Can You Generate Alpha in US Equities Using Corporate Reputation?

Corporate Reputation Data

The Corporate Reputation Index (CRI), developed by Seldonix and distributed in partnership with Social Market Analytics, allows traders to identify and compare over- and under-priced stocks that are affected by a company’s actions. Corporate Reputation is the controllable portion of a company’s stock price, considered to be an intrinsic driver of company performance, and a major contributor to daily stock price fluctuation.

The Corporate Reputation Index is the first daily business metric for monitoring corporate reputation. Centered on “0”, companies with negative values are deemed undervalued relative to market while those with positive values are overvalued.

The CRI is created after the close of markets and is currently available before markets open the next day at any time after 9:00 PM Eastern US Time the night before.

Each data record features a set of fields:

  • The timestamp associated with each field is the market close date of the data used;
  • Symbol;
  • CRI

Basis of Research using Seldonix data

The purpose of the research described in this paper is to determine if there are opportunities to generate alpha in US equities traded on the NYSE and NASDAQ using Seldonix data as a basis for daily market movement prediction (the holding period is about 6.5 hours).

We show how with the use of simple technical indicators and the popular conception of basket trading, we can exploit the CRI to generate excess returns. An evaluation of the CRI was conducted in the forecasting of stock price. The results of the testing showed a 1 – 8 day lag in the prediction of stock price, depending on the stock. Therefore our purpose is to determine the most appropriate time when the CRI generates the most suitable values for opening a position. As the CRI values change every day and are different for each ticker, we need to construct an approach which will successfully encompass both equally important collations. To identify these situations, we need to:

  • Compare short-term values to long-term;
  • Compare values within the considered universe of tickers.

The full paper can be accessed here.


This approach gives the following results for S&P 100 tested on the period from 1/5/2010 to 8/18/2014:

ParameterAll TradesLong TradesShort Trades
Net Profit/Loss21,182.8213,984.977,197.85
Total Profit113,708.7159,129.8054,578.91
Total Loss-92,525.89-45,144.83-47,381.06
Cumulated Profit %21.18 %13.98 %7.20 %

Max Drawdown-2,590.71-2,774.62-2,265.55
Max Drawdown %-2.41 %-2.66 %-2.18 %
Max Drawdown Date9/2/20119/30/20119/15/2011
Return/Drawdown Ratio8.185.043.18
Drawdown Days %80.96 %80.36 %88.29 %
Max Drawdown Duration119198188

CAGR4.40 %2.97 %1.57 %
Sharpe Ratio1.731.370.73
Annualized Volatility2.542.162.15
Sortino Ratio2.862.151.13
Information Ratio1.711.370.72
Optimal f68.3263.5133.96

All Trades #230811661142
Profitable Trades Ratio0.530.540.52
Winning Trades #1229634595
Losing Trades #1079532547

Average Trade9.1811.996.30
Average Winning Trade92.5293.2691.73
Average Losing Trade-85.75-84.86-86.62
Avg. Win/ Avg. Loss Ratio1.081.101.06
Average Profit per Share0.040.050.03

Max Consequent Winners  9119
Max Consequent Losers10109


Rules for closing

The other idea is to exploit the CR trend not only for opening but for closing position. On the figure below you can see the example of position on AAPL chart, blue line on second pad indicates CRI values:

We use the Simple Moving Average (SMA) of the last CRI values (red line on the second pad) to determine whether the instrument gets over or underpriced. Using the model from the paragraph Over/Under Priced, we will close the position for the instrument according to following rule:

  1. If CR > SMA (5, CR) (instrument is overpriced at time t, price is likely to return to suggested price at t+1) close Short position on day close;
  2. If CR < SMA (5, CR) (instrument is underpriced at time t, price is likely to return to suggested price at t+1) close Long position on day close.

The same chart with this rule shows better results:

This approach gives the following results for S&P100 tested on the period from 1/5/2010 to 8/18/2014:

ParameterAll TradesLong TradesShort Trades
Net Profit/Loss32,118.4918,265.4113,853.08
Total Profit143,984.7571,784.7972,199.96
Total Loss-111,866.26-53,519.38-58,346.88
Cumulated Profit %32.12 %18.27 %13.85 %

Max Drawdown-3,526.80-4,508.75-3,368.95
Max Drawdown %-3.08 %-4.22 %-3.07 %
Max Drawdown Date9/2/20119/30/20119/15/2011
Return/Drawdown Ratio9.114.054.11
Drawdown Days %80.45 %81.83 %88.63 %
Max Drawdown Duration95194279

CAGR6.44 %3.83 %2.95 %
Sharpe Ratio1.971.310.94
Annualized Volatility3.272.933.14
Sortino Ratio3.402.061.55
Information Ratio1.901.310.91
Optimal f60.0544.6329.93

All Trades #219011031087
Profitable Trades Ratio0.530.550.51
Winning Trades #1158603555
Losing Trades #1032500532

Average Trade14.6716.5612.74
Average Winning Trade124.34119.05130.09
Average Losing Trade-108.40-107.04-109.67
Avg. Win/ Avg. Loss Ratio1.151.111.19
Average Profit per Share0.060.070.05

Max Consequent Winners131616
Max Consequent Losers11915


Filter for stronger signal

One more idea is to filter a stronger signal when CR retains its sign from the previous day, which is a stronger signal indicating overpriced/ underpriced asset. On the chart below you can see two trades on MSFT asset, the second one is unprofitable, when the CR line changes its value from -1 to 1:

Filtering out such unstable situations gets the following results for the S&P 100 tested in the period from 1/5/2010 to 8/18/2014:

ParameterAll TradesLong TradesShort Trades
Net Profit/Loss32,810.1219,145.4513,664.67
Total Profit126,322.9964,003.7262,319.27
Total Loss-93,512.87-44,858.26-48,654.61
Cumulated Profit %32.81 %19.15 %13.66 %

Max Drawdown-2,617.49-4,409.37-2,465.16
Max Drawdown %-2.37 %-4.18 %-2.29 %
Max Drawdown Date9/2/20119/30/20119/15/2011
Return/Drawdown Ratio12.534.345.54
Drawdown Days %78.38 %80.02 %86.74 %
Max Drawdown Duration89194176

CAGR6.56 %4.00 %2.91 %
Sharpe Ratio2.161.491.03
Annualized Volatility3.042.682.84
Sortino Ratio3.822.451.70
Information Ratio2.111.501.01
Optimal f70.8055.5136.20

All Trades #1839937902
Profitable Trades Ratio0.530.550.51
Winning Trades #972512460
Losing Trades #867425442

Average Trade17.8420.4315.15
Average Winning Trade129.96125.01135.48
Average Losing Trade-107.86-105.55-110.08
Avg. Win/ Avg. Loss Ratio1.201.181.23
Average Profit per Share0.070.080.06

Max Consequent Winners121211
Max Consequent Losers111016



We presented an approach that uses CR indices as a strong predictive factor of price directionality. We developed a trading strategy that implements an algorithm based on this approach. We also constructed two other versions, both based on CRI predictive power, that raise the performance of the main approach.

The main contribution of the paper is back-testing and comparison of the different versions of the strategy. On stocks in the S&P 100, back-testing shows that the first strategy has an average Information Ratio of 1.71 over the period 2010-2014, the second and third versions achieved Information Ratios of 1.90 and 2.11 respectively, which indicates that the CRI is predictive in the forecasting of stock returns.

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The Deltix Quantitative Research Team