ml-finance-python
python scripts for finance machine learning
git clone https://9o.is/git/ml-finance-python.git
notebook.ipynb
(272880B)
1 {
2 "cells": [
3 {
4 "cell_type": "markdown",
5 "metadata": {},
6 "source": [
7 "# Factor Risk Exposure\n",
8 "\n",
9 "By Evgenia \"Jenny\" Nitishinskaya, Delaney Granizo-Mackenzie, and Maxwell Margenot.\n",
10 "\n",
11 "Part of the Quantopian Lecture Series:\n",
12 "\n",
13 "* [www.quantopian.com/lectures](https://www.quantopian.com/lectures)\n",
14 "* [github.com/quantopian/research_public](https://github.com/quantopian/research_public)\n",
15 "\n",
16 "\n",
17 "---\n",
18 "\n",
19 "##DISCLAIMER:\n",
20 "\n",
21 "As always, this analysis is based on historical data, and risk exposures estimated on historical data may or may not affect the exposures going forward. As such, computing the risk exposure of to a factor is not enough. You must put confidence bounds on that risk exposure, and determine whether the risk exposure can even be modeled reasonably. For more information on this, please see our other lectures, especially Instability of Parameter Estimates.\n",
22 "\n",
23 "##Using Factor Models to Determine Risk Exposure\n",
24 "\n",
25 "We can use factor models to analyze the sources of risks and returns in portfolios. Recall that a factor model expresses the returns as\n",
26 "\n",
27 "$$R_i = a_i + b_{i1} F_1 + b_{i2} F_2 + \\ldots + b_{iK} F_K + \\epsilon_i$$\n",
28 "\n",
29 "By modelling the historical returns, we can see how much of them is due to speculation on different factors and how much to asset-specific fluctuations ($\\epsilon_p$). We can also examine what sources of risk the portfolio is exposed to. \n",
30 "\n",
31 "In risk analysis, we often model active returns (returns relative to a benchmark) and active risk (standard deviation of active returns, also known as tracking error or tracking risk).\n",
32 "\n",
33 "For instance, we can find a factor's marginal contribution to active risk squared (FMCAR). For factor $j$, this is\n",
34 "\n",
35 "$$ \\text{FMCAR}_j = \\frac{b_j^a \\sum_{i=1}^K b_i^a Cov(F_j, F_i)}{(\\text{Active risk})^2} $$\n",
36 "\n",
37 "where $b_i^a$ is the portfolio's active exposure to factor $i$. This tells us how much risk we incur by being exposed to factor $j$, given all the other factors we're already exposed to.\n",
38 "\n",
39 "Fundamental factor models are often used to evaluate portfolios because they correspond directly to investment choices (e.g. whether we invest in small-cap or large-cap stocks, etc.). Below, we construct a model to evaluate a single asset; for more information on the model construction, check out the fundamental factor models notebook.\n",
40 "\n",
41 "We'll use the canonical Fama-French factors for this example, which are the returns of portfolios constructred based on fundamental factors."
42 ]
43 },
44 {
45 "cell_type": "markdown",
46 "metadata": {},
47 "source": [
48 "##How many factors do you want?\n",
49 "\n",
50 "In the Arbitrage Pricing Theory lecture we mention that for predictive models you want fewer parameters. However, this doesn't quite hold for risk exposure. Instead of trying to not overfit a predictive model, you are looking for any possible risk factor that could be influencing your returns. Therefore it's actually safer to estimate exposure to many many risk factors to see if any stick. Anything left over in our $\\alpha$ is risk exposure that is currently unexplained by the selected factors. You want your strategy's return stream to be all alpha, and to be unexplained by as many parameters as possible. If you can show that your historical returns have little to no dependence on many factors, this is very positive. Certainly some unrelated risk factors might have spurious relationships over time in a large dataset, but those are not likely to be consistent."
51 ]
52 },
53 {
54 "cell_type": "markdown",
55 "metadata": {},
56 "source": [
57 "## Setup\n",
58 "\n",
59 "The first thing we do is compute a year's worth of factor returns. \n",
60 "\n",
61 "### NOTE\n",
62 "\n",
63 "The process for doing this is described in the Fundamental Factor Models lecture and uses pipeline. For more information please see that lecture."
64 ]
65 },
66 {
67 "cell_type": "code",
68 "execution_count": 1,
69 "metadata": {
70 "collapsed": true
71 },
72 "outputs": [],
73 "source": [
74 "import numpy as np\n",
75 "import statsmodels.api as sm\n",
76 "import scipy.stats as stats\n",
77 "from statsmodels import regression\n",
78 "import matplotlib.pyplot as plt\n",
79 "import pandas as pd"
80 ]
81 },
82 {
83 "cell_type": "code",
84 "execution_count": 2,
85 "metadata": {
86 "collapsed": false
87 },
88 "outputs": [],
89 "source": [
90 "import numpy as np\n",
91 "from quantopian.pipeline import Pipeline\n",
92 "from quantopian.pipeline.data import Fundamentals\n",
93 "from quantopian.pipeline.data.builtin import USEquityPricing\n",
94 "from quantopian.pipeline.factors import CustomFactor, Returns\n",
95 "\n",
96 "# Here's the raw data we need, everything else is derivative.\n",
97 "\n",
98 "class MarketCap(CustomFactor):\n",
99 " # Here's the data we need for this factor\n",
100 " inputs = [Fundamentals.shares_outstanding, USEquityPricing.close]\n",
101 " # Only need the most recent values for both series\n",
102 " window_length = 1\n",
103 " \n",
104 " def compute(self, today, assets, out, shares, close_price):\n",
105 " # Shares * price/share = total price = market cap\n",
106 " out[:] = shares * close_price\n",
107 " \n",
108 " \n",
109 "class BookToPrice(CustomFactor):\n",
110 " # pb = price to book, we'll need to take the reciprocal later\n",
111 " inputs = [Fundamentals.pb_ratio]\n",
112 " window_length = 1\n",
113 " \n",
114 " def compute(self, today, assets, out, pb):\n",
115 " out[:] = 1 / pb\n",
116 " \n",
117 "def make_pipeline():\n",
118 " \"\"\"\n",
119 " Create and return our pipeline.\n",
120 " \n",
121 " We break this piece of logic out into its own function to make it easier to\n",
122 " test and modify in isolation.\n",
123 " \n",
124 " In particular, this function can be copy/pasted into research and run by itself.\n",
125 " \"\"\"\n",
126 " pipe = Pipeline()\n",
127 "\n",
128 " # Add our factors to the pipeline\n",
129 " market_cap = MarketCap()\n",
130 " # Raw market cap and book to price data gets fed in here\n",
131 " pipe.add(market_cap, \"market_cap\")\n",
132 " book_to_price = BookToPrice()\n",
133 " pipe.add(book_to_price, \"book_to_price\")\n",
134 " \n",
135 " # We also get daily returns\n",
136 " returns = Returns(inputs=[USEquityPricing.close], window_length=2)\n",
137 " pipe.add(returns, \"returns\")\n",
138 " \n",
139 " # We compute a daily rank of both factors, this is used in the next step,\n",
140 " # which is computing portfolio membership.\n",
141 " market_cap_rank = market_cap.rank()\n",
142 " pipe.add(market_cap_rank, 'market_cap_rank')\n",
143 " \n",
144 " book_to_price_rank = book_to_price.rank()\n",
145 " pipe.add(book_to_price_rank, 'book_to_price_rank')\n",
146 "\n",
147 " # Build Filters representing the top and bottom 1000 stocks by our combined ranking system.\n",
148 " biggest = market_cap_rank.top(1000)\n",
149 " smallest = market_cap_rank.bottom(1000)\n",
150 " \n",
151 " highpb = book_to_price_rank.top(1000)\n",
152 " lowpb = book_to_price_rank.bottom(1000)\n",
153 " \n",
154 " # Don't return anything not in this set, as we don't need it.\n",
155 " pipe.set_screen(biggest | smallest | highpb | lowpb)\n",
156 " \n",
157 " # Add the boolean flags we computed to the output data\n",
158 " pipe.add(biggest, 'biggest')\n",
159 " pipe.add(smallest, 'smallest')\n",
160 " \n",
161 " pipe.add(highpb, 'highpb')\n",
162 " pipe.add(lowpb, 'lowpb')\n",
163 " \n",
164 " return pipe\n",
165 "\n",
166 "pipe = make_pipeline()"
167 ]
168 },
169 {
170 "cell_type": "code",
171 "execution_count": 3,
172 "metadata": {
173 "collapsed": true
174 },
175 "outputs": [],
176 "source": [
177 "start_date = '2014-1-1'\n",
178 "end_date = '2015-1-1'"
179 ]
180 },
181 {
182 "cell_type": "code",
183 "execution_count": 4,
184 "metadata": {
185 "collapsed": false
186 },
187 "outputs": [],
188 "source": [
189 "from quantopian.research import run_pipeline\n",
190 "results = run_pipeline(pipe, start_date, end_date)\n",
191 "\n",
192 "R_biggest = results[results.biggest]['returns'].groupby(level=0).mean()\n",
193 "R_smallest = results[results.smallest]['returns'].groupby(level=0).mean()\n",
194 "\n",
195 "R_highpb = results[results.highpb]['returns'].groupby(level=0).mean()\n",
196 "R_lowpb = results[results.lowpb]['returns'].groupby(level=0).mean()\n",
197 "\n",
198 "SMB = R_smallest - R_biggest\n",
199 "HML = R_highpb - R_lowpb"
200 ]
201 },
202 {
203 "cell_type": "markdown",
204 "metadata": {},
205 "source": [
206 "How did each factor do over 2014?"
207 ]
208 },
209 {
210 "cell_type": "code",
211 "execution_count": 5,
212 "metadata": {
213 "collapsed": false
214 },
215 "outputs": [
216 {
217 "data": {
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ef/99br/9dm6++WYCAwPbDdL++F5YW1tz6623Ymdnh5eXF6GhoR2e25MU+o7C4z4kLi6u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\nN0kiJYQQQgghhBDd9DeIBBfwOMYfTwAAAABJRU5ErkJggg==\n",
219 "text/plain": [
220 "<matplotlib.figure.Figure at 0x7f199ad89450>"
221 ]
222 },
223 "metadata": {},
224 "output_type": "display_data"
225 }
226 ],
227 "source": [
228 "SMB_CUM = np.cumprod(SMB+1)\n",
229 "HML_CUM = np.cumprod(HML+1)\n",
230 "\n",
231 "plt.plot(SMB_CUM.index, SMB_CUM.values)\n",
232 "plt.plot(HML_CUM.index, HML_CUM.values)\n",
233 "plt.ylabel('Cumulative Return')\n",
234 "plt.legend(['SMB Portfolio Returns', 'HML Portfolio Returns']);"
235 ]
236 },
237 {
238 "cell_type": "markdown",
239 "metadata": {},
240 "source": [
241 "## Computing Risk Exposure\n",
242 "\n",
243 "Now we can determine how exposed another return stream is to each of these factors. We can do this by running static or rolling linear regressions between our return stream and the factor portfolio returns. First we'll compute the active returns (returns - benchmark) of some random asset and then model that asset as a linear combination of our two factors. The more a factor contributes to the active returns, the more exposed the active returns are to that factor."
244 ]
245 },
246 {
247 "cell_type": "code",
248 "execution_count": 6,
249 "metadata": {
250 "collapsed": false
251 },
252 "outputs": [
253 {
254 "name": "stderr",
255 "output_type": "stream",
256 "text": [
257 "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:13: FutureWarning: TimeSeries is deprecated. Please use Series\n",
258 " del sys.path[0]\n"
259 ]
260 }
261 ],
262 "source": [
263 "# Get returns data for our portfolio\n",
264 "portfolio = get_pricing(['MSFT', 'AAPL', 'YHOO', 'FB', 'TSLA'], \n",
265 " fields='price', start_date=start_date, end_date=end_date).pct_change()[1:]\n",
266 "R = np.mean(portfolio, axis=1)\n",
267 "\n",
268 "\n",
269 "bench = get_pricing('SPY', fields='price', start_date=start_date, end_date=end_date).pct_change()[1:]\n",
270 "\n",
271 "# The excess returns of our active management, in this case just holding a portfolio of our one asset\n",
272 "active = R - bench\n",
273 "\n",
274 "# Define a constant to compute intercept\n",
275 "constant = pd.TimeSeries(np.ones(len(active.index)), index=active.index)\n",
276 "\n",
277 "df = pd.DataFrame({'R': active,\n",
278 " 'F1': SMB,\n",
279 " 'F2': HML,\n",
280 " 'Constant': constant})\n",
281 "df = df.dropna()"
282 ]
283 },
284 {
285 "cell_type": "code",
286 "execution_count": 7,
287 "metadata": {
288 "collapsed": false
289 },
290 "outputs": [
291 {
292 "name": "stdout",
293 "output_type": "stream",
294 "text": [
295 "Sensitivities of active returns to factors:\n",
296 "SMB: -0.066887\n",
297 "HML: -0.046026\n"
298 ]
299 }
300 ],
301 "source": [
302 "# Perform linear regression to get the coefficients in the model\n",
303 "b1, b2 = regression.linear_model.OLS(df['R'], df[['F1', 'F2']]).fit().params\n",
304 "\n",
305 "# Print the coefficients from the linear regression\n",
306 "print 'Sensitivities of active returns to factors:\\nSMB: %f\\nHML: %f' % (b1, b2)"
307 ]
308 },
309 {
310 "cell_type": "markdown",
311 "metadata": {},
312 "source": [
313 "Using the formula from the start of the notebook, we can compute the factors' marginal contributions to active risk squared:"
314 ]
315 },
316 {
317 "cell_type": "code",
318 "execution_count": 8,
319 "metadata": {
320 "collapsed": false
321 },
322 "outputs": [
323 {
324 "name": "stdout",
325 "output_type": "stream",
326 "text": [
327 "SMB Risk Contribution: 0.00159645465913\n",
328 "HML Risk Contribution: 0.000973034746847\n"
329 ]
330 }
331 ],
332 "source": [
333 "F1 = df['F1']\n",
334 "F2 = df['F2']\n",
335 "cov = np.cov(F1, F2)\n",
336 "ar_squared = (active.std())**2\n",
337 "fmcar1 = (b1*(b2*cov[0,1] + b1*cov[0,0]))/ar_squared\n",
338 "fmcar2 = (b2*(b1*cov[0,1] + b2*cov[1,1]))/ar_squared\n",
339 "print 'SMB Risk Contribution:', fmcar1\n",
340 "print 'HML Risk Contribution:', fmcar2"
341 ]
342 },
343 {
344 "cell_type": "markdown",
345 "metadata": {},
346 "source": [
347 "The rest of the risk can be attributed to active specific risk, i.e. factors that we did not take into account or the asset's idiosyncratic risk.\n",
348 "\n",
349 "However, as usual we will look at how the exposure to these factors changes over time. As we lose a tremendous amount of information by just looking at one data point. Let's look at what happens if we run a rolling regression over time."
350 ]
351 },
352 {
353 "cell_type": "code",
354 "execution_count": 9,
355 "metadata": {
356 "collapsed": false
357 },
358 "outputs": [
359 {
360 "name": "stderr",
361 "output_type": "stream",
362 "text": [
363 "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py:2882: FutureWarning: The pandas.stats.ols module is deprecated and will be removed in a future version. We refer to external packages like statsmodels, see some examples here: http://statsmodels.sourceforge.net/stable/regression.html\n",
364 " exec(code_obj, self.user_global_ns, self.user_ns)\n"
365 ]
366 },
367 {
368 "data": {
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ZZ54hPDzco69BAhshhBBCCCG84PW97/ND3m6PnvPC1PNYPPrqdo9xBCenCwkJAeD7778n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\n7AAAAABJRU5ErkJggg==\n",
370 "text/plain": [
371 "<matplotlib.figure.Figure at 0x7f18e3bd9fd0>"
372 ]
373 },
374 "metadata": {},
375 "output_type": "display_data"
376 }
377 ],
378 "source": [
379 "# Compute the rolling betas\n",
380 "model = pd.stats.ols.MovingOLS(y = df['R'], x=df[['F1', 'F2']], \n",
381 " window_type='rolling', \n",
382 " window=100)\n",
383 "rolling_parameter_estimates = model.beta\n",
384 "rolling_parameter_estimates.plot();\n",
385 "plt.title('Computed Betas');\n",
386 "plt.legend(['F1 Beta', 'F2 Beta', 'Intercept']);"
387 ]
388 },
389 {
390 "cell_type": "markdown",
391 "metadata": {},
392 "source": [
393 "Now we'll look at FMCAR as it changes over time."
394 ]
395 },
396 {
397 "cell_type": "code",
398 "execution_count": 10,
399 "metadata": {
400 "collapsed": false
401 },
402 "outputs": [
403 {
404 "name": "stderr",
405 "output_type": "stream",
406 "text": [
407 "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:4: FutureWarning: pd.rolling_cov is deprecated for DataFrame and will be removed in a future version, replace with \n",
408 "\tDataFrame.rolling(window=100).cov(other=<DataFrame>,pairwise=True)\n",
409 " after removing the cwd from sys.path.\n",
410 "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:6: FutureWarning: pd.rolling_std is deprecated for Series and will be removed in a future version, replace with \n",
411 "\tSeries.rolling(window=100,center=False).std()\n",
412 " \n"
413 ]
414 }
415 ],
416 "source": [
417 "# Remove the first 99, which are all NaN for each case\n",
418 "\n",
419 "# Compute covariances\n",
420 "covariances = pd.rolling_cov(df[['F1', 'F2']], window=100)[99:]\n",
421 "# Compute active risk squared\n",
422 "active_risk_squared = pd.rolling_std(active, window = 100)[99:]**2\n",
423 "# Compute betas\n",
424 "betas = rolling_parameter_estimates[['F1', 'F2']]\n",
425 "\n",
426 "# Set up empty dataframe\n",
427 "FMCAR = pd.DataFrame(index=betas.index, columns=betas.columns)\n",
428 "\n",
429 "# For each factor\n",
430 "for factor in betas.columns:\n",
431 " # For each bar in our data\n",
432 " for t in betas.index:\n",
433 " # Compute the sum of the betas and covariances\n",
434 " s = np.sum(betas.loc[t] * covariances[t][factor])\n",
435 " # Get the beta\n",
436 " b = betas.loc[t][factor]\n",
437 " # Get active risk squared\n",
438 " AR = active_risk_squared.loc[t]\n",
439 " # Put them all together to estimate FMCAR on that date\n",
440 " FMCAR[factor][t] = b * s / AR"
441 ]
442 },
443 {
444 "cell_type": "markdown",
445 "metadata": {},
446 "source": [
447 "Let's plot this."
448 ]
449 },
450 {
451 "cell_type": "code",
452 "execution_count": 11,
453 "metadata": {
454 "collapsed": false
455 },
456 "outputs": [
457 {
458 "data": {
459 "image/png": 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y12ZLtfxraV9SRj+dJQCo71BmFE/qLGWFqbMEADPzLsG41CIcrP2638I5kspb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\n1p1Jl93CoarjnKtspc3ZALLE8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N1YiMvt6ndbWZYpaSnnmaMv8/UtP+aF4/+ixlzH8mmL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\nN4YOHeqY1r17d7Zt24bVaiU9PZ2MjAyaN2/utF7FmZzBgwfzxRdfEBUVx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460 "text/plain": [
461 "<matplotlib.figure.Figure at 0x7f199ad77bd0>"
462 ]
463 },
464 "metadata": {},
465 "output_type": "display_data"
466 }
467 ],
468 "source": [
469 "plt.plot(FMCAR['F1'].index, FMCAR['F1'].values)\n",
470 "plt.plot(FMCAR['F2'].index, FMCAR['F2'].values)\n",
471 "\n",
472 "plt.ylabel('Marginal Contribution to Active Risk Squared')\n",
473 "plt.legend(['F1 FMCAR', 'F2 FMCAR']);"
474 ]
475 },
476 {
477 "cell_type": "markdown",
478 "metadata": {},
479 "source": [
480 "###Problems with using this data\n",
481 "\n",
482 "Whereas it may be interesting to know how a portfolio was exposed to certain factors historically, it is really only useful if we can make predictions about how it will be exposed to risk factors in the future. It's not always a safe assumption to say that future exposure will be the current exposure. As you saw the exposure varies quite a bit, so taking the average is dangerous. We could put confidence intervals around that average, but that would only work if the distribution of exposures were normal or well behaved. Let's check using our old buddy, the Jarque-Bera test."
483 ]
484 },
485 {
486 "cell_type": "code",
487 "execution_count": 12,
488 "metadata": {
489 "collapsed": false
490 },
491 "outputs": [
492 {
493 "name": "stdout",
494 "output_type": "stream",
495 "text": [
496 "p-value F1_FMCAR is normally distributed 3.45995832983e-05\n",
497 "p-value F2_FMCAR is normally distributed 0.000393310890239\n"
498 ]
499 }
500 ],
501 "source": [
502 "from statsmodels.stats.stattools import jarque_bera\n",
503 "_, pvalue1, _, _ = jarque_bera(FMCAR['F1'].dropna().values)\n",
504 "_, pvalue2, _, _ = jarque_bera(FMCAR['F2'].dropna().values)\n",
505 "\n",
506 "print 'p-value F1_FMCAR is normally distributed', pvalue1\n",
507 "print 'p-value F2_FMCAR is normally distributed', pvalue2"
508 ]
509 },
510 {
511 "cell_type": "markdown",
512 "metadata": {},
513 "source": [
514 "The p-values are below our default cutoff of 0.05. We can't even put good confidence intervals on the risk exposure of the asset without extra effort, so making any statement about exposure in the future is very difficult right now. Any hedge we took out to cancel the exposure to one of the factors might be way over or under hedged.\n",
515 "\n",
516 "We are trying to predict future exposure, and predicting the future is incredibly difficult. One must be very careful with statistical methods to ensure that false predictions are not made."
517 ]
518 },
519 {
520 "cell_type": "markdown",
521 "metadata": {},
522 "source": [
523 "# Factor and tracking portfolios\n",
524 "\n",
525 "We can use factor and tracking portfolios to tweak a portfolio's sensitivities to different sources of risk.\n",
526 "\n",
527 "A <i>factor portfolio</i> has a sensitivity of 1 to a particular factor and 0 to all other factors. In other words, it represents the risk of that one factor. We can add a factor portfolio to a larger portfolio to adjust its exposure to that factor.\n",
528 "\n",
529 "A similar concept is a <i>tracking portfolio</i>, which is constructed to have the same factor sensitivities as a benchmark or other portfolio. Like a factor portfolio, this allows us to either speculate on or hedge out the risks associated with that benchmark or portfolio. For instance, we regularly hedge out the market, because we care about how our portfolio performs relative to the market, and we don't want to be subject to the market's fluctuations.\n",
530 "\n",
531 "To construct a factor or tracking portfolio, we need the factor sensitivities of what we want to track. We already know what these are in the former case, but we need to compute them in the latter using usual factor model methods. Then, we pick some $K+1$ assets (where $K$ is the number of factors we're considering) and solve for the weights of the assets in the portfolio."
532 ]
533 },
534 {
535 "cell_type": "markdown",
536 "metadata": {},
537 "source": [
538 "##Portfolio Exposure\n",
539 "\n",
540 "The portfolio exposure can be computed directly from the return stream, or as the weighted average of all the assets held."
541 ]
542 },
543 {
544 "cell_type": "markdown",
545 "metadata": {},
546 "source": [
547 "## Example\n",
548 "\n",
549 "Say we have two factors $F_1$ and $F_2$, and a benchmark with sensitivities of 1 and 1.1 to the factors, respectively. We identify 3 securities $x_1, x_2, x_3$ that we would like to use in composing a portfolio that tracks the benchmark, whose sensitivities are $b_{11} = 0.7$, $b_{12} = 1.1$, $b_{21} = 0.1$, $b_{22} = 0.5$, $b_{31} = 1.5$, $b_{32} = 1.3$. We would like to compute weights $w_1$, $w_2$, $w_3$ so that our tracking portfolio is\n",
550 "\n",
551 "$$ P = w_1 x_1 + w_2 x_2 + w_3 x_3 $$\n",
552 "\n",
553 "We want our portfolio sensitivities to match the benchmark:\n",
554 "\n",
555 "$$ w_1 b_{11} + w_2 b_{21} + w_3 b_{31} = 1 $$\n",
556 "$$ w_1 b_{12} + w_2 b_{22} + w_3 b_{32} = 1.1 $$\n",
557 "\n",
558 "Also, the weights need to sum to 1:\n",
559 "\n",
560 "$$ w_1 + w_2 + w_3 = 1 $$\n",
561 "\n",
562 "Solving this system of 3 linear equations, we find that $w_1 = 1/3$, $w_2 = 1/6$, and $w_3 = 1/2$. Putting the securities together into a portfolio using these weights, we obtain a portfolio with the same risk profile as the benchmark."
563 ]
564 },
565 {
566 "cell_type": "markdown",
567 "metadata": {},
568 "source": [
569 "##How to Use Risk Exposure Models\n",
570 "\n",
571 "Once we know our risk exposures, we can do a few things. We can not enter into positions that have high exposures to certain factors, or we can hedge our positions to try to neutralize the exposure.\n",
572 "\n",
573 "###Risk Management\n",
574 "\n",
575 "Often times funds will have a layer of protection over their traders/algorithms. This layer of protection takes in the trades that the fund wants to make, then computes the exposure of the new portfolio, and checks to make sure they're within pre-defined ranges. If they are not, it does not place the trade and files a warning.\n",
576 "\n",
577 "###Hedging\n",
578 "\n",
579 "Another method of dealing with exposure is to take out hedges. You can determine, for example, your exposure to each sector of the market. You can then take out a hedge if a particular sector seems to affect your returns too much. For more information on hedging, please see our Beta Hedging lecture. Good algorithms will have built-in hedging logic that ensures they are never over-exposed."
580 ]
581 },
582 {
583 "cell_type": "markdown",
584 "metadata": {},
585 "source": [
586 "*This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. (\"Quantopian\"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company. In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances.*"
587 ]
588 }
589 ],
590 "metadata": {
591 "kernelspec": {
592 "display_name": "Python 2",
593 "language": "python",
594 "name": "python2"
595 },
596 "language_info": {
597 "codemirror_mode": {
598 "name": "ipython",
599 "version": 2
600 },
601 "file_extension": ".py",
602 "mimetype": "text/x-python",
603 "name": "python",
604 "nbconvert_exporter": "python",
605 "pygments_lexer": "ipython2",
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