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Trading on Buyback Announcements

Share Repurchases, more commonly known as “share buybacks”, are when a company buys its own shares from the market. While there are number of ways to perform a buyback (tender offer, buying from market, ASRs), companies tend to perform share repurchases because they believe their shares are undervalued. So by buying back shares, this corporate action reduces the number of shares outstanding, increasing EPS (earnings per share) tends to along with share price.

We’ve done some prior research on buyback announcements and are now announcing it’s availability through pipeline. Buyback announcements for 4,000+ listed companies are now available from EventVestor.

For those who’ve thought about using buyback announcements in their algorithms, I’ve created a sample algorithm for you to get started with. It’s a simple drift strategy that holds securities for 5 days after a new buyback announcement versus a repeat buyback announcement.

Strategy Details:

  • Datafeed: Buyback Authorizations by EventVestor
  • Weights: The weight for each security is determined by the total number of longs we have in that current day. So if we have 2 longs, the weight for each long will be 50% (1.0/number of securities). This is a rolling rebalance at the beginning of each day according to the number of securities currently held and to order.
  • Capital base: $1,000,000
  • Days held: Positions are currently held for 5 days but are easily changeable by modifying 'context.days_to_hold'
  • Trade dates: All trades are made 1 business day AFTER a buyback announcements
  • Slippage and commissions in this backtest are default backtester settings

Dataset Details

Here are the available fields from the Buyback Authorizations dataset:

previous_date (datetime64[ns]) - The datetime that the last buyback announcement was made  
previous_type (string) - Possible values are (u'Suspends', u'Reduction', u'Additional', u'Reinstates', u'New')  
previous_amount (float64) - Amount of buyback. See previous_unit for measurement.  
previous_unit (string) - Possible values are (u'EURM', u'DKK', u'Mshares', u'%', u'CAD', u'NaN', u'GBPM', u'$M')  

For more examples using data, visit the data factor library.

Clone Algorithm
221
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Backtest from to with initial capital
Total Returns
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Alpha
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Beta
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Sharpe
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Sortino
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Max Drawdown
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Benchmark Returns
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Volatility
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Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
# Backtest ID: 573dcdd4b7971c12523182bd
There was a runtime error.
Disclaimer

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.

5 responses

This algo (and data) brought to mind a paper referenced by one of the speakers at QuantCon: https://www8.gsb.columbia.edu/sites/valueinvesting/files/files/12Ikenberry_lak.pdf

The hypothesis in the paper is that buyback announcements can be combined with value assessments to improve returns -- focusing the trades only on those companies that look to be undervalued by means of both the buyback announcement and fundamental factors.

Disclaimer

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.

Out of sample backtest

Clone Algorithm
221
Loading...
Backtest from to with initial capital
Total Returns
--
Alpha
--
Beta
--
Sharpe
--
Sortino
--
Max Drawdown
--
Benchmark Returns
--
Volatility
--
Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
# Backtest ID: 573e2d31922c3b0f85e1ab17
There was a runtime error.

Can anyone tell me why it looks like this strategy is taking some stocks short after the "days to hold' is up?

Trade top 500-1000 stocks, instead of Top1000.

Clone Algorithm
40
Loading...
Backtest from to with initial capital
Total Returns
--
Alpha
--
Beta
--
Sharpe
--
Sortino
--
Max Drawdown
--
Benchmark Returns
--
Volatility
--
Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
# Backtest ID: 5abd50efd20c7440f371bffa
There was a runtime error.

@Sergii I really like this improvement!

Good work.