Tourism is one of the fastest growing industries in the world, employing approximately 220 million people and generating over 9.4% of the world's GDP. The growing contribution of tourism is accompanied by an increased interest in understanding the major factors which influence visitation levels to those countries. Therefore, finding the right variables to understand and estimate tourism demand becomes very important and challenging in policy formulations. The purpose of this study is to introduce Economic Sentiment Indicator (ESI) to the field of tourism demand studies. Using ESI in demand analysis, this study will assist in the ability to tap into individuals' hopes and/or worries for the present and future. The study developed a demand model in which the number of tourist arrivals to Turkey from select EU countries is used as the dependent variable. ESI along with more traditional variables such as Interest Rate, Relative Price, and Relative Exchange Rate were brought into the model as the independent demand determinants. The study utilized such econometric models as ARIMA for seasonality adjustment and ARDL Bound test approach to cointegration for the long and short-run elasticities. ESI was statistically significant in 8 countries out of 13, three of those countries had a negative coefficient and five had a positive sign as proposed by the study. The study posits that ESI is a good indicator to gauge and monitor tourism demand and adding the visitors' state of mind into the demand equation could reduce errors and increase variance in arrivals. Policy makers should monitor ESI as it fluctuates over time. Since we do not have direct influence on travelers' demand for tourism, it is imperative that we use indirect approaches such as price adjustment and creating new packages or promotional expenditures in order to influence or induce demand. Using this information generated from the study, government officials and tourism suppliers could adjust their promotional activities and expenditures in origin countries accordingly. / Master of Science

Στην παρούσα εργασία μελετούμε τη σχέση μεταξύ χρηματιστηριακών αποδόσεων και δείκτη εμπιστοσύνης στις 27 χώρες-μέλη της ΕΕ για τα έτη 1985-2006. Βρήκαμε ότι για το μεγαλύτερο μέρος των χωρών της ΕΕ εμφανίζεται θετική συσχέτιση μεταξύ αποδόσεων και δείκτη εμπιστοσύνης του καταναλωτή στον βραχυχρόνιο ορίζοντα. Οι μεταβολές και στους δύο δείκτες τείνουν να κινούνται παράλληλα στην ίδια περίοδο, με εξαίρεση την πλειοψηφία των νεοεισελθέντων χωρών. Στον μακροπρόθεσμο ορίζοντα, βρήκαμε ότι για τις περισσότερες χώρες ο συντελεστής γίνεται σχεδόν μηδενικός. Για το μεγαλύτερο μέρος των χωρών της ΕΕ υφίσταται σχέση αιτιότητας μεταξύ των μεταβλητών, με τις αποδόσεις να προκαλούν κατά Granger τον δείκτη εμπιστοσύνης του καταναλωτή και τον δείκτη οικονομικής εμπιστοσύνης, αλλά το αντίστροφο δεν ισχύει. Αμφίδρομη σχέση αιτιότητας μεταξύ αποδόσεων και εμπιστοσύνης των καταναλωτών παρατηρείται μόνο για την Γαλλία οριακά, ενώ για την ΕΕ βρήκαμε οτι υπάρχει αμφίδρομη σχέση αιτιότητας μεταξύ αποδόσεων και δείκτη οικονομικής εμπιστoσύνης. / This paper studies the relationship between stock market developments and confidence index for the 27 EU countries - members over the years 1985-2006. We found that for the majority of the EU countries exists positive correlation between the stock market index and the confidence indicators (consumer confidence indicator and economic sentiment indicator) in the short horizon. The changes between these indexes tempt to move in the same direction contemporaneously and in the short horizon (of 1 month), with the new EU members to be an exception. The correlation becomes almost zero in the long horizon. For the most of the EU countries there is causality between the variables. Stock returns in general Granger-cause the Consumer Confidence Index and the Economic Sentiment Indicator, but not vice versa. We found also that there is feedback causality relationship between stock returns and confidence for France and the EU as a whole.

The objective of this study is to develop a market sentiment model for financial markets using machine learning, and to illustrate these methods using commodity price data. A market sentiment model may capture the fundamental and crowd psychology of the market, through a variable that uses positive and negative words and phrases. The commodity price used is the daily price of the spot crude oil exchange-traded fund (ETF), United States Oil Fund (USO). The forecasting power of the market sentiment model is compared with a traditional autoregressive model. The results showed that the autoregressive models did not have significant forecasting power for the oil data over the time period examined and the addition of the sentiment model did not improve the forecasting power. Machine learning is a relatively new forecasting method. Therefore, further research on this topic is needed before any firm conclusions can be drawn regarding the effectiveness of this approach. / February 2017

This paper investigates the investor trading behavior and the relationship between investor sentiment and stock returns. First we explore whether individual investors behave as the Disposition Effect stated — hold their losers too long while realize their winners too soon. Second, we apply four sentiment indicators — number of recommended stocks, margin purchase value, net fund redemption and odd-lot trade value — to examine relationship between investor sentiment and stock returns. We would like to see if past returns have anything to do with current sentiment, and if sentiment provides predictive power to future returns. First of all, from our analysis to over eight hundreds cash accounts trading records in two research periods, January to March and September to December in 2000, we found the Disposition Effect holds in average but not statistically. Second, the number of recommended stocks, weighted number of recommended stocks, margin purchase value, change in margin purchase value, net fund redemption and odd-lot trade value as proxies of investor sentiment are good at measuring the effect of past 4-week and 26-week returns on sentiment. Third, the margin purchase value, net fund redemption and odd-lot trade value provide predictive power to future 26-week returns in our study, which also implies there is likely underlying mean-reversion within half year during the research period. 　Finally, exploiting the change in margin purchase value as proxy of investor sentiment, we found the past 4-week returns volatility is inversely related with the indicator. That is, investors are scared on facing with high returns volatility.