A two-year revision: cross comparison and modeling of Goldman Sachs, Morgan Stanley, JPMorgan Chase, Bank of America, and Franklin Resources
Approximately two years ago we presented results of price modeling and extensive statistical analysis for share prices of five banks: Bank of America (BAC), Franklin Resources (BEN), Goldman Sachs (GS), JPMorgan Chase (JPM), and Morgan Stanley (MS). Using monthly closing prices (adjusted for splits and dividends) as a proxy to stock prices, we estimated the best fit (LSQ) quantitative price models based on the decomposition into two defining consumer price indices selected from a large set of various consumer price indices (CPIs). It was found that there are two pairs of similar price models BAC/MS and GS/JPM, with a standalone model for BEN. Using five estimated models we formulated a procedure for selection the company with the highest return depending on the future evolution of defining CPIs. Here, we revisit the original models with new data for the period between October 2012 and February 2014. All revised models are practically the same as the original ones that validates our approach to price modeling. For the pair Bank of America and Morgan Stanley, we correctly predicted that both prices would rise synchronously (the observed return since October 2012 is approximately 75%) as driven by a higher rate of increase in the price index of owner’s rent of primary residence and rent of shelter. Goldman Sachs and JPMorgan Chase have risen by ~40% in line with a higher rate of growth in the index of food and beverages relative to two rent related indices. Franklin Resources has risen by only 25% as defined by a different pair of CPIs. All five models are robust and do not demonstrate any signs of upcoming failure in the near future. They may be used for stock market analysis.
|Date of creation:||22 Mar 2014|
|Contact details of provider:|| Postal: Ludwigstraße 33, D-80539 Munich, Germany|
Web page: https://mpra.ub.uni-muenchen.de
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Ivan O. Kitov & Oleg I. Kitov, 2008.
"Long-Term Linear Trends In Consumer Price Indices,"
Journal of Applied Economic Sciences,
Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 3(2(4)_Summ).
- Ivan Kitov, 2012.
"Cross comparison and modelling of Goldman Sachs, Morgan Stanley, JPMorgan Chase, Bank of America, and Franklin Resources,"
- Kitov, Ivan, 2012. "Cross comparison and modelling of Goldman Sachs, Morgan Stanley, JPMorgan Chase, Bank of America, and Franklin Resources," MPRA Paper 43099, University Library of Munich, Germany.
- Kitov, Ivan, 2009. "ConocoPhillips and Exxon Mobil stock price," MPRA Paper 15334, University Library of Munich, Germany.
When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:54696. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.