This study aims to examine the challenges and applications of machine learning for financial research. Machine learning algorithms have been developed for certain data environments which substantially differ from the one we encounter in finance. Not only do difficulties arise due to some of the...

Natural disasters are negative shocks that can severely disrupt the communities in which they occur. Disasters like hurricanes, tornados, floods, wildfires, and earthquakes, moreover, can cause severe property damage, including damages to homes, businesses, and automobiles. An important issue...

ESG ratings as a stock screener for downside protection can be significantly improved when combined with sentiment indicators derived from news and social media. Following a statistical approach, consisting in evaluating thousands of long-only monthly-rebalanced random portfolios, we find...

Peer and cohort effects are important in health economics, and obesity may be related to social relationships, where obese individuals interact with other obese individuals. There were significant 19th century cohort effects, where BMIs were related to the cohort that an individual belonged....

• It is not widely emphasized in the literature that derivatives are complex random quantities which should, by custom, be characterized by their probability density functions. • It is understood that Black-Scholes style of derivatives pricing represents an expected value, i.e. the...

The financial economics literature proposes dozens of performance measures to be used, for instance, to compare, analyse, rank and select assets. There is thus a problem: which measures should be considered? We extend the current literature by comparing a large set of performance measures over...

When searching the Finnish Liiga website for statistics, I discovered an interesting statistic called Corsi%-C. It is defined as “...when the score is within a goal in the first two periods and tied in the third period”. (liiga, 2021) Essentially, this is an enhanced version of Corsi for...

We use a unique dataset of bond downgrades from a niche rating company that has been found to be reacting faster to publicly available information than its competitors. Using regime-switching models we propose risk measures to quantify stock return disturbances (distress costs) associated with...

In this paper we describe and apply the methods of Symbolic Time Series Analysis to an experimental framework. We discuss data symbolization as a tool for identifying temporal patterns in experimental data and use symbol sequence statistics in a model strategy. In particular, we introduce a...

Evaluating goalie performance in hockey is a difficult and time consuming pro- cess. However, with the introduction of advanced statistics, this process has become clear. This report combines advanced hockey analytics and statistical analysis to predict the Goals Above Replacement and Support...