Fundamental Analysis for Forex Trading Pdf

Economists have traditionally been skeptical of the value of technical analysis, the use of past price behavior to guide trading decisions in asset markets. Instead, they have relied on the logic of the efficient markets hypothesis. Christopher J. Neely briefly explains the fundamentals of technical analysis and the efficient markets hypothesis as applied to the foreign exchange market, evaluates the profitability of simple trading rules, and reviews recent ideas that might justify extrapolative technical analysis.

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F EDERAL R ESERVE B ANK OF S T . LOUIS

S EPTEMBER /OCTOBER 1997

Technical

Analysis in

the Foreign

Exchange

Market: A

Layman's Guide

Christopher J. Neely

Technical analysis suggests that a long-term rally

frequently is interrupted by a short-lived decline.

Such a dip, according to this view, reinforces the

original uptrend. Should the dollar fall below

1.5750 marks, dealers said, technical signals would

point to a correction that could pull the dollar back

as far as 1.55 marks before it rebounded.

Gregory L. White

Wall Street Journal

November 12, 1992

echnical analysis, which dates back a

century to the writings of Wall Street

Journal editor Charles Dow, is the use of

past price behavior to guide trading deci-

sions in asset markets. For example, a

trading rule might suggest buying a curr ency if

its price has risen more than 1 percent from

its value ﬁve days earlier. Such rules are

widely used in stock, commodity, and (since

the early 1970s) foreign exchange markets.

Mor e than 90 percent of surveyed foreign

exchange dealers in London report using

some form of technical analysis to inform

their trading decisions (Taylor and Allen,

1992). In fact, at short horizons—less than

a week—technical analysis predominates

over fundamental analysis, the use of other

economic variables like interestrates, and

prices in inﬂuencing trading decisions.

Investors and economists ar e interested

in technical analysis for different reasons.

Investors are concerned with "beating the

market," earning the best return on their

money. Economists study technical

analysis in foreign exchange markets

because its success casts doubt on the efﬁ-

cient markets hypothesis, which holds that

publicly available information, like past

prices, should not help traders earn

unusually high returns. Instead, the

success of technical analysis suggests that

exchange rates are not always determined

by economic fundamentals like prices and

interest rates, but rather are driven away

from their fundamental values for long

periods by traders' irrational expectations

of future exchange rate changes. These

swings away from fundamental values may

discourage international trade and invest-

ment by making the relative price of U.S.

and foreign goods and investments very

volatile. For example, when BMW decides

where to build an automobile factory, it

may choose poorly if ﬂuctuating exchange

rates make it difﬁcult or impossible to pre-

dict costs of production in the United

States relative to those in Germany.

Despite the widespread use of

technical analysis in foreign exchange

(and other) markets, economists have tra-

ditionally been very skeptical of its value.

Technical analysis has been dismissed by

some as astrology. In turn, technical

traders have frequently misunderstood

what economists have to say about asset

price behavior. What can the two learn

from each other? This article provides

an accessible treatment of recent research

on technical analysis in the foreign

exchange market.

A PRIMER ON TECHNICAL

ANALYSIS IN FOREIGN

EXCHANGE MARKETS

Technical analysis is a short-horizon

trading method; positions last a few hours

or days. Technical traders will not hold

Christopher J. Neely is an economist at the Federal Reserve Bank of St. Louis. Kent A. Koch provided research assistance.

positions for months or years, waiting

for exchange rates to return to where fun-

damentals are pushing them. In contrast,

fundamental investors study the economic

determinants of exchange rates as a basis

for positions that typically last much

longer, for months or years. Some traders,

however, use technical analysis in

conjunction with fundamental analysis,

doubling their positions when technical

and fundamental indicators agree on the

direction of exchange rate movements.

Three principles guide the behavior

of technical analysts.

The ﬁrst is that

market action (prices and transactions

volume) "discounts" everything. In other

words, all relevant information about an

asset is incorporated into its price history,

so there is no need to forecast the

fundamental determinants of an asset's

value. In fact, Murphy (1986) claims that

asset price changes often precede observed

changes in fundamentals. The second

principle is that asset prices move in

trends. Predictable trends are essential to

the success of technical analysis because

they enable traders to proﬁt by buying

(selling) assets when the price is rising

(falling), or as technicians counsel, "the

trend is your friend." Practitioners appeal

to Newton's law of motion to explain the

existence of trends: Trends in motion tend

to remain in motion unless acted upon by

another force. The third principle of tech-

nical analysis is that history repeats itself.

Asset traders will tend to react the same

way when confronted by the same

conditions. Technical analysts do not

claim their methods are magical; rather,

they take advantage of market psychology.

Following from these principles, the

methods of technical analysis attempt to

identify trends and reversals of trends.

These methods are explicitly extrapolative;

that is, they infer future price changes

from those of the recent past. Formal

methods of detecting trends are necessary

because prices move up and down around

the primary (or longer-run) trend. An

example of this movement is shown in

Figure 1, where the dollar/deutsche mark

($/DM) exchange rate ﬂuctuates around an

apparent uptrend.

To distinguish trends from shorter-run

ﬂuctuations, technicians employ two types

of analysis: charting and mechanical rules.

Charting, the older of the two methods,

involves graphing the history of prices

over some period—determined by the

practitioner—to predict future patterns

in the data from the existence of past

patterns. Its advocates admit that this

subjective system requires the analyst to

use judgement and skill in ﬁnding and

interpreting patterns. The second type of

method, mechanical rules, imposes consis-

tency and discipline on the technician by

requiring him to use rules based on mathe-

matical functions of present and past

exchange rates.

Charting

T o identify tr ends thr ough the use of

char ts, practitioners must ﬁrst ﬁnd peaks

and troughs in the price series. A peak is

the highest value of the exchange rate

within a speciﬁed period of time (a local

maximum), while a trough is the lowest

value the price has taken on within the

same period (a local minimum). A series

of peaks and troughs establishes downtrends

and uptrends, r espectively. For example, as

These principles and a much

more comprehensive treatment

of technical analysis are provid-

ed by Murphy (1986) and

Pring (1991). Rosenberg and

Shatz (1995) advocate the

use of technical analysis with

more economic explanation.

Figure 1 shows only closing

prices. In this, it differs from

most charts employed by tech-

nical traders, which might show

the opening, closing, and daily

trading range.

S EPTEMBER /OCTOBER 1997

F EDERAL R ESERVE B ANK OF S T . LOUIS

Figure 1

Peaks, Troughs, Trends, Resistance

and Support Levels Illustrated for

the $/DM

Sell signal from a

0.5% filter rule

Local troughs

Resistance level

Trendline

Buy signal from a 0.5% filter rule

Support level

Local peak

NOTES: Not all buy and sell signals from the filter rule are identified.

0.72

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SeptAugJulyJuneMay

0.60

0.66

0.70

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0.68

1992

$ per DM

shown in Figure 1, an analyst may establish

an uptr end visually by connecting two

local tr oughs in the data. A trendline is

drawn below an appar ent up trend or

above an apparent downtrend. As mor e

tr oughs touch the trendline without

violating it, the technician may place mor e

conﬁdence in the validity of the tr endline.

The angle of the trendline indicates the

speed of the trend, with steeper lines indi-

cating faster appreciation (or depreciation)

of the for eign currency.

After a trendline has been established,

the technician trades with the tr end,

buying the for eign currency if an uptr end is

signaled and selling the for eign currency if

a downtr end seems likely. When a market

par ticipant buys a foreign currency in the

hope that it will go up in price, that par tici-

pant is said to be long in the currency. The

opposite strategy , called shorting or selling

short , enables the participant to make

money if the for eign currency falls in price.

A shor t seller borrows foreign currency

today and sells it, hoping the price will fall

so that it can be bought back more cheaply

in the futur e.

Spotting the reversal of a trend is just

as important as detecting trends. Peaks

and troughs are important in identifying

reversals too. Local peaks are called resis-

tance levels, and local troughs are called

support levels (see Figure 1). If the price

fails to break a resistance level (a local

peak) during an uptrend, that may be an

early indication that the trend may soon

reverse. If the exchange rate signiﬁcantly

penetrates the trendline, that is considered

a more serious signal of a possible reversal.

Technicians identify several patterns

that are said to foretell a shift from a trend

in one direction to a trend in the opposite

direction. An example of the best-known

type of reversal formation, called "head

and shoulders," is shown in Figure 2. The

head and shoulders reversal following an

uptrend is characterized by three local

peaks with the middle peak being the

largest of the three. The line between the

troughs of the shoulders is known as the

"neckline." When the exchange rate

penetrates the neckline of a head and

shoulders, the technician conﬁrms a

reversal of the previous uptrend and

begins to sell the foreign currency. There

are several other similar reversal patterns,

including the V (single peak), the double

top (two similar peaks) and the triple

top (three similar peaks). The reversal

patterns of a downtrend are essentially

the mirrors of the reversal patterns for

the uptrend.

Mechanical Rules

Charting is very dependent on the

interpretation of the technician who is

drawing the charts and interpreting the

patterns. Subjectivity can permit emotions

like fear or greed to affect the trading

strategy. The class of mechanical trading

rules avoids this subjectivity and so is

more consistent and disciplined, but,

according to some technicians, it sacriﬁces

some information that a skilled chartist

might discern from the data. Mechanical

trading rules are even more explicitly

extrapolative than charting; they look for

trends and follow those trends. A well-

known type of mechanical trading rule is

the "ﬁlter rule," or "trading range break"

rule which counsels buying (selling) a cur-

rency when it rises (falls) x percent above

(below) its previous local minimum (max-

imum). The size of the ﬁlter, x, which is

S EPTEMBER /OCTOBER 1997

F EDERAL R ESERVE B ANK OF S T . LOUIS

Figure 2

The Head and Shoulders Reversal Pattern

Illustrated for the $/DM

Right shoulder

Neckline

Left shoulder

Head

0.66

0.56

MayDec AprFeb MarJanNovOctSept

0.60

0.64

0.58

0.62

1991-92

$ per DM

Exchange rate

penetrates the

neckline sell

signal

chosen by the technician from past experi-

ence, is generally between 0.5 percent and

3 percent. Figure 1 illustrates some of the

buy and sell signals generated by a ﬁlter

rule with ﬁlter size of 0.5 percent.

A second variety of mechanical trading

rule is the "moving average" class. Like

trendlines and ﬁlter rules, moving averages

bypass the short-run zigs and zags of the

exchange rate to permit the technician to

examine trends in the series. A moving

average is the average closing price of the

exchange rate over a given number of

pr evious trading days. The length of the

moving average "window"—the number of

days in the moving average—governs

whether the moving average r eﬂects long- or

short-run tr ends.

Any moving average will

be smoother than the original exchange-

rate series, and long moving averages will

be smoother than short moving averages.

Figure 3 illustrates the behavior of a 5-day

and a 20-day moving average of the exchange

rate in relation to the exchange rate itself.

A typical moving average trading rule pre-

scribes a buy (sell) signal when a short

moving average crosses a longer moving

average from below (above)—that is,

when the exchange rate is rising (falling)

relatively fast. Of course, the lengths of

the moving averages must be chosen by

the technician. The length of the short

moving average rule is sometimes chosen

to equal one, the exchange rate itself.

A ﬁnal type of mechanical trading rule

is the class of "oscillators," which are said

to be useful in non-trending markets, when

the exchange rate is not trending up or

down strongly . A simple type of oscillator

index, an example of which is shown in

Figur e 4, is given by the dif ference between

two moving averages: the 5-day moving

average minus the 20-day moving average.

Oscillator rules suggest buying (selling) the

foreign currency when the oscillator index

takes an extremely low (high) value. Note

that the oscillator index, as a difference

between moving averages, also generates

buy/sell signals from a moving average rule

when the index crosses zero. That is,

when the short moving average becomes

larger than the long moving average, the

moving average rule will generate a buy

signal. By deﬁnition, this will happen

when the oscillator index goes from nega-

tive to positive. Therefore, an oscillator

chart is also useful for generating moving

average rule signals.

Other Kinds of Technical Analysis

Technical analysis is more complex

and contains many more techniques than

those described in this article. For

For example, the ﬁve-day mov-

ing average of an exchange

rate series is given by:

where

denotes the closing

price of the spot exchange rate

at day

S EPTEMBER /OCTOBER 1997

F EDERAL R ESERVE B ANK OF S T . LOUIS

Figure 3

5- and 20-Day Moving Averages

Sell signal, moving

average rule

5-Day moving average

Buy signal, moving average rule

20-Day moving average

Exchange rate

NOTES: These moving averages smooth the exchange rate and can be used to

generate buy and sell signals in the foreign exchange market.

0.65

0.60

JunMayAprMarFeb

0.59

0.62

0.64

0.61

0.63

1992

$ per DM

The Oscillator Index

Oscillator rule buy signal

Difference in

moving averages

Moving

average

rule buy

signal

Moving

average rule

sell signal

Oscillator rule sell signals

NOTES: The 5-day moving average minus the 20-day moving average can also be

used to generate buy and sell signals.

1.0

–0.2

JunMayAprMarFeb

–0.4

0.4

0.8

0.2

0.6

1992

Normalized difference in moving averages

–0.8

–1.0

–0.6

example, many technical analysts assign a

special role to round numbers in support

or resistance levels. When the exchange

rate signiﬁcantly crosses the level of 100

yen to the dollar, that is seen as an indica-

tion that further movement in the same

direction is likely.

Other prominent types

of technical analysis use exotic mathemat-

ical concepts such as Elliot wave theory

and/or Fibonacci numbers.

Finally,

traders sometimes use technical analysis of

one market's price history to take positions

in another market, a practice called inter-

market technical analysis.

EFFICIENT MARKETS AND

TECHNICAL ANALYSIS

T echnical analysts believe that their

methods will permit them to beat the

market. Economists have traditionally been

skeptical of the value of technical analysis,

afﬁrming the theory of efﬁcient markets

that holds that no strategy should allow

investors and traders to make unusual

retur ns except by taking excessive risk.

Investing in the Foreign

Exchange Market

To understand the efﬁcient markets

hypothesis in the context of foreign

exchange trading, consider the options

open to an American bank (or ﬁrm) that

temporarily has excess funds to be invested

over night. The bank could lend that money

in the overnight bank money market,

known as the federal funds market. The

simple net retur n on each dollar invested

this way would be the overnight interest

rate on dollar deposits. The bank has other

investment options, though. It could

instead convert its money to a foreign cur-

rency (e.g., the deutsche mark), lend its

money in the overnight German money

market (at the German interest rate) and

then convert it back to dollars tomorrow.

This return is the sum of the German

overnight interest rate and the change in

the value of the DM. Which investment

should the bank choose? If the bank were

not concerned about risk, it would choose

the investment with the higher expected

return. While the U.S. and German

interest rates are known, the bank must

base its decision on its forecast of the rate

of appreciation of the DM. If market par-

ticipants expect the return to investing in

the German money market to be higher

than that of investing in the U.S. money

market, they will all try to invest in the

German market, and none will invest in

the U.S. money market. Such a situation

would tend to drive down the German

return and raise the U.S. return until the

two were equalized. The excess return on a

German investment over an investment in

the U.S. money market (R

), at date t,

from the point of view of a U.S. investor is

deﬁned as

(1) R

; i

D S

– i

where i

is the German over night inter est

rate, D S

is the percentage rate of apprecia-

tion of the DM against the dollar over night,

and i

is the U.S. overnight interest rate.

If market participants cared only about the

expected return on their investments, and

if their expectations about the change in the

exchange rate were not systematically wr ong,

the expected excess retur n on foreign

exchange should equal zero, every day .

The assumption that market partic-

ipants care only about the expected return

is too strong, of course. Surely, participants

also care about the risk of their investment.

Risk can come from either the risk of default

on the loan or the risk of sharp changes in

the exchange rate, or both. If investing in

the Ger man market is signiﬁcantly riskier

than investing in the U.S. market, investors

must be compensated with a higher expected

return in the German market, or they will

not invest there. In that case, the expected

excess return would be positive and equal

to a risk premium. The expected risk-

adjusted excess r eturn would be equal

to zer o. That is,

(2) E[R

] – RP

= 0,

where E [*] is a function that takes the

expected value of the term inside the

"The 100 yen level for the dol-

lar is still a very big psychologi-

cal barrier and it will take a few

tests before it breaks. But once

you break 100 yen, it's not

going to remain there for long.

You'll probably see it trade

between 102 and 106 for a

while," said Jorge Rodriguez,

director of North American

Sales at Credit Suisse, as

reported by Creswell (1995).

Murphy (1986) discusses Elliot

wave theory, Fibonacci num-

bers, and many other technical

concepts.

Samuelson (1965) did seminal

theoretical work on the modern

theory of efﬁcient markets.

The excess return may also be

considered the return to some-

one borrowing in dollars and

investing those dollars in

German investments.

Market participants may be

concerned about the

liquidity

of their position as well as

the expected return and risk.

Liquidity is the ease with which

assets can be converted

into cash.

S EPTEMBER /OCTOBER 1997

F EDERAL R ESERVE B ANK OF S T . LOUIS

brackets [*] and RP

is the risk premium

associated with the higher risk of lending

in the German market.

Efﬁcient Markets

The idea that the expected risk-adjusted

excess return on for eign exchange is zer o

implies a sensible statement of the efﬁ cient

markets hypothesis in the for eign exchange

context: Exchange rates r eﬂect information

to the point where the potential excess r eturns

do not exceed the transactions costs of acting

(trading) on that information.

In other

wor ds, you can' t proﬁ t in asset markets

(like the foreign exchange market) by

trading on publicly available information.

This description of the efﬁcient markets

hypothesis appears to be a restatement of

the ﬁrst principle of technical analysis:

Market action (price and transactions

volume) discounts all information about

the asset' s value. There is, however, a subtle

but important distinction between the efﬁ-

cient markets hypothesis and technical

analysis: The efﬁcient markets hypothesis

posits that the curr ent exchange rate adjusts

to all information to prevent traders fr om

r eaping excess retur ns, while technical

analysis holds that curr ent and past price

movements contain just the information

needed to allow proﬁ table trading.

What does this version of the efﬁcient

markets hypothesis imply for technical

analysis? Under the efﬁ cient markets

hypothesis, only current inter est rates and

risk factors help predict exchange rate

changes, so past exchange rates ar e of no

help in forecasting excess foreign exchange

returns—i.e., if the hypothesis holds, tech-

nical analysis will not work. Malkiel' s

summary of the attitude of many economists

towar d technical analysis in the stock

market is based on similar r easoning:

The past history of stock prices cannot

be used to predict the future in any

meaningful way. Technical strategies

ar e usually amusing, often comforting,

but of no real value. (Malkiel, 1990,

p. 154.)

How do prices move in the hypothetical

efﬁ cient market? In an efﬁcient market,

proﬁt seekers trade in a way that causes

prices to move instantly in response to new

infor mation, because any information that

makes an asset appear likely to become

mor e valuable in the future causes an

immediateprice rise today. If prices do

move instantly in response to all new

information, past information, like prices,

does not help anyone make money. If

there were a way to make money with little

risk from past prices, speculators would

employ it until they bid away the money to

be made. For example, if the price of an

asset rose 10 percent every Wednesday,

speculators would buy str ongly on T uesday,

driving prices past the point where anyone

would think they could rise much further,

and so a fall would be likely . This situation

could not lead to a predictable pattern of

rises on T uesday, though, because specula-

tors would buy on Monday. Any pattern in

prices would be quickly bid away by market

par ticipants seeking proﬁ ts. Indeed, there

is considerable evidence that markets often

do work this way . Moorthy (1995) ﬁnds

that foreign exchange rates react very

quickly and efﬁciently to news of changes

in U.S. employment ﬁgur es, for example.

Because the efﬁ cient markets hypothesis

is frequently misinterpreted, it is important

to clarify what the idea does not mean. It

does not mean that asset prices ar e unrelated

to economic fundamentals.

Asset prices

may be based on fundamentals like the pur-

chasing power of the U.S. dollar or German

mark. Similarly, the hypothesis does not

mean that an asset price ﬂuctuates randomly

ar ound its intrinsic (fundamental) value.

If this were the case, a trader could make

money by buying the asset when the price

was relatively low and selling it when it was

r elatively high. Rather , "efﬁcient markets"

means that at any point in time, asset prices

repr esent the market's best guess, based on

all curr ently available information, as to the

fundamental value of the asset. Future price

changes, adjusted for risk, will be close to

unpredictable.

But if any pattern in prices is quickly

bid away, how does one explain the

There are a number of versions

of the efﬁcient markets hypoth-

esis. This version is close to

that put forward by Jensen

(1978).

For an example of an incorrect

interpretation of the efﬁcient

markets hypothesis, see

Murphy (1986, p. 20-21) who

offers, "The theory is based on

the

efﬁcient markets hypothesis

which holds that prices ﬂuctuate

randomly about their intrinsic

value. . . . it's just unrealistic to

believe that

all

price movement

is random."

S EPTEMBER /OCTOBER 1997

F EDERAL R ESERVE B ANK OF S T . LOUIS

apparent tr ends seen in charts of asset

prices like those in Figure 1? Believers in

efﬁ cient markets point out that completely

random price changes—like those generated

by ﬂipping a coin—will produce price

series that seem to have trends (Malkiel,

1990, or Paulos, 1995). Under ef ﬁcient

markets, however, traders cannot exploit

those tr ends to make money , since the

tr ends occur by chance and are as likely to

r everse as to continue at any point. (For

example, some families have—pur ely by

chance—strings of either boys or girls, yet

a family that already has four girls and is

expecting a ﬁfth child still has only a 50

per cent chance of having another girl.)

EVALUATING TECHNICAL

ANALYSIS

The efﬁcient markets hypothesis

requires that past prices cannot be used

to predict exchange rate changes. If the

hypothesis is true, technical analysis

should not enable a trader to earn proﬁts

without accepting unusual risk. This sec-

tion examines how two common types of

trading rules are formulated and how the

returns generated by these rules are

measured. Problems inherent in testing

the rules, measuring risk, and drawing

conclusions about the degree of market

efﬁciency are discussed.

Finding a Trading Rule

A basic problem in evaluating

technical trading strategies is that rules

r equiring judgement and skill are impossible

to quantify and therefore unsuitable for

testing. A fair test r equires ﬁxed, objective,

commonly used trading rules to evaluate.

An "objective" rule does not rely on indi-

vidual skill or judgement to determine buy

or sell decisions. The rule should be com-

monly used to reduce the problem of

drawing false conclusions from "data

mining"— a practice in which many

different rules are tested until, purely by

chance, some are found to be proﬁtable

on the data set. Negative test results are

ignored, while positive results are

published and taken to indicate that

trading rule strategies can yield proﬁts.

For example, there is a vast literature on

pricing anomalies in the equity markets,

summarized by Ball (1995) and Fortune

(1991), but Roll (1994) has found that

these aberrations are difﬁcult to exploit in

practice; he suggests that they may be par-

tially the result of data mining.

Trading Rules

With these considerations, two kinds

of trading rules have been commonly

tested: ﬁlter rules and moving average rules.

As a preceding section of this article

explained, ﬁlter rules give a buy signal

when the exchange rate rises x percent

over the previous recent minimum.

The analyst must make two choices to

construct a ﬁlter rule: First, how much

does the exchange rate have to rise, or

what is the size of the ﬁlter? Second, how

far back should the rule go in ﬁnding a

recent minimum? The ﬁlter rules studied

here will use ﬁlters from 0.5 percent to 3

percent and go back ﬁve business days to

ﬁnd the extrema.

A moving average rule

gives a buy signal when a short moving

average is greater than the long moving

average; otherwise it gives a sell signal.

This rule requires the researcher to choose

the lengths of the moving averages. The

moving average rules to be tested will use

short moving averages of 1 day and 5 days

and long moving averages of 10 days and

50 days. Both the ﬁlter rules and the

moving average rules are extrapolative, in

that they indicate that the trader should

buy when the exchange rate has been

rising and sell when it has been falling.

Proﬁts

The trading rules switch between long

and short positions in the foreign currency.

Recall that a long position is a purchase

of foreign currency—a bet that it will go

up—while a short position is the reverse,

selling borrowed foreign currency now in

the hope that its value will fall. Denoting

the percentage change in the exchange rate

A number of previous studies

have documented evidence of

proﬁtable technical trading rules

in the foreign exchange market:

Sweeney (1986); Levich and

Thomas (1993); Neely, Weller,

and Dittmar (1997).

As with most aspects of techni-

cal analysis, the choice of ﬁlter

size and window lengths has

been determined by practition-

ers through a process of trial

and error.

S EPTEMBER /OCTOBER 1997

F EDERAL R ESERVE B ANK OF S T . LOUIS

($ per unit of foreign currency) from date t

to t+1 by DS

, and the domestic (foreign)

overnight interest rate by i

), then the

overnight return from a long position is

approximately given by Equation 1:

(1) R

; i

+ DS

– i

The return to a short position is the nega-

tive of the return to a long position. The

return to a trading rule over a period of

time is approximately the sum of daily

returns, minus transactions costs for each

trade. Transactions costs are set at 5 basis

points (0.05 percent) for each round trip in

the currency. A r ound trip is a move from

a long position to a short position and

back or vice-versa.

Evidence from Ten Simple Technical

Trading Rules

Six ﬁlter rules and four moving average

rules were tested on data consisting of the

average of daily U.S. dollar bid and ask

quotes for the DM, yen, pound sterling, and

Swiss franc.

All exchange rate data begin

on 3/1/74 and end on 4/10/97. These four

series are called $/DM, $/¥, $/£, and $/SF.

Because the results for the four exchange

rates were similar, full results from only

the $/DM will be reported in the tables.

Table 1 shows the annualized

percentage return, monthly standard

deviation (a measure of the volatility of

returns), number of trades per year, and

two measures of risk, the Sharpe ratio and

the CAPM beta, for each of the 10 trading

strategies for the $/DM. The Sharpe ratio

and CAPM betas are discussed in some

detail in the shaded insert. The mean

annual return to the 10 rules was 4.4 per-

cent, and 38 of the 40 trading rules were

proﬁtable (had positive excess return) over

the whole sample. These results cast doubt

on the efﬁcient markets hypothesis, which

holds that no trading strategy should be

able to consistently earn positive excess

The estimate of transactions

costs used here is consistent

with recent ﬁgures. Levich and

Thomas (1993) consider a

round-trip cost of 0.05 percent

realistic, as do Osler and Chang

(1995).

The exchange rate data were

obtained from DRI and were

collected at 4:00 p.m. local

time in London from Natwest

Markets and S&P Comstock.

Daily overnight interest rates

are collected by BIS at 9:00

a.m. London time. Interest

rates for Japan were unavail-

able before 3/1/82, so the

interest rates before this date

were set to 0 for the $/¥ case.

S EPTEMBER /OCTOBER 1997

F EDERAL R ESERVE B ANK OF S T . LOUIS

T echnical T rading Rule Results for the $/DM

Moving A verage Rule Results

Monthly Estimated Standard

Annual Standard Number of Sharpe CAPM Error of

Short MA Long MA Return Deviation Trades Ratio Beta Est. Beta

1 10 6.016 2.979 928 0.583 – 0.022 0.091

1 50 7.546 3.155 268 0.690 – 0.135 0.085

5 10 6.718 3.064 576 0.633 – 0.144 0.084

5 50 6.671 3.236 146 0.595 – 0.134 0.080

Filter Rule Results

Monthly Estimated Standard

Annual Standard Number of Sharpe CAPM Error of

Filter Return Deviation Trades Ratio Beta Est. Beta

0.005 5.739 3.057 1070 0.542 – 0.071 0.089

0.010 6.438 2.951 584 0.630 – 0.092 0.093

0.015 3.323 3.255 382 0.295 – 0.037 0.085

0.020 1.934 3.348 234 0.167 – 0.128 0.087

0.025 0.839 3.236 142 0.075 – 0.118 0.082

0.030 – 1.541 3.578 92 – 0.124 – 0.086 0.077

NOTES: The ﬁrst two columns of the top panel characterize the length of the short and long moving averages used in the moving-average

trading rule. The third column is the annualized asset return to the rule, while the fourth column is the monthly standard deviation of

the return. The ﬁfth column is the number of trades over the 23-year sample. The sixth column is the Sharpe ratio, and the last two

columns provide the CAPM beta with the S&P 500 and the standard error of that estimate. The lower panel has a similar structure,

except that the ﬁrst column characterizes the size of the ﬁlter used in the rule. All extrema for ﬁlter rules were measured over the

previous ﬁ ve business days.

Table 1

retur ns. The number of trades over the

23-year sample varied substantially over the

10 rules, ranging from 4 trades per year

to almost 50 trades per year. The moving

average rules were somewhat more proﬁ table

than the ﬁlter rules.

There is little evidence that these

excess returns are compensation for

bearing excessive risk. The ﬁrst measure

of risk, the Sharpe ratio, is the mean

annual return divided by the mean annual

standard deviation. The moving average

rules had higher Sharpe ratios (0.6 vs.

0.25) than the ﬁlter rules. Six of the 10

Sharpe ratios are better than the 0.3

obtained by a buy-and-hold strategy in

the S&P 500 over approximately the same

period. This result indicates that the

average return to the rules is very good

compared to the risk involved in

following the rules.

The second measure of risk, the CAPM

betas, reﬂects the correlation between the

monthly trading rule returns and the

monthly returns to a broad portfolio of

risky assets (the S&P 500). Signiﬁcantly

positive betas indicate that the rule is

bearing undiversiﬁable risk. These CAPM

betas estimated from the 10 rules generally

indicate negative correlation with the S&P

500 monthly returns. None of them is

signiﬁcantly positive, statistically or econom -

ically . In other words, there is no systematic

risk in these rules that could explain the

positive excess returns.

For Whom is Technical Trading

Appropriate?

The discussion of risk and r eturns sug-

gests that technical analysis may be very

useful for banks and lar ge ﬁnancial ﬁrms

that can borr ow and lend freely at the

overnight interbank interest rate and buy

and sell in the wholesale market for for eign

exchange, where transactions sizes are in

the millions of dollars. Technical trading is

much less useful for individuals, who

would face much higher transactions costs

and must consider the opportunity cost of

the time necessary to become an expert on

for eign exchange speculating and to keep

up with the market on a daily basis. How

lar ge would transactions costs have to be to

eliminate the excess r eturn to the technical

rules? If we assume a 6 percent annual

excess return to the rule and 230 trades (10

trades a year), round-trip transactions costs

would have to be greater than 0.6 per cent

to produce zer o excess returns.

In addition to higher transactions

costs, individual investors following tech-

nical rules also must accept the risk that

such a strategy entails. Figure 5 illustrates

the risk by depicting, at monthly intervals,

the one-year-ahead excess return from

1974 through 1996 for the (1,10) moving

average rule on the $/DM and, for compar-

ison, the total excess return on buying and

holding the S&P 500 index, a popular

measure of returns to a stock portfolio.

The ﬁgure shows that the excess returns to

both portfolios vary considerably at the

annual horizon, often turning negative.

While the technical trading rule excess

return is less variable than the S&P excess

return, it can still lead to signiﬁcant losses

for some subperiods. Two ways to

measure losses over subperiods are the

maximal single-period loss (maximum

drawdown) and maximum loss in a

calendar year. Over the period from March

1974 through March 1997, the maximum

S EPTEMBER /OCTOBER 1997

F EDERAL R ESERVE B ANK OF S T . LOUIS

Figure 5

One-Year Moving Average, Forward-

Looking Excess Returns to the (1,10)

Moving Average Trading Rule

Excess return to $/DM

(1,10) moving average rule

Excess return to

S&P 500 index

–30

–10

–20

9690 92 9480 8884 868278761974

Moving Average of Annual Return

March 1974-March 1996

that an investor could have lost by using the

moving average trading rule was – 28.2 per-

cent; this loss, which would have occurred

between March 7, 1995, and August 2, 1995

(a period of 149 days), translates into an

annual rate of –69.2 percent. In other

words, an investor using this rule would

have lost almost 30 percent of his capital

over this ﬁve-month period. Similarly, the

maximum loss for this technical trading

rule in a complete calendar year was –9.8

percent in 1995, but –17.8 percent for the

S&P 500 in 1981.

Perhaps the biggest obstacle to

exploiting technical rules is that while the

returns to stocks depend ultimately on the

proﬁtability of the ﬁrms in which the stock

is held, the source of returns to technical

analysis is not well understood; therefore,

the investor does not know if the returns

will persist into the future or even if they

continue to exist at the present. Indeed,

Figure 5 shows that the post-1992 return

to the (1,10) moving average rule for the

$/DM has been negative.

Do These Results Measure the

Degree of Market Efﬁciency?

There are a number of problems asso-

ciated with inferring the degree of market

efﬁciency from the apparent proﬁtability of

these trading rules. The ﬁrst problem is

the data. To test the proﬁtability of a

trading rule, the researcher needs actual

prices and interest rates from a series of

simultaneous market transactions. Unfor-

tunately, simultaneous quotes for daily

exchange rates and interest rates are not

generally available for a long time span.

For example, these exchange-rate data

were collected late in the afternoon, while

the interest rates were collected in the

morning. Although most economists

judge this problem to be very minor, some

argue that the trading rule decisions could

not have been executed at the exchange

rates and interest rates used.

The second problem is that without a

good model of how to price risk, positive

excess returns resulting from the use of

trading rules cannot be used to measure

the degree of inef ﬁciency . Risk is notoriously

difﬁcult to measure. In fact, a major area

of study for macr o and ﬁnancial economists

for the last 10 years has been to explain

why the return on stocks is so much higher

than that on bonds, a phenomenon called

the equity premium puzzle. Of course, at

least part of the answer is that stocks are

much riskier than bonds, but there is no

generally accepted model of risk that will

explain the size of the return difference.

Defenders of the efﬁcient markets hypoth-

esis maintain that the discovery of an

apparently successful trading strategy may

not indicate market inefﬁ ciency but, rather ,

that risk is not measured properly.

Another problem is that of "data

mining": If enough rules ar e tested, some—

purely by chance—will produce excess

returns on the data. These rules may not

have been obvious to traders at the begin-

ning of the sample. In fact, the rules tested

here are certainly subject to a data-mining

bias, since many of them had been shown

to be proﬁtable on these exchange rates

over at least some of the subsample. Closely

related to the data-mining problem is the

tendency to publish r esear ch that overturns

the conventional wisdom on efﬁcient

markets, rather than research that shows

technical analysis to be ineffective. One

solution to the data-mining problem is

suggested by Neely, Weller, and Dittmar

(1997), who apply genetic programming

techniques to the foreign-exchange market.

Genetic programming is a method by which

a computer searches through the space of

possible technical trading rules to ﬁnd a

gr oup of good rules (i.e., rules that generate

positive excess return). These good rules

are then tested on out-of-sample data to

see if they continue to generate positive

excess returns.

RETHINKING THE EFFICIENT

MARKETS HYPOTHESIS

Early research in ﬁnance on the

efﬁcient markets hypothesis was very

supportive; little evidence was found of

proﬁtable trading rules after transactions

costs were accounted for (Fama, 1970).

The returns for complete calen-

dar years were available from

1975 through 1995.

Kocherlakota (1996) and

Siegel and Thaler (1997) dis-

cuss the equity premium puzzle

extensively.

S EPTEMBER /OCTOBER 1997

F EDERAL R ESERVE B ANK OF S T . LOUIS

The success of technical trading rules

shown in the previous section is typical

of a number of later studies showing that

the simple efﬁcient markets hypothesis

fails in important ways to describe how

the foreign exchange market actually func-

tions. While these results did not surprise

market practitioners, they have helped

persuade economists to examine features

of the market like sequential trading,

asymmetric information, and the role of

risk that might explain the proﬁtability of

technical analysis.

The Paradox of Efﬁcient Markets

Gr ossman and Stiglitz (1980) identiﬁed

a major theoretical problem with the

hypothesis termed the paradox of efﬁcient

markets, which they developed in the con-

text of equity markets. As applied to the

foreign exchange market, the argument

starts by noting that exchange rate returns

are determined by fundamentals like

national price levels, interest rates, and

public debt levels, and that information

about these variables is costly for traders

to gather and analyze. The traders must

be able to make some excess returns by

trading on this analysis, or they will not do

it. But if markets were perfectly efﬁcient,

the traders would not be able to make

excess retur ns on any available information.

Therefore, markets cannot be perfectly efﬁ-

cient in the sense of exchange rates' always

being exactly where fundamentals suggest

they should be. Of course, one resolution

to this paradox is to recognize that market

analysts can recover the costs of some fun-

damental research by proﬁting from having

marginally better information than the rest

of the market on where the exchange rate

should be. In this case, the exchange rate

remains close enough to its fundamental

value to prevent less informed people from

proﬁting from the difference. Partly for

these reasons, Campbell, Lo, and MacKinlay

(1997) suggest that the debate about per-

fect efﬁciency is pointless and that it is

more sensible to evaluate the degree of

inefﬁciency than to test for absolute

efﬁciency.

Empirical Reasons to Suspect Failure

of Efﬁcient Markets

The miserable empirical perfor mance

of standard exchange rate models is another

r eason to suspect the failure of the efﬁ cient

markets hypothesis. In an important paper ,

Meese and Rogoff (1983) persuasively

showed that no existing exchange rate model

could forecast exchange rate changes better

than a "no-change" guess at forecast horizons

of up to one year . This was true even when

the exchange rate models were given true

values of future fundamentals like output

and money . Although Mark (1995) and

others have demonstrated some forecasting

ability for these models at forecasting hori-

zons greater than three years, no one has

been able to convincingly overtur n the

Meese and Rogoff (1983) r esult despite 14

years of research. The efﬁcient markets

hypothesis is frequently misinterpreted as

implying that exchange rate changes should

be unpredictable; that is, exchange rates

should follow a random walk . This is incor-

r ect. Equation 2 shows that interest rate

differ entials should have forecasting power

for exchange rate changes, leaving excess

returns unpr edictable. There is, however ,

convincing evidence that interest rates

are not good forecasters of exchange rate

changes.

According to Frankel (1996),

this failure of exchange rate forecasting

leaves two possibilities:

• Fundamentals are not observed well

enough to allow forecasting of

exchange rates.

• Exchange rates are detached from

fundamentals by (possibly irrational)

swings in expectations about future

values of the exchange rate. These

ﬂuctuations in exchange rates are

known as bubbles.

Which of these possibilities is

more likely? One clue is given by the

relationship between exchange rates and

fundamentals when expectations about the

value of the exchange rate are very stable,

as they are under a ﬁxed exchange rate

Engel (1995) reviews the

failure of this theory, called

uncovered interest parity

Swings in expectations that

are subsequently justiﬁed by

changes in the exchange rate

are known as

rational bubbles

Swings that are not consistent

with the future path of exchange

rates are

irrational bubbles

S EPTEMBER /OCTOBER 1997

F EDERAL R ESERVE B ANK OF S T . LOUIS

regime. A ﬁxed exchange rate regime is

a situation in which a government is com-

mitted to maintaining the value of its

currency by manipulating monetary policy

and trading foreign exchange reserves.

Fixed exchange rate r egimes ar e contrasted

to ﬂoating r egimes, in which the government

has no such obligation. For example, most

countries in the European Union had a

type of ﬁxed exchange rate regime, known

as a target zone, from 1979 through the

early 1990s. Fixed exchange rates anchor

investor sentiment about the future value

of a currency because of the government's

commitment to stabilize its value. If

fundamentals, like goods prices, or expec-

tations based on fundamentals, rather than

irrationally changing expectations, drive

the exchange rate, the relationship

between fundamentals and exchange rates

should be the same under a ﬁxed exchange

rate regime as it is under a ﬂoating regime.

This is not the case. Countries that move

from ﬂoating exchange rates to ﬁxed

exchange rates experience a dramatic

change in the relationship between prices

and exchange rates. Speciﬁcally, real

exchange rates (exchange rates adjusted

for inﬂation in both countries) are much

more volatile under ﬂoating exchange rate

regimes, where expectations are not tied

down by promises of government interven-

tion. Figure 6 illustrates a typical case:

When Germany and the United States ceased

to ﬁx their currencies in March 1973, the

variability in the real $/DM exchange rate

incr eased dramatically . This r esult suggests

that, contrary to the efﬁcient markets

hypothesis, swings in investor expectations

may detach exchange rates fr om fundamental

values in the short run.

Why Do Bubbles Arise?

If traders might proﬁt by anticipating

swings in investor expectations, then the

efﬁcient markets hypothesis needs signiﬁ-

cant adjustment. The structure of the

foreign exchange market has several

features that might help drive these swings

in expectations that produce bubbles.

Most foreign exchange transactions are

conducted by large commercial banks in

ﬁnancial centers like London, New York,

Tokyo, and Singapore. These large banks

"make a market" in a currency by offering

to buy or sell large quantities (generally

more than $1 million) of currencies for a

speciﬁc price in another currency (e.g.,

the dollar) on request. The exchange rates

at which they are willing to buy or sell dol-

lars are known as the bid and ask prices,

respectively. The market is highly compet-

itive, and transactions occur 24 hours a day

over the telephone and automated trading

systems. The ﬁrst feature of this market that

might inﬂuence technical trading is that spe-

ciﬁc transactions quantities and prices are

not public information; the market is non-

transpar ent. But the bid and ask exchange

rates are easy to track, as banks fr eely quote

them to any participant. Second, the trades

take place sequentially—i.e., there is time to

learn from pr evious trades. Third, the partic-

ipants in this market differ from one another

in the information they have and their will-

ingness to tolerate risk.

In other words, the

participants ar e heterogeneous.

How might these features combine to

produce bubbles? To the extent that some

participants are better informed about cer-

tain fundamentals than other agents (for

instance, they will know more about their

own and their customers' demand for

foreign exchange), the trading behavior of

It has long been assumed that

there is little or no private infor-

mation in foreign exchange

markets, but this view has

been forcefully challenged with

respect to intraday trading by

Ito, Lyons, and Melvin (1997).

S EPTEMBER /OCTOBER 1997

F EDERAL R ESERVE B ANK OF S T . LOUIS

Figure 6

NOTES: These changes become much more volatile after the end of the Bretton Woods 

system of fixed exchange rates in March 1973. The vertical line denotes this break 

date in the series. Data cover January 1960–February 1997.

82787470661962

–4

–8

–12

Monthly Percentage Changes in the

$/DM Real Exchange Rate

86 90 94 98

the informed participants will reveal

some of their private information to the

uninformed agents. For example, if the

informed agents know of fundamental

for ces that are likely to make the exchange

rate rise in the future, they are likely to buy

the foreign curr ency and thereby bid up the

publicly observed bid and ask prices. The

uninformed agents might infer from the rise

that the rate will continue to rise and, as

a result, they might buy more foreign

exchange, pushing the rate up themselves

in a self-fulﬁlling prophecy .

This inference

from past price behavior is extrapolative tech -

nical analysis: It assumes that the exchange

rate will continue moving as it has in the

r ecent past. The uninformed traders may

continue to buy foreign exchange past the

point where it is suppor ted by fundamentals.

Although this story is most plausible for ver y

high-frequency (intraday) trading, it might

also generate longer -term swings in the

exchange rate.

There are other explanations for

extrapolative trading that jettison the

assumption of rational behavior in favor of

the study of how people really make deci-

sions. This ﬁeld, called behavioral ﬁnance,

has concentrated on examples of seeming

irrationality in decision making. Two ﬁnd-

ings of this ﬁeld are that (1) experimental

participants seem unusually optimistic

about their chances for success in games

and (2) the behavior and opinions of

members of a group tend to reinforce

common ideas or beliefs.

For example,

members of a jury may become more con-

ﬁdent about their individual verdicts if the

other members of the group agree.

Either explanation for extrapolative

trading implies that bubbles may be produced

by slow dissemination of private information

into the market, coupled with extrapolative

trading rules. There is some evidence to

support this explanation. Eichenbaum and

Evans (1995) found that foreign exchange

markets reacted gradually to money supply

shocks, over a period of many months,

instead of instantly incorporating the new

information. Surveys revealed that foreign

exchange market participants' expectations

are extrapolative at horizons up to six

months. That is, if the exchange rate has

risen recently, market participants expect it

to continue to rise in the near future

(Frankel and Froot, 1987). Also, the suc-

cess of extrapolative traders tends to feed

on itself. Frankel and Froot (1990) argue

that extrapolative traders' success during

the early part of the large dollar apprecia-

tion of 1981-1985 convinced many other

traders to follow extrapolative rules,

driving the dollar up even further.

Central Bank Intervention

The other popular explanation for the

apparent proﬁtability of technical trading

rules is that technical traders are able to

proﬁt consistently from central bank inter-

vention. Some central banks frequently

intervene (buy and sell currency) in the

foreign exchange market to move the

exchange rate to help inﬂuence other vari-

ables like employment or inﬂation.

Because these actions are designed to con-

trol macroeconomic variables rather than

to make money, central banks may be

willing to take a loss on their trading.

Trading rule proﬁts may represent a

transfer from central banks to technical

traders. Lebaron (1996) found that most

trading rule proﬁts were generated on the

day before a U.S. intervention. Neely and

Weller (1997) ﬁnd that "intelligent"

trading rules tend to trade against the Fed;

that is, they tend to buy dollars when they

ﬁnd out the Fed is selling dollars. This

tantalizing story does not ﬁt all the facts,

however. For example, Leahy (1995) ﬁnds

that U.S. foreign exchange operations

make positive proﬁts, on average.

This

ﬁnding is inconsistent with the idea that

central banks are giving money away to

technical traders.

Why Are the Proﬁts Not

Arbitraged Away?

Whether the trends or inefﬁciencies in

exchange rates are created by swings in

expectations or by central bank intervention,

efﬁcient market advocates would ask why

any predictable returns in exchange rates

Treynor and Ferguson (1985),

Brown and Jennings (1989),

Banerjee (1992), and Kirman

(1993) construct models of

behavior in which information

is inferred from the actions of

others. One easily understood

example is the problem of con-

sumers who must choose

between two restaurants. One

seemingly sensible strategy for

choosing would be to go to the

more crowded restaurant on

the theory that it is likely to be

crowded because it has better

food. This phenomenon

depends on

asymmetric

information.

Shiller (1988) and Shleifer and

Summers (1990) discuss

behavioral ﬁnance in more

detail. Ohanian (1996) consid-

ers the reasons for the collapse

of bubbles.

In the United States, the

Federal Reserve and the U.S.

Treasury generally collaborate

on foreign exchange interven-

tion decisions, and operations

are conducted by the Federal

Reserve Bank of New York on

behalf of both.

See Szakmary and Mathur

(1996) for more on central

bank intervention and trading

rule proﬁts.

S EPTEMBER /OCTOBER 1997

F EDERAL R ESERVE B ANK OF S T . LOUIS

should not be arbitraged away. One answer

to this question is that speculators have

short horizons and are deterred from spec-

ulating against the trends by the risk that

such a strategy would incur. There ar e sev-

eral reasons for this: First, traders typically

operate on margin, borrowing some of the

money with which they trade. W ith a lim-

ited line of credit, the borrowing costs

would add up if traders were not able to

tur n a quick proﬁ t. Second, a trader' s

per formance is typically evaluated on

r elatively shor t horizons (less than a year).

Thir d, there may be institutional or legal

r estrictions that prevent some types of

enterprises from taking on "excessive"

exchange risk. And ﬁnally , traders do not

know the equilibrium value of the exchange

rate with any certainty, so they cannot dis-

tinguish bubbles from movements in

fundamentals. Investors who bet on long-

run r eversion to fundamental values in

exchange rates may be wiped out by short-

run deviations away from those values.

Explaining the success of technical

trading r ules with bubbles begs one mor e

question: Why do destabilizing extrapolative

traders not lose their money? Friedman

(1953) showed that destabilizing specula-

tion is doomed to lose money and so drive

the speculators out of the market. Friedman

ar gued that speculation can only destabilize

asset prices if the speculators consistently

buy when the asset price is above its equi-

Both Shleifer and Summers

(1990) and Shleifer and

Vishny (1997) discuss the

importance of risk in speculat-

ing against bubbles.

Essentially the same argument

is presented more simply in

Shleifer and Summers (1990).

S EPTEMBER /OCTOBER 1997

F EDERAL R ESERVE B ANK OF S T . LOUIS

HOW TO MEASURE RISK?

The simplest widely used measure of risk is the Sharpe ratio or the ratio of the

average annual excess return to a measure of excess return volatility called the stan-

dard deviation. Higher Sharpe ratios are desirable because they indicate either higher

average excess returns or less volatility. A commonly used benchmark of a good

Sharpe ratio is that of the S&P 500, which Osler and Chang (1995) estimated to be

about 0.32 from March 1973 to March 1994.

A major drawback to Sharpe ratios is that they ignore an important idea in

ﬁnance: An investment is risky only to the extent that its return is correlated with

the return to a broad measure of the investments available. To see this, consider the

risk associated with holding a portfolio of assets whose returns are each individually

volatile but completely independent of each other. Each year, the assets in the portfo-

lio that do unusually well will tend to offset those that do unusually poorly. The

portfolio as a whole will be much less risky than any of the individual assets. The

more assets in the portfolio, the less risky it will be. In fact, if enough of these inde-

pendent assets are grouped together into a portfolio, the return on this portfolio

becomes certain. This means that investors do not need to be compensated for hold-

ing risky assets that are not correlated with all the other assets they can buy (the mar-

ket portfolio), because the risk of each uncorrelated asset can be reduced to zero if

the portfolio contains a large enough variety of these assets. On the other hand,

assets for which returns are positively correlated with those of the other assets on

the market need a higher expected return to convince investors to hold them.

This idea motivates the second measure of riskiness, the CAPM beta: the coefﬁ-

cient from the linear regression of an asset's (or trading rule's) excess return on the

excess return of a proxy for the market portfolio, the return to a broad equity index

like the S&P 500. An estimated beta equal to zero means that the trading rule is

bearing no systematic risk, while signiﬁcantly positive betas indicate that a trading

strategy is bearing some risk, and a beta equal to one means that the trading rule

moves closely with the market, so that following it requires the investor to accept

signiﬁcant risk.

librium value (driving the price up fur ther)

and sell when the asset price is below its

equilibrium value; as the destabilizing

speculators lose their money , he

maintained, they will have less effect on the

market. The cor ollary to this ar gument is

that all successful speculation is stabilizing.

Delong, Schleifer, Summers, and W aldman

(1989) constructed a "noise trader" model

that questioned this logic, however .

They

showed that irrational ("noise") traders

could cr eate so much risk in asset markets

that the r eturns to those assets would have

to be unusually high for rational traders to

trade in them at all. In other words, the

irrational traders make unusually high

returns (on average) by foolishly pursuing

risky strategies. Some go out of business,

but, on average, this group increases its

market position.

CONCLUSION

T echnical analysis is the most widely

used trading strategy in the foreign exchange

market. Traders stake large positions on

their interpretations of patterns in the data.

Economists have traditionally rejected the

claims of technical analysts because of the

appealing logic of the efﬁ cient markets

hypothesis. More r ecently , however, the dis -

covery of proﬁ table technical trading rules

and other evidence against efﬁ cient markets

have led to a rethinking about the importance

of institutional features that might justify

extrapolative technical analysis such as pri-

vate information, sequential trading, and

central bank intervention, as well as the

r ole of risk.

The weight of the evidence now

suggests that excess retur ns have been

available to technical foreign exchange

traders over long periods. Risk is hard to

deﬁ ne and measure, however , and this

difﬁculty has obscured the degree of inefﬁ-

ciency in the foreign exchange market.

Ther e is no guarantee, of course, that tech-

nical rules will continue to generate excess

retur ns in the future; the excess returns

may be bid away by market participants.

Indeed, this may already be occurring.

Continued research on high-frequency

transactions data or experimental work

on expectations formation may provide a

better understanding of market behavior .

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... 97 Ebenfalls konnte Taylor die von Domowitz und Hakkio (1985) festgestellten zeitvariablen Risikoprämien nicht beobachten. 98 Froot und Frankel (1989) haben ebenfalls festgestellt, dass sich die Terminkursverzerrung nicht durch zeitvariable Risikoprämien erklären lässt. 99 Charles Engel (1996), welcher ebenfalls auf Basis der vorangegangenen Ergebnisse 100 den FRB mittels Risikoprämien zu erklären versuchte, kam zu der Erkenntnis, dass die Differenz vom durch den Terminkurs prognostizierten Wechselkurs und dem tatsächlich eingetretenen Wechselkurs zu groß sei, um lediglich durch Risikoprämien erklärt zu werden. ...

... Taylor (1989) [118]. 98 Vgl. Taylor (1989) [118], S. 9. f. 99 Vgl. ...

... Vgl. Neely (1997)[98] und vgl.Copeland (2014)[29], S. 301.50 Vgl.Neely (1997)[98], S. 32 und vgl.Levich, Thomas (1991)[82]. ...

Jonathan Bergmann

Die verfasste Arbeit beschäftigt sich mit der Handelsstrategie Carry Trades. Grundlage dieser Strategie ist das Ausnutzen von Zinsunterschieden, welche zwischen zwei Währungsräumen vorherrschen, und einer Wechselkursanpassung, die diese Unterschiede nicht komplett kompensiert. Investiert ein Anleger beispielsweise in eine ausländische Währung mit höherem Zinsniveau, so müsste sich der Wechselkurs gemäß der Zinsparitätentheorie in der Folge so anpassen, dass der höhere Ertrag durch die Zinsen beim Rücktausch der Währung vollständig egalisiert wird. Ziel dieser Arbeit war eine empirische Untersuchung für die Währungen der G10 auf wöchentlicher Handelsbasis sowie die Konstruktion und Berücksichtigung von ex ante Sharpe-Ratios als Handelsindikator.

... Usually this process is judgmental and not based on a statistical model of price movements. The rationale for technical analysis is that prices are not driven by a single underlying data generation process, but by market psychology which is episodic but leaves distinctive patterns in prices, perhaps through the channels of fundamental news flow (Neely, 1997) and order-flow (Osler 2001 and. The same technical analysis techniques are applied to foreign exchange, metals, agricultural commodities, energy commodities, indices, equities, debt instruments and derivatives serving all these markets (Murphy, 2000). ...

... Unrealistically simple mechanical rules employed by the existing literature often constitute a "poor caricature" (Batchelor and Kwan, 2003) of practical technical analysis. It is wrongly asserted by the literature that technical analysis is used exclusively for short-time horizons (for example Neely, 1997) -this is not correct. Frequencies ranging from intra-day tick data to monthly or even quarterly data are in fact used (Achelis, 2000, Gann, 1949, Pring, 1998, Edwards and Magee, 2001and Murphy, 2000. ...

... Hamilton (1922) stated that those who successfully applied Dow Theory would rarely make in excess of four or five trades annually, a range echoed by Gann (1942 and1949), s application of other longer tenn techniques. This must be contrasted with the general academic assumption that technical analysis is only used with short-time horizons (for example Neely, 1997). Dow Theory assumes that manipulation of the primary, long-tenn, trend is not possible and it must reflect trends in the underlying fundamentals as the "averages discount everything" (Hamilton, 1922). ...

Richard Ramyar

- Technical and fundamental analysis of financial markets and macroeconomic cycles - Behavioural financial economics and heuristics - Forecasting surveys - Behaviour surveys - Non parametric statistics - Computational statistics - Machine learning and big data - Bootstrap methodologies - Kernel Density Estimators - Structural instability and breaks - Technical analysis is the study of price movements in traded markets so as to forecast future movements or identify trading opportunities. Following a review of the history and research of technical analysis, three empirical chapters evaluate a number of propositions popular among technical analysts. One approach used widely over the last century assumes that support and resistance levels can be predicted by projecting the ratios between the length and duration of successive trends, in particular using Fibonacci ratios like 1.618. This proposition is rejected for the Dow Jones Industrial Average by identifying turning points and testing for clustering by developing a block bootstrap procedure. A few significant ratios appear to support such anchoring by the market, but no more than would be expected by chance. The thesis then reports a survey based experiment that tests whether individuals themselves do have an in-built tendency to anchor forecasts of future trends on previous trends. The significance of the survey results are tested using a novel kernel density estimator based bootstrap methodology. Respondents' forecasts do bear some relationship to the size of the most recent trend by certain whole-number ratios by more often than would be expected by chance. The third experiment addresses the criticism that academic studies do not use a rich enough characterisation of technical analysis. 120 active market-timing strategies are tested using a regression based framework of equity fundamentals, macroeconomic fundamentals, behavioural variables and a diverse set of mainstream statistical indicators from technical analysis. Our recursive approach uses time-invariant rolling and expanding estimation windows as well as conditional windows based on the presence of structural breaks, identified using the conditional reverse ordered cusum method (ROC), of Pesaran and Timmermann (2002). Models that include both fundamental and technical indicators perform well, even allowing for realistic levels of transactions costs. And accounting for structural instability via the ROC method also improves performance.

... In the implementation of dataset preprosessing process, the classification uses the Technical Analysis and Fundamental Analysis approaches. The purpose of classification in this case is to predict the class or category labels [22][23][24]. The classification is divided into two types, which are: a. Supervised classification (classification) and b. ...

Imam Cholissodin
Sutrisno Sutrisno

Rainfall is a natural factor that is very important for farmers or certain institutions to predict the planting period of a plant. The problem is that rainfall is very difficult to predict. Trials to get optimal rainfall prediction have been carried out by BMKG through research with variety of methods in various fields, including meteorology, climatology and geophysics. The results of the study unfortunately obtained a less optimal success rate in predicting rainfall. Today, there are many new methods for predicting events. These methods include deep learning (DL) and Particle swarm optimization (PSO). The use of the deep learning method is very susceptible to initial weights that are less than optimal, so it requires a process of optimization using a metaheuristic technique, which is the PSO algorithm, because this algorithm has a level of complexity that is much lower than genetic algorithms. In this study, this method is utilized to predict rainfall by determining the exact regression equation model according to the number of layers in hidden nodes based on the size of the kernel and the weight between the layers. This research is approved achieved get more optimal rainfall prediction results that those of previous research that without optimization with PSO.

... A trading strategy based on technical trading rules that is profitable in the long term is inconsistent with the weak form of the efficient market hypothesis. Some earlier studies supporting the profitability of technical analysis in the foreign exchange markets (Sweeney 1986;Levich and Thomas, 1993;Neely, 1997;LeBaron, 1999LeBaron, , 2002. In theory, the foreign exchange market should be efficient because of very high turnover and domination of professional traders that should not be influenced by the sentiment of retail investors (Sager and Taylor, 2006;Menkhoff and Taylor, 2007). ...

Miroslav Svoboda
Martina Sponerová

This paper provides a comparison between the strategy based on technical analysis and the strategy based on random trading on a highly liquid EUR/USD foreign exchange market. The authors analyze three years of data, and in every intraday trading session. Technical analysis strategy uses essential indicators such as moving averages (MA). Every trading position will have the risk-reward ratio (RRR) 3 to 1. In addition, another trading positions on the EUR/USD currency pair will be opened at the same time each day, without technical analysis. The time of entry into position will be indicated by past high liquidity on a given currency pair at a given time with a similar risk-reward ratio (RRR) 3 to 1. This paper aims to compare the strategy of technical analysis and the random strategy in intraday trading concerning the profitability of these trades.

... These patterns are not limited to the stock market domain. Similar patterns are observed and used for analysis in other domains such as crude oil price [5] [6] [7], gold and silver price [8], and foreign exchange rates of various currencies [9] [10] providing advantageous insight of future behavior of data. ...

With the advent of high volume data streams, we have seen the need for real time analytic techniques like Complex Event Processing. This paper extends a Complex Event Processing Engine to support real time identification of technical chart patterns from streaming data. Technical chart patterns are known interesting recurring patterns on time series data, and they are used by experts in time series data analysis domains such as stock market and currency exchange rates. However, the automated identification of these patterns is challenging due to high volatility and noise of data. The paper focuses on identifying suitable technique to filter out volatility and a set of algorithms to query the data streams continuously and identify patterns. The resulting solution is a toolkit for chart pattern recognition which is a composition of a set of complex CEP queries and a Kernel regression smoother applied on moving windows. Same toolkit can be used to detect chart patterns in other domains such as Gold and Oil prices.

... Technical analysis [15,22,24], as one of essential approaches in quantitative investment, focuses on interpreting and forecasting stock movements in terms of its price and volume. The central assumption in technical analysis lies in that all relevant information for investment decision is reflected by price and volume movement. ...

As one of the most important investing approaches, technical analysis attempts to forecast stock movement by interpreting the inner rules from historic price and volume data. To address the vital noisy nature of financial market, generic technical analysis develops technical trading indicators, as mathematical summarization of historic price and volume data, to form up the foundation for robust and profitable investment strategies. However, an observation reveals that stocks with different properties have different affinities over technical indicators, which discloses a big challenge for the indicator-oriented stock selection and investment. To address this problem, in this paper, we design a Technical Trading Indicator Optimization(TTIO) framework that manages to optimize the original technical indicator by leveraging stock-wise properties. To obtain effective representations of stock properties, we propose a Skip-gram architecture to learn stock embedding inspired by a valuable knowledge repository formed by fund manager's collective investment behaviors. Based on the learned stock representations, TTIO further learns a re-scaling network to optimize the indicator's performance. Extensive experiments on real-world stock market data demonstrate that our method can obtain the very stock representations that are invaluable for technical indicator optimization since the optimized indicators can result in strong investing signals than original ones.

... On the contrary, in technical analysis historical data of prices is used to drive signals about future prices. Lately, nevertheless, technical trading rules have been commonly used by investors and financial analysts to make investment decisions (Neely, 1997;Taylor and Allen (1992)). More recently, however, (Han, Zhou, & Zhu, 2016) find results that favor the persistent profitability of the MA trading rule. ...

... Investors' priority is what determines the selection of the analysis technique. Technical analysis is more focused on market indicators including analysis based on stock prices movements, the volume of stock trade, financial forecasts, and market trends while ignoring the company's basic or fundamental published financial data in making investment strategies (Neely 1997). The salient features and differences of the two analysis techniques are given below as: ...

Shakeel Muhammad

Fundamental analysis has gained huge popularity among capital markets researchers in last decades. It uses current and past financial reports (Piotroski 2000, 2004; Fama and French, 2004; Elleuch 2009, Seng 2012), along with political and economic data in order to assign intrinsic value to firms and help to identify mispriced securities (Kothari, 2001). Both fundamental and technical analyses are used to forecast stock returns with the aim to buy stock when they are under-priced and sell when they are overpriced.Our study aimed to investigate the ability of the historical accounting data in predicting future stock returns using fundamental analysis especially in emerging economy i.e. Pakistan. Data were collected for the eleven-year period from 2007 to 2017 for 115 non-financial companies listed on Karachi stock exchange (KSE) with available ten years consecutive data. This paper utilizes five indicators from multiple areas i.e. profitability ratios, liquidity ratios, leverage ratios, and market-based ratios. For analysis, this study used penal data analysis (common effect model, fixed effect model, and random effect model). The results indicates that the fundamental analysis can predict future stock returns in Pakistani listed companies and end up with the implications and future directions. Keywords: Fundamental analysis, Penal data analysis, emerging economy i.e. Pakistan,

Imam Cholissodin
Sutrisno Sutrisno

Prediction of rainfall is needed by every farmer to determine the planting period or for an institution, eg agriculture ministry in the form of plant calendars. BMKG is one of the national agency in Indonesia that doing research in the field of meteorology, climatology, and geophysics in Indonesia using several methods in predicting rainfall. However, the accuracy of predicted results from BMKG methods is still less than optimal, causing the accuracy of the planting calendar to only reach 50% for the entire territory of Indonesia. The reason is because of the dynamics of atmospheric patterns (such as sea-level temperatures and tropical cyclones) in Indonesia are uncertain and there are weaknesses in each method used by BMKG. Another popular method used for rainfall prediction is the Deep Learning (DL) and Extreme Learning Machine (ELM) included in the Neural Network (NN). ELM has a simpler structure, and non-linear approach capability and better convergence speed from Back Propagation (BP). Unfortunately, Deep Learning method is very complex, if not using the process of simplification, and can be said more complex than the BP. In this study, the prediction system was made using ELM-based Simplified Deep Learning to determine the exact regression equation model according to the number of layers in the hidden node. It is expected that the results of this study will be able to form optimal prediction model.Keywords: prediction, rainfall, ELM, simplified deep learning

We use genetic programming techniques to identify optimal technical trading rules. We find strong evidence of economically significant out-of-sample excess returns to the rules for each of six exchange rates ($/DM, $/Yen, $/SF, $/£, DM/Yen, SF/£), over the period 1981–95. Some of the rules have a structure similar to those used by technical analysts. Betas calculated for the returns according to various benchmark portfolios provide no evidence that the returns to these rules are compensation for bearing systematic risk. 'Bootstrapping' results for the $/DM indicate that the trading rules are detecting patterns in the data that are not captured by standard statistical models.

David P. Brown

Technical analysis, or the use of past prices to infer private information, has value in a model in which prices are not fully revealing and traders have rational conjectures about the relation between prices and signals. A two-period dynamic model of equilibrium is used to demonstrate that rational investors use historical prices in forming their demands and to illustrate the sensitivity of the value of technical analysis to changes in the values of the exogenous parameters.

Richard M. Levich
Lee R. Thomas

In this paper, we present new evidence on the profitability and statistical significance of technical trading rules in the foreign exchange market. We utilize a new data base, currency futures contracts for the period 1976–1990, and we implement a new testing procedure based on bootstrap methodology. Our results suggest that simple technical trading rules have very often led to profits that are highly unusual. Splitting the entire sample period into three 5-year periods reveals that on average the profitability of some trading rules declined in the latest period although profits remained positive (on average) and significant in many cases. (JEL F31, F47, G15).

Fundamental Analysis for Forex Trading Pdf

Source: https://www.researchgate.net/publication/5047117_Technical_Analysis_in_the_Foreign_Exchange_Market_A_Layman's_Guide

Fundamental Analysis for Forex Trading Pdf

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