Dow Jones Index
Donated on 10/22/2014
This dataset contains weekly data for the Dow Jones Industrial Index. It has been used in computational investing research.
Dataset Characteristics
Time-Series
Subject Area
Business
Associated Tasks
Classification, Clustering
Feature Type
Integer, Real
# Instances
750
# Features
15
Dataset Information
Additional 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.
Has Missing Values?
No
Introductory Paper
By M. S. Brown, M. Pelosi, Henry Dirska. 2013
Published in IAPR International Conference on Machine Learning and Data Mining in Pattern Recognition
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| quarter | Feature | Integer | no | ||
| stock | Feature | Categorical | no | ||
| date | Feature | Date | no | ||
| open | Feature | Categorical | no | ||
| high | Feature | Categorical | no | ||
| low | Feature | Categorical | no | ||
| close | Feature | Categorical | no | ||
| volume | Feature | Integer | no | ||
| percent_change_price | Feature | Continuous | no | ||
| percent_change_volume_over_last_wk | Feature | Continuous | no |
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Additional Variable Information
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
Dataset Files
| File | Size |
|---|---|
| dow_jones_index.data | 87 KB |
| dow_jones_index.names | 4.3 KB |
Papers Citing this Dataset
Sort by Year, desc
By Tarique Siddiqui, Zesheng Wang, Paul Luh, Karrie Karahalios, Aditya Parameswaran. 2018
Published in ArXiv.
0 to 2 of 2
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset dow_jones_index = fetch_ucirepo(id=312) # data (as pandas dataframes) X = dow_jones_index.data.features y = dow_jones_index.data.targets # metadata print(dow_jones_index.metadata) # variable information print(dow_jones_index.variables)
Brown, M. (2013). Dow Jones Index [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5788V.
Keywords
Creators
Michael Brown
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.