ASR's Proprietary Indicators
ASR’s proprietary indicators provide clients with a range of datasets designed to inform the investment process and aid the construction of portfolios.
Click HERE to contact us for more information on any of these products.
Thematic ETF Tracker
follows the performance of the ‘ASR Thematic 100’ list, a screen of exchange traded funds that all play on a range of investment themes. We sub-divide these into 14 sub-indices, including demographics, cyber security, robotics & AI, and water.
Click HERE to see sample.
Recent years have underlined the value of analysing sentiment trends across assets. On this front, we have developed two proprietary sets of indicators that quantify sentiment across a wide range of assets.
ASR ‘poll-of-polls’ indicators are based on combining several sentiment surveys into a single indicator, that provide a broad view of investor sentiment on a number of markets. However, a number of markets are currently not covered by sentiment surveys.
ASR Sentiment Barometer Indicators (SBIs) were developed to fill this gap. The SBIs use price action to create sentiment series that have a strong relationship with the output derived from actual sentiment surveys. They combine several algorithms which quantify behavioural elements such as ‘price anchoring’ and trend ‘overconfidence’, as well as shorter-term momentum and have an 80% correlation with our ‘poll-of-polls’ indicators.
Making Use of SBIs Sentiment Barometer Indicators flag up when a market is at risk from over-extended optimism or pessimism. They are a timing tool, to be used alongside other strategies, highlighting when it may be worth considering a contrarian trade or adding portfolio protection.
Our ASR SBIs now cover over 1000 markets, encompassing global equity indices, global bonds, fixed income, FX and commodities.
A couple of examples of SBIs in action are as follows:
ASR Leading Indicators
Provide a lead of around 6-12 months on the business cycle in Europe, the US, and Asia. Compared with the OECD leading indicators, our metrics provide a longer lead on the cycle as well as excluding all market-based indicators.
ASR Surprise Indicators
The ASR Surprise Indicators measure the extent to which economic data have deviated from the expectations of professional forecasters on a daily basis over the previous quarter, providing a timely and high frequency indicator of economic news. In contrast to some other Surprise Indicators we do not use weights to aggregate our indicators, in order to capture a more pure measure of surprise.
ASR Expectations Indicators
Provide a real-time indication of analysts’ economic expectations. The indicators measure a broader definition of ‘economic activity’ but work well as a real-time daily series of Consensus GDP forecasts. Our Expectations Indicators are constructed in a broadly similar way to the Surprise Indicators. Median consensus forecasts are taken for the same set of variables as are used in the surprise indicators, and these are then normalised and aggregated using equal weights.
ASR Business Cycle Indicators
The ASR Business Cycle Indicators (BCIs) classify the current stage of the business cycle in real time (i.e. recovery, mid cycle, late cycle and recession). They capture a cycle lasting between three and ten years from peak to trough and are based on five underlying components: the investment to GDP ratio, unemployment rate, corporate margins, consumer willingness to buy durable goods, and growth in credit to the private sector.
ASR Financial Stress Indicators
Seek to capture stress in the banking sector, equity and foreign exchange markets. We use seven components to create the overall FSI banking sector beta, Ted spreads, inverted term spreads; corporate spreads, stock market returns, time-varying stock volatility and time-varying real effective exchange rate volatility. We identify episodes of ‘financial stress’ as extreme values of the composite FSI, where the index is greater than one standard deviation away from its historic trend.
ASR Logistics Indicators
Comprising volume data for freight activity at the busiest airports and container ports in Asia, Europe and the US, ASR’s Logistics Indicators are designed to provide a timely read on global trade growth. Our Air Freight Indicators, which are more sensitive to changes in demand, also provide a crosscheck on a number of cyclical indicators such as the performance of Cyclical vs Defensive Equities, Commodity prices and EPS growth.
ASR High Frequency Data Pack: The COVID-19 Lockdown Monitor.
Available on a Friday. Client-only.
ASR / WSJ Newsflow Indexes
Absolute Strategy Research (ASR) and the Wall Street Journal (WSJ) are now working together on the production and development of the Absolute Strategy / WSJ Newsflow indices. ASR is responsible for the production of the data and the analysis embodied in this research report. WSJ will simultaneously publish the data and provide independent commentary.
What do the ASR Macro NewsFlow Indexes measure?
The ASR Macro NewsFlow Indexes track the difference between the number of positive news stories and the number of negative news stories. Every month we search Factiva, the Dow Jones news database, for six key variables, and count the number of articles where these key words are found in a positive context and compare that to the number of stories where they are found in a negative context. One of the strengths of Factiva is that it has a long history which allows us to see how the net balance (i.e. the difference between the number of positive and negative stories) has moved over the past two decades. We can then compare each net balance with an actual economic indicator to verify that it tracks well. As well as the six key variables, we have also created a Composite NewsFlow Index (CNI) which provides an overall measure of economic surprise.
Can we compare the U.S. results with that of other countries?
Cross-country comparisons are possible but with one important qualification: all our searches are done in the English language. At first, we thought this would be a major problem, but the results for countries such as Japan and China have been quite good despite relying solely on English language sources for what is happening in those countries. Eventually we would like to expand our searches to include other languages as this would make the indices more robust.
What can investors and economy-watchers glean from the monthly indexes?
We believe that the ASR Macro NewsFlow Indexes are a good way of monitoring economic surprises, particularly surprises that journalists regard as sufficiently newsworthy to merit an article or report. Whereas we can all read one daily newspaper, such as the Wall Street Journal, it is not possible for us to read multiple newspapers from around the world. Because Factiva draws on an extensive range of different news sources, it allows us to see patterns. In effect what we have is a macro surprise indicator, curated by what journalists think are important and relevant.
The series do not lead the traditional data releases, but they provide a very timely tracking of the global economy. We normally publish the previous month’s results on the Tuesday after the US non-farm payroll release. The ASR Macro NewsFlow Indexes thus give us a cross check on economic growth, analyst estimate revisions to corporate earnings, revenue/sales, employment, inflation and the perception of monetary policy. We publish a global aggregate as well as six country/regional breakdowns. Interestingly, the Composite NewsFlow Indicators (the CNIs) provide an additional insight for asset allocators with a useful correlation to the year-on-year change in stocks versus bonds.
Are the indexes seasonally adjusted?
We do a simple seasonal adjustment of the results. We also adjust for the fact that there are more news sources on the Factiva database today than there were twenty years ago. An alternative approach would of course be to identify news sources that had been on Factiva over the entire period, but we feared that this might limit the sample size excessively.
What inspired the indexes?
Investment strategists have used simple macro word counts before. Indeed, one of the worst jobs for a summer intern was to be asked to go through back copies of the Wall Street Journal or Financial Times and count the number of references to ‘recession’ or ‘inflation’! But few people have systematically worked through 20 years of Factiva data and created series that so clearly track key economic variables. What makes ASR’s searches unusual is the way we have tried to add context, to try and sort the positive stories from the negative. After all, there is a big difference between going INTO recession – and coming OUT of recession – keyword search simply won’t pick that up that distinction. This is what makes the Factiva software so well suited to this task. What inspired us back in 2007 was what we saw as an untapped source of macroeconomic data, namely a news database curated by journalists, together with software that allowed us to interrogate that database going back twenty years. We saw the potential to create quantitative measures where no reliable data existed (e.g. for some emerging markets), as well as the scope to track interest in investment themes and for industrial sectors.
How have these indexes, looking back, tracked other long-established sentiment indexes as well as other rough measures of economic confidence, such as the S&P 500?
When we look at the current deck of Macro NewsFlow Indicators, we are astonished at how close some of the relationships are with some of the official economic data or with Markit PMI components. We believe the process we have developed has the potential to create similar indicators for stock market indices at both the country and sector level. In principle it should be possible to track the fundamental NewsFlow associated with the constituents of the Dow Jones Index or the S&P500. However, so far we have not yet attempted to create new measures of stock-market sentiment, but the potential is there.
Who to contact
For further information about the ASR/WSJ Newsflow Indices, please email email@example.com
Our Dividend Dynamo methodology was developed created to screen for stocks that are at a low risk of dividend cuts, and incorporate those stocks into an index that avoids sector bias.
We rank each stock on the following criteria: EPS growth; EPS volatility; interest cover; cash conversion; and case return on equity. These rankings are added together with equal weights to get the Dividend Dynamo score.
We then then take the best three in each ICB Industry to get the ASR Dividend Dynamos, or the worst three stock for the ASR Dividend Dilemma.
These screens are based on the ASR Europe 600, ASR Eurozone 500, ASR Global 1500, ASR US 500, ASR UK 350 and ASR Japan 300 stock lists. Constituents of these indexes are available upon request. Indices are rebalanced on the first day of each quarter.
Sample index performance is shown in the chart below.
Equity Risk Premium
Over the long run, US equities have given investors a higher return than US bonds. As total return data from Dimson, Marsh and Staunton show, US equities have had a total return of 9.9% pa since 1955, while US bonds have returned 7.1%, a difference of 2.8% each year. Of course there is a reason for this: holding equities in a portfolio is more hair-raising, since their price fluctuates more. This additional return is a premium for taking on risk and is called the Equity Risk Premium.
Since the art of portfolio construction is based around balancing risk and return, understanding and estimating future equity returns is a vital step in building a multi-asset portfolio. However, using this historic ERP in portfolio construction requires faith that the future is going to be like the past. In our view, a better, more forward looking approach is to use the ERP that is implied by current market prices.
All the major textbook methods of estimating the implied ERP have issues: the simpler ones have model risk (that the model is not representative of the real world), while the more complex ones have input risk (that the input variables might be incorrect). One solution to this issue is to harness ‘the wisdom of the crowds,’ in which the error of a set of estimates is lower than the error of a single one. So we combined 9 models into a composite. We have aggregated using the median, rather than the first component of a PCA analysis since it possibly detects more of the shifts in the underlying ERP, and is less affected by simple price moves.
We have built composite ERP estimates for 19 countries around the world. While the calculation of the Eurozone ERP as an aggregate of countries or a single entity might appear to be an important conceptual difference, in practice we have found the results of both methods to be similar. We have also created an ASR Global Composite ERP, weighted by equity market size.
The Equity Risk premium dataset is produced by Absolute Strategy Research Ltd, which is authorised and regulated by the Financial Conduct Authority. The ERP data provides general information only and is not intended to form the basis of an investment decision. The information on which the ERP data is based was obtained from sources that we believe to be reliable but we do not guarantee that it is accurate or complete, and it should not be relied upon as such.