This website aims to introduce the results of our research on sector rotation in the US financial markets and is based on quantitative and Intermarket analysis. On the website you will find a brief description of the approach used in the modelling phase. This approach is supported by the Intermarket analysis within the Business Cycle. All the findings in our research are based on a quantitative approach rather than a value investing or macroeconomic approach. This does not mean that the advances made have nothing to do with the equity valuation or the macroeconomic indicators but it demonstrates that the performance of the sectors/industries can be explained by a forward-looking approach based on bond, equity and commodity prices and the development of the Business Cycle. Our work Our work on Intermarket correlations and sector rotation in the US financial markets is based on quantitative analysis and no fundamentals are involved. The research and the models are based on 110 years of US equity market data, 70 years of Standard & Poor’s (S&P) GICS sectors/industries data and 75 years of Barron’s (now Dow Jones) industries data. We have also back-tested a set of data provided by Prof. Kennet R. French which covers US sectors and industries (total returns in this case) based on their four-digit SIC code from 1926. On our website you will soon also be able to find models relating to the European, Asian and American (excluding the US) markets. However, given the limited availability of data for such markets, our research and modelling focus mainly on the US equity markets. Some quantitative techniques used for the US markets are applicable to the rest of the world and we are keen to develop further research/modelling in other geographic areas. Business Cycle and Intermarket models On the following pages we discuss more in detail our view regarding the correlations between the Business Cycle and our Intermarket model and how sector investing can take advantage of the different phases of the Business Cycle. This website also introduces and explains our view regarding the effect of the Business Cycle on the Intermarket correlations and the Financial Markets. Our work consists in putting together the assumptions of the Business Cycle and the principles of Intermarket to create an investment strategy which is able to outperform the market. Our research and modelling demonstrate that a sector/industry investing strategy can outperform the market and achieve higher returns and lower risk in a consistent way in both the short and long-term. The Intermarket analysis is not new to many market technicians as it was introduced by Martin J. Pring in the 90s with the book The All Season Investor (Wiley 1991). Also John Murphy, another technical analyst, has written relevant books about Intermarket analysis. However, the first study on Intermarket analysis was published in 1939 by Leonard P. Ayres, in his book “Turning Points in Business Cycles” and it covers Business Cycles and their turning points from 1831 to 1939. In our opinion the Intermarket correlations are still valid and they have not changed to any great extent. Academics and investment institutions have published many papers in the recent years about financial markets and how to outperform the equity market average (benchmark). Some of the most notable papers that have attracted our attention are Momentum Investing from London Business School (LBS), Seasonal Effect in financial markets by Ben Jacobsen from Massey University, and some research papers and books published by the major investment institutions (e.g. Standard & Poor’s). Some of these documents demonstrate that it is possible to consistently outperform the market in the long-run and they were prepared by some of the brightest minds of the financial industry with the highest financial skills. Those which most appeal to us have in common the use of quantitative analysis. Benjamin Graham [1] said: “I should greatly welcome an effort by security analysts to deal intelligently with speculative operations. To my mind the prerequisite here is for the quantitative approach, which is based on the calculation of the probabilities in each case, and a conclusion that the odds are strongly in favour of the operation’s success”. We favour the use of a quantitative approach rather than methodologies that have been proved not to work. We believe that a quantitative approach is more rational and objective than any other approach that has not been back tested. Because we completed the first part of our research/modelling into the US equity markets, we have decided to compare our findings with those of the above mentioned papers. The challenge Although future performance cannot be predicted accurately, one of the challenges of our approach is to demonstrate that a sector rotation approach based on the Intermarket analysis can achieve a long-term capital growth (~18%) by far more than the most well-known benchmark in the world, the S&P 500 Total Return (~8.9%). The results The results of our research/model demonstrate that investing in sectors or industries is by far more profitable and less risky than stock picking a few companies. The evidence suggests that buying a whole sector of the economy is less risky than buying a few selected shares. Sector Rotation There are a lot of charts available on sector rotations (i.e. Merrill Lynch’s investment clock and S&P’s sector rotation chart) but we have not yet found any study which explains how investors should rotate their money, what the drivers are, what the performance of a model would look like and mainly the timing of entry into and exit from the market. In our research we have used what we would call the “drivers” of the Business Cycle borrowed from the Intermarket analysis and we have created our timing sector rotation models. The results of our models are based on the entire period from 1900 to 2017. However, when we developed our models we had to consider from 1926, 1936 and 1942 to date only due to the lack of data on sectors and industry groups prior to those dates. Some numbers One of our back tested models has 80% probability of outperforming the benchmark within 18 months, 90% probability within three years, 95% probability within four years. On those few occasions that the models underperform the benchmark, they do so for a short period of time with a small negative premium. One of the sector rotation models has 100% probability to beat the benchmark within five years. Most of them have 95% within four years. Back-testing methodology All the above is based on past performances from 1926, 1936 and 1942 to date and clearly there is no absolute guarantee it is going to happen again. However, while some models are a very good example of data mining (i.e. what would have been the best asset allocation in the last 70 years?) and can be used only as an indicator for future performances, other models are robust and have been properly back tested. For example the data has been modelled from 1926 to 2007 and back tested from 2008 to 2017 or modelled from 1942 to 2007 and back tested from 2008 to 2017. Those results are available on our website pages with equity curve data available in a spreadsheet for your own analysis and for the sake of transparency. However, in the LBS momentum investing model, there is an high turnover of shares (the authors admit that transaction costs can seriously compromise the performance), and sometimes momentum strategy can earn negative returns for five to seven years and underperform the benchmark for much longer. Sector rotation works Sector rotation works as an investment strategy firstly because it is more likely that the same sector or industry performs similarly in the same economic conditions. Secondly, there is a business correlation between most of the sectors/industries of the economy. Bonds and Commodities are then added as a hedge to economic fluctuations and financial market volatility. Our quantitative and probability based approach helps to map the relationships between asset classes and to allocate money to the right sector/industry at the right time in the right quantity at any phase of the Business Cycle and thereby we believe that the results obtained with our models are able to enhance the manager’s fund performance, return them extra performance fee and attract more capital into their funds. Please see the Business case. [1] Graham is considered the first proponent of value investing. He began teaching at Columbia Business School in 1928 and subsequently refined with David Dodd through various editions of their famous book Security Analysis. Warren Buffet is the most famous investor who follows this approach. This business case outlines how our asset allocation models can help fund/hedge fund managers to enhance their performance. Our business case discusses assumptions, performance measures, fee calculation and simulation results. It also considers also the benefits our models would bring to fund/hedge fund managers’ fees and their fund size if applied. Fund managers typically charge their funds both a management fee and a performance fee. In this business case we present the results of investing with our models to those fund managers whose are willing to enhance their performance and increase their profit from fees. We also aim to show how fund managers can benefit from investing using our quantitative models and we lay the ground rules of how we can improve the managers’ performance and profits. When a fund is outperforming its benchmark (Hurdle Rate) in a rising market, it can be argued that performance fees provide investors and managers with a win-win situation. Typically, the manager is paid a proportion of the total amount by which the fund outperforms (Alpha) its benchmark. These performance fees provide an incentive for the manager to generate Alpha (rather than just absolute returns) on the basis that the fund manager will be rewarded for his superior skills. When the fund outperforms its benchmark in a falling market (with the fund showing negative returns), it can be argued that performance fees should not be paid to the manager who is losing the client money. However, in this document we also consider the case where the investor would benefit from a fund which outperforms the benchmark in spite of negative returns. Assumptions The following assumptions have been made for the purposes of our business case: 1. Fund/hedge fund managers may charge a % management fee and/or a % performance fee. 2. The management fees are calculated as a percentage of the fund’s Net Asset Value (NAV) and typically are 2% a year. The management fees are paid whether the fund makes or loses money. These fees aim to cover the operational costs of the fund and a proportion of the fund manager’s profits. 3. The performance fees are typically 20% of the returns above the Hurdle Rate during any year. These fees include a hurdle, so that a fee is only paid on the fund’s performance in excess of a benchmark rate. This ensures that the manager is only rewarded if the fund generates returns in excess of the returns that the rational investor would have received if they had invested their capital elsewhere. 4. We have used the S&P 500 Total Return as benchmark. 5. We have assumed that management and performance fees are calculated and paid on an annual basis for simplicity. Management fees are paid at the beginning of the year while the performance fee is calculated at the end of the year. 6. The manager has flat transaction cost fees for 0.8% per year of the fund value, i.e. of the NAV. We have also considered the case for a High Water Mark whose function is to ensure that a manager who has made money for an investor and then loses part of that capital cannot take a performance fee until the loss has been made up. This is needed to avoid the situation where the benchmark falls by 40% and the fund only falls by 30%; the fund has outperformed its benchmark by 10% but has still lost the investor money. This means that a performance fee only applies to net profits. This fee is paid on positive returns where the fund outperforms both the benchmark and the High Water Mark. This avoids a situation where the client would be charged a fee while a fund is declining. However, one could argue that since the manager’s client could alternatively invest in the SPDR S&P 500 (SPY), in a falling market, a smaller loss is a gain for the next period e.g. if the benchmark falls by 40% and the fund falls only by 30%, the following year the manager would need to achieve smaller returns to break even. For example, with a 40% drop in the SPY, $100 would become $60 while with a 30% drop in the fund, $100 would become $70. In the next period, if the SPY grows 50%, $60 will become $90 while it would need only 30% growth for the fund to get back to $91 thereby outperforming the SPY on a two year basis. Fee calculations In general, funds are typically open-ended, meaning that investors can invest and withdraw money at regular intervals e.g. end of the month. However, in our calculations we assume that throughout the year the monthly capital outflow equals the monthly capital inflow i.e. the invested capital is the same as at the beginning of the year. Performance measures and results We have applied the above rules to one of our models to estimate what the manager’s fund performance would look like if we make the above assumptions. 1. 2% management fee: • 2% management fee 2. 2% management fee and 20% performance fee with Hurdle: • 2% management fee • 20% performance fee on Alpha • 0.8% in operational costs 3. 20% performance fee with Hurdle: • 20% performance fee on Alpha 4. 2% management and 20% performance fees with Hurdle and High Water Mark: • 2% management fee • 20% performance fee on Alpha with High Water Mark • 0.8% in operational costs 5. 2% management and 20% performance fees on absolute returns: • 2% management fee • 20% performance fee on Alpha • 0.8% in operational costs As mentioned above, management fees are paid at the beginning of the year while the performance fee is calculated at the end of the year. Both are calculated on the NAV which grows at the rate of change of our model. Conclusions Unless the fund manager is able to consistently deliver Alpha, a rational investor should not invest in his fund. Rather the investor should invest in an Exchange Traded Fund (ETF), able to track the S&P 500 Total Return e.g. the SPDR S&P 500 (SPY). Tables 2.1a and 2.1b below show the results of our fee calculation from 1942 to 2017 while table 2.2 considers only the last 10 years (2008-2017). The aim of this exercise is to capture different possible fee regimes used by different fund managers. From the results below it can be clearly seen that the fund NAV performance is better that the S&P 500 TR in all five cases although the results are different. Options 2) and 5), for example, favour the manager rather than the client while options 1) and 3) favour the client and not the manager. However, with a fund growing at 14.6% or 15.1% compound annual rate of growth, it is arguable that the manager would gain in the medium and long run by attracting additional capital to their fund and gaining in this way from economies of scale. Our models offer the fund manager the opportunity to enhance their performance thereby attracting more capital. In this way the managers of large funds would be able to benefit from economies of scale as their management fee could generate a significant part of the fund manager’s profits. Although future performance cannot be predicted accurately, our approach demonstrates that sector rotation can achieve above the norm long-term capital growth and when transaction costs and performance fees have been taken into account in our calculation, the NAV of our funds are still highly competitive compared to most of the top performing Hedge Funds. It is also worth noting in table 3.1b that the fund has positive returns in 87% of the years from 1942 to 2017 for options 1, 2, 4 and 5, and 93% of the years for option 3. We believe that the results obtained with our models are able to enhance the manager’s fund performance, return them extra performance fee and attract more capitals into their funds. Table 2.1a: Results for the five different fee regimes (1942-2017) Table 2.1b: Results for the five different fee regimes (1942-2017) Table 2.2: Results for the five different fee regimes (2007-2017) based on a $10m fund at the beginning of the period.
However, the greatest challenge for our models is to compare them with well-known top performers such as Warren Buffet (~21% since 1980, assuming we can benchmark Warren Buffet’s performance to Berkshire Hathaway) and the LBS Momentum investing model (~17.8% from 1980 and ~15.2% from 1900).
The extra transaction costs due to the higher number of shares to be bought and sold (almost zero using sector ETFs) are more than compensated by short, medium and long-term extra premium. The long-term returns of our models are highly competitive with the LBS model, Warren Buffet and most of the top performing Hedge Funds.
We have developed four core models: Business Cycle, Intermarket, Ranking & Timing and the Conservative models. All the models are rotation models but they have four different types of buy and sell signals. The models are invested in sectors/industries, bonds and commodity ETFs.
When performances are measured in absolute returns, the results are even more impressive with some models being able to achieve a positive return within 12 months with 95% probability and a compound annual rate of growth of ~18% since 1942. The four models compound annual rate of growth ranges from 12% to 20%. However, there are models that, when invested in industries rather that sectors, can achieve a 25% compound annual rate of growth.
Most of the models have an annualised risk-adjusted return of between 0.9 and 1.5 and some of them have a historical maximum drawdown of 10%-12%. This means that at any point in the past 70-80 years the value of the portfolio has never gone below 10%-12% of its peak value. All the models consider end of month prices only and transactions are assumed to be executed at the close of the last trading day of each month.
The evidence suggests that our sector rotation models are less expensive than the LBS Momentum investing model which, for several reasons, is a good benchmark for comparison. Firstly, it comes from one of the most prestigious institution in the world. Secondly, the model covers more than 100 years of history. Thirdly, its performance is similar to that of the best Hedge Funds.
On the following pages we present the results for the five different fee regimes which are based on what has been discussed so far and on the parameters below:
• 0.8% in operational costs
Fee regime options
S&P 500 TR CARG
Fund NAV CARG
Average Net Manager’s fee/year (%)
Average Performance fee/year (%)
Manager’s preference
Client’s preference
Winner
1) 2% management fees
9.14%
12.93%
1.20%
0.00%
5
2
Client
2) 2% management fees and 20% performance fee with hurdle
9.14%
11.61%
2.40%
1.20%
2
4
Manager
3) 20% performance fee with hurdle
9.14%
13.61%
1.42%
1.42%
4
1
Client
4) 2% management and 20% performance fees with hurdle and high water mark
9.14%
11.88%
2.15%
0.95%
3
3
Both
5) 2% management and 20% performance fees on absolute returns
9.14%
10.33%
3.58%
2.38%
1
5
Manager
Fee regime options
S&P 500 TR (+ve) Returns (%/+ve yrs)
Fund NAV (+ve) Returns (%/+ve yrs)
Fund NAV (+ve) Alpha (%/+ve yrs)
Performance fees (%/+ve yrs)
1) 2% management fees
77.46%
85.92%
56.34%
0.00%
2) 2% management fees and 20% performance fee with hurdle
77.46%
85.92%
56.34%
57.97%
3) 20% performance fee with hurdle
77.46%
90.14%
61.97%
63.77%
4) 2% management and 20% performance fees with hurdle and high water mark
77.46%
85.92%
56.34%
50.72%
5) 2% management and 20% performance fees on absolute returns
77.46%
85.92%
56.34%
85.51%
Fee regime options
S&P 500 TR CARG
Fund NAV
CARGAverage Net Manager’s Fee/year (%)
Net Manager’s Fees ($m)
Manager’s preference
Client’s preference
Winner
1) 2% management fees
7.70%
6.29%
1.20%
1.73
4
2
Client
2) 2% management fees and 20% performance fee with hurdle
7.70%
5.55%
2.01%
2.36
2
4
Manager
3) 20% performance fee with hurdle
7.70%
7.40%
0.92%
0.93
5
1
Client
4) 2% management and 20% performance fees with hurdle and high water mark
7.70%
6.28%
1.21%
1.76
3
3
Both
5) 2% management and 20% performance fees on absolute returns
7.70%
4.79%
2.79%
3.81
1
5
Manager