Whitepaper on quantitative stock selection

Welcome to introduction to Saemor’s Whitepaper on Quantitative Stock Selection – covering 4 subjects related to our models. The purpose is to bring our investors up to date on specific quantitative research and techniques we conduct.

The growing importance of the macro-economy in driving asset returns has resulted in a testing environment for investors. Many of the traditional stock selection models have been challenged as they are largely a play on stock-specifics. Our models are different as they do not obey to the “one size fits all” and “all-weather” concept.

Quantitative managers are as different from each other as fundamental managers are. They can be stock pickers, high-frequency traders, GTAA’s and CTA’s. The ones which focus primarily on stock selection often use multi-factor models. Within the multi-factor space managers also take a different approaches in terms of which factors to include, how to combine them etc. Saemor Capital applies a highly distinct method when selecting alpha drivers.

Our quantitative stock selection models select stocks based on ‘alpha drivers’ or ‘factors’-mostly fundamental criteria such as the Price/Earnings and Debt/Assets ratios. These factors are supported by academic and other research and are applied by both quantitative and fundamental investors alike. We employ a tailored approach for specific stocks, sectors and market segments and changing macro-economic regimes. It has helped us to withstand the headwinds from the European sovereign debt crises.

We spend a large amount of time trying to generate new ideas and investigating how stock selection approaches perform in different environments. In our research we look at all aspects of the quantitative investment process, from alpha generation to portfolio construction to risk management.

This whitepaper highlights some of our most successful techniques with regard to alpha generation, including:

  • Style Timing, or Dynamisation of Multifactor Models: Quant investors are becoming wary of sudden style shifts, severe macro events and shorter macro cycles – and style timing is gaining prominence in the investment process as dynamic factor weighting has come in vogue. Here we focus on ‘style momentum’, i.e. the continuation of style performance from one period to the next and find high levels of persistence in style performance.
  • Conditional Factor Efficacy: All quant investors aim to select the best set of alpha drivers and a combination several factors lies at the heart of this process. Most quantitative investment processes have different models for each sector, but neglect other important characteristics of stocks. Considering more angles than only sector classification can vastly improve quantitative models for stock selection.
  • Asymmetry of Alpha: Have you ever wondered why on the one hand, debt-ridden companies underperform healthy ones, while on the other hand, triple AAA companies do not outperform average ones? Welcome to the world of factor asymmetric alpha. In this paper, Rani Piputri explores this subject and shows how to benefit from this phenomenon.
  • Gradual Information Diffusion, by Professor Ben Jacobson and PhD candidate Helen Lu. Ben is an advisor to Saemor Capital: Is the oil price a leading indicator for stock returns? And if so, how? Do some industries lead the market? And which ones? Questions like these are on many investors’ minds. Gradual information diffusion – a new theory describing how information flows across markets – is providing more of an answer for quant investors. If the supporting empirical evidence uncovered to date is anything to go by, this method is quickly becoming a promising means for predicting market returns. Employing signals from other markets could potentially help us in the allocation of risk to different sources of alpha..
  • Please contact us if you are interested in reading the full pdf articles.

Posted on January 25, 2015