Pairs Trading: Performance of a Relative Value Arbitrage Rule

We test a Wall Street investment strategy known as pairs trading' with daily data over the period 1962 through 1997. Stocks are matched into pairs according to minimum distance in historical normalized price space. We test the profitability of several trading rules with six-month trading periods over the 1962-1997 period, and find average annualized excess returns of up to 12 percent for a number of self-financing portfolios of top pairs. Part of these profits may be due to market microstructure effects. Nevertheless, our historical trading profits exceed a conservative estimate of transaction costs through most of the period. We bootstrap random pairs in order to distinguish pairs trading from pure mean-reversion strategies. The bootstrap results suggest that the pairs' effect differs from previously documented mean reversion profits.

Uncovering the Internal Structure of the Indian Financial Market: Cross-correlation behavior in the NSE

The cross-correlations between price fluctuations of 201 frequently traded stocks in the National Stock Exchange (NSE) of India are analyzed in this paper. We use daily closing prices for the period 1996-2006, which coincides with the period of rapid transformation of the market following liberalization. The eigenvalue distribution of the cross-correlation matrix, $\mathbf{C}$, of NSE is found to be similar to that of developed markets, such as the New York Stock Exchange (NYSE): the majority of eigenvalues fall within the bounds expected for a random matrix constructed from mutually uncorrelated time series. Of the few largest eigenvalues that deviate from the bulk, the largest is identified with market-wide movements. The intermediate eigenvalues that occur between the largest and the bulk have been associated in NYSE with specific business sectors with strong intra-group interactions. However, in the Indian market, these deviating eigenvalues are comparatively very few and lie much closer to the bulk. We propose that this is because of the relative lack of distinct sector identity in the market, with the movement of stocks dominantly influenced by the overall market trend. This is shown by explicit construction of the interaction network in the market, first by generating the minimum spanning tree from the unfiltered correlation matrix, and later, using an improved method of generating the graph after filtering out the market mode and random effects from the data. Both methods show, compared to developed markets, the relative absence of clusters of co-moving stocks that belong to the same business sector. This is consistent with the general belief that emerging markets tend to be more correlated than developed markets.Comment: 15 pages, 8 figures, to appear in Proceedings of International Workshop on "Econophysics & Sociophysics of Markets & Networks" (Econophys-Kolkata III), Mar 12-15, 200

Unifying Time-to-Build Theory

Several contributions have recently reconsidered the role of the time to build assumption in explaining some relevant stylized facts. In this paper, the similarities and differences which may emerge when the time to build structure of capital is introduced in a continuous or discrete time framework are studied and enlightened. The most striking difference lies in the dimensionality of the two frameworks, which is always finite in discrete but infinite in continuous time. Then, the deterministic version of the traditional time to build model developed by Kydland and Prescott is presented, and it is shown how the typical time to build model setup in continuous time can be obtained. Moreover, the richest dynamics in continuous time is investigated and, more importantly, it is shown that the predictions in terms of capital, output, and consumption behavior are not signi¯cantly di®erent from its discrete version once the economy is calibrated properly

The Size Effect in Value and Momentum Factors: Implications for the Cross-Section of International Stock Returns

We document a consistent and robust relation between expected equity premia and common risk factors constructed on the basis of small stocks. Empirically, we show that (i) small-stock components of traditional value and momentum factors capture patterns in returns on regional and global portfolios of stocks; (ii) size-effect models substantially outperform benchmark models in finance; (iii) global small-stock value and momentum components are priced but regional models lead to more accurate asset evaluations; (iv) funding liquidity risk is a partial explanation of these findings

Pension fund performance and costs: small is beautiful.

Abstract Using the CEM pension fund data set, we document the cost structure and performance of a large sample of US pension funds. To date, self-reporting biases and a deficiency of comprehensive return and cost data have severely hindered pension fund performance studies. The bias-free CEM dataset resolves these issues and provides detailed information on fund-specific returns, benchmarks and costs for all types of pension plans and equity mandates. We find that pension fund cost levels are substantially lower than mutual fund fees. The domestic equity investments of US pension funds tend to generate abnormal returns (after expenses and trading costs) close to zero or slightly positive, contrasting the average underperformance of mutual funds. However, small cap mandates of defined benefit funds have outperformed their benchmarks by about 3% a year. While larger scale brings costs advantages, liquidity limitations seem to allow only smaller funds, and especially small cap mandates, to outperform their benchmarks. JEL Classifications : G23, G11, G14 Acknowledgements Our thanks to Keith Ambachtsheer, CEM Benchmarking Inc. for providing the pension fun