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  • Leapday Quantitative Research Team

Market-neutral portfolio construction with an event-based signal


In this report we build a market-neutral portfolio using Leapday’s REACTION signals which are generated just before the close on the first day of trading subsequent to a company’s earnings announcement. To present realistic portfolio results we include conservative trading cost assumptions and constraints on position sizing based on liquidity. The result is a portfolio with an annualized return of 11.0% between 2016-2020, a Sharpe ratio of 1.90, a maximum drawdown of -4.3%, and a correlation to SPY of 0.10.

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In our whitepaper “Trading Events: Predicting Reactions After Earnings Announcements” (available upon request by emailing, we present metrics including average return per trade and hit rate to demonstrate the predictive power of the REACTION signals to forecast individual stock returns. We are often asked for additional metrics such as annual return and Sharpe Ratio, which are best answered in a portfolio setting. With that motivation, in this report we set out to build a portfolio using the REACTION signals and provide the associated performance metrics for this portfolio.

It should be noted that portfolio construction for an event-driven strategy, such as the one required for trading the REACTION signals after earnings announcements, introduces nuances and additional considerations that are not present in traditional portfolio construction. For example, earnings activity is cyclical and there are short periods of time in the middle of a quarter where there is considerable activity and larger stretches of time when a portfolio is mostly in cash. We address these topics throughout this report.

We will proceed by introducing the signals, stating the portfolio assumptions, presenting the results, and discussing the findings.


Leapday’s REACTION signals are available for 3 entry points after an event day, which is the first day the market is open subsequent to a company’s earnings announcement. These 3 entry points are: (1) 60 minutes after the open on the event day; (2) just before the market close on the event day; (3) before the market open on the next trading day after the event day.

For this study, like other reports we have recently published, we focus on the market close signal. This signal is available for 4 holding periods: 3d, 5d, 10d, and 15d. We use the 3d hold signal in the portfolio construction below like we do in our other published reports.

The REACTION signals range from -100 to +100 indicating the direction and magnitude of the signal. For this study, we will use approximately the top one-third of signals ordered by signal magnitude. This approach provides a balance between using the strongest signals and having enough trading activity to utilize available capital. Table 1 below shows the number of signals these settings correspond to between 2016-2020. With this constraint we observe over 1,000 trades a quarter.

Portfolio Construction

As outlined in the previous section, we will use the strongest one-third of REACTION signals that are released just before the market close on the first day the market is open subsequent to a company’s earnings announcement to build a simulated portfolio. We will focus on the 3d hold signal. Below are all the assumptions used in our portfolio construction.

  • Enter a trade at the market close on the first day the market is open subsequent to a company’s earnings announcement (t0 close) and exit the trade on the close 3 days later (t3 close).

  • Create a market-neutral portfolio by hedging trades using an opposite side trade in SPY with a beta-adjusted notional value that offsets the beta risk of the event trade.

  • Starting capital of $50M and assuming constant capital throughout the portfolio (i.e. - we do not reinvest profits).

  • Portfolio leverage used during periods of high activity.

  • Trade no more than 6 percent of the starting capital per position.

  • Trade no more than 2 percent of a company’s historical average trade value per position. (The historical average trade value is calculated using the trailing 20 day average traded value.)

  • Beyond the two constraints on trade size stated above, trade equal weight.

  • Round-trip trading costs are based on breakpoints for liquidity group as follows:

  • Low (<$3M historical average trade value): 20 bps

  • Mid ($3M-$25M historical average trade value): 10 bps

  • High (>$25M historical average trade value): 5 bps

  • Costs are allocated half on entry and half on exit

  • In the metrics presented below, we assume $0 in hedge costs (for the SPY hedge) and the hedge does not use any equity or margin


Below we present metrics from a portfolio that trades the top one-third of the t0 close 3d signal and holds those trades for 3 days using the portfolio assumptions stated above.

The metrics associated with the chart above are as follows. We include metrics without costs (Table 2) and with costs (Table 3).

Below in Table 4 are additional metrics for the portfolio between 2016-2020 including maximum drawdown and correlation to the broader market.

It is worthwhile to note that the above metrics for annual return are based on constant capital in which trade size does not grow with the portfolio and instead is based on the starting capital. There is no reinvestment of profits. When we present annual portfolio growth metrics above, this is inherently understating the annual return because the return is calculated using cumulative portfolio value while trade sizing is held constant (e.g. - the 2018 portfolio annual return is based on growth of portfolio since the end of 2017, not the starting capital in January 2015 that the trade size is based on). Below in Table 5 we present annual metrics based on the constant capital used:

One additional caveat to note is that the majority of events take place during a few weeks toward the middle of each quarter. Therefore a portfolio trading these signals will be in mostly cash during the beginning and end of each quarter. Table 6 below shows that the median day uses only 20% of the initial capital. Thus, annualized returns presented above are conservative for a portfolio setting since an investor would have significant capital to deploy into additional strategies during much of the year and return on capital used is significantly higher.

Finally, all the metrics presented above do not filter out any companies based on market capitalization or trade value. Given the starting portfolio size and the restrictions on the portfolio construction that cap how much of a company’s trade value can be traded, lower liquidity companies do not have a significant impact on the total return. However, to give additional reassurance of the value of the REACTION signals across high liquidity companies, we present metrics below in Tables 7 - 9 for a portfolio that only trades names with trade value over $25M using all the same assumptions as before.

As shown above, constraining trades to only highly liquid companies results in a very small deterioration in the overall performance of the portfolio.


We have shown that building a market-neutral portfolio based on trading Leapday’s REACTION signals produces strong annual returns, high Sharpe ratios, and small drawdowns. We believe that these results, which are robust to trading costs and various market regimes, highlight the power of using Leapday’s REACTION signals in a strategy. Each REACTION signal is an individual prediction for a company’s short term return and collectively they produce a portfolio with a unique return stream.

The metrics presented above are based on a simple standalone strategy and sophisticated investors can use the signals in a variety of ways to boost performance even further. The signals can be used as an input in a model or any decision making framework alongside other inputs to outperform the standalone strategy. Furthermore, investors can use more advanced portfolio construction methods. In this study, we treat all signals that fall in the top one-third of signals equally. In our whitepaper, however, we demonstrate that the top 5% of signals outperform the top 10% and so forth. Therefore, further boosts in performance could be realized by weighting trades appropriately. Finally, the entry and exit rules used in this portfolio are simple. We enter at the close on the event day and exit on the close 3 days later. The portfolio performance can be further improved by optimizing the hold to incorporate additional information gauged from the onset of the trade until the optimal exit.


In this report we build a market-neutral portfolio using signals generated after earnings announcements to profit from short-term price reactions. We focus on the REACTION t0 close 3d signal, which is generated just before the close on the first day of trading subsequent to a company’s earnings announcement, and create a portfolio that uses the top one-third of these signals based on signal magnitude to trade the t0 close and hold that trade for 3 days. Inclusive of costs, this portfolio has an annualized return of 11.0% between 2016-2020, a Sharpe ratio of 1.90, a maximum drawdown of -4.3%, and a correlation to SPY of 0.10. If trading is restricted only to highly liquid companies, metrics remain similarly strong. The metrics presented in this report are conservative given the trade costs assumptions, portfolio position sizing constraints that we use, and the cyclical nature of earnings events which allows investors to deploy capital elsewhere during periods of low earnings activity. These results indicate that investors and traders can use the REACTION signal to drive an independent trading strategy or as an input for a model or investment decision making framework.

In Part 2 of this report, we will construct a market-neutral portfolio using the REACTION signals generated 60 minutes after the open on the event day. We will then build a combined portfolio using both the signals from the close used in this report and those from 60 minutes after the open used in the next report. To read Part 2, click here.



This material is solely for informational purposes and is not an offer or solicitation for the purchase or sale of any security, nor is it to be construed as legal or tax advice. References to securities and strategies are for illustrative purposes only and do not constitute buy or sell recommendations. The information in this report should not be used as the basis for any investment decisions. We make no representation or warranty as to the accuracy or completeness of the information contained in this report, including third-party data sources. The views expressed are as of the publication date and subject to change at any time. Hypothetical performance has many significant limitations and no representation is being made that such performance is achievable in the future. Past performance is no guarantee of future performance.

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