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2IQ INSIDER SCORE ENHANCED MODEL METHODOLOGYGetting the most of Insider Trading Data: building an enhanced insider trading signal

September 2018

2 Enhanced Model Methodology

SUMMARYThe 2iQ insider transactions scoring model leverages more than a decade of experience working with insider transaction data with the expertise of the world’s pre-eminent quantitative investment managers. The model scores companies based on insider sentiment to identify which transactions and insiders have the highest predictive strength, delivering a trading signal in the form of a score or ranking which can be used as an input to the quantitative portfolio construction process.

Not all trades are created equal - only some will shed light on a company’s prospects. For example the fact that a COO sells part of her equity awards for tax purposes says very little about her sentiment towards the company. However when a CEO of a tech company substantially increases his position while the stock’s momentum is in an uptrend – that transaction tells us much more.

Given that some trades contain a lot more information than others, we have developed a framework that allows us to identify the most relevant trades to be used in the model. In this paper we will outline the specific methodology used to create the 2iQ Insider Score and present its performance results.

3Enhanced Model Methodology

IDENTIFYING RELEVANT TRADES

To extract the full potential of insider transaction data we must consider three primary characteristics which will help us to identify which transactions are most likely to deliver alpha: trade, insider and company.

The interplay between these dimensions will allow us to determine the relevance of the trade as well as its potential to deliver excess returns.

1 - TRADE CHARACTERISTICS

a. Transaction Type (Buy/Sell/Award)b. Size of the trade in dollars (using the log of this value)c. Very large trades (above 5 million USD are flagged to allow for a nonlinear behavior)d. Percentage of the insider position being tradede. Size of the trade compared to last 24 months of activity of the insiderf. Trade flag such as Tax related, Automated Sell (like 10b5-1 plan) or trades related to an employee

compensation plan.

2 - INSIDER CHARACTERISTICS

a. Insider level (A, B, C, …)b. Whether the insider has a finance related role (CFO, CAO, Controller, etc)c. How active is the insider or group of insiders:i. Number of buys or sells of significance 2-3 has the insider done in the last 2 years.ii. Did the insider buy(sell) on a subsequent price increase or decrease


IDENTIFYING RELEVANT TRADES

3- COMPANY CHARACTERISTICS

a. Valueb. Sizec. Betad. Long term Momentum and more specifically extreme momentum (independently of direction)e. Short Term momentumf. Sector classification, as a proxy for volatility, R&D activity and expected level of insider knowledge

compared to the broader market (for example Biotech and Technology)g. Countryh. Ohslon O-Score. Excluding financials and Utilities, the score is transformed in a probability of

default.

We calibrate the model using residual returns to a risk model. This allows us to capture the effect of company characteristics on the information content of insider transactions rather than the alpha content of those characteristics. For details in the risk model please see Appendix A.


TRANSACTION SCOREThe above characteristics feed into a regression model which delivers a score for each individual transaction. The regression step is explained here, followed by details of how these transaction scores feed into the company level model.

FORECASTING OF TRADE RETURNSWe calibrate the regression coefficients by dividing the transaction set across a matrix of trade types (buys, sells, awards) and regions (North America, Europe and Asia Pacific); resulting in nine different models. We use a robust regression with appropriate constraints on coefficients for insider level, sector and country to avoid rank deficiency. Each transaction type and region uses slightly different characteristics in the regression. The factor selection is made on the basis of previous academic or practitioner research with strong rationale. This allows us to forecast the 20 day return from the date each transaction is reported. Again, we use residual returns to calibrate the model to ensure that we purely capture the insider transaction effect.

ECONOMIC FILTERINGWe have taken active steps to filter how buys and sells are considered in the model. A buy transaction has either zero relevance or is a signal of positive excess returns, with sell transaction containing zero relevance or a signal of negative excess returns. A buy cannot signal negative excess returns, and a sell cannot signal positive excess returns.

● Sells that have a score > 0 are eliminated● Buys that have a score < 0 are eliminated● Awards between -0.2% and 0.2% are eliminated● Around 60-70% of sells and 50% of buys are affected

On average buys are more informative than sells, but sells can be informative in some cases. More specifically, the above process retains only 30-40% of sells in the model, while at least 50% of buys are retained.

This regression model allows us to attach an expected significance level to each individual trade. The information can then be used to construct an aggregated signal for a cross-sectional risk premia/alpha factor at company level.


With this calculation we can also consider different types of filtering at insider or trade relevance level. We observe that smart filtering with hard rules can be beneficial - but this has marginal impact compared to the enhanced methodology.

Our enhanced methodology combines the signal values using the enhanced trade information by calculating a sum of all trade forecasts derived from the regression model above and dividing that by the number of relevant trade forecasts to calculate the enhanced ratio.

Our research considered a range of lookback values between 1 and 3 months with the objective of balancing turnover with performance and concluded a lookback window of 3 months was most appropriate.

The obvious goal of using insider transactions data is to arrive at data inputs for an investment process which provides a clear alpha signal to the investor. One can arrive at such a score using a simple methodology:

EnhancedRatio_ni,t =TradeForecasti,[t:t-n]

#(TradeForecasti,[t:t-n] ≠ 0)

CountRatio_ni,t =CountBuysi,[t:t-n] - CountSellsi,[t:t-n]

CountBuysi,[t:t-n] + CountSellsi,[t:t-n]

∑

COMPANY SCORE

SUMMARYThe 2iQ Enhanced Insider Score Methodology was created and circulated to clients in September 2015. At that time we ran a series of backtests across various regions (North America, Europe, Asia, Global) comparing its performance versus the naive model. We considered the information coefficient and a stick man, L/S quintile portfolio as metrics to evaluate performance. Most notably, as we will highlight in the below results, we ran an out of sample portfolio using actual results to demonstrate how the score has performed since its creation in September 2015.


TABLE 1 - GLOBAL BACKTESTING – MONTHLY FREQUENCYThis backtest and the following are run on the S&P BMI universe from December 2003 to September 2015. We use residual returns from our risk model as described above. Long Short spreads (LS) are computed using the top 20% and bottom 20% scores for each signal.

TABLE 2 - GLOBAL BACKTESTING – WEEKLY FREQUENCY

AutoC = Autocorrelation of the signal (1 month)IC = Rank order correlation between the signal and forward returns at 1, 3 and 6 monthsLS = Long Short spread based on a long top quintile scores, short bottom quintile scores, rebalanced monthly, returns are annualized

Horizons in this table are over 1, 2 and 4 weeks.

RESULTS BACKTESTING

Name AutoC IC1 IC3 IC6 LS1 LS3 LS6 Std1 Std3 Std6 IR1 IR3 IR6CountRatio1M 61% 1.1% 1.6% 1.4% 4.2% 3.0% 1.8% 3.9% 3.9% 3.5% 1.1 0.7 0.5CountRatio3M 88% 0.8% 1.2% 0.9% 3.8% 2.5% 1.6% 3.2% 3.4% 3.1% 1.2 0.7 0.5EnhancedRatio1M 57% 3.4% 4.5% 5.3% 11.7% 8.5% 6.2% 4.3% 4.3% 4.4% 2.7 2.0 1.4EnhancedRatio3M 88% 2.9% 3.9% 4.5% 9.3% 6.8% 5.3% 4.1% 3.9% 3.8% 2.3 1.7 1.4

Name AutoC IC1 IC2 IC4 LS1 LS2 LS4 Std1 Std2 Std4 IR1 IR2 IR4CountRatio1W 70% 2.4% 2.3% 2.2% 17.4% 12.0% 7.6% 4.1% 4.4% 4.5% 4.2 2.7 1.7CountRatio4W 95% 0.9% 1.0% 1.1% 8.7% 6.6% 4.6% 3.6% 3.7% 3.7% 2.5 1.8 1.2CountRatio13W 98% 0.3% 0.4% 0.7% 5.5% 4.6% 3.9% 3.2% 3.3% 3.3% 1.7 1.4 1.2EnhancedRatio1W 74% 3.5% 4.2% 4.6% 28.8% 22.6% 16.9% 7.0% 6.7% 6.3% 4.1 3.4 2.7EnhancedRatio4W 96% 2.2% 2.8% 3.4% 17.1% 14.3% 11.6% 4.9% 4.7% 4.2% 3.5 3.1 2.8EnhancedRatio13W 98% 1.6% 2.1% 2.8% 12.0% 10.7% 9.5% 4.5% 4.4% 4.2% 2.6 2.5 2.3


CHART 1 – LONG SHORT SPREADS – 1 M RETURNS

CHART 2 – LONG SHORT SPREADS – 3M RETURNS

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CHART 3– INFORMATION COEFFICIENT TIME SERIES – 1 M RETURNS

CHART 4 – INFORMATION COEFFICIENT TIME SERIES – 3 M RETURNS

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TABLE 3 – REGION MONTHLY RESULTS: NORTH AMERICA

TABLE 4 – REGION MONTHLY RESULTS: EUROPE

TABLE 5 – REGION MONTHLY RESULTS: ASIA PACIFIC

AutoC = Autocorrelation of the signal (1 month)IC = Rank order correlation between the signal and forward returns at 1, 3 and 6 monthsLS = Long Short spread based on a long top quintile scores, short bottom quintile scores, rebalanced monthly, returns are annualizedIR = Annualized returns / annualized standard deviation


Name AutoC IC1 IC3 IC6 LS1 LS3 LS6 Std1 Std3 Std6 IR1 IR3 IR6CountRatio1M 59% 0.3% 0.5% 0.2% 2.4% 1.3% 0.6% 5.3% 5.7% 4.9% 0.5 0.2 0.1CountRatio3M 87% 0.2% 0.3% -0.1% 1.7% 1.0% 0.5% 4.4% 4.4% 3.8% 0.4 0.2 0.1EnhancedRatio1M 51% 2.1% 2.8% 3.4% 8.9% 5.6% 3.9% 5.5% 5.3% 5.2% 1.6 1.1 0.8EnhancedRatio3M 85% 1.9% 2.4% 2.8% 6.7% 4.4% 3.4% 3.7% 3.7% 3.5% 1.8 1.2 1.0


TABLE 6 – CORRELATIONS

Count Ratio

Enh Ratio

Beta Size BToM EToP Value BP+EP

Mom 1M

Mom 12-1M

Abs Mom

CountRatio3M 100% 52% -11% -15% 21% 13% 20% -7% -16% 6%EnhancedRatio3M 52% 100% 20% -24% 15% 10% 17% -2% -20% 5%Beta (1yr) -11% 20% 100% 11% -12% -1% -2% -2% -3% 14%Size -15% -24% 11% 100% -27% -4% -17% 9% 20% -19%Book To Market 21% 15% -12% -27% 100% 40% 81% -12% -30% 2%Earnings To Price 13% 10% -1% -4% 40% 100% 85% -10% -26% -1%Value(BM+EP) 20% 17% -2% -17% 81% 85% 100% -13% -34% 0%Momentum 1M -7% -2% -2% 9% -12% -10% -13% 100% 3% -2%Momentum 12-1M -16% -20% -3% 20% -30% -26% -34% 3% 100% 7%Absolute Momentum 6% 5% 14% -19% 2% -1% 0% -2% 7% 100%


TABLE 7 - OUT OF SAMPLE PERFORMANCESince the model creation, we have collected end of month signals and ran analysis each month on the model performance following the same methodology we use for backtesting. Those performance numbers were collected month after month with no restatement and represent a true out of sample comparison to our backtest results.

RESULTS OUT OF SAMPLE

Global Europe N America Asia Pac09/2015 -1.6% 1.7% -4.0% 3.3%10/2015 1.8% 1.8% 1.9% 4.5%11/2015 1.1% 1.7% 0.0% 1.4%12/2015 -0.2% 1.2% 0.5% 1.4%01/2016 -0.2% 1.5% -1.8% 3.5%02/2016 1.0% 0.2% 0.2% 2.2%03/2016 0.4% -1.2% -0.2% 2.2%04/2016 1.3% -0.1% 1.4% 2.0%05/2016 1.2% 0.2% 1.5% 0.4%06/2016 1.8% 0.2% -0.8% 1.8%07/2016 1.7% 2.5% 0.4% 4.3%08/2016 2.2% 1.7% 1.4% 2.9%09/2016 0.4% -1.7% 0.3% 0.5%10/2016 -0.1% -2.6% 0.6% 2.6%11/2016 0.9% 0.8% 0.7% -0.5%12/2016 0.7% -1.8% 1.9% -0.1%01/2017 0.2% -0.4% 0.8% 3.0%02/2017 0.5% 2.1% 0.4% 2.1%03/2017 0.6% -0.1% -0.8% 3.7%04/2017 1.5% 0.2% 1.5% 2.4%05/2017 1.0% 2.1% 1.6% 1.2%06/2017 1.9% 1.8% 1.0% 3.0%07/2017 0.1% 0.4% 0.8% 2.1%08/2017 1.0% -0.8% 2.9% 0.1%09/2017 2.2% 2.1% 0.2% 3.3%10/2017 -0.7% 0.8% 0.1% -0.1%11/2017 1.7% 4.3% 1.8% -0.3%12/2017 -1.0% -0.7% -1.2% 0.0%01/2018 -1.0% -0.2% -0.4% -0.2%02/2018 0.4% -0.6% 0.9% 0.6%03/2018 1.2% 1.1% 0.7% -0.6%04/2018 0.2% -0.8% -1.2% 1.2%05/2018 -0.4% -1.7% -1.3% -0.3%06/2018 1.0% 3.3% 0.6% 0.5%07/2018 0.2% -0.2% 0.3% 0.8%08/2018 0.6% -1.4% 1.3% 0.4%

Year Global Europe N America Asia Pac2016 11.9% -0.3% 5.7% 24.2%2017 9.3% 12.1% 9.3% 22.5%

EnhancedRatio3M L/S spread - 1 Month return*


CHART 5 – GLOBAL OUT OF SAMPLE - LONG SHORT SPREADS 1M

CHART 6 – NORTH AMERICA OUT OF SAMPLE - LONG SHORT SPREADS 1M

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CHART 7 – EUROPE OUT OF SAMPLE - LONG SHORT SPREADS 1M

CHART 8 – ASIA PAC OUT OF SAMPLE - LONG SHORT SPREADS 1M

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CONCLUSIONThe 2iQ enhanced insider model demonstrates consistent excess returns across market cycles. We show that, scoring companies based on insider sentiment to identify the transactions and insiders with the highest predictive strength and using this information to create a trading signal for the portfolio construction process, is an effective means to deliver excess returns to quantitative or fundamental portfolios.

The results prove that the enhanced methodology is largely superior to the naive method of calculating insider scores based on 2iQ’s underlying data. The score works well across regions, with especially strong performance in Europe and Asia Pacific. The out-of-sample performance is in line with the in-sample performance, demonstrating that the model is robust with particularly strong out-of-sample performance in the Asia Pacific region. The difference in performance can be attributed to different legislative and reporting frameworks across regions (with North America being the most stringent).

It is notable that the 2iQ insider model demonstrates very low levels of correlation with other traditional quantitative factors - a strong indication of additive returns to an existing quantitative or fundamental investment process.

The work here shows how well the standardized model performs and is useful in providing an overall benchmark for an investor to consider use of this model, within their process. The methodology is flexible and can be adapted across various investment processes and contexts. We also offer a white-box approach where the user can build variants of the methodology to suit their needs.

APPENDIXRisk model

In order to have a fair representation of how the factor will behave we run the backtest on excess returns using a risk model we developed. Those excess returns are also used for the calibration of the regression model.

In this risk model we neutralize daily returns using a cross sectional regression methodology used by major risk model vendors.

The model includes simple and common risk constraints and risk factors present in portfolios: a valuation score (combining Book to Price and Earnings to Price), 12 months Momentum, 1 month Momentum, Beta, Size, Sector and Country. Therefore our returns are neutralized for those effects.

Those daily excess returns are then combined to compute 1, 3 and 6 months forward returns used in the backtest and for the calibration of the regression model.

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