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Alterations in the Liquidity Premium as an Effect of Exchange Traded Funds : A Study Performed on Nasdaq Composite between 1997 and 2016Andersson, Axel, Svanberg, Emanuel January 2018 (has links)
Investors have historically demanded a return premium for taking on the risk of illiquidity both in terms of characteristic and systematic liquidity risk. Recent research have presented results suggesting that the liquidity premium is diminishing. The increasing popularity of passive investments such as Exchange Traded Funds (ETFs) have been proposed as a driving force for the declining trend. Despite the popularity of ETFs, there is limited research how they impact the financial markets. The purpose of this thesis is to investigate how the liquidity premium has developed in the United States between 1997 and 2016 and to explore if developments in the liquidity premium can be linked to the capital inflow to the United States ETF market. The thesis uses measures of stocks’ spreads and order book depths as proxies for the characteristic and systematic liquidities. The proxies are used to test if liquidity has influenced stock returns over 1-year, 5-years and the entire 20-year period. The empirical results obtained through Fama-MacBeth regressions show that the liquidity premium can fluctuate by both sign and magnitude year by year. The characteristic risk premium is negative and significant for the entire 20-year period and the 1-year regressions suggests a clear negative trend. The systematic liquidity premium on the other hand is positive and significant for the entire 20-year period but the 1-year regressions do not show a clear trend. The empirical results show no statistical significance that ETFs influence the liquidity premium. However, the graphical interpretation of the 1-year regressions suggests that the characteristic liquidity premium is negatively correlated with the growth of ETFs. The negative characteristic premium implies that investors are not being adequately compensated for the risk of illiquidity and should therefore avoid a liquidity-based investing strategy which has generated excess return in the past.
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Geo-Political Risk-Augmented Capital Asset Pricing Model and the Effect on Long-Term Stock Market ReturnsNakhjavani, Arya 01 January 2018 (has links)
This paper examines the capital - asset pricing model (CAPM) which has been extended with a factor for geo-political risk. I use monthly stock return data for all stocks listed on a major US exchange from January 1990 to December 2016 and utilize a Fama-Macbeth Regression with Newey-West standard errors to test the geo-political augmented Sharpe-Lintner CAPM. The paper first determines if increased sensitivity to geopolitical risk lead s to lower average returns and second assesses if geo-political risk as an explanatory variable is a significant enough to expose a failure of the CAPM to capture expected returns fully through beta. The results of our regressions do not confirm the hypothesis that firms with high sensitivities to geo-political risk have expressly different returns in the long run. Furthermore, our Fama-Macbeth regression does not find expressly significant average slopes for geo-political risk as a variable.
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Multifraktalita a prediktabilita finančních časových řad / On multifractality and predictability of financial time seriesHeller, Michael January 2021 (has links)
The aim of this thesis is to examine an empirical relationship between multifrac- tality of financial time series and its returns. We approach the multifractality of a given time series as a measure of its complexity. Multifractal financial time series exhibit repeating self-similar patterns. Multifractality could be a good predictor of stock returns or a factor which can be used in asset pricing. We expected that capturing the complexity of a given time series by a model, a positive or a negative risk premia for investing into "more multifractal assets" could be found. Daily prices of 31 stock indices and daily returns of 10-years US government bonds were downloaded. All the data were recorded between 2012 and 2021. After estimation the multifractal spectra, applying MF-DFA method, of all stock indices, we ordered all stock indices from the lowest to the most multifractal. Then, we constructed a "multifractal portfolio" holding a long position in the 7 most multifractal and holding a short position in the 7 least multifractal stock indices. Fama-MacBeth regression with market risk premia and multifractal variable as independent variables was applied. Multi- fractality in all examined financial time series was found. We also found a very low negative risk premia for holding "a multifractal...
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