We develop statistical methods to detect informed trading in options markets. We apply these methods to 31 companies from various sectors over 14 years analyzing approximately 9.6 million option prices. We find that option informed trading tends to cluster prior to certain events, takes place...

This appendix extends the empirical results in Chesney, Crameri, and Mancini (2011). Informed trading activities on put and call options are analyzed for 19 companies in the banking and insurance sectors from January 1996 to September 2009. Our empirical findings suggest that certain events such...

We develop the idea of using Monte Carlo sampling of random portfolios to solve portfolio investment problems. In this first paper we explore the need for more general optimization tools, and consider the means by which constrained random portfolios may be generated. A practical scheme for the...

An investment is a commitment of funds made in the expectation of some positive rate of return. A fundamental principle of investments is diversification, where investors diversify their investments into different types of financial assets. The different stocks can be clubbed in one portfolio....

With the advances in time-series prediction, several recent developments in machine learning have shown that integrating prediction methods into portfolio selection is a great opportunity. In this paper, we propose a novel approach to portfolio formation strategy based on a hybrid machine...

Following Levy and Roll [2010], we posit that the market portfolio is the efficient tangent Markowitz portfolio, i.e., it is mean-variance efficient. We then reverse engineer the expected returns and variance terms with constraints imposed by empirical data on a hierarchy of asset baskets. This...