Investment Strategies

Alpha Insights Driven by Investment Fundamentals

Bram Zeigler is a Portfolio Manager at Algert Global, sub-advisor to the International Small Cap Fund. Using behavioral finance principles and his research-based knowledge, over 20 years of practical experience has shaped his insightful understanding of economic and market efficiency.

Additionally, he holds a patent for NASDAQ’s SuperMontage trading system, the exchange’s fully integrated order display and execution engine. Bram shares his views on international small caps and the search for alpha using Algert Global’s unique process.

How does your investment process aim to capture alpha?

We believe that there are market dislocations caused by emotional and cognitive biases of investors. Our process aims to identify companies that we believe are fundamentally mis-valued and are most likely to converge towards our model’s estimate during our investment horizon (2 to 3 earning cycles). We build fundamental models in a systematic way and test our assumptions over long time horizons.

How is your modeling unique from an index ETF or smart beta product?

Many active or passive managers are using fundamental data to measure what we consider to be generic market premia. For example, momentum is a commonly used factor. We have a Catalyst model that looks at attention and sentiment, which analyzes the sources of momentum in contrast to other products that measure the mere existence of momentum. For example, looking at text from a company earnings or analyst call using natural language processing to measure sentiment indicators could be a source of momentum.

How do you tailor your process depending on global region?

Another differentiator we believe we have is tailored signals contingent on geography and industry. The accuracy of our process depends on an appropriate relative peer group ranking. The Algert investment team is comprised of regional sub-portfolio specialists that share ideas implemented globally in a highly collaborative way. New signals are regularly deployed in a thoughtful, customized way by region and sector.

What risk management oversight is utilized in the investment process?

We intend to generate alpha from stock-specific returns, as opposed to unintended market risk, on a consistent basis steadily through time while seeking the fund’s objective of long-term capital appreciation. We estimate that approximately 90% of the portfolio’s active risk is due to stock specific factors. Further, our process aims to rank stock level projected returns independent of any returns expected from common market factors (e.g. country, size, industry, beta). This is often called ‘neutralization’ and is the ability to systematically assess a company’s characteristics independent from multiple common characteristics.

International small cap stocks trade with less volume than large cap. How do you manage transaction costs?

We forecast and measure transaction costs on each trade pre- and post-execution. Our trading philosophy is to trade often and in small size. We use this to both mask our investment intentions from other market participants and minimize market impact. For each trade, we also measure the performance of the executing broker relative to an estimate of realized cost based on our order history with similar characteristics.

Stock selection and transaction costs appear to be key parts of the investment process. How do you integrate them?

The portfolio construction process we use to implement our portfolio is optimized according to multiple targets. These include beta targets, benchmark constraints, and tolerance for systematic and idiosyncratic risk. Liquidity, holding and trading parameters also go into the final portfolio construction process. Our goal is to maximize the portfolio’s exposure to our stock selection rankings, which we believe is our value-add, subject to trading and risk constraints.

How many data points do your quantitative models processing each day?

We use mainly proprietary data signals and on any given trading day, our models process thousands of individual data points. As you can see this is far more than any individual or team could evaluate at an individual stock level. We conduct ongoing research to develop new signals that we believe give a more accurate picture of market investment opportunities. Our driving theme is consistency and repeatability with innovative data processing techniques that reflect our fundamental view of the market.

Beta is a measure of the volatility of a fund relative to the overall market.
Smart Beta strategies attempt to deliver a better risk-return profile than conventional market cap weighted indices by using alternative weightings schemes based on calculated measures.
Alpha is a measure of the portfolio’s risk-adjusted performance. When compared to the portfolio’s beta, a positive alpha indicates better-than-expected portfolio performance and a negative alpha worse-than-expected portfolio performance.

Important Risks: Equity securities, such as common stocks, are subject to market, economic and business risks that may cause their prices to fluctuate. Investments made in small capitalization companies may be more volatile and less liquid due to limited resources or product lines and more sensitive to economic factors. The Funds may invest in foreign securities which involves certain risks such as currency volatility, political and social instability and reduced market liquidity. Emerging markets may be more volatile and less liquid than more developed markets and therefore may involve greater risks. The Funds may invest in ETFs (Exchange-Traded Funds) and is therefore subject to the same risks as the underlying securities in which the ETF invests as well as entails higher expenses than if invested into the underlying ETF directly.

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