Bank Income Diversification and its Impact on Profitability and Risk: Evidence from Nordic Markets

Financial industry has recently encountered many changes in the business environment. Increased regulation together with growing competition is forcing commercial banks to rethink their business models. In order to maintain profitability in the new environment, banks are focusing more into activities that yield noninterest income. This is a shift away from the traditional intermediation function of banks. This study aims to answer the question if the shift from traditional income yielding activities to more innovative noninterest activities is logical in terms of profitability and risk in Nordics. This study also aims to answer the question if diversification within the noninterest income categories has impact on profitability and risk and if there are certain categories of noninterest income that are better than others in terms of profitability and risk in Nordics. Results show that diversification between interest and noninterest activities and increase in the share of noninterest income have a negative impact on the risk adjusted returns and risk profile. Results also show that further diversification within the noninterest income categories has negative impact on risk adjusted profitability and risk while an increase of the share of commission and fee income category of total noninterest income has a positive impact on risk adjusted profitability and risk. Results are logical and in line with previous research (De Young & Roland, 2001; Stiroh, 2004). Results provide useful information to banks and help them better evaluate outcomes of different income diversification strategies.

PERFORMANCE ANALYSIS OF A TACTICAL ASSET ALLOCATION – Evidence from the Black–Litterman model based fixed income portfolio

Over time the demand for quantitative portfolio management has increased among financial institutions but there is still a lack of practical tools. In 2008 EDHEC Risk and Asset Management Research Centre conducted a survey of European investment practices. It revealed that the majority of asset or fund management companies, pension funds and institutional investors do not use more sophisticated models to compensate the flaws of the Markowitz mean-variance portfolio optimization. Furthermore, tactical asset allocation managers employ a variety of methods to estimate return and risk of assets, but also need sophisticated portfolio management models to outperform their benchmarks. Recent development in portfolio management suggests that new innovations are slowly gaining ground, but still need to be studied carefully. This thesis tries to provide a practical tactical asset allocation (TAA) application to the Black–Litterman (B–L) approach and unbiased evaluation of B–L models’ qualities. Mean-variance framework, issues related to asset allocation decisions and return forecasting are examined carefully to uncover issues effecting active portfolio management. European fixed income data is employed in an empirical study that tries to reveal whether a B–L model based TAA portfolio is able outperform its strategic benchmark. The tactical asset allocation utilizes Vector Autoregressive (VAR) model to create return forecasts from lagged values of asset classes as well as economic variables. Sample data (31.12.1999–31.12.2012) is divided into two. In-sample data is used for calibrating a strategic portfolio and the out-of-sample period is for testing the tactical portfolio against the strategic benchmark. Results show that B–L model based tactical asset allocation outperforms the benchmark portfolio in terms of risk-adjusted return and mean excess return. The VAR-model is able to pick up the change in investor sentiment and the B–L model adjusts portfolio weights in a controlled manner. TAA portfolio shows promise especially in moderately shifting allocation to more risky assets while market is turning bullish, but without overweighting investments with high beta. Based on findings in thesis, Black–Litterman model offers a good platform for active asset managers to quantify their views on investments and implement their strategies. B–L model shows potential and offers interesting research avenues. However, success of tactical asset allocation is still highly dependent on the quality of input estimates.