Background This study aims to clarify the role of FinTech digital banking start-ups in the financial industry. We examine the impact of the funding of such start-ups on the stock returns of 47 incumbent US retail banks for 2010 to 2016. Methods To capture the importance of FinTech start-ups, we use data on both the dollar-volume of funding and number of deals. We relate these to the stock returns with panel data regression methods. Results Our results indicate a positive relationship exists between the growth in FinTech funding or deals and the contemporaneous stock returns of incumbent retail banks. Conclusions Although these results suggest complementarity between FinTech and traditional banking, we note that our results at the banking industry level are not statistically significant, and that the coefficient signs for about one-third of the banks are negative, but not statistically significant. Since the FinTech industry is young and our sample period short, we cannot rule out that our findings are spurious.

This paper focuses on the estimation of mutual fund styles by return-based style analysis. Often the investment style is assumed to be constant through time. Alternatively, time variation is sometimes implicitly accounted for by using rolling regressions when estimating the style exposures. The former assumption is often contradicted empirically, and the latter is inefficient due to its ad hoc chosen window size. Here, the Kalman filter is used to model time-varying exposures of mutual funds explicitly. This leads to a testable model and more efficient use of the data, which reduces the influence of spurious correlation between mutual fund returns and style indices. Several stylized examples indicate that more reliable style estimates can be obtained by modelling the style exposure as a random walk, and estimating the coefficients with the Kalman filter. The differences with traditional techniques are substantial in these stylized examples. The results from the empirical analyses indicate that the structural model estimated by the Kalman filter improves style predictions and influences results on performance measurement.

To better understand the potential and limitations of the tokenization of real asset markets, empirical studies need to examine this radically new organization of financial markets. In our study, we examine the financial and economic consequences of tokenizing 58 residential rental properties in the US, particularly those in Detroit. Tokenization aims at fragmented ownership. We found that the residential properties examined have 254 owners on average. Investors with a greater than USD 5,000 investment in real estate tokens, diversify their real estate ownership across properties within and across the cities. Property ownership changes about once yearly, with more changes for properties on decentralized exchanges. We report that real estate token prices move according to the house price index; hence, investing in real estate tokens provides economic exposure to residential house prices.