Stocks with Outperform Ratings Beat the Market

Introduction

I recently began investing and was wondering how good analysts are at predicting the future of a company. So here is a short data analysis of my curiosity!

In short, we will be answering these hypotheses:

1. Price targets can accurately reflect the future price of a company.
2. Some analysts can predict better than others.
3. A “buy” or “outperform” rating will on average predict a stock moving up.
4. Some analyst ratings are better than others.
5. If we were to invest only in stocks with “buy”/”outperform” ratings, we can beat the market.

A price target is the price a financial analyst believes that a stock will reach in a year.

A performance rating is the rating a financial analyst assigns a stock that comes from their combined research and analysis of the company.

Methodology

Since my investments are mostly in Canada, I will be focusing on Canadian equities. To reduce the amount of noise, I looked at companies with the following conditions:

• Listed on the TSX
• Market cap over $1 billion
• Stock price over $5

The source data for companies can be found here: http://www.theupside.ca/list-tsx-stocks-market-capitalization/.

All source code can be found here: http://github.com/ayoungprogrammer/price-targets

Next, to get the price targets and performance ratings, I used Marketbeat and for stock price information, I used the “unofficial” Yahoo Finance api. One restriction is that Marketbeat only had ratings for the last 2 years but it should be enough data to look back at enough ratings.

For each analyst rating assignment, I looked at the 10 day average centered around when it was assigned and the 10 days average centered around a year in time.

After some webscraping and html/json parsing we have the dataframe with sample rows:

	ticker	analyst	target	rating	aver_close_at_analysis	analysis_date	aver_close_at_12m	12m_date
0	RY	TD Securities	78.00	Hold	70.282856	2016-03-02	97.832857	2017-03-02
1	RY	Scotiabank	77.00	Outperform	70.282856	2016-03-02	97.832857	2017-03-02
2	RY	TD Securities	80.00	Buy	69.029999	2016-02-25	97.656251	2017-02-25

Each row corresponds to a rating issued by an analyst with the following attributes:

• ticker: Ticker symbol
• analyst: Analyst who rated
• target: Target price issued by analyst
• rating: Rating issued by analyst
• aver_close_at_analysis: 10 days average stock price centered on analysis date
• analysis_date: When the analysis was issued
• aver_close_at_12m: 10 days average stock price centered 12 months from analysis date
• 12m_date: 1 year from the analysis date

Results

We can calculate the error between the target price and actual price as follows:

Let

\( t = \) target price,

\(p_0 = \) price at analysis,

\(p_1 = \) price at 12 years after analysis

\(error = 100 \times \frac{t – p_0}{p_0} – \frac{p_1 – p_0}{p_0} \)

Intuitively, this is difference in percentage change from the prediction and the actual. For example, error = 5 means the target price was 5% higher than then actual percentage change.

In code:

df['target_perc'] = (df['target'] - df['aver_close_at_analysis'])/df['aver_close_at_analysis'] * 100
df['real_perc'] = (df['aver_close_at_12m'] - df['aver_close_at_analysis'])/df['aver_close_at_analysis'] * 100
df['error'] = (df['target_perc']-df['real_perc'])
df['abs_error'] = abs(df['error'])

	ticker	analyst	target	aver_close_at_12m	target_perc	real_perc	error
0	RY	TD Securities	78.00	97.832857	10.980122	39.198750	-28.218627
1	RY	Scotiabank	77.00	97.832857	9.557300	39.198750	-29.641449
2	RY	TD Securities	80.00	97.656251	15.891643	41.469292	-25.577649

A quick glance at the data shows that some of the ratings have very high variance. Therefore, we should try to reduce the noise of our error measurement by getting rid of some outliers. We will do so by removing outliers in the 10th and 90th percentiles. We also remove analysts with less than 100 ratings, so we can compare the most important analysts.

With pandas, we can easily group the data by analyst and aggregate attributes with different functions:

def filter_tail(data, p1=10, p2=90):
    q1 = np.percentile(data, p1)
    q3 = np.percentile(data, p2)
    return data[(data > q1) & (data < q3)]

analysts = df.groupby(['analyst'], as_index=False)['error'].agg({
        'mean_abs_err': lambda xs:np.mean(np.abs(filter_tail(xs))),
        'count': 'count',
        '10p': lambda xs: np.percentile(xs, q=10),
        '90p': lambda xs: np.percentile(xs, q=90),
        'mean': lambda xs: np.mean(filter_tail(xs)),
        'std': lambda xs: np.std(filter_tail(xs)),
    })
analysts = analysts[analysts['count'] > 100].sort_values(by='mean_no_outliers')

	analyst	std	mean	mean_abs_err	10p	90p	count
48	National Bank Financial	16.365463	-2.532164	13.425469	-40.092491	30.074729	251
3	BMO Capital Markets	18.982738	-2.282366	14.435614	-55.866637	35.678834	248
7	Barclays PLC	16.120963	-0.586090	13.128664	-35.657920	37.853971	212
20	Desjardins	18.972093	-0.542214	15.355082	-49.563907	33.312509	115
12	Canaccord Genuity	18.101054	4.703447	14.967193	-35.795798	47.363220	302
10	CIBC	19.098723	4.960119	15.799013	-31.113204	48.409297	446
58	Royal Bank of Canada	18.198846	5.544864	15.402881	-34.362247	50.268754	584
66	TD Securities	20.179027	5.868849	17.002451	-44.254427	47.655748	551
54	Raymond James Financial, Inc.	21.500147	7.580748	18.711069	-42.025280	51.284896	269
62	Scotiabank	18.735960	7.741048	15.962621	-32.351408	53.038985	706

We can also plot the means and standard deviations as error plots:

From the aggregate table, we see that Barclays PLC has the least mean absolute error, i.e., its error is closest to 0 and is the most accurate. Barclays PLC also has the “tightest” standard deviation, so it is also the most precise. However, we see that the standard deviations for each analyst is very large; so the precision of each analyst is very low. Barclays PLC has a standard deviation of 16% which we can interpret as 95% of price targets will be +/- 32% off. For example, if TD Bank current stock price is $100 and Barclays PLC gives a price target for $100, all we can reasonably expect is the stock price to range from ~$70 to ~$130.

Thus we can answer our first two hypotheses:

1. Analysts are on average, accurate in their predictions with their mean error close to 0. However, price targets cannot precisely predict the future of a company in 12 months.
2. According to the data, Barclays PLC has the most accurate and precise price targets, but only by a small margin.

A more intuitive image of precision vs accuracy:

Next, we will look at analyst ratings and explore their relation to stock performance.

Using pandas again, we can easily filter out price targets with no rating and only take the ratings from analysts that care about (in the previous table). We can also easily group by each analyst and rating and aggregate with different functions on different attribute.

ratings = df[(df['rating'] != 'NaN') & (df['analyst'].isin(analysts['analyst']))]
ratings_agg = ratings.groupby(['analyst', 'rating'], as_index=False).agg({
        'error': {
            'mae': lambda xs: np.mean(np.abs(filter_tail(xs))),
        },
        'real_perc': {
            'mean': 'mean',
            'median': 'median',
            '10p': lambda xs: np.percentile(xs, 10),
            '90p': lambda xs: np.percentile(xs, 90),
            'count': 'count',
        },
        'target_perc': {
            'median': 'median',
            '10p': lambda xs: np.percentile(xs, 10),
            '90p': lambda xs: np.percentile(xs, 90),
        }
    })

ratings_agg.columns = list(map('_'.join, ratings_agg.columns.values))
ratings_agg[ratings_agg['real_perc_count'] > 10]

Attributes:

• target_perc_10p: 10th percentile for price target change percentage
• target_perc_90p: 90th percentile for price target change percentage
• tarc_perc_median: median for perice target change percentage
• error_mae: mean absolute error
• real_perc_10p: 10th percentile for real price change percentage
• real_perc_90p: 90th percentile for real price change percentage
• real_perc_median: median for real price change percentage
• real_perc_count: number of ratings
• real_perc_mean: mean of real price change percentage

Sample rows:

	analyst_	rating_	target_perc_10p	target_perc_90p	target_perc_median	error_mae	real_perc_10p	real_perc_count	real_perc_mean	real_perc_90p	real_perc_median
0	BMO Capital Markets	Market Perform	-0.249542	23.049416	7.826429	17.048387	-13.624028	82	29.306269	86.571109	11.962594
2	BMO Capital Markets	Outperform	9.333289	34.091310	18.863216	13.267287	-13.430220	116	22.215401	64.711223	15.109094
5	Barclays PLC	Equal Weight	-5.964636	10.834989	4.672844	11.317883	-23.540832	72	11.511754	37.461244	9.239600

Now we take only analyst ratings with at least 20 and then sort by stock performance (change in stock price over a year). We can take the top 10 and perform more analysis on those.

top_ratings = ratings_agg[ratings_agg['real_perc_count'] > 20]
top_ratings = top_ratings.sort_values('real_perc_mean', ascending=False)
top_ratings['analyst_rating'] = top_ratings['analyst_'] + ' ' + top_ratings['rating_']
top_analyst_ratings = top_ratings['analyst_rating'].head(10)
top_analyst_ratings

Sorted by real price change percentage:

	analyst_	rating_	target_perc_10p	target_perc_90p	target_perc_median	error_mae	real_perc_10p	real_perc_count	real_perc_mean	real_perc_90p	real_perc_median	analyst_rating
32	National Bank Financial	Sector Perform	0.277827	45.337101	8.695651	14.170428	-5.738096	81	29.887596	61.182293	17.410111	National Bank Financial Sector Perform
0	BMO Capital Markets	Market Perform	-0.249542	23.049416	7.826429	17.048387	-13.624028	82	29.306269	86.571109	11.962594	BMO Capital Markets Market Perform
54	TD Securities	Action List Buy	18.333724	77.701954	38.001830	19.629657	-0.893705	36	28.334038	71.459495	15.928740	TD Securities Action List Buy
21	Canaccord Genuity	Buy	8.340735	61.226203	24.085974	18.796959	-16.561733	177	27.086690	86.234464	18.039215	Canaccord Genuity Buy
31	National Bank Financial	Outperform	7.705539	53.579343	18.929633	12.407454	-1.526925	115	24.524428	59.093628	22.213398	National Bank Financial Outperform
2	BMO Capital Markets	Outperform	9.333289	34.091310	18.863216	13.267287	-13.430220	116	22.215401	64.711223	15.109094	BMO Capital Markets Outperform
14	CIBC	Sector Outperformer	9.714648	59.846481	33.652243	21.688542	-14.518562	44	22.205022	60.020932	22.218981	CIBC Sector Outperformer
43	Royal Bank of Canada	Sector Perform	-0.678788	36.516168	11.101983	13.099112	-11.590060	220	21.330160	51.282201	10.498096	Royal Bank of Canada Sector Perform
36	Raymond James Financial, Inc.	Outperform	9.437804	54.483693	21.236522	15.914385	-18.695776	106	20.773835	59.159844	13.004491	Raymond James Financial, Inc. Outperform
49	Scotiabank	Outperform	7.239955	50.402485	19.082141	15.926344	-21.632875	246	18.407885	51.023159	14.897374	Scotiabank Outperform

We can make an error plot for the mean and standard deviation of the real percentage change for each analyst rating:

We can see that stocks with the top analyst ratings go up on average 25% in a year which is very good. Based on the error plot, TD Security Action List Buy seems to perform the best in terms of high mean and lower variance. Although there is high variance, the mean is more meaningful in this case. If we were to invest $1000 in each of the stocks when were given the rating, we would make about $1250 on average after a year, which is what we really care about. The TSX index went up 11% and TSX index annualized return is 9.1%. So we’re actually beating the market by ~16% with this strategy!

However, keep in mind that this data is for the last 2 years and is not indicative of future performance. On the other hand, I believe this strategy could make sense since analysts put significant effort and research into their rating and also because of the influence of the rating. People probably trust the analysts and would likely invest knowing that the stock has a good rating thus self fulfilling the rating.

With this analysis, we can conclude our last 3 hypotheses:

1. A buy or outperform rating will on average go up on average by 15-20%.

2. TD Security Action List Buy appears to be the strongest indicator for a stock to perform well.

3. If we buy stocks with the top 10 ratings when they get issued and sell in exactly one year, we will beat the market by ~16%.

Conclusion

• Price targets aren’t a good indicator of where the price of a stock will go.
• The top performance ratings are a good indicator for a stock performing well.
• You could possibly beat the market by only buying stocks with sector outperforms or buy ratings and selling in one year.

Please keep in mind that I am by no means a financial expert and am not certified to give financial advice.

All the code can be found here: https://www.github.com/ayoungprogrammer/price-targets