Cloud computing makes your service a commodity
Just a brief blog post as I'm mostly busy working hard on my dissertation (which should be made available here in a few weeks) and applying for jobs. Hopefully the job thing should be signed in a few weeks as well, as there are a few interesting in the pipeline at the moment.
Anyway, thought it was time to post something, so here goes. Been following Brightbox for some time. They are a new cloud computing provider (but not a new hosting provider in general), and they give off a good first impression. Yesterday they published their pricing plan for the soon to be non-beta platform. I don't have any reason for doubting the quality of their service, and I often like to support the "little guys", but man, cloud computing really have turned servers into a commodity.
Prior to pay-as-you-go hosting, there was a million different aspects you should consider when choosing a hosting provider. I'm not saying most people did consider all those aspects, and mainly I assume that's because it was often rather time consuming. Unless you're doing a big deal with someone, who bothers too much, right? You look at the basic price, other more or less useful metrics (reputation, web site quality, gut feeling...) and make a decision. Now? You just compare hourly prices. And that's the first thing I did with Brightbox as well, having their price plan in one tab, and Rackspace's in another (btw: I really hate the chat pop-up on Rackspace's website!). As the disk and memory sizing are equal, it makes it trivial to do this. I'm not saying it's accurate. I should compare the performance, but I can't really be bothered right now. Given the almost flawless reputation of Rackspace, in addition to being cheaper, why would I spend a few hours this Saturday creating performance metrics? Maybe I will at one point, but only if I'm in need of wast amounts of what they offer.
A look at Fudge Proto
OpenGamma is an interesting startup headquartered here in London. They provide open source software for analytics and risk analysis for the financial service industry. As that's right up my alley I decided to start looking at their code this Friday. Among all that code there's one project which looked like a good starting point, namely Fudge Proto. I took my usual approach and read what documentation I felt was relevant while browsing the source code at the same time. Experimenting with the code and learning by examples has always worked for me, so in no time I had some code up and running.
In brief you can compare the purpose of Fudge Proto to that of Google Protocol Buffers and Apache Thrift. You have one or more objects and can then easily serialize these, store them, send them over a network, and deserialize them again when needed. The target output format would typically be binary. Binary is beneficial over for instance XML and JSON as it's more compact. It should generally also be quicker to parse as the format is more fixed with less fuss. Of course it's not easily human readable, but that's not the point either.
Fudge Proto and the underlying Fudge Messaging system is fairly young, with version 0.3 released early this year. I wanted to see how it stacks up to Google's Protocol Buffers. I understand Protocol Buffers is heavily used internally at Google and given Google's scale I'm sure it's heavily optimized. Although I've dealt with serialization before, and also designed some low level messaging systems for use on very limited devices, I'm not claiming to be an expert on any of these systems. Therefore, should you spot some obvious issues in my experimentations later on, feel free to contact me by email or leave a comment. I'll be happy to rerun these tests with improvements to both Protocol Buffers and Fudge Proto.
Excessive Optimism and Analyst Recommendations
This blog post taps into some of the theories outlined by behavioral finance. Specifically I’m going to look at research done on what is called overconfidence.
Overconfidence among investors can manifest itself in many ways. One of these forms is excessive optimism (or pessimism) with regards to beliefs in future outcome. Carleton, Chen, and Steiner (1998) and Jegadeesh, and Kim (2003) study the value of analyst recommendations. There are two aspects worth considering specifically in this context. The first is the quality of the recommendations and the second is the market reaction to them. The value of an analyst report can be defined as its impact on the market, thus the quality of the report and market reaction is not mutually exclusive but rather tightly linked. This poses some potential issues as there might be a feedback loop at work here, where analysts with a broader audience could potentially have a bigger impact than more unknown analysts with less exposure. However, given that the topic in question is an anomaly of the efficient market hypothesis, this potential feedback loop is of interest. In an efficient market, any new information in the form of a buy/sell recommendation must only have an immediate impact without any positive serial correlation in future abnormal returns if it represents new information. I will begin by analyzing a number of aspects related to analyst recommendations and proceed to cover market impact.