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Stock analysis: AFI
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.gitignore
.ipynb_checkpoints
Raw
README.md

Stock analysis

An IPython notebook for analysing Australian stock prices and average returns over time.

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afi-performance.ipynb
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"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"AFI dividends"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Checking whether dividend payouts for AFI are included in Yahoo Finance's overall return."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Preamble"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"import pandas.io.data as web"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Fetching dividend info"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We fetch dividends directly from the AFI web site, parsing an HTML table on the page."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"AFI_URL = 'http://www.afi.com.au/Dividend-history.aspx'\n",
"\n",
"import pyquery as pq\n",
"import dateutil\n",
"\n",
"def parse_date(s):\n",
" s = s.replace('\\xa0', ' ')\n",
" s = s.rstrip(' *')\n",
" return dateutil.parser.parse(s).date()\n",
"\n",
"def fetch_dividends():\n",
" doc = pq.PyQuery(AFI_URL)\n",
" table = doc('table')[0]\n",
" rows = []\n",
" for row in doc('tr'):\n",
" row = [td.text_content().strip()\n",
" for td in row.findall('td')]\n",
" rows.append(row)\n",
" \n",
" rows = rows[1:] # drop header\n",
" header = ['date_paid', 'dividend', 'franking_level', 'drp_price']\n",
" df = pd.DataFrame.from_records(\n",
" [{k: v for k, v in zip(header, r)} for r in rows],\n",
" columns=header,\n",
" index='date_paid',\n",
" )\n",
" df.index = df.index.map(parse_date)\n",
" df.franking_level = df.franking_level.map(\n",
" lambda f: float(f.rstrip('%')) / 100.0\n",
" )\n",
" df = df[df.dividend != '1.00']\n",
" return df\n",
"\n",
"div = fetch_dividends()\n",
"div.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dividend</th>\n",
" <th>franking_level</th>\n",
" <th>drp_price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014-08-29</th>\n",
" <td> 14.00</td>\n",
" <td> 1</td>\n",
" <td> </td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-02-21</th>\n",
" <td> 8.00</td>\n",
" <td> 1</td>\n",
" <td> $5.86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-08-30</th>\n",
" <td> 14.00</td>\n",
" <td> 1</td>\n",
" <td> $5.64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-02-22</th>\n",
" <td> 8.00</td>\n",
" <td> 1</td>\n",
" <td> $5.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-08-31</th>\n",
" <td> 13.00</td>\n",
" <td> 1</td>\n",
" <td> $4.36</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 3 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
" dividend franking_level drp_price\n",
"2014-08-29 14.00 1 \n",
"2014-02-21 8.00 1 $5.86\n",
"2013-08-30 14.00 1 $5.64\n",
"2013-02-22 8.00 1 $5.37\n",
"2012-08-31 13.00 1 $4.36\n",
"\n",
"[5 rows x 3 columns]"
]
}
],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Fetching price data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use Yahoo Finance to get historical price data. It only goes back to 2001."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"SYMBOL = 'AFI'\n",
"price = web.get_data_yahoo('{0}.AX'.format(SYMBOL), start='1/1/1990')\n",
"price.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Adj Close</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td> 2.97</td>\n",
" <td> 2.97</td>\n",
" <td> 2.97</td>\n",
" <td> 2.97</td>\n",
" <td> 0</td>\n",
" <td> 2.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-02</th>\n",
" <td> 2.56</td>\n",
" <td> 2.58</td>\n",
" <td> 2.55</td>\n",
" <td> 2.57</td>\n",
" <td> 131900</td>\n",
" <td> 2.57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-03</th>\n",
" <td> 2.56</td>\n",
" <td> 2.58</td>\n",
" <td> 2.55</td>\n",
" <td> 2.55</td>\n",
" <td> 229800</td>\n",
" <td> 2.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-04</th>\n",
" <td> 2.57</td>\n",
" <td> 2.59</td>\n",
" <td> 2.55</td>\n",
" <td> 2.58</td>\n",
" <td> 233000</td>\n",
" <td> 2.58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-05</th>\n",
" <td> 2.58</td>\n",
" <td> 2.58</td>\n",
" <td> 2.54</td>\n",
" <td> 2.56</td>\n",
" <td> 276600</td>\n",
" <td> 2.56</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 6 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
" Open High Low Close Volume Adj Close\n",
"Date \n",
"2001-01-01 2.97 2.97 2.97 2.97 0 2.97\n",
"2001-01-02 2.56 2.58 2.55 2.57 131900 2.57\n",
"2001-01-03 2.56 2.58 2.55 2.55 229800 2.55\n",
"2001-01-04 2.57 2.59 2.55 2.58 233000 2.58\n",
"2001-01-05 2.58 2.58 2.54 2.56 276600 2.56\n",
"\n",
"[5 rows x 6 columns]"
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}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"diff = (price['Close'] - price['Adj Close']).abs().sum()\n",
"diff"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"0.0"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For US stocks, the _adjusted close_ is supposed to include dividend returns in the stock price, and take into account stock splits. However, for ASX stocks there's no difference. This just confirms that we need to include them manually."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Combining dividends with close price"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Firstly, we convert our dividend return to what percentage that is of that day's close price."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"close = price['Close']\n",
"divc = pd.merge(div, pd.DataFrame({'close': close}, index=price.index),\n",
" left_index=True,\n",
" right_index=True).drop(['franking_level', 'drp_price'], axis=1)\n",
"divc['dividend'] = divc.dividend.map(float) / 100.0\n",
"divc['dividend_ind'] = divc['dividend'] / divc['close']\n",
"divc.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dividend</th>\n",
" <th>close</th>\n",
" <th>dividend_ind</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014-08-29</th>\n",
" <td> 0.14</td>\n",
" <td> 6.13</td>\n",
" <td> 0.022838</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-02-21</th>\n",
" <td> 0.08</td>\n",
" <td> 6.05</td>\n",
" <td> 0.013223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-08-30</th>\n",
" <td> 0.14</td>\n",
" <td> 5.69</td>\n",
" <td> 0.024605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-02-22</th>\n",
" <td> 0.08</td>\n",
" <td> 5.46</td>\n",
" <td> 0.014652</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-08-31</th>\n",
" <td> 0.13</td>\n",
" <td> 4.32</td>\n",
" <td> 0.030093</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 3 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 41,
"text": [
" dividend close dividend_ind\n",
"Date \n",
"2014-08-29 0.14 6.13 0.022838\n",
"2014-02-21 0.08 6.05 0.013223\n",
"2013-08-30 0.14 5.69 0.024605\n",
"2013-02-22 0.08 5.46 0.014652\n",
"2012-08-31 0.13 4.32 0.030093\n",
"\n",
"[5 rows x 3 columns]"
]
}
],
"prompt_number": 41
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Performance with dividends"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's calculate a returns index, basically your returns per dollar once the price-per-stock is factored out. This lets us easily calculate the return given a buy and sell date."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"returns = pd.DataFrame({'price': close.pct_change().fillna(0)})\n",
"returns['dividend'] = divc.dividend_ind\n",
"returns['dividend'].fillna(0, inplace=True)\n",
"returns['combined'] = returns['price'] + returns['dividend']\n",
"\n",
"ret_index = (1 + returns).cumprod()\n",
"ret_index.plot(figsize=(13, 5))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 45,
"text": [
"<matplotlib.axes.AxesSubplot at 0x1088a7908>"
]
},
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"metadata": {},
"output_type": "display_data",
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" target = d + relativedelta.relativedelta(years=y)\n",
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" target += timedelta(days=1)\n",
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" purchase sale years gross_return adj_return\n",
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"text": [
" Average annual return: 1.119\n",
" Purchase dates: 2001-01-01 -> 2013-09-04\n"
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Raw
rba-inflation.csv
View raw

(Sorry about that, but we can’t show files that are this big right now.)

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