Examined one company Becton Dickinson and Co (BDX) Global medical technology company

1. An Analysis of Financial News and the Effect on Stock Price By Michael Cohn Table 1. Parameter Estimates Table 3. Summary Statistics Abstract Methods INTRODUCTION: Stock prices change in response to unexpected fundamental information. There has been a lot of past research into finding a way to turn qualitative informationsuch as news articles in to quantifiable and measureable data. METHODS: In this study I attempt to examine whether or not the direction in movement is correlated with good or bad news. I attempt to find whether the occurrence of a positive news article causes stock price to increase or the contrary if a negative news article causes stock price to decrease. I model this by examining daily returns for one company Becton Dickinson and Co (BDX) against dummy variables for the occurrence of positive or negative news articles. In doing so I attempt to isolate the relevant news as my source of variation by controlling for other factors such as market volatility, sector trends and behavior of substitutes. RESULTS: My model offered up non conclusive results. I did find a negative sign for my negative news coefficient and a positive one for my positive news coefficient. This could be interpreted as negative news causing stock price to decrease and visa versa. However the coefficients were found to be insignificant. CONCLUSION: Due to limitations in my study I was unable to come up with conclusive results. However further research along with the use of text mining methods could yield more conclusive findings. • Examined one company Becton Dickinson and Co (BDX) • Global medical technology company • Traded on the New York Stock Exchange • S&P 500 • Big market cap and high liquidity. Good for my purposes because it is more responsive to material news however does not have “too much” media coverage • Examined BDX over a 3 ½ month time span • January 1 – April 17 2013 • Used dummy variables to account for positive news articles and negative ones • Days that had neither were labeled “neutral” • 73 observations • Unit of observation: A single day of trading • Used a variety of news sources to avoid bias as well as omitted duplicate stories • Examined daily returns of BDX while trying to isolate relevant news as my source of variation • Note that only about half of all observations exhibited days containing relevant news. (either positive or negative) Limitations and Conclusions • We do see a positive relationship with our POSNEWS dummy and a negative relationship with our NEGNEWS dummy • Supports our hypothesis that positive news drives stock price up and vice versa • However these coefficients are found to be statistically insignificant • Most variables with the exception of Index were found to be insignificant • However most of the signs of the coefficients resulted as expected • The R-Square and Adjusted R-Square measures are seen to be very small • The model does not account for very much of the variation in the dependent variable BDX • This points to probable omitted variable bias Prior Studies • Small sample size • Only examined one company • Short time period • Only 73 Observations • There is an external validity issue that comes from only examining one company • For example small cap firms are probably much less affected by financial news • Technology available to me • Human error • Omitted variable bias • Possible multicollinearity • Although the coefficients are insignificant they do support the hypothesis • Further investigation could provide much stronger results • “Text Mining Systems for Market Response to News” • Mittermayer and Knolmayer • Use past situations to predict how asset price will react in the future. • Used to predict short term price trends not price levels themselves • Examines both cause and effect • UBS uses a model that analyzes news headlines to predict exchange rate trends over a 3 hour time horizon • “Which News Moves Stock Prices?” • Boudoukh, Feldman, Kogan and Richardson • Textual Analysis • Identified news, Unidentified news and no news days • Found Higher Volatility in the stock market on days there was related news stories • “Exchange Rates and Fundamentals: New Evidence from Real Time Data” • Ehrmann and Fratzscher • Examined the relationship between macroeconomic news and the exchange rate • Examine the effects in the first moment after the news release Model/Results • BDX=β1 + β2POSNEWS + β3NEGNEWS + β4Index + β5VIX + β6ABT + β7Sector + β8Gold • BDX: The daily returns for Becton Dickinson and Co (BDX) • POSNEWS: The occurrence of a positive news article pertaining to BDX • NEGNEWS: The occurrence of a negative news article pertaining to BDX • Index: Daily returns for the S&P 500 as a whole • VIX: Volatility index which measures expected percent change in the S&P 500 for the next 30 days • ABT: The daily returns for Abbott Laboratories. A competitor with BDX and also part of the S&P 500 • Gold: SPDR GLD – An ETF that tracks the price of a tenth of an ounce of gold • Sector: The difference in closing price from previous day closing price for the S&P 500 healthcare sector Table 2. R-Square and Adjusted R-Squared