HINDE CAPITAL RESEARCH · Robo-type passive investing is modelled on over three decades of declining interest rates, which in some countries are still negative. The buy to hold, buy

HINDE CAPITAL RESEARCHHINDE VALUE STRATEGYSEPTEMBER 2018

56 Shoreditch High St, London E1 6JJ

Email: [email protected]

HINDE CAPITAL

Mark Mahaffey Lukas DillowAalok Sathe

CONTRIBUTORS

Co-Founder of HINDE CAPITAL

Visiting ResearcherFund Manager

02 Hinde Capital Research

Hinde Value Strategy

INTRODUCTION

INDEX

Any reader of the current financial press will not have far to go to find an article on the benefits of passive index investing, which usually recommends a low-cost provider with an ETF (Exchange Traded Fund). Passive investing relies on the belief that an investor will be better off ‘merely tracking’ a whole equity index, like the FTSE, rather than pursuing a more actively managed strategy, where a basket of individual stocks is selected in a portfolio targeting an outperformance to the index, especially in bear markets. The argument typically relies on the fact that the historically high costs charged by active fund managers have been far greater than any outperformance achieved, especially over the long-term.

We could debate this issue at length, but briefly we believe that once you add in all the extras to your ‘cheap’ model, such as IFA fees, platform and dealing fees, the actual charge is very different from the headline price, rather like a budget airline pricing structure, i.e. not much different to before!

While the statistics may show that the ‘average’ active strategy has slightly underperformed the index, it also means that many have outperformed, some markedly so. We are also well reminded that all Robo-type passive investing is modelled on over three decades of declining interest rates, which in some countries are still negative. The buy to hold, buy on dips, pound-cost averaging, fully invested strategies have tended to work out, even after two 50% declines in 2002 and 2008 but, unfortunately, valuations on any historic metrics are now insanely overstretched.

The research paper that follows is a small selection of our work, undertaken to demonstrate our commitment to the knowledge that active strategies, both in the actual stock selection and cash levels in portfolios, can have material differences in, not just wealth creation, but more importantly wealth protection. Despite seeing investors struggle for returns in low interest rate and quiet markets currently, we expect to see much less interest in passive investing at the bottom of the coming bear markets.

THE COMPANY

04UNDERSTANDING

HINDE VALUE MODELTrend IndexLong-Term RSIWhat are we testing?

12SIMULATING PROFIT-TAKING RULES

Profit Taking Based on Return Structure (%)Profit Taking Based on Day Count

21APPENDIX

Examples Picked at Random

09BREAKDOWN

20CONCLUSION

06VISUAL DEPICTION

03OVERVIEW


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After several years of research, the decision was taken to combine and test Hinde Capital’s most powerful models to identify long-term equity opportunities. This procedure brought together the Hinde Value Model (fundamental), the Hinde Trend Index (technical) and a Long-term RSI Signal (technical).

This methodology involved taking all the constituents from the FTSE 350 and ranking them using the quantitative equity model. These constituents were then churned through the tactical trading models (Trend Index & Long-Term RSI) helping to combine the three models. Each stock was then allocated a score and a trading signal, if a trading signal was generated. This research was undertaken to understand significance of combining these models and considering how best to exit each signal.

The results presented an observation skew of 65:35 (positive to negative results) with an average positive return ranging from 10-48% (relative to the index) across a series of time horizons. The most basic testing that was carried out only took into consideration a stop rule to the downside (each signal was stopped if it ever underperformed the index by (25%)).

This system was later enhanced by using profit-taking measures (based on time or a specified return target) to ensure a more systematic method on both the upside and downside. Testing showed that taking profit based on a specified day count, rather than a targeted relative return, was more beneficial.

The first batch of signals was implemented (from 2009 to 2018) into an index enhancement strategy, which yielded over 133% in comparison to the broad market index that generated 86%. The second batch of signals generated between 2006 and 2009 (including the financial crisis) was also implemented into a similar index enhancement strategy that produced a return of 48% relative to the broader market that generated 9%.

Overview



UNDERSTANDING

Trend Index

Hinde Value Model

LONG-TERM RSI (Relative Strength Index)

The trend index looks to assess the compressions and expansions of a stock. A large movement results in the trend index rising and, conversely, it falls, if a stock trends sideways for a period.

The indicator ranges from 0 to 100 and the time frame for comparing up periods to down periods can be chosen at will. In our analysis, we have chosen an 18-month period as prior research has shown it to be statistically significant. An RSI value within the 10th percentile tends to identify strong undervalued opportunities and those in the 90th percentile tend to identify overvalued opportunities.

This is a quantitative factor model that assesses four key areas:

The Relative Strength Index (or RSI) is a momentum indicator that focuses on recent price changes in any liquid asset, helping to identify oversold or overbought conditions. The RSI is calculated by using the formula below:

RSI = 100 - 100 / (1 + RS)

RS = Average gain of up periods during the specified time frame

Average loss of down periods during the specified time frame


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We looked to test when the constituents were:

• Ranked highly on the Hinde Value model• Trend exhausted (to the downside)• RSI Oversold

The simulation was run from December 2009 and helped to compile a list of constituents that fit the criteria.

This report looks to document the findings of this testing on what are believed to be valuable, longstanding buying opportunities that would help investors to compound significant wealth. The combination of factors described can create one of four possible key states (as shown below). In this report, the team is clearly modelling the one highlighted below:

What are we testing?



The first two charts illustrate the proprietary trend index and the signals that it generates.

Visual Depiction

The final chart shows the long-term RSI score.


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• The analysis period from 2010 to 2018 produced 152 signals that on average presented 18 investment opportunities annually.

• Of these signals generated, 72 observations would have been stopped out at some point over a two-year trading period. However, it is important to understand that of these 72 signals, 37 were straight losses whilst 35 made significant gains before falling to their stop levels. Therefore, if we only had a strict stopping rule and not profit-taking criteria, then 114 (or 76%) observations were highly profitable and only 37 (or 24%) were true losers. It is important to understand that of the 72 observations, 37 were straight losses whereas 35 made significant gains and then later fell to their stop levels. Therefore, 114 (or 76%) observations were highly profitable and only 37 (or 24%) were real losers.

• Based on the assumption that all losing trades were closed out when they underperformed the index by 25%, we can see that the positive to negative returns were skewed 65:35 on average across all time horizons.

• Over a 12-month period, 71% of stocks produced significant returns while the rest would have been stopped out, delisted or taken over. This number falls to 58% as we reach the 24-month period.

Key Findings



• The chart above displays how our signals were split by industry group. For example, approximately 11% of the 152 signals over the observation period came from within the mining sector etc. Note: signals were only taken if the same equity did not appear in the previous month to avoid duplication. Another note is that if the date for the equities that produced a signal in recent years was beyond the last valuation date, we used the last valuation price as an alternative. Therefore, some of the values for the 2017 and 2018 results were over a shorter period.

• If we negated all the stocks that made less than 5% (those that dropped straight away), approximately 50% of all the signals reached their high within 140 days. Furthermore, 60% of all the signals reached their high within 180 days.


September 2018 09

BREAKDOWN

According to Table 1, 52% of the observations were purely positive opportunities.

According to Table 2, 23% of the observations initially rose to become profitable positions, but they were then eventually stopped out as they underperformed relative to the index. For example, Hunting plc appeared on 30th of January 2015 and rose rapidly relative to the index up until the 30th of June 2015. After this point, the stock collapsed rapidly on a relative basis and would have been stopped out, down 25% versus the index. This oil and gas position would have been closed on the 8th of January 2016.

Table 3, shows the distribution of the highs that the 23% of observations initially reached before they got stopped out on a relative basis to the index. 77% of the 35 observations rose to between 10% and 30% against the index. Finally, 23% of the 32 observations rose above 30% relative to the index before falling.

Table 4, splits all the observations that produced a positive return and were never stopped out.

Table 3

Table 1

Table 2

Table 4



Table 5, shows all the observations that would have potentially enabled an investor to produce a positive return on a relative basis to the index.

Table 6, demonstrates the average number of days it took for a stock to reach its high – for both the stocks that fell and the stocks that stayed high. Furthermore, we have shown the average number of days taken for a stock to reach its stop, if it did fall over the 2-year period.

Table 5

Table 6


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Graph 1

Graph 2

Graph 1, shows the frequency of signals that reached their high between the range shown on the x-axis. This data includes both tickers which rose before falling, as well as the tickers which never dropped. The 38 stocks that fell straight away have not been included. 8% of the signals reached their maximum between 45 to 60 days and proved to be the modal interval.

Graph 2, The graph above shows the proportion (as % of total signals generated) of signals that reached their high between the day ranges shown on the x-axis. Approximately, 50% of occurrences reached their peak within 180 days and 80% of signals reached their peak within 390 days.

Based on the analysis conducted, it would be advisable to take profit where the proportion of signals are greater than the trendline (between 100 and 300 days). With the average of this being 200 days, this may be the preferable time to sell. There is a clear plateau in the proportion at around 250-300 days. After this point, there is a rise, but the data suggests that waiting longer than 250 days may not provide a greater reward.



SIMULATING PROFIT-TAKING RULES

Profit Taking Based on Return Structure (%)

Profit Taking Based on Day Count

It is vital to have a structured method for taking profit on any opportunity, just as it is important to have hard stops. This process was conducted by starting with our original data that only considered the hard stop. It was evident throughout our analysis that any profit-taking rules would be based on:

• Time• Target Return (%)

We firstly overlaid our data with boundaries based on a return (%) target or a time factor (day count). The two graphs below show the average maximum or minimum return (%):


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A subset of the original simulation (that consisted of only the stop rule) was created and enabled us to observe how much each position gave back from its high to its closing NAV within a day count. It was evident that approximately 50% of winning trades lost up to 15% before reaching their closing NAV. Therefore, the importance of profit-taking rules was very clear.

It was then decided to apply both the time and return conditions concurrently to help drive the scenario testing shown below. Profit was taken on any trade that either reached a specific relative return (%) or reached the end of a defined period. The numbers below illustrate the average maximum positive relative returns (%). It was evident that as you increased either factor the rate of increase in returns diminished. Therefore, targeting a relative return or day count beyond 60% or 180 days seemed inefficient as the extra alpha generated was trivial.



Testing % Return Limits: Observing Boundary to Closing NAV Performance`

Applying the Final Rules

It was vital to understand whether setting a relative return (%) limit helped to improve /capture a larger proportion of the overall NAV for each signal. It was apparent that even as the boundary was raised from 110 to 170, the average positive return (%) failed to fall. Beyond these boundary points, those stocks that traded higher tended to be driven by momentum and this momentum usually came due to idiosyncratic reasons that are tough to model. Therefore, it is difficult to set a consistent profit taking rule based on a return (%) target, across the entire signal database.

After evaluating all the results, it is evident that the best performance is achieved with (25%) stop relative to the index and on the upside, having a strict 180-day count limit. The return structure over a strict 180-day count limit proved to be much more uniform and therefore consistent to model. Based on the overlays suggested above, this gave a start and end date for every signal generated. As a result, please see the 30 biggest winners and losers on an absolute return basis (including dividends) below:


September 2018 15



Index Enhancement Strategy

Following on from the observations above, the next step was to show that if each signal was employed as an alternative to a proportion of the FTSE100, this would enhance the overall performance of the index. Every time our model generated a signal, 2.5% would be allocated to each live signal and the remaining assets would be allocated to the FTSE100.


September 2018 17

As shown above, the enhanced strategy from 2010 to date has generated 133% compared to the index that gained 86% over the same period (total return). The true effect of the model stock picks would only have taken effect from 2012 onwards as this was when the number of live signals rose to an effective level. In conclusion, it is evident that the signals generated (including all the stop and profit-taking rules) had a statistically significant impact on the strategy’s overall performance in comparison to the mainstream index.

Number of signals held over time



Assessing Signals Generated between 2006 & 2009

2006 to 2009 was a significantly volatile period, during which there were 145 unique signals. Out of this sample set, 59 signals generated a relative NAV greater than 125. If held to the end of the 180-day period, 60% of stocks would have been losing positions; however, 75% of these could have been closed at a profit before being stopped out.


September 2018 19

The positive to negative skew (based on the final closing NAV) was 40:60 with the average return being 8.7% on a relative basis. The average maximum positive return was +32.5% and the average maximum negative return was (17.3%).

Over a four-year period that included the financial crisis, the index enhanced strategy meaningfully outperformed the broader market, generating 48.8% and 9% respectively. Looking at the charts above, it is clear that the model stock picks added a large amount of alpha.

From 2006 to 2018, there were 296 signals, which presented approximately 24 trades per annum. Of these 296 signals, 34 (or 11%) were truly losing trades as these trades never achieved a positive return relative to the index. The modal group was 0 to 10%, which according to the data suggests that 24% of observations within their lifetime could have achieved a maximum return between 0 and 10% relative to the index. Finally, 60% of the signals achieved a high ranging between 10 and 90% relative to the index during their live period.

For All Trades (2006 to 2018)



The testing carried out has demonstrated that the model/signal generation produced a significant positive return skew. The models were generated on a monthly basis to help reduce noise, especially within volatile periods. This was evident over a variety of periods with every signal being maintained using strict stop/profit-taking rules. Opportunities that traded unfavourably were closed out down (25%) relative to the index.

Conversely, signals that traded well could run for up to 180 days, given that the research showed this was the most optimal trading period. A day count profit-taking rule was employed given that a methodology based on positive relative returns was not as effective (and not a possible model). Rules set on a day count methodology generated far greater/stable returns in comparison. The average maximum return over the period was higher on average along with the average minimum return.

This simulation was run in two parts. It was initially applied to the 2010 – 2018 period. This was followed by its application on the dates ranging from 2006 to 2009 (which included the financial crisis). Across the two periods, the quantitative model produced 145 and 152 opportunities respectively.

Applying these signals to an enhancement strategy showed that it had a significant impact on the performance of the broader index. For the initial period from 2006 to 2009, the enhancement strategy generated a total return of 48% in comparison to the index that generated 9%. Similarly, during the 2010 to 2018 period, the enhancement strategy generated over 133%, whereas the broader index produced a return of 86% (total return) from 2009 to 2018. In both periods, it is clear that the model stock picks added significant value to the portfolio.

Conclusion


September 2018 21

Appendix

Examples Picked at Random

Hunting plc rose to a significant profit which would have been taken before, but then fell relative to the index. This is an example of a signal which fell to a NAV of 75 relative to the index, but was originally a profitable trade.

Brown (N) Group plc was similar to Hunting plc, as it initially rose before falling to its stop level relative to the index. This profit would’ve also been taken.

Hunting plc(HTG LN EQUITY)

Brown (N) Group plc (BWNG LN EQUITY)

Start Date – 30/01/2015 Stop Date – 08/01/2016




Man Group plc never reached the stop level and rose to a significant profit which would’ve been taken.

Marks & Spencer Group plc was a straight loss from its signal. It did not gain significant profit before falling. However, the stop would’ve prevented large losses.

Man Group plc (EMG LN EQUITY)

Marks & Spencer Group plc (MKS LN EQUITY)

Start Date – 31/05/2012 Stop Date – N/A



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IG Group Holdings plc never reached its stop level and was one of the most highly profitable trades.

Game Group plc conversely was potentially one of the worst trades but the stop would have prevented significant wealth losses.

IG Group Holdings plc (IGG LN EQUITY)

Game Group plc (GMG LN EQUITY)

Start Date – 30/12/2016 Stop Date – N/A




Hinde Capital DATA TABLESThe tables below show the relative results each year since 2010. This again only includes the stops at down 25%, relative to the index. If a signal was stopped, it was removed for the periods after.


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HINDE VALUE STRATEGYSEPTEMBER 2018

56 Shoreditch High St, London E1 6JJ

Email: [email protected]

HINDE CAPITAL

www.hindecapital.com

Important Information

This document is communicated by Hinde Capital Ltd which is authorised and regulated by the UK Financial Conduct Authority. Hinde Capital Ltd is a company registered in England and Wales with company number 06207559. Its registered office at 56 Shoreditch High St, London E1 6JJ. The information herein does not constitute an offer to sell or the solicitation of an offer to buy any securities. The value of an investment can fall as well as rise, and investors may not get back the amount originally invested. Past performance is not indicative of future results. The information in this document is believed to be materially correct but Hinde Capital makes no representation or warranty as to its accuracy or completeness and accepts no liability for any inaccuracy or omission. Information obtained from third parties has not been independently verified by Hinde Capital. This document contains information from various data sources including Bloomberg LLP.

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HINDE CAPITAL RESEARCH · Robo-type passive investing is modelled on over three decades of declining interest rates, which in some countries are still negative. The buy to hold, buy