the extractalpha tactical model 1 (tm1)

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The ExtractAlpha Tactical Model 1 (TM1) Global performance

Vinesh Jha, CEO

October 2018

Abstract

The ExtractAlpha Tactical Model 1 (TM1) is a quantitative stock selection model

designed to capture the technical dynamics of single equities over short

horizons. TM1 is a tactical factor, in that it can assist a longer-horizon investor in

timing their entry or exit points, or be used in combination with existing

systematic or qualitative strategies with similar holding periods.

TM1 was first released for U.S. equities in 2016. In August 2018, coverage was

expanded to include developed markets in North America, Europe, and Asia.

This paper details the model changes which were implemented for the coverage

increase, and performance measures for these new markets.

For full details on TM1’s methodology, please refer to the TM1 White Paper for

U.S. equities

About ExtractAlpha

ExtractAlpha is an independent research firm dedicated to providing unique, curated,

actionable data sets to institutional investors. We apply our extensive experience in

quantitative analysis and the design of investment analytics products to interesting new

data sets and tools. Our rigorously built quantitative models are designed for institutional

investors to gain a measurable edge over their competitors.

ExtractAlpha’s management held senior research and sales positions in the founding

team at StarMine and at top quantitative hedge fund groups including Morgan Stanley’s

Process Driven Trading (PDT).

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Introduction

Reversal, the most basic technical stock selection factor, is often used by statistical arbitrage trading

groups in some form, the simplest of which is to purchase stocks which have recently dropped and to

short stocks which have recently rallied. TM1 expands upon simple reversal factors in several ways.

Reversal itself is modeled using residual returns – returns adjusted for common risk factors – to better

capture idiosyncratic stock movements. The reversal effect is further refined by focusing on price

movements which are more likely to be driven by liquidity demand rather than by information, and

therefore more likely to revert.

Reversal is only one of four TM1 components. TM1 also models other, distinct short-term market

dynamics which are not captured by reversal strategies: short-term momentum in institutional demand

for risk factors; shocks to liquidity; and seasonality effects.

TM1 is designed to be a tactical factor, in that it can assist a longer-horizon investor in timing their entry

or exit points, or be used in combination with existing systematic or qualitative strategies with similar

holding periods. ExtractAlpha’s clients have access to the various components of TM1 so that they can

use those factors which suit their investment style, or which are most orthogonal to their existing

strategies.

TM1 values are computed overnight in each of the three broad regions (North America, Europe, and

Asia), and made available prior to the first market open in that region every weekday. A complete daily

historical file containing TM1 and component scores is available for evaluation, with data from 2002

through to a recent date.

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Model structure

TM1 captures four effects, each with a corresponding numerical component that is available to users:

Reversal, Factor Momentum, Liquidity Shock, and Seasonality.

The four factors are combined using dynamic weights that reflect their recent profitability.

Reversal Factor

Momentum

Liquidity

Shock Seasonality

Dynamic profitability-based

component weighting

TM1

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Factors

Reversal captures the idea that large stock moves in one direction typically reverse over the short term,

especially when calculated after adjusting for common risk factors (i.e., by using residual returns) and

especially when accompanied by relatively light volume, since news-driven moves are less likely to

revert.

Factor momentum looks at recent moves in common fundamental factors such as value and growth, and

extrapolates those forward, since large institutions tend to move into and out of factors across multiple

days.

Liquidity shock identifies stocks with sudden increases in volume, which can lead to higher attention and

to inflows from large investors.

Seasonality captures the documented trend of particular stocks to experience outperformance

repeatedly at the same time of year each year.

TM1 dynamically weights its four components over time, based on their performance in the recent

several years.

TM1 values, and the component values, are available to data feed subscribers as relative 1-100 ranks,

with 100 representing the stocks which are most likely to outperform their regional peers according to

that component or to TM1 overall.

Regions

TM1 Global was initially released with three markets: Developed Americas, Developed Europe, and

Developed Asia Pacific. The three corresponding emerging market regions (Latin America, Emerging

Europe and Middle East, and Emerging Asia Pacific) will be available in a future release. The dominant

market in the Emerging Markets datasets will be China A Shares.

Developed Americas (AM) consists of Canada and Bermuda.

Developed Europe (EU) consists of Austria, Belgium, Denmark, Finland, France, Germany, Iceland,

Ireland, Israel, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and

the United Kingdom.

Developed Asia Pacific (AP) consists of Australia, Hong Kong, Japan, New Zealand, Singapore, South

Korea, and Taiwan.

Ongoing data for each region will be produced prior to the open of markets in that region, that is, three

times a day.

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Changes from the U.S. model

No changes were made to the concepts or structure of TM1 when expanding to global coverage. The in

sample and out of sample methodology was also maintained, as was our reliance on FactSet’ pricing and

fundamental data.

The following changes were made to facilitate the global expansion:

• In developed markets, regional research and regression universes consists of stocks with at least

US$100m in market capitalization and US$1m in average daily trading volume. A minimum price

was not imposed due to the variation across markets in the distribution of prices.

• In emerging markets, the universes consist of stocks with at least US$1b in market cap and at

least US$5m in average daily trading volume, to account for the generally lower liquidity in

these markets

• When processing fundamental data, we accounted for the longer lags between period end dates

and filing dates in non-U.S. markets

• We did not require the presence of quarterly fundamental data

• Change variables such as revenue growth in the risk model were computed in USD rather than in

the local currency

• Country dummy variables were introduced into our risk models and regressions. In emerging

markets, we used industries rather than sectors in our regressions to account for the narrower

universe

• When Winsorizing variables, we did so by country and universe

• TM1’s components were slowed down to account for the higher trading costs outside of the U.S.

For example,

o Reversal looks back 30 trading days rather than 15

o Factor Momentum uses country dummy variables in developed markets (in Europe, U.K.

and Europe ex-U.K. dummy variables) and looks back 5 days rather than 1. In Canada,

we still look back 1 day

o Liquidity Shock looks back one month rather than one day

o Seasonality looks at a wider window from today’s date as well

• We combine factors based on their ability to predict forward 10-day returns (not 2-day returns),

other than in Canada which inherited the U.S. coefficients

• The final percentile ranks are done by region and universe

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Universe Size

The below charts show the number of stocks per country which pass our universe screens in each of the

Developed regions over time. The data begins in 2002 for all regions, though we begin our Canadian

research in 2005 due to the thin coverage for the first three years of the sample.

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Stocks in research universe - Developed Europe

AT BE DK FI FR DE IE IL

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Stocks in research universe - Developed Asia Pacific

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Historical performance

The plot below shows the cumulative returns to a market neutral portfolio which is long the top decile

(scores 91 to 100) and short the bottom decile (scores 1 to 10) for each TM1 component and the overall

score. The summary statistics also compare TM1 to a basic reversal factor which is computed using

average price returns over the prior N days, with N selected to match to the turnover of TM1 in each

region (5 days in Canada, 15 in Europe, and 15 in Asia).

Subgroup results by country, sector, Value/Growth, and Low/High Volatility are long/short quintile

portfolios rather than decile portfolios. Returns are only shown for subgroupings in which we have a

sufficient number of stocks on a sufficient number of days, which is why for example some sector results

are not shown in some regions.

First we note that the returns are nearly monotonic across deciles in all regions:

Next we examine each developed region in greater detail.

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Annualized Return by TM1 Decile

Canada Developed Europe Developed Asia Pacific

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Developed Americas ex-US (i.e., Canada)

In Canada, reversal strategies work quite well but are relatively low-Sharpe, partly due to the somewhat

narrow universe of liquid Canadian stocks. TM1’s Reversal Component alone improves upon a basic

reversal strategy’s Sharpe ratio, and the other components provide incremental value as well.

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TM1 and Component Returns - Developed Americas (ex-US)

Reveral Factor Momentum Liquidity Shock Seasonality TM1

Annualized

Return

Sharpe

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Days

Daily

Turnover

Reversal Component 26.7% 1.29 52% 22%

Factor Momentum Component 8.7% 0.27 51% 78%

Liquidity Shock Component 8.5% 0.52 51% 16%

Seasonality Component 6.7% 0.52 51% 25%

TM1 33.3% 1.45 53% 45%

Basic Reversal 28.2% 0.81 52% 41%

TM1 Value Added 5.1% 0.64 1%

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Developed Americas ex-US TM1 Basic Reversal Value Added

Companies

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Overall 252 33.3% 1.45 53% 45% 28.2% 0.81 52% 5.1% 0.64 1%

2005 211 49.3% 3.41 58% 30% 96.1% 4.49 61% -46.8% (1.08) -3%

2006 255 54.1% 2.96 59% 29% 41.8% 1.47 51% 12.3% 1.49 8%

2007 290 -1.9% (0.11) 48% 30% 23.6% 1.08 51% -25.5% (1.19) -3%

2008 249 71.5% 1.89 57% 30% 78.3% 1.30 55% -6.8% 0.59 2%

2009 206 46.4% 2.24 54% 32% -26.3% (0.73) 47% 72.7% 2.97 7%

2010 253 45.2% 2.84 53% 46% 21.7% 0.86 48% 23.5% 1.98 5%

2011 287 18.1% 0.78 48% 57% 98.0% 2.97 56% -79.9% (2.19) -8%

2012 249 40.7% 2.20 55% 49% 43.1% 1.82 56% -2.4% 0.38 -1%

2013 242 51.3% 2.66 59% 50% 15.9% 0.53 52% 35.4% 2.13 7%

2014 260 -0.7% (0.03) 52% 51% -6.7% (0.20) 51% 6.0% 0.17 1%

2015 247 46.7% 1.68 51% 53% 0.3% 0.01 49% 46.4% 1.67 2%

2016 260 9.8% 0.31 49% 62% -18.4% (0.41) 51% 28.2% 0.72 -2%

2017 260 28.2% 1.38 51% 57% -14.8% (0.59) 50% 43.0% 1.97 1%

2018 through Oct 17 266 10.2% 0.43 48% 50% 45.3% 1.18 53% -35.1% (0.75) -5%

Small Caps 215 31.7% 1.34 53% 45% 27.6% 0.78 52% 4.1% 0.56 1%

Growth stocks 131 16.0% 0.78 51% 42% 16.5% 0.57 51% -0.5% 0.21 0%

Value stocks 123 29.1% 1.59 54% 44% 11.5% 0.46 52% 17.6% 1.13 2%

Low volatility stocks 105 29.6% 2.76 56% 50% 24.4% 1.40 54% 5.2% 1.36 2%

High volatility stocks 148 20.3% 0.97 52% 42% 6.6% 0.23 50% 13.7% 0.74 2%

Energy 67 16.3% 0.77 51% 40% -2.0% (0.08) 50% 18.3% 0.85 1%

Materials 80 32.3% 1.19 53% 41% 13.4% 0.43 50% 18.9% 0.76 3%

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Developed Europe

The TM1 Reversal Component has worked well for some time in Developed European markets, with the

other components providing some additional value. The benchmark basic reversal strategy, however,

has had a fairly low Sharpe ratio.

As shown on the next page, TM1’s value added over the benchmark strategy is fairly consistent across

most slices of European stocks.

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TM1 and Component Returns - Developed Europe


Annualized

Return

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Ratio

% Positive

Days

Daily

Turnover





TM1 28.6% 3.27 58% 27%

Basic Reversal 15.6% 0.91 51% 30%

TM1 Value Added 13.0% 2.36 7%

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Developed Europe TM1 Basic Reversal Value Added

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Overall 962 28.6% 3.27 58% 27% 15.6% 0.91 51% 13.0% 2.36 7%

2002 449 68.3% 4.74 65% 31% -8.7% (0.28) 49% 77.0% 5.02 16%

2003 628 64.2% 6.42 66% 26% 38.0% 2.06 55% 26.2% 4.36 11%

2004 781 29.7% 4.65 63% 26% 9.3% 0.83 50% 20.4% 3.82 13%

2005 941 18.0% 3.13 55% 26% 10.8% 1.26 52% 7.2% 1.87 3%

2006 1072 37.1% 5.64 62% 26% 33.2% 2.49 57% 3.9% 3.15 5%

2007 1268 22.3% 3.14 57% 26% 14.5% 1.10 54% 7.8% 2.04 3%

2008 1080 44.9% 3.13 61% 27% 45.2% 1.42 52% -0.3% 1.71 9%

2009 904 70.5% 6.30 66% 27% 38.0% 1.58 50% 32.5% 4.72 16%

2010 936 15.1% 2.04 52% 27% 12.7% 0.97 53% 2.4% 1.07 -1%

2011 951 20.4% 2.24 54% 28% 26.1% 1.37 47% -5.7% 0.87 7%

2012 863 5.9% 0.77 51% 29% 2.0% 0.13 49% 3.9% 0.64 2%

2013 928 33.1% 5.19 61% 30% 2.4% 0.24 48% 30.7% 4.95 13%

2014 1027 21.3% 3.50 61% 29% 8.4% 0.71 47% 12.9% 2.79 14%

2015 1071 12.1% 1.60 51% 29% 6.0% 0.47 48% 6.1% 1.13 3%

2016 1071 9.6% 1.15 50% 28% 25.8% 1.51 52% -16.2% (0.36) -2%

2017 1183 1.4% 0.25 52% 27% -2.7% (0.32) 49% 4.1% 0.57 3%

2018 through Oct 17 1232 17.0% 2.63 55% 29% 1.1% 0.12 52% 15.9% 2.51 3%

Large Caps 505 31.1% 3.07 59% 31% 24.3% 1.28 53% 6.8% 1.79 6%

Small Caps 467 25.9% 2.12 53% 26% 6.3% 0.34 49% 19.6% 1.78 4%

France 106 34.8% 2.69 56% 24% 16.2% 0.95 52% 18.6% 1.74 4%

Germany 112 21.8% 1.63 53% 22% 8.3% 0.49 50% 13.5% 1.14 3%

Italy 86 22.1% 1.39 52% 24% 7.9% 0.43 51% 14.2% 0.96 1%

Spain 71 22.4% 1.47 54% 22% -9.7% (0.52) 48% 32.1% 1.99 6%

Sweden 82 20.1% 1.57 55% 23% 13.6% 0.90 51% 6.5% 0.67 4%

Switzerland 77 24.4% 1.84 54% 24% 5.9% 0.37 50% 18.5% 1.47 4%

UK 296 17.0% 1.79 54% 20% -1.7% (0.11) 49% 18.7% 1.90 5%

Growth stocks 489 21.8% 2.90 57% 24% 11.3% 0.81 50% 10.5% 2.09 7%

Value stocks 478 21.2% 2.80 58% 25% 8.0% 0.58 50% 13.2% 2.22 8%

Low volatility stocks 415 21.7% 3.92 60% 26% 17.4% 1.62 55% 4.3% 2.30 5%


Commercial Services 72 16.2% 1.20 53% 23% 8.0% 0.53 52% 8.2% 0.67 1%

Consumer Discretionary 137 19.7% 1.69 53% 23% 5.6% 0.34 49% 14.1% 1.35 4%

Energy 102 22.5% 1.44 54% 23% 7.9% 0.42 51% 14.6% 1.02 3%

Finance 199 17.3% 1.57 54% 25% 12.4% 0.70 51% 4.9% 0.87 3%

Healthcare 82 18.1% 1.21 53% 24% 6.9% 0.44 50% 11.2% 0.77 3%

Industrials 103 22.3% 1.62 53% 24% 18.6% 1.19 51% 3.7% 0.43 2%

Materials 107 12.9% 0.97 53% 24% 3.4% 0.19 50% 9.5% 0.78 3%

Technology 92 17.6% 1.16 52% 23% 5.6% 0.33 49% 12.0% 0.83 3%

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Developed Asia Pacific

The Reversal Component shows a much higher Sharpe ratio than a basic reversal factor in Developed

Asian markets, which are dominated by Japan, and it does not show the same degree of decay over time

as in some other regions. The other components do not actually add value to TM1 as a whole because

of this strong Reversal contribution; because the TM1 algorithm forces some weight onto these other

components, the Reversal Component actually outperforms TM1 as a whole.

Some investors who already implement a reversal strategy in Asia, however, may find these other

components – particularly Seasonality – to be additive to their strategies since they are uncorrelated.

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TM1 and Component Returns - Developed Asia Pacific


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TM1 31.3% 3.03 59% 29%

Basic Reversal 32.1% 1.44 53% 29%

TM1 Value Added -0.8% 1.59 6%

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Developed Asia Pacific TM1 Basic Reversal Value Added

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Overall 1764 31.3% 3.03 59% 24% 32.1% 1.44 53% -0.8% 1.59 6%

2002 from February 453 35.2% 2.54 57% 23% 88.4% 2.95 54% -53.2% (0.41) 3%

2003 752 4.8% 0.33 53% 18% 66.6% 2.88 54% -61.8% (2.55) -1%

2004 1021 16.3% 1.35 52% 17% 23.1% 1.03 51% -6.8% 0.32 1%

2005 1289 16.9% 1.75 54% 17% 6.8% 0.45 50% 10.1% 1.30 4%

2006 1610 29.8% 2.54 57% 21% 65.7% 2.64 57% -35.9% (0.10) 0%

2007 1910 48.8% 4.42 61% 24% 11.6% 0.54 50% 37.2% 3.88 11%

2008 1532 57.8% 4.44 60% 24% 35.2% 1.02 54% 22.6% 3.42 6%

2009 1523 80.2% 6.98 68% 25% 52.0% 2.19 53% 28.2% 4.79 15%

2010 1705 38.9% 4.09 63% 26% 35.1% 2.24 55% 3.8% 1.85 8%

2011 1743 44.6% 3.95 61% 26% 26.8% 1.07 51% 17.8% 2.88 10%

2012 1726 41.3% 5.05 64% 26% 12.1% 0.88 50% 29.2% 4.17 14%

2013 2140 24.3% 2.43 58% 27% 24.3% 1.25 55% 0.0% 1.18 3%

2014 2294 21.1% 3.77 60% 26% 5.9% 0.45 51% 15.2% 3.32 9%

2015 2488 22.7% 2.82 59% 26% 25.5% 0.68 52% -2.8% 2.14 7%

2016 2359 20.4% 3.15 61% 27% 32.9% 2.25 52% -12.5% 0.90 9%

2017 2614 13.1% 2.56 57% 29% 3.6% 0.37 49% 9.5% 2.19 8%

2018 through Oct 17 2885 23.6% 2.61 55% 29% 31.1% 2.12 53% -7.5% 0.49 2%

Large Caps 364 30.4% 1.83 55% 28% 48.3% 1.69 54% -17.9% 0.14 1%

Small Caps 1421 32.1% 2.87 58% 24% 27.8% 1.24 52% 4.3% 1.63 6%

Australia 166 18.9% 1.41 53% 21% 7.3% 0.38 50% 11.6% 1.03 3%

Hong Kong 289 29.6% 2.17 56% 21% 10.6% 0.56 49% 19.0% 1.61 7%

Japan 827 23.2% 2.31 56% 19% 16.4% 0.93 50% 6.8% 1.38 6%

Korea 330 25.6% 1.43 55% 23% 25.3% 1.18 52% 0.3% 0.25 3%

Singapore 81 10.7% 0.51 50% 23% -6.3% (0.31) 51% 17.0% 0.82 -1%

Taiwan 304 29.2% 1.98 57% 20% 2.9% 0.14 50% 26.3% 1.84 7%

Growth stocks 913 25.3% 2.76 58% 21% 15.5% 0.82 51% 9.8% 1.94 7%

Value stocks 857 21.9% 2.75 58% 21% 17.9% 1.06 52% 4.0% 1.69 6%

Low volatility stocks 819 21.4% 3.47 59% 23% 25.0% 1.70 53% -3.6% 1.77 6%


Commercial Services 109 25.8% 1.37 53% 22% 15.8% 0.65 52% 10.0% 0.72 1%

Consumer Discretionary 246 25.4% 2.24 56% 20% 15.6% 0.85 52% 9.8% 1.39 4%

Consumer Non-Durables 112 24.1% 1.54 53% 23% 13.1% 0.64 52% 11.0% 0.90 1%

Energy 116 24.9% 1.44 53% 23% 17.8% 0.75 52% 7.1% 0.69 1%

Finance 278 18.6% 1.36 55% 22% 14.5% 0.59 51% 4.1% 0.77 4%

Healthcare 139 27.7% 1.54 54% 23% 16.2% 0.64 52% 11.5% 0.90 2%

Industrials 202 21.2% 1.76 55% 21% 19.0% 1.00 52% 2.2% 0.76 3%

Materials 239 22.9% 1.75 54% 21% 21.2% 1.03 52% 1.7% 0.72 2%

Technology 332 29.9% 2.25 56% 21% 11.5% 0.56 51% 18.4% 1.69 5%

Transportation 86 25.2% 1.43 53% 24% -2.2% (0.07) 50% 27.4% 1.50 3%

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Latency and transaction costs

The above returns assume that positions are entered into at the open, held for one day, and rebalanced,

with no transaction costs. Below we show the effect of waiting until the close on that same day to enter

positions, exiting on the subsequent close. Results in Canada degrade quickly, but in European and

Asian markets the decay is fairly small.

Region Entry at: Return Sharpe % Pos Days

AM Open 33.3% 1.45 53%

Close 13.4% 0.63 52%

EU Open 28.6% 3.27 58%

Close 22.8% 2.70 57%

AP Open 31.3% 3.03 59%

Close 24.6% 2.66 59%

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Risk exposures

Finally, we examine the average risk factor exposures by decile of TM1. Here the risk factors are

constructed to be mean 0 and standard deviation 1, and we see that although TM1 can take factor bets

on an individual day due to the Factor Momentum component, overall there are no pervasive factor

bets. The extreme stocks, not surprisingly, do tend to be somewhat more volatile than stocks in the

middle deciles.

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