Systematic Value portfolio
The Bellmont Systematic Value Portfolio is a value investing share portfolio which uses a rules-based approach to select and invest in Australia’s highest quality stocks trading at reasonable prices. Our objective is to offer a managed share portfolio that is designed to generate higher total returns than simply investing in the broad market*.
At a high level, we have designed and built a rules based stock selection process that eliminates us from the cognitive biases that we all inherit as humans. Free from these biases that impede all investors, our model examines years worth of institutional grade financial statement data filtering out the bad (and potentially bad) and selecting only those stocks that meet our exact criteria.
The model initially filters out stocks with evidence or characteristics that suggest potential issues in the financial statement data. This method alone helps us avoid stocks that could be at risk of bankruptcy. Once these stocks have been removed we look at identifying business characteristics which provide them a competitive advantage on their peers. Finally, we select only those stocks that we can purchase at reasonable prices to ensure the best possible likelihood of attractive long term investment returns. In short, our process is as follows:
- Enhance our margin of safety by avoiding stocks at risk of bankruptcy
- Identify high quality companies
- Identify companies that trade at reasonable prices
The Bellmont Systematic Value Portfolio provides investors with hands off exposure to the Australian sharemarket.
- A diversified Australian share portfolio
- Rules based approach
- 20 stock equal weight
- Direct ownership of stocks
- Full transparency
- Tax effective ownership
- Full real-time reporting
- Low Base Fee with performance fee to align incentives**
* Benchmark : ASX 200 Accumulation Index
** 0.65% annual fee + 20% outperformance fee
Trusting the data and model – ensuring reliability of back test results
The Systematic Value Portfolio is unapologetically mechanical. By design, it is free from the behavioural biases that influence the decisions of most fund managers. The decisions to buy and sell are instead driven by the rules we have intentionally built into the model. Inherently, this systematic approach also allows us to better understand the performance and risk of our strategy through simulation and back testing, providing investors with more information up front.
Investors are right to question the reliability of back-test results, particularly those showing excess returns over the benchmark. Critics often point to data mining (the practice of examining large volumes of data in order to find an optimal investment method). In an effort to illustrate the ridiculousness of this practice, David J Leinweber (a physics and computer science graduate from MIT) famously gathered hundreds of data sets and found that the S&P500 could be predicted by looking at butter production in Bangladesh! If we examine enough data sets we are bound to find more meaningless coincidental patterns.
Rather than blindly examine any type of data set, we limit our analysis to those factors identified in academic research, published in top rated peer reviewed academic journals, and that have continued to exhibit ongoing excess returns subsequent to their discovery and publication. It is by sticking to this time tested approach i.e. examining only those metrics that have been accepted and proven through both interrogation by peers, as well as through the passage of time that we can truly have confidence that we have a sound investment methodology rather than stumbling on some contrived data correlation.
Further to the criticism of data mining, sceptics often question the validity of backtest results with most arguments falling into two camps. These potential pitfalls will be outlined below as well as how we treat them to ensure we can have confidence in our model.