Despite our relaxing weekend, it was time to get right back to businesses this Monday. We had a full slate that required us to leave the hotel at 9am and spend the entire day at the University of Nicosia. Today consisted of 3 meetings: a conference with Fleet Management Ltd, a lecture on blockchain and cryptocurrency with the CEO of UNIC, and another lecture on data forecasting with Spyros Makridakis, one of the world’s most respected data forecasters.
Our meeting with Fleet Management helped me really understand how important ship management is to the Republic of Cyprus. They told us that the growth of the shipping industry will reflect the growth of the entire Cypriot economy, and they have clients in Japan, Europe, the Middle East, Asia, and the United States. Along with that, they explained how they make their profit despite such large costs that they have to take on. The costs can average around $8,000-$9,000 per ship per day, but their profit comes from charging fees to the ship owner. The ship management will see gradual highs and lows, but their revenue per ship will generally translate to a profit. For examples, they boomed during the pandemic, making around $50,000 per ship per day. Fleet was particularly interesting to me because they were the company that my group researched during our pre-departure meetings.
The two lectures that we attended in the afternoon probably held the most informational value that we can translate to our lives back at home. First we listened to the CEO of the university, who is way ahead of the crypto curve. He told us that UNIC started accepting Bitcoin as payment all the way back in 2013, and he broke down crypto and NFTs in an insightful and understandable way for us. The main gist of cryptocurrency is that it has no administrator or database, it is completely digital, and it only holds value because we perceive it to hold value.
Data forecasting is something that really interests me (it’s even what I wrote about in my application to Pitt), so it was really cool to hear Makridakis speak on how he can minimize uncertainty and maximize accuracy so well. Some of the key takeaways that I got were that statistically complex methods do not necessarily lead to higher accuracy than simpler ones, combining methods leads to higher accuracy and lower uncertainty, and machine learning will continue to provide much more accurate forecasting than statistics.
Sidenote: I didn’t take any pictures today so enjoy a picture of a cat I took on Sunday as my cover photo 😀