AGILE & DISTRIBUTED PROJECT MANAGEMENT: A CASE STUDY REVEALING WHY SCRUM IS USEFUL

Scrum has gained surprising momentum as an agile IS project management approach. An obvious question is why Scrum is so useful? To answer that question we carried out a longitudinal study of a distributed project using Scrum. We analyzed the data using coding and categorisation and three carefully selected theoretical frameworks. Our conclusion in this paper is that Scrum is so useful because it provides effective communication in the form of boundary objects and boundary spanners, it provides effective social integration by building up social team capital, and it provides much needed control and coordination mechanisms by allowing both local and global articulation of work in the project. That is why Scrum is especially useful for distributed IS project management and teamwork

Market Liquidity and Funding Liquidity

We provide a model that links an asset's market liquidity - i.e., the ease with which it is traded - and traders' funding liquidity - i.e., the ease with which they can obtain funding. Traders provide market liquidity, and their ability to do so depends on their availability of funding. Conversely, traders' funding, i.e., their capital and the margins they are charged, depend on the assets' market liquidity. We show that, under certain conditions, margins are destabilizing and market liquidity and funding liquidity are mutually reinforcing, leading to liquidity spirals. The model explains the empirically documented features that market liquidity (i) can suddenly dry up, (ii) has commonality across securities, (iii) is related to volatility, (iv) is subject to "flight to quality", and (v) comoves with the market, and it provides new testable predictions.

Predatory Trading

This paper studies predatory trading: trading that induces and/or exploits other investors' need to reduce their positions. We show that if one trader needs to sell, others also sell and subsequently buy back the asset. This leads to price overshooting and a reduced liquidation value for the distressed trader. Hence, the market is illiquid when liquidity is most needed. Further, a trader profits from triggering another trader's crisis, and the crisis can spill over across traders and across markets.

Asset Pricing with Liquidity Risk

This paper solves explicitly an equilibrium asset pricing model with liquidity risk -- the risk arising from unpredictable changes in liquidity over time. In our liquidity-adjusted capital asset pricing model, a security's required return depends on its expected liquidity as well as on the covariances of its own return and liquidity with market return and market liquidity. In addition, the model shows how a negative shock to a security's liquidity, if it is persistent, results in low contemporaneous returns and high predicted future returns. The model provides a simple, unified framework for understanding the various channels through which liquidity risk may affect asset prices. Our empirical results shed light on the total and relative economic significance of these channels.

Liquidity and Asset Prices

We review the theories on how liquidity affects the required returns of capital assets and the empirical studies that test these theories. The theory predicts that both the level of liquidity and liquidity risk are priced, and empirical studies find the effects of liquidity on asset prices to be statistically significant and economically important, controlling for traditional risk measures and asset characteristics. Liquidity-based asset pricing empirically helps explain (1) the cross-section of stock returns, (2) how a reduction in stock liquidity result in a reduction in stock prices and an increase in expected stock returns, (3) the yield differential between on- and off-the-run Treasuries, (4) the yield spreads on corporate bonds, (5) the returns on hedge funds, (6) the valuation of closed-end funds, and (7) the low price of certain hard-to-trade securities relative to more liquid counterparts with identical cash flows, such as restricted stocks or illiquid derivatives. Liquidity can thus play a role in resolving a number of asset pricing puzzles such as the small-firm effect, the equity premium puzzle, and the risk-free rate puzzle.Liquidity; Liquidity Risk; Asset Prices

Valuation in Over-the-Counter Markets

We provide the impact on asset prices of search-and-bargaining frictions in over-the-counter markets. Under certain conditions, illiquidity discounts are higher when counterparties are harder to find, when sellers have less bargaining power, when the fraction of qualified owners is smaller, or when risk aversion, volatility, or hedging demand are larger. Supply shocks cause prices to jump, and then "recover" over time, with a time signature that is exaggerated by search frictions. We discuss a variety of empirical implications.

Over-the-Counter Markets

We study how intermediation and asset prices in over-the-counter markets are affected by illiquidity associated with search and bargaining. We compute explicitly the prices at which investors trade with each other as well as marketmakers' bid and ask prices in a dynamic model with strategic agents. Bid-ask spreads are lower if investors can more easily find other investors, or have easier access to multiple marketmakers. With a monopolistic marketmaker, bid-ask spreads are higher if investors have easier access to the marketmaker. We characterize endogenous search and welfare, and discuss empirical implications.

Slow Moving Capital

We study three cases in which specialized arbitrageurs lost significant amounts of capital and, as a result, became liquidity demanders rather than providers. The effects on security markets were large and persistent: Prices dropped relative to fundamentals and the rebound took months. While multi-strategy hedge funds who were not capital constrained increased their positions, a large fraction of these funds actually acted as net sellers consistent with the view that information barriers within a firm (not just relative to outside investors) can lead to capital constraints for trading desks with mark-to-market losses. Our findings suggest that real world frictions impede arbitrage capital.

When to use what? – Selecting systems development method in a Bank

Since the very first systems development method was defined it has been discussed how and when to select which method(s). Several books and consultants have claimed to have found the philosophers stone, but never the less it seems that there is no single method that will ever work for (nearly) all development situations. The question then arises: When to use what? And how can one help a concrete organization decide on systems development method(s)? To answer this question we undertook an action research study in ScandiBank that employs 1700 IS developers. The action research took place in three learning cycles. The first cycle started in 2001, the latter ended in 2005. We report from the three learning cycles emphasizing and explaining the learning that took place in each cycle. The result – our answer to the research question on when to use what? – is a framework focusing on the final product, with a few well-chosen trails through the “maze” of possibilities, and some rules for election of method parts