean square error, mean absolute error, and root‐mean‐square error to find the optimal ANN model. Furthermore, for the risk evaluation, the probability of default of loans is computed using logistic regression. Investors in the P2P lending market can share their assets between different loans, so the procedure of P2P investment is similar to portfolio optimization. In this context, we minimize the risk of a portfolio for a minimum acceptable level of return. To analyse the effectiveness of our proposed model, we compare our decision‐making algorithm with the output of a traditional model. The experimental results on a real‐world data set show that our model leads to a better investment concerning both risk and return.     

The influencing factors of satisfaction and lending intention in online lending investment: an empirical study based on the Chinese market

This paper develops a platform‐based influencing factors model which considers value perception, risk prevention measure, non‐default experience, trust and incentive gap, to better examine the impact of platforms on investors’ satisfaction and lending intention based on the Chinese market. The results reveal that the first four factors positively influence the satisfaction of the investors, while the incentive gap has a negative impact, and there is a positive association between investors’ satisfaction and lending intention. Some specific features of China’s online lending market are identified, which provides valuable insights for online lending platforms and the government.     

Do commodities add value in multi-asset portfolios? An out-of-sample analysis for different investment strategies

An essential motive for investing in commodities is to enhance the performance of portfolios traditionally including only stocks and bonds. We analyze the in-sample and out-of-sample portfolio effects resulting from adding commodities to a stock-bond portfolio for commonly implemented asset allocation strategies such as equally- and strategically-weighted portfolios, risk-parity, minimum-variance as well as reward-to-risk timing, mean-variance and Black–Litterman. We analyze different commodity groups such as agricultural and livestock commodities that currently are critically discussed. The out-of-sample portfolio analysis indicates that the attainable benefits of commodities are much smaller than suggested by previous in-sample studies. Hence, in-sample analyses, such as spanning tests, might exaggerate the advantages of commodities. Moreover, the portfolio gains greatly vary between different types of commodities and sub-periods. While aggregate commodity indices, industrial and precious metals as well as energy improve the performance of a stock-bond portfolio for most asset allocation strategies, we hardly find positive portfolio effects for agriculture and livestock. Consequently, investments in food commodities are not essential for efficient asset allocation.     

Asymptotic behaviour of mean-quantile efficient portfolios

In this paper we investigate portfolio optimization in the Black–Scholes continuous-time setting under quantile based risk measures: value at risk, capital at risk and relative value at risk. We show that the optimization results are consistent with Merton’s two-fund separation theorem, i.e., that every optimal strategy is a weighted average of the bond and Merton’s portfolio. We present optimization results for constrained portfolios with respect to these risk measures, showing for instance that under value at risk, in better markets and during longer time horizons, it is optimal to invest less into the risky assets.This research was partially supported by the National Science and Engineering Research Council of Canada, and the Mathematics of Information Technology and Complex Systems (MITACS) Network of Centres of Excellence.     

Determinants of the ARM share of national and regional lending

We model the ARM share of mortgage lending and provide several unique contributions to the mortgage choice literature. First, we motivate the use of the price spread between fixed- and adjustable-rate credit as a regressor by constraining the effect of FRM and ARM prices to be symmetric and show that the data support this restriction. Second, our data span a far longer time period (six years) than previous research. Third, we estimate separate share equations by region, allowing us to contrast geographic variation in ARM shares. Fourth, we examine the effect of convertible ARMs—which became prevalent in mid-1987—on overall ARM lending.     

Small caps in international equity portfolios: the effects of variance risk

We show that predictable covariances between means and variances of stock returns may have a first order effect on portfolio composition. In an international asset menu that includes both European and North American small capitalization equity indices, we find that a three-state, heteroskedastic regime switching VAR model is required to provide a good fit to weekly return data and to accurately predict the dynamics in the joint density of returns. As a result of the non-linear dynamic features revealed by the data, small cap portfolios become riskier in bear markets, i.e., display negative co-skewness with other stock indices. Because of this property, a power utility investor ought to hold a well-diversified portfolio, despite the high risk premium and Sharpe ratios offered by small capitalization stocks. On the contrary, small caps command large optimal weights when the investor ignores variance risk, by incorrectly assuming joint normality of returns.      

Financial expertise on audit committees of loan applicants: a research note to test the effects on lending decisions

The purpose of this study is to examine whether audit committee financial expertise matters when making commercial lending decisions. Commercial lenders rely on audited financial statements in making lending decisions, and the quality of these financial statements is impacted by the capabilities of audit committees having oversight of financial reporting. It is widely believed that this oversight is enhanced when audit committees contain members with financial expertise. A behavioural experiment is conducted where commercial lending officers make risk assessments and provide probabilities of granting loans based on a hypothetical scenario. This paper finds insufficient evidence to conclude that the existence of financial expertise on audit committees makes a difference to lenders. When replacing audit committee members, however, financial expertise does appear to matter to lenders in some cases.     

The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform

There have been concerns about the use of alternative data sources by fintech lenders. We compare loans made by LendingClub and similar loans that were originated by banks. The correlations between the rating grades (assigned by LendingClub) and the borrowers’ FICO scores declined from about 80% (for loans originated in 2007) to about 35% for recent vintages (originated in 2014–2015), indicating that nontraditional data (not already accounted for in the FICO scores) have been increasingly used by fintech lenders. The rating grades perform well in predicting loan default. The use of alternative data has allowed some borrowers who would have been classified as subprime by traditional criteria to be slotted into “better” loan grades, allowing them to obtain lower priced credit.     

The mechanism of credit risk contagion among internet P2P lending platforms based on a SEIR model with time-lag

The credit risk contagion of Internet peer-to-peer (P2P) lending platforms is an important part of Internet financial risk management and supervision. This study analyzes the contagion path of credit risk in Internet P2P lending. Based on complex network theory and the theory of infectious disease dynamics, the characteristics of Internet P2P lending development are combined to construct a SEIR model of credit risk transmission among Internet P2P lending platforms with time lag, and the robustness of the model is analyzed and proven. The influence of platform correlations, the susceptible immune rate, the platform elimination rate, contagion latency, the saturation coefficient, and the susceptibility input rate on credit risk contagion behavior among Internet P2P lending platforms is analyzed, using the equilibrium point and threshold value. The impact of each variable is analyzed by simulation. Corresponding countermeasures and suggestions are proposed to prevent and control credit risk contagion among these platforms.     

The effects of government capital and liquidity support programs on bank lending: Evidence from the syndicated corporate credit market

This study jointly evaluates the effects of the U.S. Treasury's Troubled Asset Relief Program (TARP), the Federal Reserve's Discount Window (DW), and Term Auction Facility (TAF) on bank syndicated lending during the 2007–2009 financial crisis, using a unique data set that tracks the exposure of each lender in each syndicated credit facility in each year. By comparing lending changes within a group of banks that lend to the same facility of the same firm in the same year, it eliminates the impacts of demand-side factors that often bias the results of empirical studies on bank credit supply. Overall, I find that TARP, DW, and TAF played only a marginal role in increasing bank syndicated lending. By examining lending changes at the facility-lender and firm-lender levels, this study is less prone to the reverse causality problem that is inherent in studies using bank-level data. Therefore, this study complements studies using bank-level data and provides policymakers with a balanced view on the effects of these programs.     

The effect of banking market structure on the lending channel: Evidence from emerging markets

This paper analyses the extent to which the level of bank competition influences monetary policy transmission. Using a large panel dataset of 978 banks from 55 countries, and employing the Lerner index model as a measure of market structure, our results show that an increase in banking sector competition weakens the effectiveness of monetary policy on bank lending. The findings are robust to a broad array of sensitivity checks including control of alternative measurements of the Lerner index, different samples and different methodological specifications. By extension, these results have important policy implications for regulators in assessing the effectiveness of monetary policy transmission mechanisms.     

Portfolio selection under the condition of value preservation

Besides risk and return, investors often are interested in choosing a portfolio such that the portfolio value is preserved. However, the traditional utility-maximizing approach generally fails to provide such a solution. As a different approach value preservation is formulated as an equilibrium problem. Following this approach it is shown that under reasonable assumptions a value preserving solution exists. The solution only depends on the set of feasable portfolio decisions. Contrary to this, the Bernoulli principle in addition requires a utility function that is independent from this set.     

Risk adjusted discount rates and the present value of risky costs

In capital budgeting the correct risk adjusted discount rate for future cash flows is independent of whether the flow is a cost or a revenue. Contrary to a widely disseminated view in some popular textbooks and elsewhere, costs are not especially safe (nor risky), and accordingly costs should not be discounted at especially low risk adjusted discount rates. This paper analyzes capital budgeting within a portfolio model in which revenues and costs appear as “long’ and “short’ portfolio positions, respectively, and proves that costs are neither more nor less intrinsically risky than revenues.     

Multinational banks in the crisis: Foreign affiliate lending as a mirror of funding pressure and competition on the internal capital market

We investigate how the lending activities abroad of a multinational bank’s local and hub affiliates have been affected by funding difficulties during the financial crisis. We find that affiliates’ local deposits and performance have been stabilizing loan supply. By contrast, relying on short-term wholesale funding has increasingly proven to be a disadvantage in the crisis, which has seen inter-bank and capital markets freeze. By introducing a liable approximate measure for intra-bank flows, we detect competition for intra-bank funding between the affiliates abroad as well as an increasing focus on the parent bank’s home market activities. In addition, the more an affiliate abroad relies on intra-bank funding in the crisis, the greater its dependence on its parent bank having a stable deposit and long-term wholesale funding position. We consider changes in long-term lending to the private sectors of 40 countries by the affiliates of the 68 largest German banks. To obtain a more precise picture, we clean our lending data from valuation effects.     

Risk optimizations on basis portfolios: The role of sorting

This paper investigates the mean–variance and diversification properties of risk-based strategies executed on style or basis portfolios. We show that the performance of these risk strategies is highly sensitive to the sorting procedure used to form the basis assets. Whereas the extant literature provides mixed support for the outperformance of smart beta strategies based on scientific diversification, our designed strategies outperform both the market model and multifactor model. Our testing framework is based on bootstrapped mean–variance spanning tests and shows valid conclusions when controlling for multiple testing, transaction costs, and luck from random basis portfolio construction rules. Economically, our results are supported by diversification-based properties.     

Remarks on a copula-based conditional value at risk for the portfolio problem

We deal with a multivariate conditional value at risk. Compared with the usual notion for the single random variable, a multivariate value at risk is concerned with several variables, and thus, the relation between each risk factor should be considered. We here introduce a new definition of copula-based conditional value at risk, which is real valued and ready to be computed. Copulas are known to provide a flexible method for handling a possible nonlinear structure; therefore, copulas may be naturally involved in the theory of value at risk. We derive a formula of our copula-based conditional value at risk in the case of Archimedean copulas, whose effectiveness is shown by examples. Numerical studies are also carried out with real data, which can be verified with analytical results.