Life Must Be Tough for Some People

It must be really hard to be a real estate agent in Florida...

Man vs. Machine

Since the "flash crash" in May, there has been lots of talk about how computer trading algorithms  (i.e. algo trading) can crash the market by overwhelming the market with sell orders triggered after some event-based signal takes place.

Using computers to trade in the milisecond frequency makes it almost impossible for human beings to beat machines because there is no way to match their trading speed.

The FT last week had a very interesting story on how two (independent) Norwegian traders exploited a flaw in the algorithm of a U.S. firm to make money out of illiquid Norwegian stocks. So far so good, but the traders have been handed suspended prison sentences for market manipulation and a fine equal to their trading profits.

I find these charges a little odd. How come a computer that has a built in set of rules is not "manipulating" the markets when it places their orders? Two investors, doing a careful research job, find a flaw in the strategy and placed their bets, taking up the risk along with it. What's wrong with that? T

he U.S. firm is to blame for having a poor algorithm in the first place.Unfortunately this reminds me of that story saying that financial firms enjoy the profits but ask someone to bail them out in case of losses.

It's alive!

Those things don't happen often (at least not as often as I hoped!), but here is the link to my latest paper. It is joint work with Reena Aggarwal and Jason Sturgess (from Georgetown University)

To those of you interested in knowing a bit more about my research, this is it. Any comments are highly welcome!

Does Proxy Voting Affect the Supply and/or Demand for Securities Lending? 

We use a comprehensive proprietary data set consisting of shares available to lend (supply), shares borrowed (demand), and loan fee to study the securities lending market in the United States. We provide a better understanding of the securities lending market; examine the role of institutional investors in the voting process by analyzing the supply of lendable shares around the time of a proxy vote; and to address some of the issues related to empty voting we examine the changes in borrowing demand around the time of a proxy vote. On average, 19.57 percent of a firm’s market capitalization is available for lending, 3.3 percent is actually borrowed, and the annualized loan fee is 42 basis points. During our sample period, 2005-2009, there are 105,143 proxy agenda items. At the time of a proxy vote, there is a significant reduction in the supply of shares available to lend because institutions restrict or recall their loaned shares prior to a vote. The reduction in the supply of lendable shares is most pronounced in cases associated with significant events such as mergers, and with agenda items for which ISS recommends voting AGAINST the proposal. Our findings are consistent with institutional investors responsibly recalling shares, hence reducing supply ahead of material proposals. Most of the increase in loan fee around the time of a vote is associated with the reduction in supply which is related to the desire of institutions to vote their shares. We find statistically significant evidence of increase in demand however this increase in demand is economically small relative to the reduction in supply. In contrast, we find that the large increase in loan fee around the time of the ex-dividend date is driven by an increase in borrowing demand for cash flow reasons, with no change in the supply of lendable shares.

Why There is No System Like Capitalism...

Many in Brazil are fond of socialism, tough none moved to Cuba or China. Here is maybe why:

Alphas, Betas, and Sales Pitch

On Friday I had lunch with a colleague that told me about a job interview when he used to be at Goldman Sachs for a position with on the sales team.

Being asked how good he was at sales the candidate - an Insead MBA student - said:

"I will sell your beta as if it were alpha."

If I was sure he came up with this answer on his own, I would have given him the job on the spot. Maybe I would prefer that he had told the truth and explained how he could sell high beta as a good market timing move, but I still fiound it a greant answer. For those of you that don't remember.know what is the difference between beta and alpha, here goes a (maybe not so) simple explanation.

In modern models returns are explained as a function of their exposure to underlying risk factors (like the stok market, liquidity, etc.). A fund that takes up lots of market-related risk will have a high "beta" relative to the movements of the stock market. Thus, any over-performance relative to market would simply be due to higher exposure to risk rather than a superior managerial skill (i.e. a high "alpha") possessed by the fund's manager. Alpha is the return over an above the expected given the fund's exposure to sources of risk.