With my experience in statistical analysis and after further gaining information on equity markets I was curious to explore the possibility of generating alpha by combining the two skills. I got an opportunity where I worked on a similar idea and then I extended it on the data I had downloaded from yahoo.
In this strategy I try to find a statistical equivalent of pairs trading and try to extract inefficiencies in price movements.
Abstract
The objective of this analysis is to obtain an alpha that is based on statistically exploring ineffi ciencies in stock prices. The strategy involves decomposing stock prices in each industry into principal components that explain the most variance and then regress the stock prices on those components to obtain the stock's dependence on them. Next the components are forecasted using GARCH model and hence the forecasted evolution of the stocks is also obtained based on the regression results. Based on these forecasts I will create a long-short neutral arbitrage strategy with the aim of achieving high risk adjusted returns.
Statistical Arbitrage on US Equity
https://github.com/kunalrajani/statistical-arbitrage
Disclaimer: Please read
Please note that this is my independent work where I have used data from yahoo finance to explore statistical concepts from a course I took in statistics. It may inadvertently have an overlap with a work that somebody else has already done and I have no intentions of replicating it. I would be glad to know of any such clash and post a clarification on this post.
I am open to having my work being redistributed or used but only after due credit and a reference has been made. Feel free to contact me to avoid any misunderstandings or if you need more details from this paper.
*The analysis is still underway and the conclusions are under review
In this strategy I try to find a statistical equivalent of pairs trading and try to extract inefficiencies in price movements.
Abstract
The objective of this analysis is to obtain an alpha that is based on statistically exploring ineffi ciencies in stock prices. The strategy involves decomposing stock prices in each industry into principal components that explain the most variance and then regress the stock prices on those components to obtain the stock's dependence on them. Next the components are forecasted using GARCH model and hence the forecasted evolution of the stocks is also obtained based on the regression results. Based on these forecasts I will create a long-short neutral arbitrage strategy with the aim of achieving high risk adjusted returns.
Statistical Arbitrage on US Equity
https://github.com/kunalrajani/statistical-arbitrage
Disclaimer: Please read
Please note that this is my independent work where I have used data from yahoo finance to explore statistical concepts from a course I took in statistics. It may inadvertently have an overlap with a work that somebody else has already done and I have no intentions of replicating it. I would be glad to know of any such clash and post a clarification on this post.
I am open to having my work being redistributed or used but only after due credit and a reference has been made. Feel free to contact me to avoid any misunderstandings or if you need more details from this paper.
*The analysis is still underway and the conclusions are under review
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