Optimizing share market return using fundamental financial analytics

Reading time: 5 mins

Here, authors Cary Helenius, Kevin Gomes and Graham Taylor discuss analytic techniques to identify the influence of company financials on shareholder return, and whether their model can predict future market out-performance.

This article is a follow-up to our 2014 article “Fundamental financial analytics”  in which we discussed the use of analytic techniques to identify which and what level of company financials influence shareholder return. One future direction mentioned in the article was whether an alternative statistical model could have funds management applications – whether we can use the findings to predict future market out-performance. The results of that investigation are presented here.

Our research

The milestones of our research can be summarised as follows:

  • “Market Eye Q” Understanding the Drivers of Shareholder Return presentation at the Actuaries Summit 2013 where we identified statistically significant relationships between listed companies’ financial ratios and total shareholder return (TSR). For example, companies that release financials showing a high level of ROE and a positive change in ROE will, on average, have a larger positive change in their share price relative to other companies.
  • Market Eye Q” Analysis of financial management levers for stock selection” paper at the Actuaries Summit 2015 where we presented our findings of better than market average returns from using our alternative model coupled with simple prior price momentum filters to select companies in which to invest.

Before our presentation at the Actuaries Summit 2015 we optimized the future actual return by back testing combinations of prior price momentum and TSR predicted by our model although those initial findings did not make it into the paper.   Over the 2 years to December 2016 we have monitored, without influence of hindsight, the return from companies selected by our model/prior price momentum filter by the following groups:

  • Group A - top 50% of companies predicted by our model of TSR.
  • Group B - top 50% of companies predicted by our model of TSR with a prior price momentum filter.
  • Group C - top 25% of companies predicted by our model of TSR.
  • Group D - top 25% of companies predicted by our model of TSR with prior price momentum filter.
  • Group E - companies predicted by our optimized model TSR and prior price momentum filter.


The return for companies selected by our model is calculated from the end of the day of release of the half-year/full-year company’s financials to the end of the next half year, around 3.5 months later, (typically to the end of June or December) and prior to the next release of financial reports.  We then averaged the return over the companies selected (assuming equal investment in each) at each half-year and compounded the return over the 4 periods of investment to December 2016.  The results for Group A to E are shown in the table below.

Some commentary on the table:

  • Including a prior price momentum filter improves the return – compare Group A to B and C to D. This makes intuitive sense – a company may release a good set of financials but the market deems the outlook poor – this was the case for a number of mining engineering companies.
  • The optimized model TSR and prior price momentum filter (Group E) produces the largest return (40%) although from selecting only 13 companies.

The table below shows a sample of model picks from Group B (some highs and lows) over the 2 years to December 2016 and the actual return from end of day of release of the financial report to around one month before release of next half-year or full year financial report.

Results in context

The returns in the first table compare favourably to the change in the ASX 200 which increased 5%, from 5,411 to 5,665, between end December 2014 and end December 2016. (Noting for this analysis we have excluded dividends from our returns to make them comparable to the ASX 200 index performance, and our group of modelled companies excludes banks and miners as their reported financials are not comparable to the broader market.)

To put the results into context with respect to the companies we have included in our modelling[*], we have plotted below the actual investment returns shown above for Groups B, D and E against the distribution of investment returns formed by the simulation of 55, 30 and 13 randomly selected companies respectively at each half-year for the 4 half-years to December 2016 (and calculating the compounded return as we did for our selected companies).

Some commentary on the charts:

  • In each case, there is fairly compelling evidence of out performance. For example, the 40% return seen for Group E would only have occurred by luck 1/15th of the time. Equivalently, the 1/15 is a measure of the statistical significance of the result, when compared to random selections of portfolios of the same size.
  • The significance of the result B and E is similar, despite the higher return for E. This reflects the shape of the (slightly skewed) bell curve; it is harder to achieve the same level of outperformance with a larger portfolio.
  • The return of 26% for Group B is the same as the return for Group D, although the statistical significant of the result for Group D is not quite as strong.

There are also some pragmatic considerations when interpreting the results

  • These results may change if monitored over a longer period than 2 years (they are subject to a degree of random influence)
  • Some of the financials in our model, at least over this 2-year period, may benefit from re-weighting (noting that the prior price momentum filter did not have as large an impact on the top 25% of model selected companies as it did for the top 50% - see first table and compare return for Group A to B and for Group C to D).


  • Companies that report strong financial performance, on average, outperform the investment returns of those that do not. Market outperformance from this portfolio of companies can be achieved by investing in these companies at the end of the trading day following the release of financials and holding these stocks to a month or so before the release of the next financials.
  • The investment performance of this portfolio can be further increased by including a prior price momentum filter across the companies that have reported strong financial performance.
  • Significantly higher than average market returns can be achieved by investing in companies selected by our optimized model TSR and prior price momentum filter.

[*] We include around 200 ASX companies at any point in time excluding banks and miners.


*DISCLAIMER: This article should not be considered as financial advice.

CPD Actuaries Institute Members can claim two CPD points for every hour of reading articles on Actuaries Digital.

About the authors

Graham Taylor

Graham has over 25 years of experience as an actuarial analyst in general insurance. He worked for Tillinghast - Towers Perrin for 12 years before joining Taylor Fry when it formed in 1999. Graham has extensive experience in valuation of long tail classes of business including CTP, medical malpractice and workers' compensation. Over the last 15 years he has used various statistical software packages to build models to explain a diverse range of data from predicting the probability of common law claims to the outcome of AFL and NRL matches.

Kevin Gomes

Kevin has over 25 years experience as an actuary working across a wide variety of fields, including life insurance and investment management. Since 1996, Kevin has specialised in general insurance, including working as a consulting actuary since 2002. Kevin currently works at Taylor Fry Consulting Actuaries, where he sits on the Board of Directors. Kevin believes strongly in contributing to the actuarial profession, having served on the General Insurance Practice Committee from 2006 to 2010 and chairing the Organising Committee for several Actuaries Institute seminars. He also served for many years as a member of the Actuaries and Accountants Liaison Committee. In his spare time, Kevin is a keen sharemarket investor and enjoys searching for tools which might give him that elusive edge.

Cary Helenius

Cary has been a qualified fellow of the Actuaries Institute for over 20 years and has over 25 years experience in financial services and equity markets. He was formerly an equity partner and Executive Director of Goldman Sachs JBWere where he spent 10 years as a senior research analyst covering the insurance and gaming sectors and 5 years in the investment banking division. Cary is based in Melbourne and is currently an Executive Director of Capital Advice and Market Eye, and the Director of Equity Risk Management. Cary has wide experience in equity market research, investment banking and corporate advice. He has been involved in the Actuaries Institute Superannuation Committee, Committee for the Future, and presented at various Institute and other industry conferences over the past 20 years. From 2004 to 2012, he lectured on Investments as part of the actuarial education provided though the University of Melbourne and the Actuaries Institute professional education program for APC 1, APC 2 and the recently introduced investments based APC3.

Comment on the article (Be kind)


No Comments

Also this month