Four Factor Model in Indian Equities Market

Statistical Arbitrage in the U.S. Equities Market

We study model-driven statistical arbitrage in U.S. equities. The trading signals are generated in two ways: using Principal Component Analysis and using sector ETFs. In both cases, we consider the residuals, or idiosyncratic components of stock returns, and model them as mean-reverting processes. This leads naturally to “contrarian” trading signals. The main contribution of the paper is the co...

Topological Structures in the Equities Market Network

We present a new method for articulating scale-dependent topological descriptions of the network structure inherent in many complex systems. The technique is based on “Partition Decoupled Null Models,” a new class of null models that incorporate the interaction of clustered partitions into a random model and generalize the Gaussian ensemble. As an application we analyze a correlation matrix der...

A Multi-factor risk model for the Indian Stock Market

The paper attempts to find the evidence of a multi factor model for explaining stock price returns in the Indian stock market. It makes use of the technique of statistical factor analysis. The results of the factor analysis show that a five factor model is appropriate for explaining the returns generation process in India. The explanatory power of this five factor model is significantly better ...

Optimal trade execution of equities in a limit order market

Stochastic Trends and Cointegration in the Market for Equities

We used a no-arbitrage, cost-of-carry pricing model to examine whether equity spot and futures markets are cointegrated. A stock index and its futures price should be cointegrated if the cost of carry is stationary. Otherwise, the appropriate cointegrating relationship is trivariate. We found that the Standard and Poor’s 500 index, associated futures series, and interest rate are all nonstation...