As conventional wisdom has it, markets shut down during financial crises, as sellers struggle to find their buyers. However, an empirical assessment of what really goes on with secondary market liquidity in periods of financial distress is far from trivial, as a quick survey of the related literature illustrates. Whereas the relation between liquidity and market returns in the US has been studied extensively in both directions, the results differ according to the measure of liquidity in use. Moreover, much less is known about the behavior of secondary market liquidity (in its different dimensions) in periods of financial turmoil, a critical test to evaluate both the functioning of financial markets and the mechanics of financial crises. This paper contributes to fill in this gap, conducting the first systematic empirical study of secondary market liquidity under stress across emerging market crises.
A generally accepted theoretical argument relating liquidity and market returns is the collateral-based view: pronounced falls in asset prices reduce the value of financial intermediaries’ capital and increase their margin calls, forcing them to liquidate their positions, thereby inducing wider bid-ask spreads and increasing the price response to trading. Since net withdrawals are a function of the intermediaries’ performance, when the value of assets drop the short-term inflow of funds decreases or even reverses, forcing financial intermediaries to sell, adding to the price downturn, and generating a spiraling fall in some liquidity measures. Therefore, market liquidity is closely related to intermediaries’ funding needs, and this mutually reinforcing relation can generate sudden spikes in illiquidity indicators. While collateral-based theories assume that outside capital does not enter the market during downturns, fire-sale theories highlight precisely the role of outside capital: lower asset prices reward liquid outside buyers who profit from illiquid asset holders. Fire-sales (namely, forced wide-spread selling from distressed funds when investors redeem their capital en masse) put downward pressure on prices, as outside buyers demand additional compensation for providing needed liquidity.
Can an uninformed trader profit from buying and then selling an asset? Despite Friedman's (1953) conclusion that arbitrage would make such a strategy self-defeating, some financial economists have concluded that profitable "trade-based" manipulation is feasible. Allen and Gale (1992) and Aggarwal and Wu (2004) demonstrate the possibility of a pooling equilibrium in which the typical trader cannot distinguish informed investors and would-be manipulators, allowing the latter to influence prices strategically. Other models rely on investor irrationality or other departures from market efficiency. In Jarrow's (1992) model, prices have momentum, enabling a manipulator to establish a price trend and then profit by trading against it. Mei, Wu and Zhou (2004) demonstrate that a trader can exploit investor biases by buying to drive up prices, then selling at a profit.
As a course on programming, it emphasizes the analysis and solution of problems, the development of correct and efficient algorithms and data structures that embody the solutions, and the expression of the algorithms and data structures in a form suitable for processing by a computer. The focus is more on the human thought processes than on the computer execution processes.
As a course on functional programming, it approaches programming as the construction of definitions for (mathematical) functions and data structures. Functional programs consist of expressions that use these definitions. The execution of a functional program entails the evaluation of the expressions making up the program. Thus the course’s focus is on problem solving techniques, algorithms, data structures, and programming notations appropriate for the functional approach.
