1. Title

: Weekly stock data for Dow Jones Index

2. Source:

 This dataset comprises data reported by the major stock exchanges.

3. Past Usage
This dataset was first used in:

Brown, M. S., Pelosi, M. & Dirska, H. (2013). Dynamic-radius Species-conserving Genetic Algorithm for 
the Financial Forecasting of Dow Jones Index Stocks. Machine Learning and Data Mining in Pattern 
Recognition, 7988, 27-41.

We request that you provide a citation to this paper when using the dataset.  We welcome you to 
compare your results against ours in (Brown, Pelosi & Dirska, 2013).



4. Relevant Information
	In predicting stock prices you collect data over some period of time - day, week, month, etc.
	But you cannot take advantage of data from a time period until the next increment of the time period. 
	For example, assume you collect data daily.  When Monday is over you have all of the data
	for that day.  However you can invest on Monday, because you don't get the data until the
	end of the day.  You can use the data from Monday to invest on Tuesday.  

	In our research each record (row) is data for a week.  Each record also has the percentage
	of return that stock has in the following week (percent_change_next_weeks_price). Ideally,
	you want to determine which stock will produce the greatest rate of return in the following
	week.  This can help you train and test your algorithm.

	Some of these attributes might not be use used in your research.  They were
	originally added to our database to perform calculations.  (Brown, Pelosi & Dirska, 2013)
	used percent_change_price, percent_change_volume_over_last_wk, days_to_next_dividend, 
	and percent_return_next_dividend.  We left the other attributes in the dataset
	in case you wanted to use any of them. Of course what you want to maximize is
	percent_change_next_weeks_price.

	Training data vs Test data:
	In (Brown, Pelosi & Dirska, 2013) we used quarter 1 (Jan-Mar) data for training and
	quarter 2 (Apr-Jun) data for testing.

	Interesting data points:
	If you use quarter 2 data for testing, you will notice something interesting in 
	the week ending 5/27/2011 every Dow Jones Index stock lost money.

	The Dow Jones Index stocks change over time.  The stocks that made up the index in 2011 were:
		3M		 	MMM
		American Express 	AXP
		Alcoa			AA
		AT&T 			T
		Bank of America		BAC
		Boeing 		 	BA
		Caterpillar 	 	CAT
		Chevron 	 	CVX
		Cisco Systems 		CSCO
		Coca-Cola 	 	KO
		DuPont 		 	DD
		ExxonMobil 	 	XOM
		General Electric 	GE
		Hewlett-Packard		HPQ
		The Home Depot 	 	HD
		Intel 		 	INTC
		IBM 		 	IBM
		Johnson & Johnson 	JNJ	
		JPMorgan Chase 	 	JPM
		Kraft			KRFT
		McDonald's 		MCD
		Merck 		 	MRK
		Microsoft 	 	MSFT
		Pfizer 		 	PFE
		Procter & Gamble 	PG
		Travelers 	 	TRV
		United Technologies 	UTX
		Verizon 	 	VZ
		Wal-Mart 	 	WMT
		Walt Disney 	 	DIS





5. Number of Instances

There are 750 data records.  360 are from the first quarter of the year (Jan to Mar).

390 are from the second quarter of the year (Apr to Jun).



6. Number of Attributes
There are 16 attributes.  

7. For each Attribute


	quarter:  the yearly quarter (1 = Jan-Mar; 2 = Apr=Jun).
	stock: the stock symbol (see above)
	date: the last business day of the work (this is typically a Friday)
	open: the price of the stock at the beginning of the week
	high: the highest price of the stock during the week
	low: the lowest price of the stock during the week
	close: the price of the stock at the end of the week
	volume: the number of shares of stock that traded hands in the week
	percent_change_price: the percentage change in price throughout the week
	percent_chagne_volume_over_last_wek: the percentage change in the number of shares of 
		stock that traded hands for this week compared to the previous week
	previous_weeks_volume: the number of shares of stock that traded hands in the previous week
	next_weeks_open: the opening price of the stock in the following week
	next_weeks_close: the closing price of the stock in the following week
	percent_change_next_weeks_price: the percentage change in price of the stock in the 
		following week days_to_next_dividend: the number of days until the next dividend
	percent_return_next_dividend: the percentage of return on the next dividend

8. Missing Attribute Values:

      None