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An investigation of the relative strength index

January 2015
Banks and Bank Systems 10(1):92-96

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Using daily data for the Swiss franc/US dollar exchange rate, this paper studies the trading profitability of the technical indicator Relative Strength Index (RSI). The authors find that for the past decade or so, using the standard configuration of RSI < = 30 and RSI > = 70 as buy or sell threshold, RSI offers no trading profit, but a small loss instead. However, when the buy/sell threshold parameters are altered, to deviate from the combination most commonly used, using RSI as the trading signal still yields profits. The authors also provide an explanation of this phenomenon. One implication of our findings is that consistent profit opportunities should no longer exist in what is already commonly and widely known, but taking a path less travelled could still lead to profit opportunities not yet discovered and utilized.

The daily US Dollar/Swiss Franc exchange rate in units of pips Figure 2 plots the Relative Strength Index (RSI) computed from the daily exchange rate. RSI was computed based on the past 14 time periods, which is a time period length commonly used in practice. The figure shows that RSI, during this decade for USD/CHF exchange rate, can go occasionally as high as 90 or above, or 10 or below, which is considered rather extreme. If we consider the times when

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The Relative Strength Index (RSI) technical indicator for the daily US Dollar/Swiss Franc exchange rate.

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“An investigation of the relative strength index”

AUTH ORS Bing Anderson

Shuyun Li

ARTICLE INFO Bing Anderson and Shuyun Li (2015). An investigation of the relative strength

index. Banks and Bank Systems, 10(1), 92-96

JOURNAL "Banks and Bank Systems"

FOUNDER LLC “Consulting Publishing Company “Business Perspectives”

NUMBER OF REFERENCES

NUMBER OF FIGURES

NUMBER OF TABLES

businessperspectives.org

Banks and Bank Systems, Volume 10, Issue 1, 2015

Bing Anderson (USA), Shuyun Li (China)

An investigation of the relative strength index

Abstract

Using daily data for the Swiss franc/US dollar exchange rate, this paper studies the trading profitability of the technical

indicator Relative Strength Index (RSI). The authors find that for the past decade or so, using the standard configura-

tion of RSI < = 30 and RSI > = 70 as buy or sell threshold, RSI offers no trading profit, but a small loss instead. How-

ever, when the buy/sell threshold parameters are altered, to deviate from the combination most commonly used, using

RSI as the trading signal still yields profits. The authors also provide an explanation of this phenomenon. One implica-

tion of our findings is that consistent profit opportunities should no longer exist in what is already commonly and wide-

ly known, but taking a path less travelled could still lead to profit opportunities not yet discovered and utilized.

Keywords: currency market, financial indicator, market efficiency.

JEL Classification: G14, G15, F31.

Introduction

Technical analysis has long been a part of the

finance practice. It has been studied in the academic

finance literature, too. For example, Park and Irwin

(2007) surveyed both the early and the modern stu-

dies on technical analysis, and found that “early

studies indicate that technical trading strategies are

profitable in foreign exchange markets and futures

markets, but not in stock markets. Modern studies

indicate that technical trading strategies consistently

generate economic profits in a variety of speculative

markets at least until the early 1990s”.

The opposite of technical analysis is the fundamen-

tal analysis. However, fundamental analysis and

technical analysis are not necessarily incompatible

with each other. It is documented by de Zwart,

Markwat, Swinkels and van Dijk (2009) that com-

bining technical analysis with fundamental analysis

enhances the profitability of trading and investment

strategies.

Literature review

Falbo and Pelizzari (2011) mention that there are

many different methods of technical analysis, or dif-

ferent technical indicators. Not surprisingly, the profit-

ability of technical indicators has long been subject to

debate in the academic literature. It is likely also true

that the profitability of technical indicators actually

changes over time, as suggested by Lim and Brooks

(2011).

Mitra (2011) suggests that moving average based

technical trading rules are profitable in the stock mar-

ket of India. Analyzing a survey of 692 fund managers

in five countries, Menkhoff (2010) finds that the vast

majority of them do use technical analysis, which is

indirect evidence that technical analysis is useful in

actual trading and investment. Szakmary, Shen and

Sharma (2010) suggest that trend-following technical

trading strategies are profitable in the commodity

 Bing Anderson, Shuyun Li, 2015.

futures market. And, Menkhoff and Taylor (2007) try

to explain why technical analysis could be profitable.

On the contrary, Pukthuanthong-Le and Thomas

(2008) find that “the profitability of trend following

eroded for major currencies and their associated cross

exchange rates around the mid-1990s”. Park and Irwin

(2010) also suggest “that technical trading rules gen-

erally have not been profitable in the U.S. futures

markets” Neely, Weller and Ulrich (2009) discover

that market adapts over time and the profitability of

technical trading rules changes over time.

1. Objectives of this study

This paper contributes to this important debate on the

profitability of technical analysis by focusing on one

particular technical indicator: the Relative Strength

Index (RSI). The RSI was created by Wilder and first

published by him in 1978. Despite that it is an impor-

tant technical indicator that has been around for dec-

ades, we are aware of only one study that is dedicated

to the RSI itself, a study by Rodriguez-Gonzalez,

Garcia-Crespo, Colomo-Palacios, Iglesias and

Gomez-Berbis (2011). Their study, however, is not a

study about the profitability of the RSI itself, but in-

stead is a study on modifying the RSI using neural

networks to make it forward-looking.

This paper also contributes to the important topic of

market efficiency, in the context of the currency mar-

ket. If the market is completely efficient, we should

find no profitability for the Relative Strength Index.

On the other hand, if profitability exists, that is evi-

dence that the market is not completely efficient. It

is also possible, and perhaps quite likely, that mar-

ket, being an aggregate of its participants, behaves

just like an individual participant, or a human be-

ing, in that it takes time for the market to absorb

and adapt to information, and therefore become

more efficient than before, but this process of

learning never ends. The outcome of this study

should shed light on this interesting hypothesis,

and contribute to the discussion on market efficiency.

Banks and Bank Systems, Volume 10, Issue 1, 2015

2. Data and research method

Calculation of the Relative Strength Index (RSI) starts

with examining the change of the closing price from

one day to the next. For each day, an up change UC

and a down change DC are calculated. For a day when

the closing price is higher than that of the previous

day, DC is zero, and UC is that day’s close minus the

previous day’s close. That is, UC is how much the

closing price has increased from the previous day to

this day.

UC = closetoday  closeyesterday, (1)

DC = 0.

On the other hand, for a day when the closing price is

lower than that of the previous day, UC is zero, and

DC is the previous day’s close minus that day’s close.

That is, DC is how much the closing price has de-

creased from the previous day to this day, in its abso-

lute value.

UC = 0,

DC = closeyesterday  closetoday. (2)

Both UC and DC are always non-negative. For a day

when that day’s closing price is the same as that of the

previous day, both UC and DC are zero.

A relative strength RS is defined as the ratio of the

n-day exponential moving average of the UC time

series and the n-day exponential moving average of

the DC time series. Very often, a 14-day exponential

moving average is used.

RS = EMA (UC, n) / EMA (DC, n). (3)

Finally, RS is converted to the RSI by:

RSI = 100  100/ (1 + RS). (4)

We obtain the daily data on US Dollar/Swiss Franc

exchange rate, from January 5, 1998 to May 22, 2009,

courtesy of the TradeStation Group. We will be using

mostly observation numbers rather than date to refer to

data points, with Observation 1 being that for January

5, 1998, and Observation 2955 being that for May 22,

2009.

Figure 1 plots the exchange rate itself over time in

units of pips. For example, a reading of 15000 on the

vertical axis means the actual exchange rate is 1.5000.

That is, it takes 1.5 Swiss Francs (CHF) to buy one US

Dollar (USD). As can be seen from the figure, for the

first 800 to 900 observations, the general trend is that

CHF is depreciating against the USD. For the rest

of the time, CHF is in general appreciating

against the USD.

Fig. 1. The daily US Dollar/Swiss Franc exchange rate in units of pips

Figure 2 plots the Relative Strength Index (RSI)

computed from the daily exchange rate. RSI was

computed based on the past 14 time periods, which

is a time period length commonly used in practice.

The figure shows that RSI, during this decade for

USD/CHF exchange rate, can go occasionally as

high as 90 or above, or 10 or below, which is consi-

dered rather extreme. If we consider the times when

RSI goes above 80 or below 20, there are more of

these cases, but still not very common. Just from a

visual inspection of the figure, in the majority of the

observations, RSI is indeed between 30 and 70,

which is consistent with the conventional wisdom

that an RSI below 30 or above 70 represents over-

sold or overbought conditions and therefore are

buying or selling opportunities.

Banks and Bank Systems, Volume 10, Issue 1, 2015

Fig. 2. The Relative Strength Index (RSI) technical indicator for the daily US Dollar/Swiss Franc exchange rate.

However, if one buys when RSI reaches 30 and sells

when RSI reaches 70, as conventional wisdom dic-

tates, will that be a profitable trading strategy? We

investigate.

We start with Observation 15, the first observation

where RSI is available and computed, and progress

over time observation by observation. The first time

we encounter an RSI value that is 30 or below, or 70

or above, a buy or sell transaction is carried out. For

example, without loss of generality, the first such RSI

we encounter is 70 or above, and we sell at the price

corresponding to that RSI, creating a short position.

Once such an open position is created, it can only be

closed when an opposite trading signal is encountered.

Continuing with our example, once we have a short

position, the position will remain open until we en-

counter an RSI that is 30 or below, when we close the

short position at the corresponding exchange rate,

book the profit or loss, then also open a long position

at that same exchange rate. The long position won’t be

closed until an RSI that is 70 or above is encountered,

when we close the long position at the exchange rate

at that time, book the profit or loss, then also open a

short position at that same exchange rate. And the

process goes on and on. The last open position, be-

cause it cannot be closed based on the data we have,

will not count either as a profit or a loss. The way

profit or loss is booked is simply take the difference in

pips of the entry and exit points. For example, if we

long at 1.5000 and close the position at 1.5145, that is

a profit of 145 pips. If we long at 1.5000 and close the

position at 1.4000, that is a loss of 1000 pips.

Results

The trading simulation with RSI at 30 and 70 being

the buy/sell threshold comes back disappointing. The

total profit is -3009 pips, or in other words a loss of

3009 pips. If we count opening a position and then

later closing that same position as a trade, there are 53

trades in total. The trade with the biggest loss has a

loss of 1702 pips.

The RSI as a technical indicator was first published by

Wilder in 1978. For more than three decades, the in-

vestment community has been using it, most common-

ly with 30 and 70 being the buy/sell threshold. It is not

surprising that an indicator that has been known for so

long and has been used so widely can no longer give

one any edge in trading.

But would it be possible for a different threshold com-

bination to still be generating trading profits? We ex-

periment with 20/80 first.

The trading simulation with RSI at 20 and 80 being

the buy/sell threshold comes back with a small profit.

The total profit is 2387 pips. If we count opening a

position and then later closing that same position as a

trade, there are 23 trades in total. The trade with the

biggest loss has a loss of a staggering 2442 pips,

which is even bigger than the total profit. This para-

meter combination is eventually a bit profitable, how-

ever. One has to be able to stomach quite a bit of loss

in order to reach that eventual profit. The reduced

number of trades is because the threshold for trading is

now higher than the previous case of 30/70, and there-

fore less trades meet the standard and are carried out.

We move one step further in the same direction, and

try the 10/90 combination next. The trading simulation

with RSI at 10 and 90 being the buy/sell threshold

comes back with an even smaller profit. The total

profit is 1094 pips. There are only 6 trades in total.

The trade with the biggest loss has a loss of 3622 pips,

which is even bigger than that of the 20/80 parameter

combination.

Banks and Bank Systems, Volume 10, Issue 1, 2015

Given the degeneration of performance when we

move from the 20/80 parameter combination to the

10/90 parameter combination, we naturally decide to

go the other way and try 40/60 instead.

Surprisingly, the trading simulation with RSI at 40 and

60 being the buy/sell threshold performs the best

among all the parameter combinations we have tested

so far. The total profit is 5206 pips. There are 125

trades in total. The trade with the biggest loss has a

loss of 1876 pips.

To be complete, and to give us an idea about the pa-

rameter stability in terms of profitability, we investi-

gate the cases in between, too. The first of these cases

is the 35/65 parameter combination. The total profit

is 6621 pips. There are 93 trades in total. The trade

with the biggest loss has a loss of 1461 pips. The

second of these intermediate cases is the 25/75

parameter combination. For this case, the total

profit is 863 pips. There are 41 trades in total. The

trade with the biggest loss has a loss of 1380 pips.

It is interesting that the famous and widely used

30/70 parameter combination generates a loss, but

a little bit above and a little below that, they are

both profitable cases. We suspect this is exactly

because that the 30/70 parameter combination is so

famous and so widely used. When a market ineffi-

ciency is known and exploited by many for a long

period of time, the very activities of making profit

off this market inefficiency tend to reduce the

market inefficiency itself, and eventually remove

the profit opportunity. It has to be this way. Oth-

erwise, if a market inefficiency can be used by

many people to make as much money as they

please, and the market inefficiency and the profit

opportunity never reduces or disappears, it would

have become an infinite fountain of free wealth,

which exists only in myth, not in reality.

To complete our investigation, we carry out trading

simulation with RSI at 15 and 85 being the buy/sell

threshold. It is also a profitable case. The total profit is

4616 pips. There are 10 trades in total. The trade with

the biggest loss has a loss of 1946 pips.

Conclusion and recommendation

The significance of the findings in this paper is two-

folded. On one hand, we find that for the past decade

or so, using the standard configuration of RSI < = 30

and RSI > = 70 as buy or sell threshold, RSI offers no

trading profit, but a small loss instead. Once a technic-

al indicator is well known and a standard parameter

configuration for the technical indicator is well used in

practice, its profitability diminishes.

However, there is hope left. As the rest of our findings

indicate, when the buy/sell threshold parameters are

altered, to deviate from the combination most com-

monly used, using RSI as the trading signal still yields

profits.

This is merely an initial investigation of the Relative

Strength Index. Much more can be possibly done in

subsequent studies. For example, the same trading

simulation can be carried out on price data in the stock

or the futures markets. It is also possible for more

complicated trading rules to be derived based on RSI,

but not completely dependent on the RSI. For exam-

ple, we could use RSI as a threshold to open a posi-

tion, but once a position is opened, a trailing stop loss

order, rather than another RSI threshold, will be used

for determining when to close the position.

For practitioners, the recommendation coming out of

this study is to take the path less travelled. What is

already commonly known and commonly used hardly

offers any opportunity for profit any more. However,

even for an indicator as widely known as the RSI,

altering parameter configuration to an uncommon

combination still finds profits that have yet to be dis-

covered and picked up.

For academics, this study has implications on the

theory of market efficiency. What can be seen from

this study is that the market is neither completely effi-

cient, nor persistently inefficient, but rather an entity

that learns and adapts, and gradually becomes more

efficient but never absolutely and perfectly efficient.

Each and every participant in the market learns and

adapts in this way. The market is just an aggregate of

its participants. Why should not the market behave in

this way too?

References

1. de Zwart, G., T. Markwat, L. Swinkels and D. van Dijk. (2009). The economic value of fundamental and technical

information in emerging currency markets, Journal of International Money and Finance, No. 28. pp. 581-604.

2. Falbo, P. and C. Pelizzari. (2011). Stable classes of technical trading rules, Applied Economics, No. 43. pp. 1769-1785.

3. Lim, K.P. and R. Brooks. (2011). The Evolution of Stock Market Efficiency over Time: A Survey of the Empirical

Literature, Journal of Economic Surveys, No. 25, pp. 69-108.

4. Menkhoff, L. (2010). The use of technical analysis by fund managers: International evidence, Journal of Banking

& Finance, No. 34, pp. 2573-86.

5. Menkhoff, L. and M.P. Taylor. (2007). The obstinate passion of foreign exchange professionals: Technical

analysis, Journal of Economic Literature, No. 45, pp. 936-72.

6. Mitra, S.K. (2011). How rewarding is technical analysis in the Indian stock market? Quantitative Finance, No. 11,

pp. 287-297.

Banks and Bank Systems, Volume 10, Issue 1, 2015

7. Neely, C.J., P.A. Weller and J.M. Ulrich. (2009).The Adaptive Markets Hypothesis: Evidence from the Foreign

Exchange Market, Journal of Financial and Quantitative Analysis, No. 44, pp. 467-88.

8. Park, C.H. and S.H. Irwin. (2007). What do we know about the profitability of technical analysis? Journal of

Economic Surveys, No. 21, pp. 786-826.

9. Park, C.H. and S.H. Irwin. (2010). A Reality Check on Technical Trading Rule Profits in the US Futures Markets,

Journal of Futures Markets, No. 30, pp. 633-59.

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12. Szakmary, A.C., Q. Shen and S.C. Sharma. (2010). Trend-following trading strategies in commodity futures: A re-

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We all must agree that the word "trend" is now the buzzword of the stock market. As a part of investment strategy and analysis, it is always suggested that the investors should keep an eye on medium-term and short-term changes in addition to longer-term (secular) patterns. Traders and investors use the RSI as a momentum indicator. Overbought and oversold situations are indicated by RSI values between 70 and 30. Over the past two decades, several techniques have been developed to analyze NIFTY 50 data for investment purposes. In this paper, we have estimated the returns by looking at the two trends i.e., 50-50 and 60-40. In addition to this, how to trade and back-test our strategy is also explained. Applying these two RSI strategies to the NIFTY 50 chart revealed that 50-50 offers a higher long-term return, while 60-40 provides a superior short-term return. Finally, the strategies' returns F-statistics and P-values were calculated and analyzed to determine their significance level and acceptability.

"Investor sentiment measurement based on technical analysis indicators affecting stock returns: Empirical evidence on VN100"

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Full-text available

Dec 2021

The purpose of this study is to examine whether investor sentiment as measured by technical analysis indicators has an impact on stock returns. The research period is from 2015 to mid-2020. 1-year government bond yields, financial data, transaction data of 57 companies in the VN100 basket, and VNIndex are analyzed. The investor sentiment variable is measured by each technical analysis indicator (Relative Strength Index – RSI, Psychological Line Index – PLI), and the general sentiment variable is established based on extracting the principal component from individual indicators. The paper uses two regression methods – Fama-MacBeth and Generalized Least Square (GLS) – for five different research models. The results show that sentiment plays an important role in stock returns in the Vietnamese stock market. Even controlling the factors such as cash flow per share, firm size, market risk premium, and stock price volatility in the studied models, the impact of sentiment is significant in both the model using individual technical indicators and the model using the general sentiment variable. Furthermore, investor sentiment has a stronger power to explain excess stock returns than their trading behavior. The implication from the results shows that the Vietnamese stock market is inefficient, in which psychology is a very important issue and participants need to pay due attention to this factor. AcknowledgmentThis study was funded by the Industrial University of Ho Chi Minh City (IUH), Vietnam (grant number: 21/1TCNH03).

The Adaptive Markets Hypothesis: Evidence from the Foreign Exchange Market

Article

Full-text available

Apr 2009
J FINANC QUANT ANAL

We analyze the intertemporal stability of excess returns to technical trading rules in the foreign exchange market by conducting true, out-of-sample tests on previously studied rules. The excess returns of the 1970s and 1980s were genuine and not just the result of data mining. But these profit opportunities had disappeared by the early 1990s for filter and moving average rules. Returns to less-studied rules also have declined but have probably not completely disappeared. High volatility prevents precise estimation of mean returns. These regularities are consistent with the Adaptive Markets Hypothesis (Lo (2004)), but not with the Efficient Markets Hypothesis.

New Concepts in Technical Trading Systems

Article

Jan 1978

J. W. Jr. Wilder

Weak-Form Efficiency in Currency Markets

Article

Sep 2008

Many past studies have found that currencies trend, so technical trading rules produced statistically and economically significant profits. In other words, foreign exchange markets were weak-form inefficient. The study reported here reexamined this phenomenon with use of a new database of currency futures for 1975-2006 that includes old and newly liquid currencies. The findings from the recent data are contradictory. The profitability of trend following eroded for major currencies and their associated cross exchange rates around the mid-1990s. Newly liquid currencies after 2000 do trend, however, just as major currencies did in earlier years. The evidence is consistent with early weak-form inefficiency followed by vanishing trends as traders learn and adapt their strategies.

A reality check on technical trading rule profits in the U.S. Futures markets

Article

Oct 2009
J FUTURES MARKETS

This article investigates the profitability of technical trading rules in U.S. futures markets during the years 1985–2004. Statistical significance of performance across the trading rules is evaluated using White's Bootstrap Reality Check and Hansen's Superior Predictive Ability tests, which can directly measure the effect of data snooping by testing the performance of the best rule in the context of the full universe of technical trading rules. Results show that the best rules generate statistically significant economic profits for only two of 17 futures markets after correcting for data snooping biases. This evidence suggests that technical trading rules generally have not been profitable in the U.S. futures markets. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:633–659, 2010

How rewarding is technical analysis in the Indian stock market?

Article

Feb 2011

Subrata Mitra

This paper analyses the profitability of moving average based trading rules in the Indian stock market using four stock index series. The study finds that most technical trading rules are able to capture the direction of market movements reasonably well and give significant positive returns both in long and short positions. But these returns cannot be exploited fully due to real world transaction costs. Although the transaction cost has come down over the years, various components of transaction costs due to the bid-ask spread, brokerage, etc. will never be zero. The trading rules based on short term moving averages may be able to detect trends in financial series very quickly, but those rules also generate a large number of trades causing higher transaction costs. Thus technical traders have to pay more attention to minimizing transaction costs while choosing a trading rule. Nevertheless, profit opportunities from technical analysis continue to remain an interesting and debatable issue in the Indian stock market.

Stable classes of technical trading rules

Article

May 2011

Technical analysis includes a huge variety of trading rules. This fact has always been a serious hindrance to the large number of market efficiency studies implemented either to demonstrate the profitability of market-beating systems or to deny their operational feasibility. For evident reasons it is practically impossible and theoretically weak to systematically analyse the entire body of all trading rules. We therefore propose a novel method to form natural classes of trading rules which are found to be robust to changing market scenarios. In particular, groups are formed adopting a similarity measure based on the investing signals of the trading rules. Our clustering methodology adopts a Markov chain bootstrapping technique to generate differentiated scenarios preserving volume and price joint distributional features. An application is developed on a sample of 674 trading rules. Results show that six groups (here identified as trading styles) are sufficient to explain the large portion of the investing signals variance. We also suggest applications of our results to fund performance measurement and the analysis of financial markets.

The evolution of stock market efficiency over time: A survey of the empirical literature

Article

Feb 2011
J ECON SURV

This paper provides a systematic review of the weak-form market efficiency literature that examines return predictability from past price changes, with an exclusive focus on the stock markets. Our survey shows that the bulk of the empirical studies examine whether the stock market under study is or is not weak-form efficient in the absolute sense, assuming that the level of market efficiency remains unchanged throughout the estimation period. However, the possibility of time-varying weak-form market efficiency has received increasing attention in recent years. We categorize these emerging studies based on the research framework adopted, namely non-overlapping sub-period analysis, time-varying parameter model and rolling estimation window. An encouraging development is that the documented empirical evidence of evolving stock return predictability can be rationalized within the framework of the adaptive markets hypothesis.

The Use of Technical Analysis by Fund Managers: International Evidence

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Nov 2010
J BANK FINANC

Lukas Menkhoff

The use of technical analysis by financial market professionals is not well understood. This paper thus analyzes survey evidence from 692 fund managers in five countries, the vast majority of whom rely on technical analysis. At a forecasting horizon of weeks, technical analysis is the most important form of analysis and up to this horizon it is thus more important than fundamental analysis. Technicians are as experienced, as educated, as successful in their career and largely just as overconfident in decision-making as others. However, technical analysis is somewhat more popular in smaller asset management firms. What we find most significant is the relation of technical analysis with the view that prices are heavily determined by psychological influences. Consequently, technicians apply trend-following behavior.

The economic value of fundamental and technical information in emerging currency markets

Article

Jan 2008
J INT MONEY FINANC

We measure the economic value of information derived from macroeconomic variables and from technical trading rules for emerging markets currency investments. Our analysis is based on a sample of 21 emerging markets with a floating exchange rate regime over the period 1997–2007 and explicitly accounts for trading restrictions on foreign capital movements by using non-deliverable forward data. We document that both types of information can be exploited to implement profitable trading strategies. In line with evidence from surveys of foreign exchange professionals concerning the use of fundamental and technical analysis, we find that combining the two types of information improves the risk-adjusted performance of the investment strategies.

CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator

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