Fundamentals of Sentiment - Dow Essay 2012

8/13/2019 Fundamentals of Sentiment - Dow Essay 2012

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The Fundamentals of

Sentiment

1st

Feb 2012

A submission to the Market Technicians Association (MTA) for the 2012 Charles H Dow Award.


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The Fundamentals of Sentiment 2012

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Introduction

Psychology is the key to understanding the predictable and repeatable components of

human behavior. This fact has long been recognized by students of market action,

particularly those schooled in the discipline of technical analysis.

However, the discipline of economics has long resisted the notion that man is anything

but a rational calculating animal. According to the Efficient Market Hypothesis (EMH),

there can be no possibility of a risk-adjusted return premium using only price data.

For decades, this has led the academic world of financial theorists and the practical

world of financial traders to lead separate lives in seemingly parallel universes. In one

corner, the practical trader has employed charts, indicators and sundry forecasting

devices to pursue profits based on publicly reported transactional data. In another

corner, the academic community constructed proofs of the impossibility of such

devices ever leading to profitable risk-adjusted returns.

Of course, there were some brave academic souls, the pioneers of behavioral finance,

who steadily and diligently amassed evidence that all was not well with the EMH.

Among these, Jegadeesh and Titman have demonstrated economic profits from the

following of price momentum, both relative and absolute [1]. Others, such as the

Khaneman and Tversky [2], provided evidence, through their experiments that led to

the Nobel prize winning Prospect Theory, that utility theory is a poor model for the

process of human decision making.

Academics have thus, by stages, warmed to the view that technical analysts have long

held to be central to a practical understanding of market action. Recently, Kirkpatrick

and Dahlquist have done much to document the modern rapprochement betweenacademic researchers and practically-minded traders [3].

Passing now from academia and the technical analyst community, we must consider

the man on the street. Recent market events have caused much despair.


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If Man is always fully aware of the likely future consequences of his present actions, then

how, one may ask, did financial markets manage to get themselves into such a pretty

predicament? In the past five years, we have had seemingly endless market crises.

It would be just fine and dandy to assume that the events of the 2008-2009 bear market,

and the global credit crisis, were simply consequences of unforeseeable circumstance,

the mere slings and arrows of outrageous fortune with no visible proximate cause.

The charitable analyst might take this view. Picture the hapless Dinosaur of Yore who is

bewitched by the fiery approach of an asteroid. How might that reptile ponder the

impending demise of his species? It was an entirely unpredictable Act of God!

Was this true of the Global Financial Crisis? How could sentiment be so positive in one

moment, and so dire the next? This shall be the focus of our enquiry.

What are the Fundamentals of Sentiment?

Cost-Basis Theory

Any professional trader who enters the market must have a theory. However devised,

constructed, borrowed, or purloined, a theory is necessary to support prudent action.

The market is too deceptive to merely trust a single price tick, daily bar, or reported

piece of official data and consequent analyst opinion. Markets are deceitful in the

most delicious and confounding fashion. It is part of their endless charm.

They will Zig just as soon as Zag, especially if our hapless trading Pilgrim has placed his

stop-loss limit order too close to a visible level and is mauled by the Pit Hounds of Hell.

Historically, economists have assumed that absolute price levels don’t matter. Traders

know differently, since they are aware of the institutional constraints upon them.

Where, in the whole crazy grand scheme of things, should we look first?

Among all options, it is nominal debt contracts that figure prominently when gearing is

involved. If you owe more than the asset is worth, rapid liquidation, even at fire-sale

prices, seems rational. This psychological insight is the essence of cost-basis theory.


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Very clearly, a trader who operates on margin will worry some if his stock should fall

below its book value of purchase. He will worry greatly when overextended with an

amount of borrowed money. Each hopeful uptick in price restores a sliver of equity.Each relentless downdraft in price washes away the very anchors of his soul.

Hence we are bold enough to make an assertion of principle. Contrary to the clear and

present assumptions of orthodox economics, we maintain that price is the fundamental

variable that determines the solvency, and thus the sentiment, of geared investors.

The Mind of the Market

What can we know about the cost-basis of the market?

Every share has an owner and a book value. That number is a precise objective fact

equal to the nominal purchase price, the cost-basis used for tax reporting.

For example, consider the mythical Acme Ltd. Every share outstanding has two labels.

Someone purchased it at a definite time and a definite price. Among all imaginable

prices, the only possible prices are those at which the security previously traded.

Let us picture that carefully, as per the sketch shown at Figure 1.

Figure 1 Price chart of the mythical ACME Ltd

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28-Jan-10 28-Apr-10 28-Jul-10 28-Oct-10 28-Jan-11

Time

P r i c e


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The past price and volume of all trades, the tape as we call it, contains some valuable

information. When shown conventionally, as per Figure 1, we see an erratic path of

prices through time, with associated fluctuating trading volumes.

For completeness, we have shown classical support and resistance levels, in red and

green, drawn at prices where there has been significant trading congestion.

However, let us now think differently and remove time from the picture.

While we cannot be certain exactly what percentage of the current shareholders will

still hold positions at previously traded prices, we can still deduce some principles.

Where there has been very great volume traded, such as a very large block traded

there is a higher probability of significant holdings still held at that cost-basis.

Furthermore, if the volume was very recent then only the subsequent trading can have

changed the holdings of the past. Since every share traded will change its cost-basis, it

will be the newer trades that dominate over the older trades.

Pulling this together, we could make a map of volume traded at price, as per Figure 2.

Figure 2 Volume Traded at Price for ACME Ltd

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4.0%

5.0%

6.0%

8.00 10.00 12.00 14.00 16.00 18.00

Price

% V o l u m e


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Against this picture of history, with time removed, we can be quite certain of where the

peaks lie. The only point of doubt is their relative height. Past prices and volumes do

matter because they impact the market memory.

The annotated peaks happen to coincide with the prior support and resistance lines.

Through the lens of cost-basis theory, we see that this is a very strong mathematical

property of financial markets. The history of price and volume data determines the

statistical shape of the reference prices at which current investors hold securities.

We can do no better than quote Jiler [4]:

Have you ever bought a stock, watched it decline in price, and yearned to sell out for what you paid? Have you ever sold a stock, watched it go up after you had sold it, and wished you

had the opportunity to buy it again? Well you are not alone. These are common human

reactions, and they show up on the stock charts by creating support and resistance.

The great advance of the last ten years [5, 6, 7] has been to realize that the foregoing

ideas can be translated into a simple estimate of the average cost-basis of the market,

and thus a simple estimate of the support and resistance levels for a market index.

The Value-Weighted Adaptive Moving Average

Now we highlight some subtle connections from academic literature into the technical

analysis literature and vice versa. These will lead us directly to establish the foundation

for a certain class of moving average indicators as being fundamental to sentiment.

Traditionally, it had been supposed that one must have access to broker accounting

data to estimate the average cost-basis. However, around 2001-2002, three separate

groups [5, 6, 7], working independently, came to the exact same conclusion.

Reasoning on the basis of the previous discussion, concerning the relationship between

historical traded price and volume and cost-basis, each group motivated the same

mathematical formula. It is simple, and provides an estimate of cost-basis.

This formula will be the basis for our fundamental measure of market sentiment.


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It takes the form of an Exponentially Weighted Moving Average (EWMA):

VWAMAT = VWAMAT-1 + TURNOVER VELOCITYT * (PRICET - VWAMAT)

The formula is conventional except for one small detail.

The smoothing parameter, marked as turnover velocity, is changeable and reflects the

pace of market trading. It is driven by the rate-of-trade compared with market value:

TURNOVER VELOCITYT = TRADED VALUET /MARKET VALUET.

One simply looks up the daily traded value and divides this by the index market cap.

The full justification for this formula is described in [5, 6, 7].

Adaptive moving averages have been introduced previously, notably the KAMA of

Kaufman [8], and others such as the MESA and FAMA of Ehlers [9].

Each of these has been shown to have merit in certain trading situations.

Our interest in the Value-Weighted Adaptive Moving Average (VWAMA) lies in the

possibility of testing some of the precepts of cost-basis theory [7].

In prior work, Fries [5] noted the correlation between cost-basis and price momentum,

while Grinblatt and Han [6] have also demonstrated an intimate relationship between

this indicator, the disposition effect, and the persistence of price momentum.

Our goal is related, but to a different purpose:

1. We seek a market signal for aggregate investor sentiment 2. We wish to investigate the trading efficacy of the moving average signal

The first point will already be clear given the previous motivation.

We have argued at length, that cost-basis is an important determinant of sentiment

since it fixes the mark-to-market profit and loss of all investors.


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Finally, we have introduced, from the above prior studies, a remarkable and perhaps

unexpected connection. Exponential moving averages, of this special type, have a

direct relationship to the unrealized profit-and-loss of existing holdings.

The simple ratio:

UNREALISED PROFIT AND LOSST = PRICET /VWAMAT – 1

measures trader gains and losses, and thus investor sentiment.

In words, if the current price is $12 and the average cost basis is $10, then the average

unrealized profit and loss is $2. This would be a condition of positive sentiment. On the

other hand, if the price is $10 and average cost basis is $12, we have a $2 loss.

The Mind of Market Moving Average

Proceeding now to our statistical study, we will introduce a minor adaptation upon the

previous literature. Our goal is to study the sentiment of markets in aggregate. In prior

work, researchers have focused on the single security version of the average.

When we come to study indices, as is necessary for any aggregate study of sentiment,

there is an immediate practical problem. Market index composition will change over

time, and the construction of a truly accurate market cost-basis indicator is a very

complex bottom-up exercise of daily re-weighting individual security indicators.

In this study, we will assume that the naïve application of the same turnover rule as has

been derived for individual securities is an adequate estimate for our purpose.

The conditions under which this assumption holds true are that the turnover of the

market as a whole is broadly similar to that of the constituent securities.

When using this approximation we call it the Mind of the Market Average (MOMA):

MOMAT = MOMAT-1 + TURNOVER VELOCITYT * (INDEX PRICET - MOMAT)

Here the index price refers to the standard price level of a market capitalization

weighted index, while the turnover velocity is an exchange reported number.


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Plan of the Study

Armed with the MOMA formula we can proceed to outline our empirical study.

We sought the maximum market coverage, by geography, and extent, that is as

consistent as possible and publicly available to the private trader at no charge.

The best source is the S&P/Citigroup Broad Market Index (BMI) [10]. This is a market

capitalization weighted daily price index, for which we were able to download

approximately 50 global markets reaching back as far as 1989.

The final list for testing was further reduced by available turnover data to the list of 45

markets exhibited in Table 1. The primary source of turnover velocity information for

each market was the World Federation of Exchanges (WFE) website [11].

In some cases, such as the Euronext and NASDAQ OMX markets, it was necessary to go

direct to the underlying exchange website, and refer to their market fact books.

The essential plan of the study was then to:

1. Compute the cost-basis and assess the sensitivity of starting values2. Examine the statistics of the profit-and loss signal historically3. Investigate initial evidence for profitable trading rules

Sensitivity of Starting Conditions

Moving Averages are known to suffer from the startup problem. We must choose some

starting value to seed the average, but over time the importance of this fades.

To test how important this is in practice, we calculated the cost-basis estimate across all

45 markets using three initial starting values.

1. MOMA0 = Index Price0 2. MOMA0 = 0.9 * Index Price0 3. MOMA0 = 1.1 * Index Price0


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In words, we seed the series with the first index price, a price 10% higher and a price

that is 10% lower. Then we see how many years lapse before they converge.

In general, this period is about five years.

The worst of markets studied was Japan, and one can see the demonstrated pathway

towards convergence after ten years in Figure 3.

Figure 3 Mind of Market Average for Japan

Since our data set is some twenty years long, we were satisfied to proceed with the

study using unedited values at the central estimate of the initial starting price.


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Trading Studies

Since we have 45 markets, with daily data over 20 or more years, there are excellent

grounds to embark upon some tests of simple trading rules.

However, there is very little experience with testing Value Weighted Adaptive Moving

Averages, so we have little to guide us but the cost-basis theory elaborated earlier.

Rather than simply mine the data for all possible rule combinations, we have chosen to

test only two strategies [12]. The discipline we follow is the logic of our cost-basis theory

and to assume that the cost-basis level is significant to sentiment.

Firstly, we examined the time-series of Profit & Loss Signal.

Figure 4 Mind of the Market Average for the United States

One can see, from Figure 4, the typical pattern of Bull and Bear trading. There are long

upward trends lasting several years where the market finds support at cost-basis. These

are punctuated by shorter periods of much more intense downward moves.


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To visualize this we plot the ratio of the market index to the cost-basis index. This variable

has the direct interpretation of measuring average unrealized trader gains and losses.

Figure 5 Unrealized Profit and Loss for the United States The corresponding chart for the United States is shown in Figure 5. Simple reasoning

suggests that this quantity should be mean reverting. We can also perceive some

possible evidence of a market tendency to avoid trading near cost basis.

It is as though this price level repels the market from both above and below, much as

the classical theory of support and resistance contends [3].

Detailed summary statistics across all 45 markets are displayed in Table 2 confirm thatreturns are higher in positive P&L periods and risks are lower.

Following these observations, we test what we call Simple MOMA (SMOMA) based on

the simple-minded idea of being short in bear markets and long in bull markets.

This is a pure Stop and Reverse (SAR) system [8], with no free parameters.


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When the index price is above the estimated cost-basis level the strategy is long. When

it makes the cross-over to negative territory the strategy is short. We model trading cost

as 50bps per transaction, which is 100bps per reversal in the SAR system.

Since ETF or index futures are available, this seems conservative.

Results for SMOMA are reported in Table 3. There are very many small losing trades and

a few large winners associated with being long in the bull market phase.

The second strategy is less obvious, and does have two free parameters which we fix on

a somewhat arbitrary, but informed basis. Typical moving average systems employ a

fast and a short moving average [8]. The speed of the MOMA is driven by market

turnover, which is a market parameter. However, the actual profit experience of traders

is driven by their gearing level. For simplicity, we assume that traders come in two basic

levered classes. Margin traders we assume are 2:1 geared. Pattern day traders we

assume are 5:1 geared. These numbers reflect the Federal Reserve System limits as a

guide to the critical level when the geared trader tips into forced-sale territory.

Following this idea, we constructed a classical fast-slow moving average system after

the pattern of Kaufman [8] and other readily available guides for the private trader.

The gearing adjustment works through the trading turnover as:

A_MOMAT = A_MOMAT-1 + A * TURNOVER VELOCITYT * (INDEX PRICET – A_MOMAT)

The new variable, A, is an acceleration factor to the velocity which speeds up MOMA.

It represents an arithmetically compounded estimate of unrealized profit and loss for a

trader who is deploying day-trading capital on an A:1 geared basis. Effectively, the A

factor is the amount by which one day of profit (or loss) is compounded by gearing.

In this system, which is again a pure Stop and Reverse (SAR) system, we simply sell whenthe 5:1 accelerated MOMA (A=5) crosses the 2:1 accelerated MOMA (A=2).

The strategy sounds complex, so it is better to simply visualize in chart form.


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Figure 6 AMOMA Strategy for Australia involving x5 speed MOMA crossing x2 speed MOMA In Figure 6, we can see the underlying BMI Index for Australia, with slow MOMA acting

as support in bull phases. For our AMOMA strategy, the rule makes money versus buy

and hold when it is short, and correct. Using the x5 accelerated average; the rule is

short whenever it falls through the x2 accelerated average.

These are covered on the reversal move, producing the sharp spike in strategy profit

during the bear market periods. The rule works to capture a developing cascade of

liquidation. In Table 4, we display the geometric return on the trading book, with a

discount of 100bps applied to each reversal of position.

Simulation Results

Table 3 and Table 4 display the results of our simulations across all 45 markets. Less than

1/3 of the cases lead to profits after transaction costs.


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This may be somewhat disheartening, until it is noted that the max gain to loss ratio and

win versus loss trade statistics show the typical footprint of a trend following strategy.

With attention to stop-losses on short sales, it seems plausible to develop a workable

trading system. The key takeaway for further work is the importance of the short side

since the system is benchmarked to a buy and hold strategy.

Conclusion

Our goal in this study was to explore the fundamentals of sentiment through the notion

of the average cost basis of the market. We have developed what appears to be the

first systematic multi-market study of simple trading strategies based on the traditional

ideas of moving average crossings [8]. While this study has entered new territory, the

results are encouraging of further research directed at developing a trend-following

trading system that can capture and protect short-sale profits in bear markets.


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Table 1 Local Currency Returns and Date Ranges for S&P/Citigroup Broad Market Indices

Country Days FirstDate LastDate FirstValue LastValue Buy & Hold Return Annualised ReturnArgentina 3842 29-Dec-94 18-Sep-09 67.28 424.46 630.91% 12.84%

Australia 5870 30-Jun-89 30-Dec-11 93.42 301.48 322.71% 5.16%Austria 5870 30-Jun-89 30-Dec-11 101.14 174.98 173.02% 2.38%Belgium 5870 30-Jun-89 30-Dec-11 116.39 197.53 169.72% 2.30%Brazil 4436 29-Dec-94 30-Dec-11 55.23 627.58 1136.33% 14.81%Canada 5870 30-Jun-89 30-Dec-11 112.36 379.44 337.69% 5.36%Chile 4436 29-Dec-94 30-Dec-11 107.39 358.08 333.43% 7.08%China 4435 30-Dec-94 30-Dec-11 108.45 312.39 288.04% 6.20%Czech Republic 4436 29-Dec-94 30-Dec-11 109.05 251.98 231.06% 4.87%Denmark 5870 30-Jun-89 30-Dec-11 130.57 625.70 479.20% 6.96%Egypt 3912 31-Dec-96 30-Dec-11 78.08 416.12 532.95% 11.38%Fin an 5870 30-Jun-89 30-Dec-11 177.25 744.14 419.84% 6.35%France 5870 30-Jun-89 30-Dec-11 98.83 218.62 221.21% 3.47%Germany 5870 30-Jun-89 30-Dec-11 104.34 271.36 260.06% 4.19%Greece 4436 29-Dec-94 30-Dec-11 49.78 28.43 57.12% -3.13%Hong Kong 5870 30-Jun-89 30-Dec-11 42.28 338.85 801.48% 9.35%Hungary 3913 31-Dec-96 30-Dec-11 45.33 164.85 363.66% 8.67%

India 4435 30-Dec-94 30-Dec-11 101.57 448.85 441.90% 8.81%Indonesia 4436 29-Dec-94 30-Dec-11 121.15 791.59 653.40% 11.25%Ireland 5870 30-Jun-89 30-Dec-11 114.62 242.45 211.52% 3.27%Israel 4435 30-Dec-94 30-Dec-11 58.97 197.58 335.03% 7.11%Italy 5870 30-Jun-89 30-Dec-11 144.01 158.58 110.11% 0.41%Japan 5870 30-Jun-89 30-Dec-11 186.81 56.95 30.48% -4.97%Malaysia 5870 30-Jun-89 30-Dec-11 74.70 233.13 312.09% 5.01%Mexico 4436 29-Dec-94 30-Dec-11 47.53 592.83 1247.15% 15.41%Morocco 3912 31-Dec-96 30-Dec-11 65.93 243.83 369.82% 8.79%Netherlands 5870 30-Jun-89 30-Dec-11 97.07 263.56 271.50% 4.38%New Zealand 5870 30-Jun-89 30-Dec-11 138.68 137.89 99.44% -0.02%Norway 5870 30-Jun-89 30-Dec-11 143.04 421.27 294.51% 4.75%Peru 4436 29-Dec-94 30-Dec-11 67.05 958.12 1428.97% 16.31%Philippines 4436 29-Dec-94 30-Dec-11 157.92 237.89 150.64% 2.35%Poland 4436 29-Dec-94 30-Dec-11 51.39 173.96 338.51% 7.17%

Portugal 4436 29-Dec-94 30-Dec-11 46.28 62.37 134.78% 1.71%Russia 3913 31-Dec-96 30-Dec-11 49.38 1323.73 2680.83% 23.59%Singapore 5870 30-Jun-89 30-Dec-11 90.24 149.12 165.26% 2.18%South Africa 4436 29-Dec-94 30-Dec-11 98.01 528.95 539.72% 10.05%Sout Korea 4436 29-Dec-94 30-Dec-11 228.47 661.21 289.41% 6.22%Spain 5870 30-Jun-89 30-Dec-11 140.75 411.29 292.21% 4.71%Sweden 5870 30-Jun-89 30-Dec-11 102.47 604.19 589.61% 7.91%Switzerland 5870 30-Jun-89 30-Dec-11 87.55 321.23 366.93% 5.74%Taiwan 4436 29-Dec-94 30-Dec-11 89.46 70.67 78.99% -1.33%Thailand 4436 29-Dec-94 30-Dec-11 357.44 232.06 64.93% -2.42%Turkey 4436 29-Dec-94 30-Dec-11 8.18 1257.61 15380.03% 33.12%United Kingdom 5870 30-Jun-89 30-Dec-11 80.66 213.54 264.73% 4.27%United States 5870 30-Jun-89 30-Dec-11 70.99 325.37 458.36% 6.75%


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Table 2 Daily Returns: Bullish Positive P&L (Up) vs Bearish Negative P&L (Dn) plus Annualized Spread

Country DayUp RtnUp St Up RngUp DayDn RtnDn St Dn RngDn Bu Per UpVsDn AnnUpS AnnDnStArgentina 3042 0.13% 1.89% 1.31% 799 -0.15% 2.90% 1.96% 79% 71% 30% 46%

Austral ia 4692 0.05% 0.80% 0.59% 1177 -0.08% 1.43% 1.03% 80% 33% 13% 23%Austria 3270 0.07% 1.06% 0.72% 2599 -0.05% 1.49% 0.95% 56% 32% 17% 24%Belgium 3400 0.05% 0.85% 0.60% 2469 -0.03% 1.42% 0.92% 58% 18% 13% 22%Brazi 3741 0.10% 1.58% 1.14% 694 -0.08% 3.54% 2.25% 84% 45% 25% 56%Cana a 4071 0.05% 0.86% 0.61% 1798 -0.04% 1.30% 0.80% 69% 24% 14% 21%Chile 3108 0.06% 0.98% 0.67% 1327 -0.03% 1.02% 0.69% 70% 22% 16% 16%C ina 2739 0.14% 1.68% 1.16% 1695 -0.12% 2.40% 1.64% 62% 66% 27% 38%Czech Republ ic 2248 0.11% 1.32% 0.95% 2187 -0.05% 1.77% 1.18% 51% 41% 21% 28%Denmar 4203 0.06% 0.89% 0.64% 1666 -0.04% 1.47% 0.99% 72% 25% 14% 23%Egypt 2194 0.14% 1.64% 1.03% 1717 -0.06% 1.83% 1.09% 56% 50% 26% 29%Fin an 3128 0.14% 1.85% 1.24% 2741 -0.07% 2.04% 1.42% 53% 51% 29% 32%France 3916 0.07% 0.98% 0.73% 1953 -0.07% 1.74% 1.22% 67% 35% 16% 28%Germany 3885 0.09% 1.02% 0.75% 1984 -0.11% 1.88% 1.32% 66% 50% 16% 30%Greece 2172 0.12% 1.65% 1.12% 2263 -0.10% 1.90% 1.32% 49% 55% 26% 30%Hong Kong 4484 0.08% 1 .26% 0 .88% 1385 -0.06% 2 .06% 1 .38% 76% 35% 20% 33%Hungary 2793 0.13% 1.73% 1.21% 1119 -0.13% 2.48% 1.71% 71% 65% 27% 39%India 2778 0.14% 1.53% 1.06% 1656 -0.11% 1.81% 1.24% 63% 63% 24% 29%

In onesia 2958 0.12% 1.62% 1.08% 1477 -0.05% 2.44% 1.60% 67% 42% 26% 39%Ire an 3094 0.05% 1.15% 0.80% 1339 -0.06% 1.93% 1.31% 70% 27% 18% 31%Israel 3519 0.06% 1.31% 0.87% 915 -0.07% 1.53% 1.06% 79% 33% 21% 24%Ita y 3129 0.08% 1.14% 0.82% 2740 -0.07% 1.61% 1.14% 53% 40% 18% 25%Japan 1450 0.08% 1.00% 0.72% 4419 -0.04% 1.40% 0.97% 25% 31% 16% 22%Ma aysia 3895 0.06% 1.06% 0.69% 1974 -0.03% 1.94% 1.13% 66% 22% 17% 31%Mexico 4118 0.09% 1.44% 1.01% 317 -0.21% 2.53% 1.83% 93% 75% 23% 40%Morocco 3625 0.04% 0.86% 0.56% 286 0.00% 0.65% 0.44% 93% 11% 14% 10%Net er an s 4110 0.07% 0.94% 0.67% 1759 -0.09% 1.73% 1.22% 70% 41% 15% 27%New Zeal and 3227 0.03% 0.91% 0.60% 2642 -0.03% 1.10% 0.79% 55% 16% 14% 17%Norway 3752 0.08% 1.08% 0.80% 2117 -0.06% 1.88% 1.29% 64% 35% 17% 30%Peru 4078 0.08% 1.53% 1.04% 357 -0.05% 1.91% 1.25% 92% 34% 24% 30%P i i ppi nes 2008 0.07% 1.18% 0.84% 2427 -0.02% 1.68% 1.12% 45% 23% 19% 27%Poland 3028 0.11% 1.55% 1.11% 1407 -0.09% 2.00% 1.45% 68% 50% 25% 32%Portuga 2215 0.08% 0.98% 0.67% 2220 -0.05% 1.25% 0.84% 50% 34% 16% 20%Russia 3412 0.15% 2.30% 1.55% 500 -0.06% 5.02% 3.41% 87% 52% 36% 80%

Si ngapore 3944 0.06% 0.99% 0.72% 1925 -0.07% 1.88% 1.29% 67% 32% 16% 30%Sout A ri ca 3815 0.06% 1 .13% 0 .81% 620 -0.08% 1 .63% 1 .09% 86% 35% 18% 26%South Korea 2841 0.09% 1 .69% 1.18% 1594 -0.03% 2.54% 1 .72% 64% 29% 27% 40%Spain 3182 0.10% 1.03% 0.76% 2687 -0.06% 1.65% 1.15% 54% 39% 16% 26%Sweden 4019 0.10% 1.20% 0.88% 1850 -0.08% 2.01% 1.43% 68% 46% 19% 32%Swi tzer an 3836 0.07% 0 .86% 0 .63% 1902 -0.05% 1 .51% 1 .05% 67% 30% 14% 24%Taiwan 2302 0.14% 1.27% 0.90% 2133 -0.13% 1.86% 1.35% 52% 68% 20% 29%Thailand 2168 0.10% 1.28% 0.92% 2267 -0.08% 2.41% 1.63% 49% 43% 20% 38%Tur ey 3217 0.27% 2.36% 1.66% 1218 -0.19% 3.15% 2.17% 73% 116% 37% 50%Uni ted Ki ngdom 4404 0.06% 0.80% 0.59% 1465 -0.10% 1.60% 1.14% 75% 40% 13% 25%Uni te Sta tes 4661 0.07% 0 .89% 0.63% 1208 -0.10% 1.85% 1 .29% 79% 40% 14% 29%


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Table 3 Results of the Simple MOMA Strategy Backtest

COUNTRY TRADES SMOMA INDEXBM BUYS SELLS GAINS LOSSES MAX_GAIN MAX_LOSS

Greece 22 5851% -41% 11 11 5 17 635.4% -2.4%Finland 61 879% 300% 30 31 10 51 920.3% -13.1%Portugal 39 417% 35% 19 20 8 31 121.4% -4.3%Egypt 34 770% 412% 17 17 7 27 885.7% -4.9%Ireland 31 231% 54% 15 16 5 26 174.3% -7.0%Thailand 57 132% -37% 29 28 9 48 144.9% -10.8%Japan 66 81% -70% 33 33 10 56 71.7% -4.5%Italy 102 116% 10% 51 51 13 89 83.7% -6.0%Belgium 47 149% 68% 23 24 5 42 111.5% -4.5%Sweden 71 539% 471% 35 36 11 60 211.2% -5.9%Norway 69 234% 187% 34 35 18 51 175.6% -10.7%Taiwan 128 2% -23% 64 64 21 107 74.7% -6.0%Morocco 4 258% 271% 2 2 3 1 189.5% -0.3%Philippines 45 23% 42% 23 22 5 40 84.5% -5.1%Denmark 51 319% 361% 25 26 11 40 174.8% -2.8%Netherlands 116 118% 172% 58 58 13 103 161.1% -4.8%

Singapore 79 -10% 56% 39 40 13 66 61.6% -8.5%Austria 98 -1% 70% 49 49 10 88 157.8% -9.1%New Zealand 93 -76% 1% 46 47 9 84 20.8% -3.8%Spain 105 93% 196% 52 53 13 92 180.9% -6.1%Israel 45 75% 214% 22 23 8 37 118.3% -6.5%Switzerland 80 95% 235% 40 40 9 71 126.8% -4.6%Germany 136 -5% 149% 68 68 21 115 97.6% -8.4%South Korea 46 23% 182% 23 23 7 39 112.3% -6.8%Malaysia 59 49% 209% 30 29 10 49 80.6% -8.4%France 132 -51% 118% 66 66 11 121 64.8% -5.2%Cana a 65 49% 228% 32 33 13 52 74.8% -4.1%Hong Kong 47 494% 682% 23 24 10 37 372.0% -4.9%Czech Republic 100 -56% 137% 50 50 11 89 251.5% -6.6%Australia 87 0% 211% 43 44 11 76 104.4% -4.5%China 113 -41% 172% 56 57 18 95 166.4% -8.1%United Kingdom 131 -56% 160% 66 65 15 116 64.8% -3.8%

United States 81 113% 345% 41 40 11 70 285.8% -4.7%Hungary 80 18% 254% 40 40 12 68 195.2% -11.2%India 83 83% 319% 41 42 17 66 183.4% -12.1%Chile 85 -34% 231% 43 42 2 83 237.6% -3.7%Poland 106 -78% 235% 53 53 9 97 86.1% -8.6%In onesia 43 153% 533% 22 21 8 35 277.2% -10.3%South Africa 41 33% 430% 21 20 6 35 129.7% -6.4%Argentina 89 -77% 530% 45 44 4 84 155.7% -9.3%Peru 21 572% 1271% 11 10 2 19 977.0% -7.8%Brazil 37 194% 1004% 18 19 10 27 308.8% -7.9%Mexico 42 115% 1165% 21 21 6 36 215.1% -7.8%Russia 32 1345% 2461% 16 16 6 26 2620.5% -8.9%Turkey 133 404% 15100% 66 67 17 116 652.4% -10.0%


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Table 4 Results of the Accelerated MOMA Backtest

COUNTRY TRADES AMOMA INDEXBM BUYS SELLS GAINS LOSSES MAX_GAIN MAX_LOSS

Greece 6 5037% -41% 3 3 3 3 635.4% -9.2%Finland 9 2032% 300% 4 5 6 3 604.6% -19.6%Sweden 11 2003% 471% 5 6 7 4 467.9% -24.6%Netherlands 14 1208% 172% 7 7 5 9 311.3% -5.5%Germany 16 727% 149% 8 8 9 7 83.2% -7.6%Portugal 7 505% 35% 3 4 3 4 126.3% -16.1%Italy 16 336% 10% 8 8 6 10 135.6% -17.9%United States 10 597% 345% 5 5 6 4 267.9% -10.4%Japan 14 182% -70% 7 7 6 8 71.4% -10.2%Switzerland 12 435% 235% 6 6 6 6 110.2% -10.0%France 14 201% 118% 7 7 4 10 171.7% -16.0%Singapore 11 133% 56% 5 6 4 7 70.7% -16.8%Taiwan 32 37% -23% 16 16 9 23 50.6% -12.4%Australia 9 253% 211% 4 5 3 6 118.5% -7.8%Thailand 15 2% -37% 8 7 6 9 178.2% -30.3%Norway 15 183% 187% 7 8 6 9 151.6% -18.5%

China 17 162% 172% 8 9 6 11 232.0% -24.2%Belgium 9 39% 68% 4 5 4 5 99.7% -24.3%Austria 12 40% 70% 6 6 4 8 121.1% -25.8%Philippines 7 4% 42% 4 3 1 6 124.3% -34.7%Canada 11 173% 228% 5 6 5 6 68.4% -11.8%New Zealand 13 -69% 1% 6 7 1 12 10.1% -13.3%United Kingdom 16 88% 160% 8 8 5 11 54.6% -11.5%Ireland 5 -29% 54% 2 3 2 3 167.7% -59.8%Czech Republic 8 35% 137% 4 4 2 6 239.0% -26.5%India 19 215% 319% 9 10 6 13 340.0% -19.0%Spain 23 84% 196% 11 12 7 16 122.7% -17.6%Morocco 4 150% 271% 2 2 1 3 248.5% -69.2%Hungary 12 133% 254% 6 6 5 7 197.1% -21.1%Malaysia 7 71% 209% 4 3 4 3 144.8% -31.9%Hong Kong 9 526% 682% 4 5 4 5 308.5% -15.2%Israel 7 5% 214% 3 4 3 4 94.3% -22.3%

Poland 12 -10% 235% 6 6 5 7 57.6% -18.0%Denmark 15 106% 361% 7 8 5 10 135.0% -15.6%Chile 7 -31% 231% 4 3 1 6 176.0% -24.1%South Korea 8 -91% 182% 4 4 2 6 30.8% -49.1%South Africa 9 -9% 430% 5 4 3 6 106.0% -16.6%Egypt 10 -79% 412% 5 5 2 8 231.8% -47.1%Argentina 9 -16% 530% 5 4 0 8 -0.2% -26.0%Indonesia 9 -47% 533% 5 4 2 7 129.1% -29.6%Mexico 4 262% 1165% 2 2 2 2 439.3% -27.0%Peru 5 337% 1271% 3 2 1 4 933.5% -17.6%Brazil 9 -2% 1004% 4 5 4 5 189.0% -22.5%Russia 6 -42% 2461% 3 3 2 4 585.7% -62.7%Turkey 25 843% 15100% 12 13 8 17 477.8% -30.1%


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References

1. Jegadeesh, Narishman and Sheridan Titman, Returns to Buying Winnersand Selling Losers, Journal of Finance 48, pp. 65-91 (1993).

2. Kahneman, Daniel and Amos Tversky, Prospect Theory: An Analysis ofDecision under Risk, Econometrica 47, pp. 263-291 (1979).

3. Kirkpatrick, Charles D III and Julie R. Dahlquist, Technical Analysis: TheComplete Resource for Financial Market Technicians 2nd Edn , New Jersey,

NJ FT Press (2005)

4. Jiler, William, How Charts Can Help You in the Stock Market, New York, NYCommodity Research Corporation (1962).

5. Fries, Christian P, Elastic Moving Averages: A Volume Sensitive Average, Stocks & Commodities V19.6, pp. 52-56 (2001).

6. Grinblatt, Mark and Bing Han, The Disposition Effect and Momentum, NBERWorking paper #834 (2002), http://www.nber.org/papers/w8734.

7. Jones, Kingsley, Reading the Mind of the Market – The Voting Line ofInvestor Sentiment, Credit Suisse First Boston, Quantitative Research Note,

Sydney August (2002).

8. Kaufman, Perry J, New Trading Systems and Methods 4th Edn, New York,NY John Wiley & Sons Inc. (2005).

9. Ehlers, John, MESA and Trading Market Cycles, New York, NY John Wiley &Sons Inc. (2002).

10. S&P/Citigroup Broad Market Index (BMI), Global Stock Markets Factbook,Standard & Poors, New York, NY (2011); www.standardandpoors.com.

11. World Federation of Exchanges (WFE); www.wfe.org. 12. Aronson, David, Evidence-Based Technical Analysis, New York, NY John

Wiley & Sons Inc. (2007).

http://www.nber.org/papers/w8734http://www.nber.org/papers/w8734http://www.nber.org/papers/w8734http://www.standardandpoors.com/http://www.standardandpoors.com/http://www.standardandpoors.com/http://www.wfe.org/http://www.wfe.org/http://www.wfe.org/http://www.wfe.org/http://www.standardandpoors.com/http://www.nber.org/papers/w8734


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Fundamentals of Sentiment - Dow Essay 2012

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