Bounceback Portfolio 2017

The Bounceback Portfolio invests in the 10 worst performing FTSE 350 stocks of the previous year and holds them for the 3-month period, January-March. 

The Bounceback Portfolio for 2016 recorded the best performance ever for the strategy: a 3-month return of 38.5%, compared with a FTSE 350 Index return of -1.4% for the same period.

The following table lists the ten worst performing FTSE 350 stocks in 2016. These are the ten stocks that will comprise the 2017 Bounceback Portfolio.

Company TIDM Return in 2016 Return since 31/12/2016
Capita CPI -56.0 -2.4
Restaurant Group (The) RTN -52.7 6.0
Sports Direct International SPD -51.7 2.3
Essentra ESNT -44.3 -1.3
easyJet EZJ -42.2 7.4
International Personal Finance IPF -40.4 -4.4
IG Group Holdings IGG -38.4 7.1
McCarthy & Stone MCS -36.6 4.8
Inmarsat ISAT -33.9 2.1
Man Group EMG -32.6 2.4

The final column gives the returns for the individual stocks for the first six days of the 2017. The portfolio as a whole has seen a return of 2.4% for the first six days of 2017, against a return of 1.9% for the FTSE 350 Index.

The Bounceback Portfolio is meant to be held until the end of the March 2017, but it is good to see that it has started the year well so far.


More articles on the Bounceback Portfolio.

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Tuesday reverses Monday

Do market returns on Tuesdays reverse those on Monday?

We first looked at this in 2013 (in this article), so time to see if anything has changed.

First, the following updates the chart to 2016 plotting Tuesday returns for the FTSE 100 Index split by whether the previous day’s returns were positive or negative. Two time periods are considered: 1984-2016 and 2000-2016.

For example, for the longer period, the average return on Tuesday when Monday was up is 0.02%, while the average Tuesday return when Monday was down is 0.09%.

FTSE 100 returns on Tuesdays when Monday was up-down

While the figures have marginally changed from the previous study in 2013, the overall finding is the same: namely that the theory that Tuesday reverses Monday does not seem to hold. Since 1984 it has done so when Monday returns have been negative, but not when they have been positive. 

As in the 2013 study, the theory has been valid for the market since 2000.

The previous study suggested that further analysis might include a filter on the size of the Monday returns. This is done in the following chart, where Tuesday returns are only considered if Monday’s returns were beyond a certain threshold (i.e. of a certain size). The (arbitrary) threshold chosen was 1 standard deviation for Monday’s returns.

FTSE 100 returns on Tuesdays when Monday was up-down (1SD filter)

It can be seen that limiting the analysis of Tuesday returns to just large movements on Monday (i.e. beyond 1 standard deviation) does help the reversal theory. In this case, if the market rises on Monday, then on average it falls the following day (albeit a pretty small average fall), and if the market falls on Monday, the market rises (fairly strongly) on the Tuesday.

Let’s now look at how the theory has been holding up in recent years.

Recent years

The following chart is similar in design to the previous charts, but this time it plots the reversal results for the discrete years 2013 – 2016.

FTSE 100 returns on Tuesdays when Monday was up-down [2013-2016]

First, when the market is up on Monday, all four of the past four years has failed to support the reversal theory as Tuesday has followed with positive returns as well. When Mondays are down, in three of the past four years Tuesdays have seen positive average returns (the exception being 2015).

Exploiting the reversal effect

OK, so how to exploit this?

The following chart plots the cumulative value of a portfolio that invests in the FTSE 100 just on Tuesdays when the previous day saw negative returns. For the rest of the time it is in cash.

In the 2013 study a variant portfolio was also considered, that as well as going long Tuesdays following negative Mondays also went short Tuesdays following positive return Mondays. There’s currently not much point in considering this as the reversal effect is not working for positive Mondays.

So, instead the variant second strategy studied here is as above (i.e. long Tuesday following a negative Monday) but with a 1 standard deviation filter applied to the Monday return (i.e. the strategy only goes long on Tuesday if the Monday negative return is a greater than 1 standard deviation return).

Strategies exploiting the Tuesday reversal effect [2000-2016]

Since 2000 it can be seen that the simple long Tuesday strategy out-performs the benchmark buy-and-hold FTSE 100 portfolio. The variant 1SD strategy only marginally out-performs the simple long Tuesday strategy, but does so with with a greatly reduced volatility.


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World’s Simplest Trading System

Here’s the system:

At the end of every month,

  • if the index is above its 10-month simple moving average: the portfolio is 100% in the market
  • if the index is below its 10-month simple moving average: the portfolio is 100% in cash

 And that’s it.

So, if we take the FTSE 100 Index as an example, if at the end of a month the FTSE 100 is above its 10-month simple moving average then either,

  • the portfolio moves into the market by buying, say FTSE 100 ETFs, (these will be the easiest instrument for most investors, but equally futures, CFDs or spread bets could be used), or
  • nothing needs to be done if the portfolio is ready in the market.

Conversely, if at the end of a month the FTSE 100 is below its 10-month simple moving average then the portfolio sells the ETFs and moves 100% to cash; if it is already in cash then nothing is done.

[NB. OK, it's possible that this isn't absolutely the simplest trading system imaginable, but apart from buy and hold it is unlikely there are many systems much simpler than this one!]

The following chart illustrates such a portfolio for the FTSE 100 Index since 1995. The diamond markers indicate the decisions made at the end of each month whether to be in the market (green diamond) or in cash (red diamond).

SMA (10M) Trading System

Roughly, one can see that the system kept the portfolio in the market in uptrends and out of the market (in cash) when the market fell.

This trading system is well-known in the US, what we will look at here is:

  1. If the trading system can be profitably applied to the FTSE 100 Index.
  2. Whether 10 months is the optimum parameter for the moving average (or would a 5-month, or 15-month, moving average produce superior results)?

Terminology: we will use SMATS(10) to refer to the 10-month simple moving average trading system. And SMATS(5) for the trading system using the 5-month simple moving average etc. Below we will analyse the trading system for 14 different parameters of the simple moving average, i.e. from SMATS(4) to SMATS(16).

Performance analysis

First, let’s look at the overall profitability of SMATS.


The following chart plots the values of the SMATS portfolios for the 14 different simple moving averages (i.e. 4-month to 16-month). As a benchmark the FTSE 100 is added (i.e. this is the value of a buy and hold FTSE 100 portfolio). All values were re-based to start at 100.

SMA Trading Systems (4M-16M) [1995-2016]

Some observations:

  1. By the end of the 20-year period all the SMATS portfolios had out-performed the FTSE 100 – except SMATS(5).
  2. By the end of the period, SMATS(10) had the highest value; although it can be seen that it wasn’t consistently the most profitable throughout the whole period.
  3. For the first six years (up to August 2001) all SMATS under-performed the FTSE 100. This was caused by the market volatility in 1998 and 2001, which caused the portfolios to be whipsawed in and out of the market.

The following chart summarises the final portfolio values in 2016 after running the trading system from 1995.

SMA trading system values [2016]

By 2016 the STATS(10) portfolio had the highest value of all portfolios at 269; the FTSE 100 buy and hold portfolio a value of 199.


We’ve looked at profitability, let’s now consider the risk incurred by each portfolio. We’ll use volatility as a (fairly standard) proxy for risk.

The following chart shows the volatility of the portfolios over the 20-year period.

SMA trading system volatility [1995-2016]

Not surprisingly the FTSE 100 had the highest volatility. The volatility of the SMATS portfolios was less due to the fact they were in cash for part of the time; broadly their volatility increased as the moving average month parameter increased.

The Sharpe Ratio combines returns with volatility to provide a comparative measure of profitability per unit of risk incurred. The ratio’s purpose is to answer questions of the form: is the profitability of a strategy justified by the risk incurred, compared to another strategy?

The following chart plots the Sharpe Ratio for the 14 portfolios. (The benchmark for the Sharpe Ratio calculation was the FTSE 100 Index.)

SMA trading system Sharpe Ratio

SMATS(10) had the highest (i.e. the best) Sharpe Ratio, although close behind were SMATS(14) and SMATS(15).

Max Drawdown

Maximum Drawdown decribes the maximum loss a portfolio suffered from a previous high value. For example, in this test SMATS(10) had a max drawdown value of 22.8%. This means that over the 20-year test period the portfolio was at most 22.8% under water (from a previous high).

Frankly, max drawdown has more significance for strategies that employ leveraged products (e.g. futures), as drawdowns incur realised losses as margins have to be paid. By contrast in the case of unleveraged equities or ETFs, drawdowns incur unrealised losses. Having said that, unrealised losses can still be uncomfortable and can have a major adverse psychological impact on the investor or trader.

The following chart shows the max drawdown values for the 14 SMATS portfolios and the FTSE 100 Index.

SMA trading system Max Drawdown

Here the SMATS(10) portfolio only had a middling relative score. The best portfolios (i.e. those with the lowest max drawdowns) were: SMATS(7), SMATS(14), SMATS(15), and SMATS(16).

Trade frequency

The following chart shows the average number of trades for the year for each portfolio. For example, over the 20-year test period SMATS(10) portfolio traded 36 times, which is an average of 1.7 times a year.

SMA trading system Average Trades-yr

As would be expected the number of trades decreases as the length of the moving average month parameter increases. In other words, systems get whipsawed less with longer moving averages.

The profitability figures above did not include transaction costs, but with the systems averaging under 2 trades per year the transaction costs would not be significant.

Summary of analysis

The following table summarises the above analysis. The values are colour-coded with green being the best value through to red being the worst for each respective analysis.

SMA trading system analysis (FTSE 100, 1995-2016)


  1. This simple moving average trading system did work for the FTSE 100 (i.e. it out-performed the FTSE 100 Index) over the 20-year period.
  2. The best performing portfolio was indeed SMATS(10), i.e. the trading using the 10-month simple moving average. It had the highest absolute profitability and also the highest Sharpe Ratio. After SMATS(10), the best portfolio was the SMATS(14), followed by SMATS(15).
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Sell in May Sector Strategy (SIMSS)

The Sell in May Effect describes the tendency of the market over the six-month period Nov-Apr to outperform the market in the other six-month period (i.e. May-Oct).

The effect can be seen in the following chart, which plots the cumulative average daily returns of the market (i.e. it gives a representation of the market moves in an average year). More information on this chart can be found here.

Santa Rally [2015] 04

As can be seen the market tends to be strong from November to April, and then flat for the six-month period May to October.

An update tracking the accuracy of this effect can be found here, and further articles here.

The problems of exploiting the Sell in May Effect

Although the effect is statistically significant, it is not an easy anomaly to exploit economically. In theory an investor might be long stocks Nov-Apr and then move to cash for May-Oct. But as can be seen in the above chart, the market doesn’t necessarily fall in the summer period (except possibly the short May-Jun period), rather it is flat. And by moving to cash the investor would forego dividends paid in the May-Oct period.

It may make sense moving to cash if interest rates were high (i.e. to benefit from high returns on cash for the summer period) – but that is not the case currently. And in any case that has to be balanced with the fact that when interest rates are high expected growth rates in equities tend to be high as well (i.e. not a time to be out of the market).

One significant reason why it may make sense to be out of the market over the summer period is that volatility is much higher then than in the Winter period (as shown here). For example, eight of the ten largest one day falls in the FTSE 100 Index happened in the Summer period. Hence, not only are returns lower in the Summer, but also risk-adjusted returns are significantly lower.

But generally, this is a little frustrating: the Sell in May Effect is a significant market anomaly, but tricky to exploit.

So, what to do?

Exploit the sector rotation

One idea is to stay in the market throughout the year but to re-balance a stock portfolio according to which sectors perform the best in the two six-month periods as defined by the Sell in May Effect.

The following two tables show the performance of the FTSE 350 sectors in the respective summer and winter periods since 1999. The tables have been ranked by average returns of the respective sectors over the 17-year period.

Sector performance in the summer period since 1999

SIM sector summer performance

 Sector performance in the winter period since 1999

SIM sector winter performance

From these tables two portfolios of sectors can be constructed that have historically performed strongly in the respective summer and winter periods.

A few filters were applied:

  1. Sectors with less than 4 component stocks were not considered
  2. Sectors must have a minimum 13-year track record
  3. Standard deviation (i.e. volatility) of a sector’s returns must be below the average standard deviation
  4. Positive returns must be over 50%

The portfolios selected were-

Summer Portfolio Winter Portfolio
Gas, Water & Multiutilities Construction & Materials
Beverages Industrial Engineering
Health Care Equipment & Services Chemicals

So, the Sell in May Sector Strategy (SIMSS) is

  • in the summer period: long sectors Gas, Water & Multiutilities, Beverages, and Health Care Equipment & Services
  • in the winter period: long sectors Construction & Materials, Industrial Engineering, and Chemicals

Performance of SIMSS

The following chart shows the simulated performance of the Sell in May Sector Strategy backdated to 1999 compared to the FTSE 100 Index.

SIMSS v FTSE 350 [1998-2016]

After 17 years the SIMSS portfolio would have grown in value to 1021 (from a starting value of 100). While the FTSE 100 (buy and hold) portfolio would have grown to 111.

This simulation does not include transaction costs, but as the strategy only trades twice a year these would not significantly change the above results.

More articles about the Sell in May Effect.

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Sell in May and come back…when?

The old saying goes “sell in May”.

But if you sell in May when should you come back into the market?

Well, in its original form the adage was, “sell in May and go away, don’t come back till St Leger Day”. And St Leger is the last big event of the UK horse-racing calendar, and usually takes place in mid-September.

A complementary anomaly (most likely originating in the US) is the Halloween Effect, which holds that stocks see the bulk of their gains in the six-month period 31 October to 1 May.

At some point it seems the sell in May saying and the Halloween Effect merged to become one. Such that today the sell in May adage is usually taken to mean that the summer period of (relatively) poor returns ends 31 October.

So, so far we have possible entries back into the market of mid-September or end October.

What does the recent data say?

The following chart shows the annual trend of the FTSE 100 Index calculated on data from 1984. (More information on this chart can be found here.)

Santa Rally [2015] 04

The chart illustrates fairly clearly the different nature of the two six-month periods:

  • 1 May – 31 October (Summer period): when the six-month return tends to be flat, and
  • 1 November – 30 April (Winter period): when the market tends to rise.

The data does support the claim that the greater part the market’s gains come in the Winter period.

Over the whole six-month Summer period the market doesn’t necessarily fall, but it does tend to be flat, and certainly the returns are less than in the Winter period.

However, it can be seen in the chart that the market is absolutely weak for the two-month period May to June.

So, according to the data since 1984, if you do sell in May, one time for coming back into the market would be the end of June.

More articles on the Sell in May Effect.

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Sell in May (update)

An update on the Sell in May Effect (also called the Six-Month Effect, or Halloween Effect in the US).

In the six months Nov 2015 to Apr 2016 (Winter period) the FTSE All-Share Index fell 1.8%. Previously, the Index had fallen 7.3% over May 2015 to Oct 2015 (Summer period).

The outperformance of the Winter market over the Summer market was therefore 5.5 percentage points, which supports the Sell in May Effect.

The following chart shows the outperformance of the FTSE All-Share Index in the Winter period over the previous Summer period since 1982.

SIM Outperformance of winter over previous summer market [1982-2016]

In the 16 years since 2000 the Winter market has outperformed the previous Summer market 11 times, with an average outperformance of 5.2 percentage points.

Other articles on the Sell in May Effect.

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Bounceback portfolio 2016

The Bounceback Portfolio invests in the 10 worst performing FTSE 350 stocks of the previous year and holds them for the 3-month period, January-March. 

Performance in 2016

The following table lists the ten worst performing FTSE 350 stocks in 2015. These ten stocks form the 2016 Bounceback Portfolio.

Company TIDM 2015 2016 (Jan-Mar)
Anglo American -75.1 84.4
Glencore -69.7 73.9
KAZ Minerals -60.3 66.7
Tullow Oil -60.0 18.8
Evraz -52.6 22.9
Vedanta Resources -52.1 24.6
Home Retail Group -52.0 66.6
Amec Foster Wheeler -49.7 4.9
Drax Group -46.9 11.3
Weir Group -46.0 10.8
FTSE 350 -2.8 -1.4

The final column in the above table also gives the returns for each stock for the period Jan-Mar 2016. For example, Anglo American shares fell 75.1% in 2015, and then rose (bounced back) 84.4% in the first three months of 2016.

The performance of the 10 Bounceback Portfolio stocks for Jan-Mar 2016 is shown in the following chart.

Bounceback portfolio 2016

On average the Bounceback Portfolio stocks had a 3-month return of 38.5%, compared with a FTSE 350 Index return of -1.4% for the same period.

Bounceback portfolio performance 2003-2016

The Bounceback Portfolio has been running since 2003. The following chart shows the comparative performance of the portfolio and the FTSE 350 Index for each year.

Bounceback portfolio v FTSE 350 [2003-2016]

As can be seen the Bounceback Portfolio scored its greatest out-performance of the FTSE 350 in 2016.

Since 2003, the Bounceback Portfolio has under-performed the index only twice (2013 and 2015).

The following chart shows the cumulative performance of the portfolio from 2003.

Bounceback portfolio cumulative performance [2003-2016]


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The Santa Rally Portfolio 2015

The Santa Rally describes the tendency of the stock market to perform strongly in the final two weeks of the year (more info).

The Santa Rally Portfolio comprises the 10 best performing FTSE 350 shares over the Santa Rally period (i.e. roughly the last two weeks of the year) over the last 10 years. The characteristics of the 10 stocks in the portfolio are:

  1. All 10 stocks have positive returns over the two-week Santa Rally period for every year since 2005.
  2. The 10 stocks have the highest average returns of all FTSE 350 stocks over the Santa Rally periods of the last 10 years.

The following table lists these ten shares and their average returns over the Santa Rally periods for the last 10 years.

Company TIDM Avg Rtn(%)
Ashtead Group 7.3
FirstGroup 6.6
Vesuvius 6.4
Croda International 5.9
Informa 5.3
British Land Co 4.9
Spectris 4.8
Pennon Group 4.4
G4S 4.4
Balfour Beatty 4.2

The following chart compares the performance of the Santa Rally Portfolio with the FTSE 350 Index for the past 10 years.

Santa Rally Portfolio [2015]Notes:

  1. The FTSE 350 Index has had positive returns in every Santa Rally period since 2005 except 2012. The average return for the Index over this period for the last 10 years is 2.2%. Last year, in 2014, although the Index was down 1.7% in December as a whole, it actually rose 4.3% in the second two weeks of the month.
  2. The Santa Rally Portfolio has had positive returns every year since 2005. The average return for the Portfolio over the last 10 years is 5.4%, thus out-performing the FTSE 350 each year by 3.2 percentage points.

More on the Santa Rally.

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Quarterly sector momentum strategy (update)

Do FTSE 350 sectors display a quarterly momentum behaviour that can be exploited?

This analysis updates the performance of two strategies, defined as:

1. Strong quarterly sector momentum strategy (Strong QSMS)

The portfolio comprises just one FTSE 350 sector, that being the sector with the strongest performance in the previous quarter. So at the end of each quarter, the portfolio is liquidated and a 100% holding established in the strongest sector of the quarter just finished. This is held for three months, when the portfolio is liquidated and re-invested in the new sector. Therefore the strategy will trade four times a year.

2. Weak quarterly sector momentum strategy (Weak QSMS)

As above, but in this case it is the weakest sector of the previous quarter that is held by the portfolio. (Strictly, perhaps, this should be called a bounceback, or reversal, strategy and not a momentum strategy.)

Only FTSE 350 sectors with at least three component companies are considered. The period studied was from 2005 to the third quarter 2015.

The accompanying chart compares the performance of the two strategies, and adds the FTSE All Share Index as a benchmark. All series are re-based to start at 100.

Quarterly (strong and weak) sector momentum strategies [2005-2015]


  1. As can be seen, both the SMS strategies out-performed the index over the period of the study. However, they did so with greater volatility (the standard deviation of the Strong SMS quarterly returns was 0.11, against comparable figures of 0.13 for the Weak SMS and 0.07 for the FTSE All Share Index).
  2. From 2012 the reversal portfolio (Weak SMS) started strongly out-performing the Strong SMS.
  3. A refinement of the strategy would be to hold the two or three best/worst performing sectors from the previous quarter instead of just the one (which would likely have the effect of reducing volatility).
  4. Costs were not taken into account in the study. But given that the portfolio was only traded four times a year costs would not have had a significant impact on the overall performance.

Extract taken from the newly published The UK Stock Market Almanac 2016.

Order your copy now!

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Shares that like/dislike July

Shares that like July

The following table lists the five FTSE 350 shares that have the best returns in July over the last ten years. For example, Elementis has an average return of 7.6% for the month of July. All stocks have risen in July for at least nine of the past ten years.

Company TIDM Avg(%)
Elementis 7.6
Brown (N) Group 6.9
Greene King 6.8
Shire 6.0
Land Securities Group 5.1

 Shares that dislike July

The following table lists the two FTSE 350 shares that have the worst returns in July over the last ten years. For example, IP Group has an average return of -4.1% for the month of July. Both stocks have fallen in at least seven of the past ten years in July.

Company TIDM Avg(%)
IP Group -4.1
SSE -2.0

An equally-weighted portfolio of the above strong July stocks would have out-performed every year an equally-weighted portfolio of the above weak July stocks by an average of 9.3 percentage points in July for the past ten years.

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