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Table of Contents Introduction. Research Goals

. Datasets

. Analysis of stock market behavior

. Our two-step approach: brief definition with major advantages over earlier methods

. Comparison of portfolios’ performances

Conclusion Introduction. Research Goals This research, foremost, is aimed to give a clue to the question of how monetary policy in the United States is transmitted to the stock market. On the one hand, the decision of the Federal Reserve System about short-term overnight lending rate (known as the Federal funds rate, or the target rate) is watched closely by market participants, and is a large common single determinant of the market response on the announcement day. On the other hand, according to the efficient-market hypothesis (EMH) developed in (Fama, 1970), in semi-strong form of market efficiency stocks must incorporate all known past information and current news. There are debates over the time that stocks take to absorb the news, and a study of (Chan, 2001) showed that stocks tend to underreact to news. Furthermore, (Bernanke & Kuttner, 2005) showed that one-month stock reaction is slightly larger than a one-day reaction to the announcement of the target rate, which implies a market under-reaction. Nevertheless, estimates of immediate market responses to announcement of the target rate are large enough to believe that the current monetary policy objectives are mostly reflected in the stock prices. We will give estimates of market response in the first part of the work.real obstacle that is debated over years is the channel of transmission of the target rate announcement to the equity market. Since (Bernanke & Blinder, 1992) defined two channels (demand and credit) of policy transmission, evidences have been presented for existence of both of them. Furthermore, in (Bernanke & Kuttner, 2005) it was clear that there can be more ways by which the change in the Federal funds rate connected to equity market response.goal of the paper is to investigate a credit channel of monetary policy transmission. We propose new financial ratios, which, to our best knowledge, haven’t been used in this field so far. Furthermore, and more interestingly, we propose a new method to investigate the existence of credit channel instead of commonly used multiple regression analysis: the tow-step approach involving clustering as a first step and t-test for the difference of portfolio means for paired data as a second step. This method is proposed foremost because current regression analysis relies on the estimate of unexpected change in monetary policy, while our methodology does not require such estimation. Scarceness of the futures data necessary for obtaining good estimates of unexpected monetary policy change makes the analysis applicable to a limited number of countries. Furthermore, ambiguity in derivation continuous futures prices makes questionable the regression estimates of credit factor effects, apart from the fact that regression equation may suffer from omitted variable bias. The latter fact means that standard errors are generally not applicable, hence, conclusions for the influence of effects should be drawn consciously. Our two-step approach does not depend on futures data, and therefore, eliminates the kind of flaws outlined above; furthermore, it enables a researcher to investigate credit channel of monetary policy for a wider range of countries. We describe the procedure, and show results for the United States in section and summarize the results in the last part of the work. 1. Data used For the main part of our research we use a sample of value-weighted returns, which we call “S&P 500*”. We searched for stock prices for 500 companies listed in the S&P 500 index as of 31 October, 2015 for the period from March 25, 1997 to December 16, 2008. Some companies were not listed during the period; some have been private for some time during the outlined period; some companies became bankrupt, and some were the objects of acquisitions. Therefore, the actual sample consists of at least of 321 and of at most 450 stock returns but represented by companies of all 10 S&P 500 sectors, defined for this index by Standard and Poor’s.would be better to use a list of S&P 500 companies most closely dated to 2008 to have more stocks in the sample but such data was limited. The sample would have been enlarged by companies, which went public after December 16, 2008, however, this was the last day of change of the Fed funds rate, which is at the core of our study. The only change in the target rate, which occurred since then, is dated at December 16, 2015, however, monthly equity market values were not available for the previous month in the main source of companies’ financial information for our research - COMPUSTAT GLOBAL. Likewise, we could not extend the scope of research beyond March 25, 1997 because the previous change in fed funds rate occurred on January 31, 1996, and we the same data on monthly equity market values for individual companies was limited to December, 1996.needed monthly equity market values in order to go from raw stock price changes to value-weighted returns, in order to construct a proxy for a broad market value-weighted return and work later with value-weighted portfolio returns. So, for example, a broad market return is just a sum across all individual value-weighted stock returns, and is equal to:; whereis a 1-day index (S&P 500*) return,is a 1-day raw stock return of individual company i on day t at month m,is an average market capitalization of a company i in the previous month m-1, henceis a weight by market capitalization of each individual stock. This weight is stable for individual companies during a particular month, and changes across months. The sum of individual companies’ returns on day t gives a value-weighted return for that day. We use this method for every day in our time range.data on monetary

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