Happy April! I’ve been slacking on these link posts.. I’m hoping to launch myself back into the habit with a varied collection of links today:
- No one really knows if HFT is good or bad. The econoblogosphere has been on fire this past week with the release of Michael Lewis’ book Flash Boys, which exposes to the general public some of the more nefarious aspects of high-frequency trading. This post by Noah Smith is a candid admission of the fact that we really don’t have the tools at this point to conclude whether many HFT practices (barring those obviously egregious techniques like front-running) help or hinder the stock market. See also “High Speed Trading and Slow-Witted Economic Policy” and “Flash Boys for the People”.
- The Alchemists: Three Central Bankers and a World On Fire revealed to me just how pivotal a role central bankers of the world have had in the recent recession and the subsequent recovery. The amount of engineering that these institutions—most notably, the Federal Reserve, ECB, and Bank of England—perform in hopes of maintaining stable employment and moderate inflation is just astounding.
- “The value of time as a student” asks a question that has been bothering me for some time: why do students of extraordinary intellectual aptitude (and no immediate financial burdens) take on menial campus jobs? Katja Grace suggests: “It seems that college students generally treat their time as low value.” We trade this time for small immediate returns, perhaps unable to conceive of how spending this same time on more positive engagements (reading, meeting with professors, building things) could yield much greater long-term returns.
- “No country for old members” models the linguistic change of communities, and reaches some interesting conclusions about how language change tells a story about the “age” or character of a community.
- “I’m sorry Dave, I’m afraid I can’t do that” is now my go-to resource for explaining some of the struggles of natural language processing. The paper gives a brief overview of the history of NLP and the challenges which it has faced or has yet to overcome.
