This thesis examines a part of the theory about financial bubbles and conducts an empirical analysis of the probability of predicting financial crises based on the stock markets of the U.S, UK, Germany and Denmark. Despite the extensive theories about financial crises and bubbles it is surprisingly difficult to establish a consensus for when an event is considered a crisis. Meanwhile the leading economists are unable to agree if bubbles even exist. Fama and his supporters believe that markets are efficient and large changes in asset prices are simply the market reacting to new information. This is in contrast to Shiller who argues that bubbles are real and happens when the asset prices deviates too far from the fundamental value and the price is primarily increasing due to the belief that the value will increase even further in the near future. The models applied to test whether or not we are able to predict financial crises are mainly the bond stock earnings yield differential model and the cyclically adjusted price to earnings ratio model. The models exhibit close to 60% accuracy when issuing a crash warning, although it fails to predict more than two out of three market corrections. The accuracy and the number of all crashes predicted, and which model performs the best depend to a large degree on the market that is being used to test the models. The performance of the models is highly influenced by the source of the data used, thus leading to large differences in the results. In addition to this, the results are very dependent on how the findings are interpreted by the individual. Despite the models seemingly mediocre performance in relation to crash predictions the bond stock earnings yield differential model is fairly successful as a long term investment strategy. Based on the past 45 years of the S&P 500 index adhering to the model would have generated an average of 33% excess return compared to the buy and hold strategy.
|Educations||MSc in Finance and Investments, (Graduate Programme) Final Thesis|
|Number of pages||100|