Nico Horstmann

Equity Market Prediction. Evidence from Netherlands (eBook, PDF)

• Format: PDF

Produktbeschreibung

Bachelor Thesis from the year 2021 in the subject Business economics - Review of Business Studies, grade: 1,0, Technical University of Munich, language: English, abstract: The focus of this bachelor thesis is the equity market of the Netherlands. The Amsterdam Stock Exchange is one of the oldest or even the oldest stock exchange of the world. Several interesting companies like Adyen (fintech company) and ASML (semiconductor company) are listed at the Netherlands market. However, this thesis is not about predicting individual stock returns, but about predicting the Netherlands stock market in general, and therefore, a broad stock index (the Netherlands-Datastream Market) is investigated, that contains (nearly) every stock of the Netherlands. Equity Market Prediction is an quite interesting topic for investment bankers and the academia. It plays an important role in topics like asset allocation, asset pricing, risk management and capital budgeting. Being able to predict the capital markets would result in a huge gain for investors. Even companies may benefit from equity market prediction, because they could time the market by deciding for example the optimal time of an initial public offering (IPO) or pricing this IPO correctly without leaving money on the table. Therefore, this bachelor thesis examines different predictor variables, that are grouped into market valuation, trend, sentiment, and macroeconomic (macro) variables. Predictor variables are variables that are said to be able to predict the equity market. To test the predictability of these predictors this thesis runs several in-sample and out-of-sample prediction trials with a defined regression framework. In-sample, both univariate as well as multivariate regressions are carried out. Out-of-sample, the predictive power of each predictor is tested stand-alone and compared to a simple benchmark model. In the end a trading strategy resulting from these return predictions may be evaluated.

Produktdetails

• Verlag: GRIN Verlag
• Seitenzahl: 64
• Erscheinungstermin: 24. August 2021
• Englisch
• ISBN-13: 9783346471543
• Artikelnr.: 62487029