Zoran Ivković

Local Does as Local is: Information Content of the Geography of Individual Investors' Common Stock Investments

Using a data set on the investments made by a large number of retail investors from 1991 to 1996, we find that households exhibit a strong preference for local investment - the average household invests nearly a third of their portfolio in firms headquartered within 250 miles. We test whether this locality bias is driven by information or by simple familiarity. The average household generates an additional return of 3.7% per year from its local holdings relative to its non-local holdings, suggesting local investors are able to exploit local knowledge. The excess return to investing locally is even larger among stocks not in the S&P 500 index (firms where informational asymmetries between local and non-local investors may be largest), while there is no excess return earned by households that invest in local S&P 500 stocks.

Day Trading International Mutual Funds: Evidence And Policy Solutions

Daily pricing of mutual funds provides liquidity to investors but is subject to valuation errors due to the inability to observe synchronous, fair security prices at the end of the trading day. This mayhurt fund investor if speculatior strategiclly seek to exploit mispricing or if the net flow of money into funds is correlated with these pricing eerrors. We show that mutual funds are exposed to speculative traders by using a simple day trading rule that yields large profits in a sample of 391 U.S.-based open-end international mutual funds. We propose a simple “fair pricing” mechanism that alleviated these concerns by correcting net asset values for stale prices. We argue that fund companies and regulatiors should look at alternatives that allow funds to offer fair prciing to investors, which, in turn, decreases the need to resort to monitoring for day traders and redemption penalties.

A fundamental restriction on fully dynamic maintenance of bin packing

Abstract This paper studies a fundamental restriction on the problem of maintaining an approximate solution for one-dimensional bin packing when items may arrive and depart dynamically. It is shown that imposing a fixed constant upper bound on the number of items that can be moved between bins per Insert/Delete operation forces the competitive ratio to be at least 4 3 , regardless of the running time allowed per Insert/Delete. Thus, the ability to move more than a constant number of items is necessary for accomplishing highly competitive, time-efficient fully dynamic approximation algorithms for bin packing.

Partially dynamic bin packing can be solved within 1 + e in (amortized) polylogarithmic time

The problem of maintaining an approximate solution for one-dimensional bin packing when items may arrive dynamically is studied. In accordance with various work on dynamic algorithms, and in contrast to prior work on bin packing, it is assumed that the packing may be arbitrarily rearranged to accommodate arriving items. In this context we show that partially dynamic bin packing (Inserts only) can be solved within 1 + e in amortized polylogarithmic time.

Fully Dynamic Bin Packing

Classic bin packing seeks to pack a given set of items of possibly varying sizes into a minimum number of identical sized bins. A number of approximation algorithms have been proposed for this NP-hard problem for both the on-line and off-line cases. In this chapter we discuss fully dynamic bin packing, where items may arrive (Insert) and depart (Delete) dynamically. In accordance with standard practice for fully dynamic algorithms, it is assumed that the packing may be arbitrarily rearranged to accommodate arriving and departing items. The goal is to maintain an approximately optimal solution of provably high quality in a total amount of time comparable to that used by an off-line algorithm delivering a solution of the same quality.