Industry specific factors

Automotive Factors

Proprietary industry-specific signals designed to predict global automotive stock returns. Systematically score stocks based on a number of metrics specific to the automobile industry. Metrics include stock-specific factors that quantify a company’s sales growth, production trends, market share, production utilization and exposure to electric vehicles.  

Utilizing robust statistical automotive data sets from S&P Global that include vehicle sales and production history, aggregated registration information, manufacturing volumes and vehicle pricing insight across various geographies, Research Signals offers 35 quantitative equity factors and 4 raw values that complement both fundamental and quantitative methods. 

Coverage: Global Automotive Industry

History: 13 years data

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Bank & Thrift Model

Model of the US Bank & Thrift Industry utilizing FDIC data (e.g. call/thrift reports). Utilizes detailed “specialty” measures that analyze asset quality, loan growth, and reserves and, in conjunction with a set of broad factors exhibiting strong performance within the bank & thrift industries, seeks to generate consistent outperformance. 

Coverage: US Bank & Thrift Industry

History: ~30 years data

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Equity REIT II Model

US REIT model utilizes proprietary data to enhance investment returns using Equity REIT’s(EREIT). We use detailed property-level information on over 42,000 properties, spread across 4,000+ cities; looking at such metrics as occupancy rate, location, and building quality to construct a bottom-up assessment of REIT net asset value and demonstrating the diversification benefit relative to the overall market.  

Coverage: US REIT Industry

History: 20 years data

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Insurance Model

Insurance model is built to enhance stock selection by accounting for the differences in accounting information between insurance companies & other financial services entities. Based on 7 sub-components, it allows end users to compare relative performance of insurance companies using a consistent valuation framework that identifies stocks with significant alpha generating potential across IHS Markit’s Insurance Universe of ~113 stocks currently (excludes ADR’s & Insurance brokers). 

Coverage: US Insurance Industry

History: ~25 years data

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Oil & Gas Factor Suite and Model

A model expansion of our original oil & gas factor suite (Industry focus: Oil & Gas Jan 2012) that builds a systematic, multifactor equity evaluation strategy based on unique analytical attributes of companies in this industry 

It systematically values firms using a multifactor strategy that employs industry-specific operating metric details such as production numbers and reserve amounts (e.g., Production Growth and Reserve-Replacement Ratio). Offers finer assessment of company performance and operating condition than items available for all industries; It builds upon exclusive corporate detail to provide a systematic evaluation process encompassing reserve metrics such as Reserve-Replacement Ratio as well as measures of operating efficiency including Relative Net Income-to-Wells, among others.

Coverage: Global Oil & Gas Industry

History: ~20 years data

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Retail Model

This is a suite of factors designed to generate excess return employing general signals alongside key industry specific measures; focusing on recent 2-yr results that highlights positive growth & momentum.

We score stocks on attributes including same store sales, store growth and operating strength, among others. Performance & attribution results based on S&P Global Retail universe (avg ~270 US names since 2005). Industry representation includes restaurants, apparel & luxury goods co.’s, food & staples retailers, and internet & catalogue vendors, among others (pg. 1 &2 detail the 7 modules & respective component factors).

Coverage: US Retail Industry

History: ~25 years data

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Sector Rotation Model

It’s an innovative multi-factor model that translates economic forecasts, market sentiment, and quantitative factors into a systematic attractiveness score of sector groups

It pairs S&P Global Sector Purchasing Managers’ Index (PMI) Survey data and proprietary CDS, securities lending and ETF sentiment indicators with momentum and fundamental factors to create a robust multi-factor signal that identifies the sectors expected to outperform and underperform over the subsequent one to twelve months.

Coverage: Developed Europe and US

History: ~25 years data

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Semiconductors Model

It’s an operating-metric based factor model (SEMC2) for the Semiconductor industry designed to generate additional alpha with greater consistency in positive return spreads.

We built it upon our original Tech Model, it utilizes four semiconductor-specific indicators around Order Bookings & Order Backlogs in conjunction with a unique Short Sentiment indicator to capture the distinct characteristics of semiconductor firms.

Coverage: Global Semiconductor Industry

History: ~15 years data

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Technology Model

Our technology model combining several industry-specific models (communication, computer, semiconductors and software) with a cross sectional overlay achieving impressive long-only and long/short returns over the test period.

Addresses complex dynamics of technology sector with industry-level model construction reflecting key tech industry attributes, combined with cross-sectional overlay to highlight more general market trends. Solves modelling difficulties within technology sector given significant business model differences with four industry groups chosen as the optimal way to delineate amongst varied firms.

Coverage: US Tech Industry

History: ~30 years data

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