EISSN : 2199-4730
Published by: Springer Science and Business Media LLC (10.1186)
Total articles ≅ 272
Latest articles in this journal
Financial Innovation, Volume 7, pp 1-33; doi:10.1186/s40854-021-00272-y
Can peer-to-peer lending platforms mitigate fraudulent behaviors? Or have lending players been acting similar to free-riders? This paper constructs a new proxy to investigate lending platform misconduct and compares the FICO score and the LendingClub credit grade. To examine whether the lack of verification by the Fintech platform affects lenders’ collection performance, I explore the recovery rate (RR) of non-performing loans through a mixed-continuous model. The regression results show that the degree of prudence taken by the lending platform in the pre-screening activity negatively affects the detection of some misreporting borrowers. I also find that the Fintech platform’s missing verification information (e.g., annual income and employment length) affects the RR of non-performing loans, thereby hampering lenders’ collection performance.
Published: 8 July 2021
Financial Innovation, Volume 7, pp 1-37; doi:10.1186/s40854-021-00268-8
This study investigates the learning curve of commercial banks regarding the efficiency of credit and value creation. However, current empirical methods for accessing the learning curve in organizations are not suitable for use in financial institutions. Considering bank-specific characteristics, we introduce a dynamic learning curve using a cost function adjusted to capture learning-by-doing in banks. Using the model, we test several hypotheses on the impact of bank intermediary experience (learning) on the efficiency of credit and value creation in Japanese commercial banks. The findings show that bank intermediary learning significantly improves the cost efficiency gain in the gross value created, total credit created, and investment. However, bank intermediary experience has no significant effect on the efficiency of the economic value created for all the banks analyzed. These findings have practical implications for evaluating cost dynamics in bank credit and value creation, risk management, lending to the real sector, and shareholder value creation.
Financial Innovation, Volume 7, pp 1-3; doi:10.1186/s40854-021-00273-x
Financial Innovation, Volume 7; doi:10.1186/s40854-021-00269-7
The explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.
Financial Innovation, Volume 7; doi:10.1186/s40854-021-00271-z
This paper proposes an original behavioural finance representative agent model, to explain how fake news’ empirical price impacts can persist in finance despite contradicting the efficient-market hypothesis. The model reconciles empirically-observed price overreactions to fake news with empirically-observed price underreactions to real news, and predicts a novel secondary impact of fake news: that fake news in a security amplifies underreactions to subsequent real news for the security. Evaluating the model against a large-sample event study of the 2019 Chinese ADR Delisting Threat fake news and debunking event, this paper finds strong qualitative validation for its model’s dynamics and predictions.
Financial Innovation, Volume 7; doi:10.1186/s40854-021-00270-0
This study investigates signal validity in equity-based crowdfunding by examining whether signals that increase crowd participation are associated with higher post-offering success. Post-offering success is measured as the probability of survival. We use a hand-collected data set of 88 campaigns with over 64,000 investments and 742 updates from a well-established and leading German equity-based crowdfunding platform, Companisto. We find that indicating that the chief executive officer holds a university degree and a higher number of business-related updates are associated with a lower risk of failure, which is in line with recent research on offering success. The number of updates on external certification, promotions, and the team is associated with a higher risk of failure. In contrast to recent findings on offering success, we find that the equity share offered is positively related to post-offering success, whereas a high number of large investments or updates on campaign development are accompanied by a higher probability of failure. Our results provide guidance for entrepreneurs and investors regarding which signals are worth sending or using. Furthermore, these results suggest that investors are partly using wrong signals and challenge the rationality and wisdom of the crowd.
Financial Innovation, Volume 7, pp 1-45; doi:10.1186/s40854-021-00265-x
The essence of this study is to investigate the influence of the board gender diversity on firms’ accounting and market-based performance using a sample of Standard & Poor’s 500 companies belonging to the information technology sector over 12 years. Using the pooled ordinary least squares (OLS) method, the outcomes provide evidence for a positive influence of women on corporate boards on both measures of company performance, except for the percentage of female executives in the case of return on assets (ROA). After estimating the fixed effects and random-effects through panel data, the econometric outcomes show no statistically significant association among board gender diversity and ROA but a positive influence of the number and percentage of women on board on price-to-earnings ratio.
Financial Innovation, Volume 7, pp 1-25; doi:10.1186/s40854-021-00267-9
This study discusses the trading behavior of foreign investors with respect to economic uncertainty in the South Korean stock market from a time-varying perspective. We employ a news-based measure of economic uncertainty along with the model of time-varying parameter vector autoregression with stochastic volatility. The empirical analysis reveals several new findings about foreign investors’ trading behaviors. First, we find evidence that positive feedback trading often appears during periods of high economic uncertainty, whereas negative feedback trading is exclusively observable during periods of low economic uncertainty. Second, the foreign investors’ feedback trading appears mostly to be well-timed and often leads the time-varying economic uncertainty except in periods of global crises. Third, lagged negative (positive) response of net flows to economic uncertainty is found to be coupled with lagged positive (negative) feedback trading. Fourth, the study documents an asymmetric response of foreign investors with regard to negative and positive shocks of economic uncertainty. Specifically, we find that they instantly turn to positive feedback trading after a negative contemporaneous response of net flows to shocks of economic uncertainty. In contrast, they move slowly toward negative feedback trading after a positive response of net flows to uncertainty shocks.
Financial Innovation, Volume 7, pp 1-24; doi:10.1186/s40854-021-00266-w
The estimation of the difference between the new competitive advantages of China's export and the world’s trading powers have been the key measurement problems in China-related studies. In this work, a comprehensive evaluation index system for new export competitive advantages is developed, a soft-sensing model for China’s new export competitive advantages based on the fuzzy entropy weight analytic hierarchy process is established, and the soft-sensing values of key indexes are derived. The obtained evaluation values of the main measurement index are used as the input variable of the fuzzy least squares support vector machine, and a soft-sensing model of the key index parameters of the new export competitive advantages of China based on the combined soft-sensing model of the fuzzy least squares support vector machine is established. The soft-sensing results of the new export competitive advantage index of China show that the soft measurement model developed herein is of high precision compared with other models, and the technical and brand competitiveness indicators of export products have more significant contributions to the new competitive advantages of China's export, while the service competitiveness indicator of export products has the least contribution to new competitive advantages of China's export.
Financial Innovation, Volume 7, pp 1-24; doi:10.1186/s40854-021-00261-1
Most loan evaluation methods in peer-to-peer (P2P) lending mainly exploit the borrowers’ credit information. However, the present study presents the maturity-based lender composition score, which exploits the investment capability of a group of lenders who fund the same loan, to enhance the P2P loan evaluation. More specifically, we extract lenders’ profiles in terms of performance, risk, and experience by quantifying their investment history and develop our loan evaluation indicator by aggregating the profiles of lenders in the composition. To measure the ability of a lender for continuous improvement in P2P investment, we introduce lender maturity to capture this evolvement and incorporate it into the aggregation process. Our empirical study demonstrates that the maturity-based lender composition score can serve as an effective indicator for identifying loan quality and be included in other commonly used loan evaluation models for accuracy improvement.