Fama and Blume (1966) applied Alexander’s filter rules to series of daily closing prices for each of 30 individual securities

Table 1 Summary of early technical analysis studies published between 1961 and 1987

Criteria: Study	Markets considered / Frequency of data	In-sample period	Technical trading systems	Benchmark strategies / Optimization	Transaction costs	Conclusion
1. Donchian (1960)	Copper futures / Daily	1959-60	Channel	Not considered	$51.50 per round-trip	The current price was compared to the two preceding week’s ranges. This trading rule generated net gains of $3,488 and $1,390, on margin of $1,000, for a single contract of the December 1959 delivery of copper and the December 1960 delivery, respectively.
2. Alexander (1961)	S&P Industrials, Dow Jones Industrials / Daily	1897-1959, 1929-59	Filter (11 rules from 5.0 to 50%)	Buy & hold	Not adjusted	Trading rules with 5, 6, and 8% filters generated larger gross profits than the B&H (buy-and-hold) strategy. All the profits were not likely to be eliminated by commissions. This led Alexander to conclude that there were trends in stock market prices.
3. Houthakker (1961)	Wheat and corn futures / Daily	1921-39, 1947-56	Stop-loss order (11 rules from 0 to 100%)	Buy & hold, Sell & hold	Not adjusted	Most stop-loss orders generated higher profits than the B&H or a sell and hold strategy. Long transactions indicated better performance than short transactions.
4. Cootner (1962)	45 NYSE stocks / Weekly	1956-60	Moving average (1/200 days with and without a 5% band)	Buy & hold	Commissions of 1% per one-way transaction	Although net returns from moving average rules were not much different from those from the B&H strategy, long transactions generated higher returns than the B&H strategy. Moreover, the variance of the trading rule was 30% less than that of the B&H.
5. Gray & Nielsen (1963)	Wheat futures / Daily	1921-43, 1949-62	Stop-loss order (10 rules from 1 to 100%)	Buy & hold, Sell & hold	Not adjusted	When applying stop-loss order rules to dominant contracts, there was little evidence of non-randomness in wheat futures prices. They argued that Houthakker’s results were biased because he used remote contracts and that post-war seasonality of wheat futures prices was induced by government loan programs.
6. Alexander (1964)	S&P Industrials / Daily	1928-61	Filter, Formula Dazhi, Formala Dafilt, moving average, and Dow-type formulas	Buy & hold	Commissions of 2% for each round-trip	After commissions, only the largest filter (45.6%) rule beat the B&H strategy by a substantial margin. Most of the other trading systems earned higher gross profits than filter rules or the B&H strategy. However, after commissions they could not beat the B&H.
7. Smidt (1965a)	May soybean futures contracts / Daily	1952-61	Momentum oscillator (40 rules)	Not considered	$0.36 per bushel per round-trip	About 70% of trading rules tested generated positive returns after commissions. Moreover, half of trading rules returned 7.5% per year or more.
8. Fama & Blume (1966)	30 individual stocks of the DJIA / Daily	1956-62	Filter (24 rules from 0.5 to 50%)	Buy & hold	0.1% per round-trip plus other costs	After commissions, only 4 of 30 securities had positive average returns per filter. Even before commissions, filter rules were inferior to the B&H strategy for all but two securities. Although three small filter rules (0.5, 1.0, and 1.5%) earned higher gross average returns (11.4%-20.9% per year) per security when considering only long positions, net returns after transaction costs were not much different from B&H returns.

Criteria:

Study

Markets considered

/ Frequency of data

In-sample period

Technical trading systems

Benchmark strategies / Optimization

Transaction

costs

Conclusion

1. Donchian

(1960)

Copper futures

/ Daily

1959-60

Channel

Not considered

$51.50 per round-trip

The current price was compared to the two preceding week’s ranges. This trading rule generated net gains of $3,488 and $1,390, on margin of $1,000, for a single contract of the December 1959 delivery of copper and the December 1960 delivery, respectively.

2. Alexander

(1961)

S&P Industrials,

Dow Jones Industrials

/ Daily

1897-1959,

1929-59

Filter (11 rules from 5.0 to 50%)

Buy & hold

Not adjusted

Trading rules with 5, 6, and 8% filters generated larger gross profits than the B&H (buy-and-hold) strategy. All the profits were not likely to be eliminated by commissions. This led Alexander to conclude that there were trends in stock market prices.

3. Houthakker

(1961)

Wheat and corn futures

/ Daily

1921-39,

1947-56

Stop-loss order

(11 rules from 0 to 100%)

Buy & hold, Sell & hold

Not adjusted

Most stop-loss orders generated higher profits than the B&H or a sell and hold strategy. Long transactions indicated better performance than short transactions.

4. Cootner (1962)

45 NYSE stocks

/ Weekly

1956-60

Moving average

(1/200 days with and without a 5% band)

Buy & hold

Commissions of 1% per one-way transaction

Although net returns from moving average rules were not much different from those from the B&H strategy, long transactions generated higher returns than the B&H strategy. Moreover, the variance of the trading rule was 30% less than that of the B&H.

5. Gray & Nielsen

(1963)

Wheat futures

/ Daily

1921-43,

1949-62

Stop-loss order

(10 rules from 1 to 100%)

Buy & hold, Sell & hold

Not adjusted

When applying stop-loss order rules to dominant contracts, there was little evidence of non-randomness in wheat futures prices. They argued that Houthakker’s results were biased because he used remote contracts and that post-war seasonality of wheat futures prices was induced by government loan programs.

6. Alexander

(1964)

S&P Industrials

/ Daily

1928-61

Filter, Formula Dazhi, Formala Dafilt, moving average, and Dow-type formulas

Buy & hold

Commissions of 2% for each round-trip

After commissions, only the largest filter (45.6%) rule beat the B&H strategy by a substantial margin. Most of the other trading systems earned higher gross profits than filter rules or the B&H strategy. However, after commissions they could not beat the B&H.

7. Smidt (1965a)

May soybean futures contracts

/ Daily

1952-61

Momentum oscillator (40 rules)

Not considered

$0.36 per bushel per round-trip

About 70% of trading rules tested generated positive returns after commissions. Moreover, half of trading rules returned 7.5% per year or more.

8. Fama & Blume

(1966)

30 individual stocks of the DJIA

/ Daily

1956-62

Filter

(24 rules from 0.5 to 50%)

Buy & hold

0.1% per round-trip plus other costs

After commissions, only 4 of 30 securities had positive average returns per filter. Even before commissions, filter rules were inferior to the B&H strategy for all but two securities. Although three small filter rules (0.5, 1.0, and 1.5%) earned higher gross average returns (11.4%-20.9% per year) per security when considering only long positions, net returns after transaction costs were not much different from B&H returns.

Table 1 continued.

Criteria: Study	Markets considered / Frequency of data	In-sample period	Technical trading systems	Benchmark strategies / Optimization	Transaction costs	Conclusion
9. Levy (1967a)	200 NYSE stocks / Weekly	1960-65	Relative strength (Ratios: 1/4 and 1/26 weeks)	Geometric average	1% per one-way transaction	Net returns of several well-performing rules were nearly two or three times the return of the geometric average, although these rules possessed slightly higher standard deviations relative to the geometric average.
10. Levy (1967b)	200 NYSE stocks / Weekly	1960-65	Relative strength (Ratio: 1/26 weeks)	Not considered	1% per one-way transaction	Stocks having the historically strongest relative strength showed an average price appreciation of 9.6% over 26 weeks (about 20.1% per year). An annual price appreciation of all stocks was 12.8%. In general, stocks that had been both relatively strong and relatively volatile produced higher profits.
11. Poole (1967)	9 exchange rates / Daily	1919-29, 1950-62	Filter (10 rules from 0.1 to 2%)	Buy & hold	Not adjusted	Four of nine exchange rates had average annual gross returns more than 25% for the best filter rules, and three of them (Belgium, France, and Italy) generated returns above 44%. Filter rules beat the B&H strategy by large differences in returns.
12. Van Horne & Parker (1967)	30 NYSE stocks / Daily	1960-66	Moving average (100, 150, and 200 days with 0, 2, 5, 10, and 15% bands)	Buy & hold	Commissions charged by members of the NYSE	No trading rule earned a total closing balance nearly as large as that generated under the B&H strategy. Even before transaction costs, gross profits from each moving average rule were less than that from the B&H.
13. James (1968)	232 to 1376 stocks from the CRSP at the Univ. of Chicago / Monthly	1926-60	Moving average (7 months = 200 days with 2 and 5% bands)	Buy & hold	Not adjusted	Moving average rules could not beat the B&H strategy. The largest average dollar difference between the moving average rules and the B&H strategy was very small.
14. Van Horne & Parker (1968)	30 NYSE stocks / Daily	1960-66	Non-weighted and exponentially weighted moving averages (200 days with 0, 5, 10, and 15% bands)	Buy & hold	1% per one-way transaction	When applying trading rules to long positions, only 55 of 480 cases (16 different combinations of rules multiplied by 30 stocks) realized profits greater than those from the B&H strategy. For long plus short positions, a smaller number of trading rules (36 out of 480 cases) outperformed the B&H.
15. Jensen & Benington (1970)	29 portfolio samples of 200 NYSE stocks / Monthly	1931-65	Relative strength (2 rules from Levy (1967a))	Buy & hold	Actual round lot rate	After transaction costs, Levy’s trading rules did not perform better than the B&H strategy. In fact, after explicit adjustment for the level of risk, the trading rules on average generated net returns less than the risk-adjusted B&H returns.

Criteria:

Study

Markets considered

/ Frequency of data

In-sample period

Technical trading systems

Benchmark strategies / Optimization

Transaction

costs

Conclusion

9. Levy (1967a)

200 NYSE stocks

/ Weekly

1960-65

Relative strength (Ratios: 1/4 and 1/26 weeks)

Geometric average

1% per one-way transaction

Net returns of several well-performing rules were nearly two or three times the return of the geometric average, although these rules possessed slightly higher standard deviations relative to the geometric average.

10. Levy (1967b)

200 NYSE stocks

/ Weekly

1960-65

Relative strength (Ratio: 1/26 weeks)

Not considered

1% per one-way transaction

Stocks having the historically strongest relative strength showed an average price appreciation of 9.6% over 26 weeks (about 20.1% per year). An annual price appreciation of all stocks was 12.8%. In general, stocks that had been both relatively strong and relatively volatile produced higher profits.

11. Poole (1967)

9 exchange rates

/ Daily

1919-29,

1950-62

Filter (10 rules from 0.1 to 2%)

Buy & hold

Not adjusted

Four of nine exchange rates had average annual gross returns more than 25% for the best filter rules, and three of them (Belgium, France, and Italy) generated returns above 44%. Filter rules beat the B&H strategy by large differences in returns.

12. Van Horne &

Parker (1967)

30 NYSE stocks

/ Daily

1960-66

Moving average (100, 150, and 200 days with 0, 2, 5, 10, and 15% bands)

Buy & hold

Commissions charged by members of the NYSE

No trading rule earned a total closing balance nearly as large as that generated under the B&H strategy. Even before transaction costs, gross profits from each moving average rule were less than that from the B&H.

13. James (1968)

232 to 1376 stocks from the CRSP at the Univ. of Chicago

/ Monthly

1926-60

Moving average

(7 months = 200 days with 2 and 5% bands)

Buy & hold

Not adjusted

Moving average rules could not beat the B&H strategy. The largest average dollar difference between the moving average rules and the B&H strategy was very small.

14. Van Horne &

Parker (1968)

30 NYSE stocks

/ Daily

1960-66

Non-weighted and exponentially weighted moving averages (200 days with 0, 5, 10, and 15% bands)

Buy & hold

1% per one-way transaction

When applying trading rules to long positions, only 55 of 480 cases (16 different combinations of rules multiplied by 30 stocks) realized profits greater than those from the B&H strategy. For long plus short positions, a smaller number of trading rules (36 out of 480 cases) outperformed the B&H.

15. Jensen &

Benington

(1970)

29 portfolio samples of 200 NYSE stocks

/ Monthly

1931-65

Relative strength

(2 rules from Levy (1967a))

Buy & hold

Actual round lot rate

After transaction costs, Levy’s trading rules did not perform better than the B&H strategy. In fact, after explicit adjustment for the level of risk, the trading rules on average generated net returns less than the risk-adjusted B&H returns.

Table 1 continued.

Criteria: Study	Markets considered / Frequency of data	In-sample period	Technical trading systems	Benchmark strategies / Optimization	Transaction costs	Conclusion
16. Stevenson & Bear (1970)	July corn and soybean futures / Daily	1957-68	Stop-loss order, filter, and combination of both systems	Buy & hold	0.5 cents per bushel for both commodities	For all systems, a 5% filter rule worked best, which generated larger net profits or greatly reduced losses relative to the B&H strategy. The filter rule also outperformed B&H for both corn and soybean futures.
17. Dryden (1970a)	U.K. stock indices, Tesco Stores stock / Daily	1962-67, 1962-64	Filter (12 rules from 0.1 to 5%)	Buy & hold	Individual stock: 0.625% per one-way transaction	Without transaction costs, filter rules consistently beat the B&H strategy for both indices and an individual stock. With transaction costs, the returns from the best filter rules were similar to those from the B&H, but long transactions beat the B&H.
18. Dryden (1970b)	15 U.K. stocks / Daily	1963-64, 1966-67	Filter (14 rules from 0.2 to 6%)	Buy & hold	Not adjusted	There was considerable variation among individual stocks’ returns. On average, filter returns were less than the corresponding B&H returns except for two smallest filter rules. However, returns only from long transactions were much higher than the B&H returns.
19. Levy (1971)	548 NYSE stocks / Daily	1964-69	32 forms of a five-point chart pattern	Buy & hold	2% per round-trip	After transaction costs, none of the 32 patterns for any holding period generated profits greater than average purchase or short-sale opportunities. Even the best-performing pattern produced adjusted relative-to-market returns of -1.1% and -0.1% for one-week and 4-week holding periods, respectively.
20. Leuthold (1972)	30 live cattle futures contracts / Daily	1965-70	Filter (1, 2, 3, 4, 5, and 10%)	Not considered	Commissions of $36 per round-trip	Four of six filters were profitable after transaction costs. In particular, a 3% filter rule generated an annual net return of 115.8% during the sample period.
21. Martell & Philippatos (1974)	September wheat and September soybean futures contracts / Daily	1956-69 (1958-70)^*	Adaptive filter model and pure information model	Buy & hold / Optimized trading rules	Adjusted but not specified	As an optimal filter size for period t, the adaptive model utilizes a filter size which has yielded the highest profits in t-1, subject to some minimum value of the average relative information gain. The pure information model chooses as an optimal filter size in period t the one with the highest relative average information gain in period t-1. Both models yielded higher net returns than the B&H only for wheat futures. However, the variance in net profits was consistently smaller than that of the B&H in both markets.

Criteria:

Study

Markets considered

/ Frequency of data

In-sample period

Technical trading systems

Benchmark strategies / Optimization

Transaction

costs

Conclusion

16. Stevenson &

Bear (1970)

July corn and soybean futures

/ Daily

1957-68

Stop-loss order,

filter, and combination of both systems

Buy & hold

0.5 cents per bushel for both commodities

For all systems, a 5% filter rule worked best, which generated larger net profits or greatly reduced losses relative to the B&H strategy. The filter rule also outperformed B&H for both corn and soybean futures.

17. Dryden

(1970a)

U.K. stock indices, Tesco Stores stock

/ Daily

1962-67,

1962-64

Filter (12 rules from 0.1 to 5%)

Buy & hold

Individual stock: 0.625% per one-way transaction

Without transaction costs, filter rules consistently beat the B&H strategy for both indices and an individual stock. With transaction costs, the returns from the best filter rules were similar to those from the B&H, but long transactions beat the B&H.

18. Dryden

(1970b)

15 U.K. stocks

/ Daily

1963-64,

1966-67

Filter (14 rules from 0.2 to 6%)

Buy & hold

Not adjusted

There was considerable variation among individual stocks’ returns. On average, filter returns were less than the corresponding B&H returns except for two smallest filter rules. However, returns only from long transactions were much higher than the B&H returns.

19. Levy (1971)

548 NYSE stocks

/ Daily

1964-69

32 forms of a five-point chart pattern

Buy & hold

2% per round-trip

After transaction costs, none of the 32 patterns for any holding period generated profits greater than average purchase or short-sale opportunities. Even the best-performing pattern produced adjusted relative-to-market returns of -1.1% and -0.1% for one-week and 4-week holding periods, respectively.

20. Leuthold

(1972)

30 live cattle futures contracts

/ Daily

1965-70

Filter (1, 2, 3, 4, 5, and 10%)

Not considered

Commissions of $36 per round-trip

Four of six filters were profitable after transaction costs. In particular, a 3% filter rule generated an annual net return of 115.8% during the sample period.

21. Martell &

Philippatos

(1974)

September wheat and September soybean futures contracts

/ Daily

1956-69

(1958-70)^*

Adaptive filter model and pure information model

Buy & hold

/ Optimized trading rules

Adjusted but not specified

As an optimal filter size for period t, the adaptive model utilizes a filter size which has yielded the highest profits in t-1, subject to some minimum value of the average relative information gain. The pure information model chooses as an optimal filter size in period t the one with the highest relative average information gain in period t-1. Both models yielded higher net returns than the B&H only for wheat futures. However, the variance in net profits was consistently smaller than that of the B&H in both markets.

^* Years in parentheses indicate out-of-sample periods.

Table 1 continued.

Criteria: Study	Markets considered / Frequency of data	In-sample period	Technical trading systems	Benchmark strategies / Optimization	Transaction costs	Conclusion
22. Praetz (1975)	Sydney wool futures / Daily	1965-72	Filter (24 rules from 0.5 to 25%)	Buy & hold	Not adjusted	For 12 of all 21 contracts of 18-month length and all three 8-year price series, the B&H strategy showed better performance than filter rules, with average differences of 0.1% and 2%, respectively. For the same data set, in 10 of 24 filters the B&H returns were greater than average filter returns. Thus, filter rules did not seem to outperform the B&H strategy consistently.
23. Martell (1976)	September wheat and September soybean futures contracts / Daily	1956-69 (1958-70)^*	Adaptive filter models and pure information model	Buy & hold / Optimized trading rules	Adjusted but not specified	A new adaptive model was developed and applied to the same data set as that used in Martell and Philippatos (1974). The new model selects its optimal filter size for next period based on profitability (e.g., the highest cumulative net profits) and information gain. Although the model outperformed the previous adaptive model for around 80% of the sample period, it neither indicated any stability with respect to the information constraint nor beat the pure information model that allows a filter size in a particular period to reflect new information.
24. Akemann & Keller (1977)	Industry groups from S&P 500 Stock Index / Weekly	1967-75	Relative strength	S&P 500 Index	2% per round-trip	The relative strength rule is designed to buy the strongest stock group in a given thirteen-week period and sell it after 52 weeks. After adjustment for transaction costs, the mean return differential between all 378 possible trials and the market index appeared to be 14.6%, although the differentials were quite volatile.
25. Logue & Sweeney (1977)	Franc/dollar spot exchange rate / Daily	1970-74	Filter (14 rules from 0.7 to 5%)	Buy & hold	0.06% per one-way transaction	Most trading rules (13 out of 14 rules) outperformed the B&H strategy after considering transaction costs. Compared to the buy and hold and invest in French government securities strategy, only four filters failed to generate higher profits.
26. Cornell & Dietrich (1978)	6 spot foreign currencies (mark, pound, yen, Canadian dollar, Swiss franc, and Dutch guilder) / Daily	1973-75	Filter (13 rules from 0.1 to 5%), and moving average (10, 25, and 50 days with 0.1 to 2% bands)	Buy & hold	Computed by using the average bid-ask spread for all trades.	For the Dutch guilder, German mark, and Swiss franc, the best rules from each trading system generated over 10% annual net returns. Although the net returns were relatively small (1% to 4%) for the British pound, Canadian dollar, and Japanese yen, they all beat the B&H strategy. Moreover, since none of the systematic risk (beta) estimates exceeded 0.12, high returns of the three currencies were less likely to be compensation for bearing systematic risk.
27. Logue, Sweeney, & Willett (1978)	7 foreign exchange rates / Daily	1973-76	Filter (11 rules from 0.5 to 15%)	Buy & hold	Not adjusted	For every exchange rate (the mark, pound, yen, lira, France franc, Swiss franc, and Dutch guilder), profits from the best filter rules exceeded those from the B&H strategy by differences ranging from 9.3% to 32.9%.

Criteria:

Study

Markets considered

/ Frequency of data

In-sample period

Technical trading systems

Benchmark strategies / Optimization

Transaction

costs

Conclusion

22. Praetz (1975)

Sydney wool futures

/ Daily

1965-72

Filter (24 rules from 0.5 to 25%)

Buy & hold

Not adjusted

For 12 of all 21 contracts of 18-month length and all three 8-year price series, the B&H strategy showed better performance than filter rules, with average differences of 0.1% and 2%, respectively. For the same data set, in 10 of 24 filters the B&H returns were greater than average filter returns. Thus, filter rules did not seem to outperform the B&H strategy consistently.

23. Martell (1976)

September wheat and September soybean futures contracts

/ Daily

1956-69

(1958-70)^*

Adaptive filter models and pure information model

Buy & hold

/ Optimized trading rules

Adjusted but not specified

A new adaptive model was developed and applied to the same data set as that used in Martell and Philippatos (1974). The new model selects its optimal filter size for next period based on profitability (e.g., the highest cumulative net profits) and information gain. Although the model outperformed the previous adaptive model for around 80% of the sample period, it neither indicated any stability with respect to the information constraint nor beat the pure information model that allows a filter size in a particular period to reflect new information.

24. Akemann &

Keller (1977)

Industry groups from S&P 500 Stock Index

/ Weekly

1967-75

Relative strength

S&P 500 Index

2% per round-trip

The relative strength rule is designed to buy the strongest stock group in a given thirteen-week period and sell it after 52 weeks. After adjustment for transaction costs, the mean return differential between all 378 possible trials and the market index appeared to be 14.6%, although the differentials were quite volatile.

25. Logue &

Sweeney (1977)

Franc/dollar spot exchange rate

/ Daily

1970-74

Filter (14 rules from 0.7 to 5%)

Buy & hold

0.06% per one-way transaction

Most trading rules (13 out of 14 rules) outperformed the B&H strategy after considering transaction costs. Compared to the buy and hold and invest in French government securities strategy, only four filters failed to generate higher profits.

26. Cornell &

Dietrich

(1978)

6 spot foreign currencies (mark, pound, yen, Canadian dollar, Swiss franc, and Dutch guilder)

/ Daily

1973-75

Filter (13 rules from 0.1 to 5%), and moving average (10, 25, and 50 days with 0.1 to 2% bands)

Buy & hold

Computed by using the average bid-ask spread for all trades.

For the Dutch guilder, German mark, and Swiss franc, the best rules from each trading system generated over 10% annual net returns. Although the net returns were relatively small (1% to 4%) for the British pound, Canadian dollar, and Japanese yen, they all beat the B&H strategy. Moreover, since none of the systematic risk (beta) estimates exceeded 0.12, high returns of the three currencies were less likely to be compensation for bearing systematic risk.

27. Logue,

Sweeney, &

Willett (1978)

7 foreign exchange rates

/ Daily

1973-76

Filter (11 rules from 0.5 to 15%)

Buy & hold

Not adjusted

For every exchange rate (the mark, pound, yen, lira, France franc, Swiss franc, and Dutch guilder), profits from the best filter rules exceeded those from the B&H strategy by differences ranging from 9.3% to 32.9%.

^* Years in parentheses indicate out-of-sample periods.

Table 1 continued.

Criteria: Study	Markets considered / Frequency of data	In-sample period	Technical trading systems	Benchmark strategies / Optimization	Transaction costs	Conclusion
28. Arnott (1979)	500 stocks from both the S&P 500 Index and the NYSE Composite Index / Weekly	1968-77	Beta-modified relative strength	Not considered	Not adjusted	Regression results indicated that for the base periods of 1 week to 18 weeks, the correlation between the change in (beta-adjusted) relative strength during the base period and that during any subsequent period was strongly negative. Hence, careless use of relative strength might lead to serious money loss.
29. Dale & Workman (1980)	90-day T-bill futures at the IMM / Daily	1976-78	Moving average (11 rules from 5 to 60 days)	Not considered	$60 per round-trip	For each individual contract, the best trading rules generated positive net returns, although the rules did not indicate consistent performances over the sample period.
30. Bohan (1981)	87 to 110 S&P industry groups / Weekly	1969-80	Relative strength	Buy & hold on S&P 500 Index	2% per year	There was a strong correlation between the performance of the strongest and weakest industry groups in one year and that of the following years, although the performance of the other groups did not have much predictive significance. For example, quintile 1 portfolio, which consists of the top 20% of industry groups, generated a return of 76% higher than the B&H on the market index, while the market outperformed quintile 5 portfolio by 80%.
31. Solt & Swanson (1981)	Gold from London Gold Market and silver from Handy & Harman / Weekly	1971-79	Filter (0.5 to 50%) and moving average (26, 52, and 104 weeks with filters)	Buy & hold	1.0% per one-way transaction plus 0.5% annual fees	For gold, a 10% filter rule outperformed the B&H strategy after adjustment for transaction costs. However, none of the filter rules dominated the B&H strategy for either gold or silver. Moving average rules were not able to improve the returns for the filter rules as well.
32. Peterson & Leuthold (1982)	7 hog futures contracts from CME / Daily	1973-77	Filter (10 rules from 1 to 10% and additional 10 rules from $0.5 to $5)	Zero mean profit	Not adjusted	All 20 filter rules produced considerable mean gross profits. It seemed that these profit levels exceeded any reasonable commission charges in most cases. In general, mean gross profits increased with larger filters, as did variance of profits.
33. Dooley & Shafer (1983)	9 foreign currencies in the New York market / Daily	1973-81	Filter (7 rules from 1 to 25%)	Not considered	Adjusted but not specified	Although results were slightly different for each currency, small filter rules (1, 3, and 5%) generally produced high profits, while larger filter rules showed consistent losses.

Criteria:

Study

Markets considered

/ Frequency of data

In-sample period

Technical trading systems

Benchmark strategies / Optimization

Transaction

costs

Conclusion

28. Arnott (1979)

500 stocks from both the S&P 500 Index and the NYSE Composite Index

/ Weekly

1968-77

Beta-modified relative strength

Not considered

Not adjusted

Regression results indicated that for the base periods of 1 week to 18 weeks, the correlation between the change in (beta-adjusted) relative strength during the base period and that during any subsequent period was strongly negative. Hence, careless use of relative strength might lead to serious money loss.

29. Dale &

Workman

(1980)

90-day T-bill futures at the IMM

/ Daily

1976-78

Moving average

(11 rules from 5 to 60 days)

Not considered

$60 per round-trip

For each individual contract, the best trading rules generated positive net returns, although the rules did not indicate consistent performances over the sample period.

30. Bohan (1981)

87 to 110 S&P industry groups

/ Weekly

1969-80

Relative strength

Buy & hold on S&P 500 Index

2% per year

There was a strong correlation between the performance of the strongest and weakest industry groups in one year and that of the following years, although the performance of the other groups did not have much predictive significance. For example, quintile 1 portfolio, which consists of the top 20% of industry groups, generated a return of 76% higher than the B&H on the market index, while the market outperformed quintile 5 portfolio by 80%.

31. Solt &

Swanson

(1981)

Gold from London Gold Market and silver from Handy & Harman

/ Weekly

1971-79

Filter (0.5 to 50%) and moving average (26, 52, and 104 weeks with filters)

Buy & hold

1.0% per one-way transaction plus 0.5% annual fees

For gold, a 10% filter rule outperformed the B&H strategy after adjustment for transaction costs. However, none of the filter rules dominated the B&H strategy for either gold or silver. Moving average rules were not able to improve the returns for the filter rules as well.

32. Peterson &

Leuthold

(1982)

7 hog futures contracts from CME

/ Daily

1973-77

Filter (10 rules from 1 to 10% and additional 10 rules from $0.5 to $5)

Zero mean profit

Not adjusted

All 20 filter rules produced considerable mean gross profits. It seemed that these profit levels exceeded any reasonable commission charges in most cases. In general, mean gross profits increased with larger filters, as did variance of profits.

33. Dooley &

Shafer (1983)

9 foreign currencies in the New York market

/ Daily

1973-81

Filter (7 rules from 1 to 25%)

Not considered

Adjusted but not specified

Although results were slightly different for each currency, small filter rules (1, 3, and 5%) generally produced high profits, while larger filter rules showed consistent losses.

Table 1 continued.

Criteria: Study	Markets considered / Frequency of data	In-sample period	Technical trading systems	Benchmark strategies / Optimization	Transaction costs	Conclusion
34. Brush & Boles (1983)	168 S&P 500 stocks / Monthly	1967-80, (two data bases were used for out-of-sample tests)	Relative strength (parameters were optimized on the development data base over 26 separate 6-month test periods)	Equal- weighted 168-stock return / Optimized models	2% per round-trip	The top decile annualized excess return of the best model was 7.1% per year over the equal-weighted 168-stock return, after adjustment for risk, dividend yield, and transaction costs. The model also produced a compounded growth of 15.2% per year after considering dividend yield and transaction costs, compared to 5.9% for the S&P 500.
35. Irwin & Uhrig (1984)	8 commodity futures: corn, cocoa, soybeans, wheat, sugar, copper, live cattle, and live hogs / Daily	1960-78 (1979-81)^, 1960-68 (1969-72)^, 1973-78 (1979-81)^*	Channel, moving averages, momentum oscillator	Zero mean profit / Optimized trading rules	Doubled commissions to capture bid-ask spread (not specified)	Trading rule profits during in-sample periods were substantial and similar across all four trading systems. Out-of-sample results for optimal trading rules also indicated that during the 1979-81 period most trading systems were profitable in corn, cocoa, sugar, and soybean futures markets. The trading rule profits appeared to be concentrated in the 1973-81 period.
36. Neftci & Policano (1984)	4 futures: copper, gold, soybeans, and T-bills / Daily	1975-80	Moving average (25, 50, and 100 days) and slope (trendline) method	Not considered	Not adjusted	Trading signals were incorporated as a dummy variable into a regression equation for the minimum mean square error prediction. Then the significance of the dummy variable was evaluated using F-tests. Overall, moving average rules indicated some predictive power for T-bills, gold, and soybeans, while the slope method showed mixed results.
37. Tomek & Querin (1984)	3 random price series (each series consists of 300 prices) generated from corn prices for each sample period / Daily	1975-80, 1973-74, 1980	Moving average (3/10 and 10/40 days)	Not considered	$50 per round-trip	From each of three random prices series, 20 sets of prices were replicated. The first 20 sets had moderate price variability, the second set large price variability, and the third set drift in prices. Both trading rules failed to generate positive average net profits for all three groups with an exception of the 10/40 rule for the relatively volatile price group. The results imply that technical trading rules may earn positive net returns by chance, although they on average could not generate positive net profits.
38. Bird (1985)	Cash and forward contracts of copper, lead, tin, and zinc from London Metal Exchange (LME) / Daily	1972-82	Filter: long positions (and cash profits) (25 rules from 1 to 25%)	Buy & hold	1% per round-trip	For cash and forward (futures) copper, over 2/3 of filter rules beat the B&H strategy. Similar results were obtained for lead and zinc but with weaker evidence. For tin, the results were inconsistent. Filter rules performed substantially better in the earlier period (1972-77).

Criteria:

Study

Markets considered

/ Frequency of data

In-sample period

Technical trading systems

Benchmark strategies / Optimization

Transaction

costs

Conclusion

34. Brush &

Boles (1983)

168 S&P 500 stocks

/ Monthly

1967-80,

(two data bases were used for out-of-sample tests)

Relative strength

(parameters were optimized on the development data base over 26 separate 6-month test periods)

Equal- weighted 168-stock return

/ Optimized models

2% per round-trip

The top decile annualized excess return of the best model was 7.1% per year over the equal-weighted 168-stock return, after adjustment for risk, dividend yield, and transaction costs. The model also produced a compounded growth of 15.2% per year after considering dividend yield and transaction costs, compared to 5.9% for the S&P 500.

35. Irwin & Uhrig

(1984)

8 commodity futures: corn, cocoa, soybeans, wheat, sugar, copper, live cattle, and live hogs

/ Daily

1960-78 (1979-81)^*, 1960-68 (1969-72)^*, 1973-78 (1979-81)^*

Channel, moving averages, momentum oscillator

Zero mean profit

/ Optimized trading rules

Doubled commissions to capture bid-ask spread (not specified)

Trading rule profits during in-sample periods were substantial and similar across all four trading systems. Out-of-sample results for optimal trading rules also indicated that during the 1979-81 period most trading systems were profitable in corn, cocoa, sugar, and soybean futures markets. The trading rule profits appeared to be concentrated in the 1973-81 period.

36. Neftci &

Policano

(1984)

4 futures: copper, gold, soybeans, and T-bills

/ Daily

1975-80

Moving average

(25, 50, and 100 days) and slope (trendline) method

Not considered

Not adjusted

Trading signals were incorporated as a dummy variable into a regression equation for the minimum mean square error prediction. Then the significance of the dummy variable was evaluated using F-tests. Overall, moving average rules indicated some predictive power for T-bills, gold, and soybeans, while the slope method showed mixed results.

37. Tomek &

Querin (1984)

3 random price series (each series consists of 300 prices) generated from corn prices for each sample period

/ Daily

1975-80,

1973-74,

1980

Moving average

(3/10 and 10/40 days)

Not considered

$50 per round-trip

From each of three random prices series, 20 sets of prices were replicated. The first 20 sets had moderate price variability, the second set large price variability, and the third set drift in prices. Both trading rules failed to generate positive average net profits for all three groups with an exception of the 10/40 rule for the relatively volatile price group. The results imply that technical trading rules may earn positive net returns by chance, although they on average could not generate positive net profits.

38. Bird (1985)

Cash and forward contracts of copper, lead, tin, and zinc from London Metal Exchange (LME)

/ Daily

1972-82

Filter: long positions (and cash profits)

(25 rules from 1 to 25%)

Buy & hold

1% per round-trip

For cash and forward (futures) copper, over 2/3 of filter rules beat the B&H strategy. Similar results were obtained for lead and zinc but with weaker evidence. For tin, the results were inconsistent. Filter rules performed substantially better in the earlier period (1972-77).

^* Years in parentheses indicate out-of-sample periods.

Table 1 continued.

Criteria: Study	Markets considered / Frequency of data	In-sample period	Technical trading systems	Benchmark strategies / Optimization	Transaction costs	Conclusion
39. Brush (1986)	420 S&P 500 stocks / Monthly	1969-84	Relative strength	Return of the equal- weighted S&P 500 Index	1% per round-trip	By avoiding the year-end effect and exploiting beta corrections and the negative predictive power of one-month trends, the best model, which was the generalized least squares beta approach, generated an annual excess return of more than 5% over the equal-weighted S&P 500, after transaction costs.
40. Sweeney (1986)	Dollar/mark and additional 9 exchange rates / Daily	1973-75 (1975-80)^*	Filter: long positions (7 rules from 0.5 to 10%)	Buy & hold / Optimized trading rules	1/8 of 1% of asset value per round-trip	Both in- and out-of-sample tests, small filter rules (0.5% to 5%) consistently beat the B&H strategy, and transaction costs did not eliminate the risk-adjusted excess returns of filter rules. Eight filter rules across 6 exchange rates produced statistically significant excess returns over the B&H in both in- and out-of sample periods.
41. Taylor (1983, 1986)	London agricultural futures: cocoa, coffee, and sugar, Chicago IMM currency futures: sterling, mark, and Swiss franc / Daily	1971-76 (1977-81)^, 1961-73 (1974-81)^, 1974-78 (1979-81)^*	A statistical price-trend model	Buy & hold and interest rate for bank deposit / Optimized trading rules	1% per round-trip for agricultural futures and 0.2% for currency futures	Taylor (1986) adds one more out-of-sample year (i.e., 1981) to the sample period in his 1983’s work. For sugar, an average net return of the trading rule was higher than that of the B&H strategy by 27% per annum. For cocoa and coffee, returns from both the trading rule and the B&H were not much different. Trading gains for currencies during 1979-80 were negligible, but in 1981 all currencies generated substantial gains of around 7% higher than the bank deposit rate.
42. Thompson & Waller (1987)	Coffee and cocoa futures in the NY Coffee, Sugar, and Cocoa Exchange / 6 weekly sets of transaction-to-transaction prices for each market	1981-83	Filter (for coffee, 5¢ through 35¢ in multiples of 5¢ per 100 lb; for cocoa, $1 through $7 per metric ton)	Not considered	Estimated execution costs	For both nearby and distant coffee and cocoa contracts, filter rules generated average profits per trade per contract substantially lower than estimated execution costs per contract in all cases in which profits were statistically significantly greater than zero. The estimated execution costs per trade per contract were $32.25 (nearby) and $69.75 (distant) for coffee futures contracts and $12.60 (nearby) and $21.80 (distant) for cocoa futures contracts.

Criteria:

Study

Markets considered

/ Frequency of data

In-sample period

Technical trading systems

Benchmark strategies / Optimization

Transaction

costs

Conclusion

39. Brush (1986)

420 S&P 500 stocks

/ Monthly

1969-84

Relative strength

Return of the equal- weighted S&P 500 Index

1% per round-trip

By avoiding the year-end effect and exploiting beta corrections and the negative predictive power of one-month trends, the best model, which was the generalized least squares beta approach, generated an annual excess return of more than 5% over the equal-weighted S&P 500, after transaction costs.

40. Sweeney

(1986)

Dollar/mark and additional 9 exchange rates

/ Daily

1973-75 (1975-80)^*

Filter: long positions

(7 rules from 0.5 to 10%)

Buy & hold

/ Optimized trading rules

1/8 of 1% of asset value per round-trip

Both in- and out-of-sample tests, small filter rules (0.5% to 5%) consistently beat the B&H strategy, and transaction costs did not eliminate the risk-adjusted excess returns of filter rules. Eight filter rules across 6 exchange rates produced statistically significant excess returns over the B&H in both in- and out-of sample periods.

41. Taylor (1983,

1986)

London agricultural futures: cocoa, coffee, and sugar, Chicago IMM currency futures: sterling, mark, and Swiss franc

/ Daily

1971-76 (1977-81)^*, 1961-73 (1974-81)^*, 1974-78 (1979-81)^*

A statistical price-trend model

Buy & hold and interest rate for bank deposit

/ Optimized trading rules

1% per round-trip for agricultural futures and 0.2% for currency futures

Taylor (1986) adds one more out-of-sample year (i.e., 1981) to the sample period in his 1983’s work. For sugar, an average net return of the trading rule was higher than that of the B&H strategy by 27% per annum. For cocoa and coffee, returns from both the trading rule and the B&H were not much different. Trading gains for currencies during 1979-80 were negligible, but in 1981 all currencies generated substantial gains of around 7% higher than the bank deposit rate.

42. Thompson &

Waller (1987)

Coffee and cocoa futures in the NY Coffee, Sugar, and Cocoa Exchange

/ 6 weekly sets of transaction-to-transaction prices for each market

1981-83

Filter

(for coffee, 5¢ through 35¢ in multiples of 5¢ per 100 lb; for cocoa, $1 through $7 per metric ton)

Not considered

Estimated execution costs

For both nearby and distant coffee and cocoa contracts, filter rules generated average profits per trade per contract substantially lower than estimated execution costs per contract in all cases in which profits were statistically significantly greater than zero. The estimated execution costs per trade per contract were $32.25 (nearby) and $69.75 (distant) for coffee futures contracts and $12.60 (nearby) and $21.80 (distant) for cocoa futures contracts.

^* Years in parentheses indicate out-of-sample periods.

Table 2 Categories for modern technical analysis studies

Out-of-sample tests

Statistical tests

Data snooping addressed

Distinctive features

Standard

Lukac, Brorsen, & Irwin (1988)

√

Conduct parameter optimization and out-of-sample tests.

Model-based

bootstrap

Brock, Lakonishok, & LeBaron (1992)

√

Use model-based bootstrap methods for statistical tests. No parameter optimization and out-of-sample tests conducted.

Genetic programming

Allen & Karjalainen (1999)

√

Use genetic programming techniques to optimize trading rules.

Reality Check

Sullivan, Timmermann, & White (1999)

√

Use White’s Reality Check Bootstrap methodology for optimization and statistical tests.

Chart patterns

Chang & Osler (1999)

√

Use recognition algorithms for chart patterns.

Nonlinear

Gençay (1998a)

√

Use nearest neighbors and/or feedforward network regressions to generate trading signals.

Others

Neely (1997)

√

Most studies in this category lack trading rule optimization and out-of-sample tests, and do not address data-snooping problems.

Table 3 Summary of standard technical analysis studies published between 1988 and 2004

Criteria: Study	Markets considered / Frequency of data	In-sample period (Out-of-sample period)	Technical trading systems	Benchmark strategies / Optimization	Transaction costs	Conclusion
1. Lukac, Brorsen, & Irwin (1988)	12 futures from various exchanges: agriculturals, metals, currencies, and interest rates / Daily	1975-83 (1978-84)	12 systems (3 channels, 3 moving averages, 3 oscillators, 2 trailing stops, and a combination)	Zero mean profit / Optimized trading rules	$50 and $100 per round-trip	Out-of-sample results indicated that 4 of 12 systems generated significant aggregate portfolio net returns and 8 of the 12 commodities earned statistically significant net returns from more than one trading system. Mark, sugar, and corn markets appeared to be most profitable during the sample period. In addition, Jensen test confirmed that the same four trading systems having large net returns still produced significant net returns above risk.
2. Lukac & Brorsen (1989)	15 futures from various exchanges: agricultural commodities, metals, currencies, and interest rates / Daily	1965-85 (various)	Channel and directional movement (both systems had 12 parameters ranging 5 days to 60 days in increments of 5)	Buy & hold / Optimized trading rules	$100 per round-trip	Technical trading rule profits were measured based on various optimization methods, which included 10 re-optimization strategies, one random strategy, and 12 fixed parameter strategies. The two trading systems generated portfolio mean net returns significantly greater than the B&H strategy. However, the trading systems yielded similar profits across different optimization strategies and even different parameters. Thus, the parameter optimization appeared to have little value.
3. Sweeney & Surajaras (1989)	An equally-weighted portfolio and a variably-weighted portfolio of currencies / Daily	Prior 250- to 1400-day prices (1980-86)	Filter, single moving average, double moving average, and the best system	Buy & hold / Optimized trading rules	Adjusted but not specified	Most trading systems generated risk-adjusted mean net profits after transaction costs, and the single moving average rule performed best. The variably-weighted portfolio approach generally outperformed the equally-weighted approach. Changing neither parameters for each trading system on a yearly basis nor amounts of data used to select optimal parameters seem to improve trading profits.
4. Taylor & Tari (1989)	IMM currency futures: pound, mark, and Swiss franc; London agricultural futures: cocoa, coffee, and sugar / Daily	1974-78 (1979-87); (1982-85)	A statistical price-trend model	Buy & hold, Zero mean profit / Optimized trading rules	Currency futures: 0.2% per round-trip; Agricultural futures: 1%	During the out-of-sample period, 1979-87, the trading rule earned aggregate mean net return of 4.3% per year for three currency futures. The mark was the most profitable contract (5.4% per year). From 1982-85, the trading rule generated a mean net return of 4.8% for cocoa, -4.26% for coffee, and 18.8% for sugar, outperforming the B&H strategy for cocoa and sugar futures.
5. Lukac & Brorsen (1990)	30 futures from various exchanges: agriculturals, metals, oils, currencies, interest rates, and S&P 500 / Daily	1975-85 (1976-86)	23 systems (channels, moving averages, oscillators, trailing stops, point and figure, a counter-trend, volatility, and combinations)	Zero mean profit / Optimized trading rules	$50 and $100 per round-trip	Only 3 of 23 trading systems had negative mean monthly portfolio net returns after transaction costs, and 7 of 23 systems generated net returns significantly above zero at 10% level. Most of the trading profits appeared to be made over the 1979-80 period. In the individual commodity markets, currency futures produced the highest returns, while livestock futures yielded the lowest returns.

Criteria:

Study

Markets considered

/ Frequency of data

In-sample period (Out-of-sample period)

Technical trading systems

Benchmark strategies / Optimization

Transaction

costs

Conclusion

1. Lukac,

Brorsen,

& Irwin (1988)

12 futures from various exchanges: agriculturals, metals, currencies, and interest rates

/ Daily

1975-83 (1978-84)

12 systems

(3 channels,

3 moving averages, 3 oscillators,

2 trailing stops, and a combination)

Zero mean profit

/ Optimized trading rules

$50 and $100 per round-trip

Out-of-sample results indicated that 4 of 12 systems generated significant aggregate portfolio net returns and 8 of the 12 commodities earned statistically significant net returns from more than one trading system. Mark, sugar, and corn markets appeared to be most profitable during the sample period. In addition, Jensen test confirmed that the same four trading systems having large net returns still produced significant net returns above risk.

2. Lukac &

Brorsen (1989)

15 futures from various exchanges: agricultural commodities, metals, currencies, and interest rates

/ Daily

1965-85

(various)

Channel and

directional movement (both systems had 12 parameters ranging 5 days to 60 days in increments of 5)

Buy & hold

/ Optimized trading rules

$100 per round-trip

Technical trading rule profits were measured based on various optimization methods, which included 10 re-optimization strategies, one random strategy, and 12 fixed parameter strategies. The two trading systems generated portfolio mean net returns significantly greater than the B&H strategy. However, the trading systems yielded similar profits across different optimization strategies and even different parameters. Thus, the parameter optimization appeared to have little value.

3. Sweeney &

Surajaras

(1989)

An equally-weighted portfolio and a variably-weighted portfolio of currencies

/ Daily

Prior 250- to 1400-day prices

(1980-86)

Filter, single moving average, double moving average, and the best system

Buy & hold

/ Optimized trading rules

Adjusted but not specified

Most trading systems generated risk-adjusted mean net profits after transaction costs, and the single moving average rule performed best. The variably-weighted portfolio approach generally outperformed the equally-weighted approach. Changing neither parameters for each trading system on a yearly basis nor amounts of data used to select optimal parameters seem to improve trading profits.

4. Taylor & Tari

(1989)

IMM currency futures: pound, mark, and Swiss franc; London agricultural futures: cocoa, coffee, and sugar

/ Daily

1974-78

(1979-87);

(1982-85)

A statistical price-trend model

Buy & hold,

Zero mean profit

/ Optimized trading rules

Currency futures: 0.2% per round-trip; Agricultural futures: 1%

During the out-of-sample period, 1979-87, the trading rule earned aggregate mean net return of 4.3% per year for three currency futures. The mark was the most profitable contract (5.4% per year). From 1982-85, the trading rule generated a mean net return of 4.8% for cocoa, -4.26% for coffee, and 18.8% for sugar, outperforming the B&H strategy for cocoa and sugar futures.

5. Lukac &

Brorsen (1990)

30 futures from various exchanges: agriculturals, metals, oils, currencies, interest rates, and S&P 500

/ Daily

1975-85

(1976-86)

23 systems (channels, moving averages, oscillators, trailing stops, point and figure, a counter-trend, volatility, and combinations)

Zero mean profit

/ Optimized trading rules

$50 and $100 per round-trip

Only 3 of 23 trading systems had negative mean monthly portfolio net returns after transaction costs, and 7 of 23 systems generated net returns significantly above zero at 10% level. Most of the trading profits appeared to be made over the 1979-80 period. In the individual commodity markets, currency futures produced the highest returns, while livestock futures yielded the lowest returns.

Table 3 continued.

Criteria: Study	Markets considered / Frequency of data	In-sample period (Out-of-sample period)	Technical trading systems	Benchmark strategies / Optimization	Transaction costs	Conclusion
6. Taylor (1992)	4 currency futures from IMM of the CME: pound, mark, yen, and Swiss franc / Daily	1977-87 (1982-87)	3 technical trading systems (filter, channel, moving average), 2 statistical price-trend models	Buy & hold / Optimized trading rules	0.2% per round-trip	All trading rules outperformed the B&H strategy across all currency futures. Among trading rules, three technical trading systems and a revised statistical trend model generated statistically significant and much higher mean net returns (3.0% to 4.0%) than that (2.0%) of the original price-trend model for most currencies. These returns could not be explained by nonsynchronous trading or time-varying risk premia.
7. Farrell & Olszewski (1993)	S&P 500 futures / Daily	1982-90 (1989-90)	A nonlinear trading strategy based on ARMA (1,1) model and 3 trend-following systems (channel and volatility systems)	Buy & hold / Optimized trading rules	0.025% per round-trip	Although the nonlinear trading strategy were slightly more profitable than the B&H strategy, the result was statistically insignificant. For the in-sample period, the nonlinear optimal trading strategy was more profitable than the B&H by nearly 5%, while for the out-of-sample period, the trading strategy was better by 3%. Meanwhile, the three trend following strategies were more profitable than the nonlinear trading strategy by around 5% to 11% during the out-of-sample period, depending on the trading strategy.
8. Silber (1994)	12 futures markets: foreign currencies, short-term interest rates, metals, oil, and S&P 500 / Daily	1979 (1980-91)	Moving average (short averages: 1 day to 15 days; long averages: 16 to 200 days)	Buy & hold (& roll over) / Optimized trading rules	Bid-ask spreads per round-trip (2 ticks for crude oil and gold; 1 tick for the rest of contracts)	After transaction costs, average annual net returns were positive for all contracts but gold, silver, and the S&P 500. In particular, most currency futures earned higher net profits (1.9% to 9.8%). For those profitable markets, moving average rules beat the B&H strategy except for 3-month Eurodollars. Test results using a Sharpe ratio criterion were similar. Hence, trading profits appeared to be robust to transaction costs and risk. Central bank intervention is one of possible explanations for the trading profits.
9. Taylor (1994)	4 currency futures from IMM: pound, mark, yen, and Swiss franc / Daily	1980-all previous contracts (1982-90)	Channel	Zero mean profits / Optimized trading rules	0.2% per one-way transaction	For price series generated by ARIMA(1,1,1) model, channel rules correctly identified the sign of conditional expected returns with around 60% probability. During 1982-90, optimal channel rules produced an average net return of 6.9% per year. The t-test indicated that the return was significant at the 2.5% level. The best trading opportunities occurred for 1985-87.
10. Menkhoff & Schlumberger (1995)	3 spot exchange rates: mark/dollar, mark/yen, and mark/pound / Daily	1981-91, 1981-85 (1986-91)	Oscillator (33 moving averages) and momentum (10 rules from 5 to 40 days)	Buy & hold / Optimized trading rules	0.0008 DM for 1$; 0.0017 DM for 1 yen; 0.003 DM for 1 BP per round-trip	During the out-of-sample period, 84% out of 129 technical trading rules tested outperformed the B&H strategy across exchange rates, after adjustment for transaction costs and risk. However, superiority of optimal trading rules during the in-sample period deteriorated in the out-of-sample period, even though they still outperformed the B&H strategy.

Criteria:

Study

Markets considered

/ Frequency of data

In-sample period (Out-of-sample period)

Technical trading systems

Benchmark strategies / Optimization

Transaction

costs

Conclusion

6. Taylor (1992)

4 currency futures from IMM of the CME: pound, mark, yen, and Swiss franc

/ Daily

1977-87

(1982-87)

3 technical trading systems (filter, channel, moving average), 2 statistical price-trend models

Buy & hold

/ Optimized trading rules

0.2% per round-trip

All trading rules outperformed the B&H strategy across all currency futures. Among trading rules, three technical trading systems and a revised statistical trend model generated statistically significant and much higher mean net returns (3.0% to 4.0%) than that (2.0%) of the original price-trend model for most currencies. These returns could not be explained by nonsynchronous trading or time-varying risk premia.

7. Farrell &

Olszewski

(1993)

S&P 500 futures

/ Daily

1982-90

(1989-90)

A nonlinear trading strategy based on ARMA (1,1) model and 3 trend-following systems (channel and volatility systems)

Buy & hold

/ Optimized trading rules

0.025% per round-trip

Although the nonlinear trading strategy were slightly more profitable than the B&H strategy, the result was statistically insignificant. For the in-sample period, the nonlinear optimal trading strategy was more profitable than the B&H by nearly 5%, while for the out-of-sample period, the trading strategy was better by 3%. Meanwhile, the three trend following strategies were more profitable than the nonlinear trading strategy by around 5% to 11% during the out-of-sample period, depending on the trading strategy.

8. Silber (1994)

12 futures markets: foreign currencies, short-term interest rates, metals, oil, and S&P 500

/ Daily

1979

(1980-91)

Moving average

(short averages: 1 day to 15 days; long averages: 16 to 200 days)

Buy & hold (& roll over)

/ Optimized trading rules

Bid-ask spreads per round-trip (2 ticks for crude oil and gold; 1 tick for the rest of contracts)

After transaction costs, average annual net returns were positive for all contracts but gold, silver, and the S&P 500. In particular, most currency futures earned higher net profits (1.9% to 9.8%). For those profitable markets, moving average rules beat the B&H strategy except for 3-month Eurodollars. Test results using a Sharpe ratio criterion were similar. Hence, trading profits appeared to be robust to transaction costs and risk. Central bank intervention is one of possible explanations for the trading profits.

9. Taylor (1994)

4 currency futures from IMM: pound, mark, yen, and Swiss franc

/ Daily

1980-all previous contracts (1982-90)

Channel

Zero mean profits

/ Optimized trading rules

0.2% per one-way transaction

For price series generated by ARIMA(1,1,1) model, channel rules correctly identified the sign of conditional expected returns with around 60% probability. During 1982-90, optimal channel rules produced an average net return of 6.9% per year. The t-test indicated that the return was significant at the 2.5% level. The best trading opportunities occurred for 1985-87.

10. Menkhoff &

Schlumberger

(1995)

3 spot exchange rates: mark/dollar, mark/yen, and mark/pound

/ Daily

1981-91,

1981-85

(1986-91)

Oscillator (33 moving averages)

and momentum (10 rules from 5 to 40 days)

Buy & hold

/ Optimized trading rules

0.0008 DM for 1$; 0.0017 DM for 1 yen; 0.003 DM for 1 BP per round-trip

During the out-of-sample period, 84% out of 129 technical trading rules tested outperformed the B&H strategy across exchange rates, after adjustment for transaction costs and risk. However, superiority of optimal trading rules during the in-sample period deteriorated in the out-of-sample period, even though they still outperformed the B&H strategy.

Table 3 continued.

Criteria: Study	Markets considered / Frequency of data	In-sample period (Out-of-sample period)	Technical trading systems	Benchmark strategies / Optimization	Transaction costs	Conclusion
11. Lee & Mathur (1996a)	6 European currency spot cross-rates / Daily	1988-92 (1989-93)	Moving average (short moving averages: 1 day to 9 days; long moving averages: 10, 15, 20, 25, and 30 days)	Zero mean profits / Optimized trading rules	0.1% per round-trip	Results of in-sample tests indicated that the trading rules did not yield significantly positive returns for all cross rates but yen/mark and yen/Swiss franc (11.5% and 8.8% per year, respectively). Out-of-sample results were even worse. Most cross rates earned negative trading returns, although long positions for the yen/mark produced marginally significant positive returns.
12. Lee & Mathur (1996b)	10 spot cross-rates / Daily	1988-92 (1989-93)	Moving average (short moving averages: 1 day to 9 days; long moving averages: 10, 15, 20, 25, and 30 days) and channel (2 to 50 days)	Zero mean profits / Optimized trading rules	0.1% per round-trip	During in-sample periods, moving average rules in general produced negative or statistically insignificantly positive net returns except the mark/yen (11.5% per year) and the Swiss franc/yen (8.8% per year). Similar results were found for channel rules. During out-of-sample periods, overall returns of the trading rules were negative or statistically insignificantly positive. Only for the mark/lira, both long positions of moving average rules and channel rules generated statistically significant profits.
13. Szakmary & Mathur (1997)	5 IMM foreign currency futures and spots: mark, yen, pound, Swiss franc, and Canadian dollar / Daily	1977-90 (1978-91)	Moving average (short moving averages: 1 day to 9 days; long moving averages: 10, 15, 20, 25, and 30 days)	Zero mean profits / Optimized trading rules	0.1% per round-trip	In-sample results indicated that moving average rules generated both statistically and economically significant returns for all currency futures but the Canadian dollar. Similar results were reported for both out-of-sample data (annual net returns ranged from 5.5% to 9.6%) and spot rates. Further analyses showed that the moving average rule profits resulted from the central bank’s “leaning against the wind intervention.”
14. Goodacre, Bosher, & Dove (1999)	254 companies in the FTSE 350 Index and 64 option trades in the U.K. / Daily	Prior 200 days (1988-96)	CRISMA (combination system of Cumulative volume, RelatIve Strength, and Moving Average)	FTSE All Share Index / Optimized parameters	0 to 2% per round-trip	The CRISMA trading system generated annualized profits ranging 6.9% to 19.3% depending on transaction costs, while an annualized return on the FTSE All Share Index over the same time period was 14.0%. When adjusted for market movements and risk, however, mean excess returns for nonzero levels of transaction costs were significantly negative. Moreover, performance of the trading system was not stable over time. With option trading, the system generated mean return of 10.2% per trade even in the presence of maximum retail costs, but only 55% of trades were profitable.
15. Kwan, Lam, So, & Yu (2000)	Hang Seng Index Futures / Daily	1986-97 (1990-98)	A statistical price-trend model	Buy & hold / Optimized parameters	0.4 to 0.5% per one-way transaction	The price-trend model performed poorer than the B&H strategy in the periods 1991-93 and 1995-96 when the market was bullish. However, the trading rule produced larger profits than the B&H in the years, 90, 94, 97, and 98 when the market became up and down. Across all years and transaction costs considered, an average net return (10.1%) of the trading rule was slightly smaller than that (13.5%) of the B&H strategy.

Criteria:

Study

Markets considered

/ Frequency of data

In-sample period (Out-of-sample period)

Technical trading systems

Benchmark strategies / Optimization

Transaction

costs

Conclusion

11. Lee &

Mathur

(1996a)

6 European currency spot cross-rates

/ Daily

1988-92

(1989-93)

Moving average

(short moving averages: 1 day to 9 days; long moving averages: 10, 15, 20, 25, and 30 days)

Zero mean profits

/ Optimized trading rules

0.1% per round-trip

Results of in-sample tests indicated that the trading rules did not yield significantly positive returns for all cross rates but yen/mark and yen/Swiss franc (11.5% and 8.8% per year, respectively). Out-of-sample results were even worse. Most cross rates earned negative trading returns, although long positions for the yen/mark produced marginally significant positive returns.

12. Lee &

Mathur

(1996b)

10 spot cross-rates

/ Daily

1988-92

(1989-93)

Moving average

(short moving averages: 1 day to 9 days; long moving averages: 10, 15, 20, 25, and 30 days) and channel (2 to 50 days)

Zero mean profits

/ Optimized trading rules

0.1% per round-trip

During in-sample periods, moving average rules in general produced negative or statistically insignificantly positive net returns except the mark/yen (11.5% per year) and the Swiss franc/yen (8.8% per year). Similar results were found for channel rules. During out-of-sample periods, overall returns of the trading rules were negative or statistically insignificantly positive. Only for the mark/lira, both long positions of moving average rules and channel rules generated statistically significant profits.

13. Szakmary &

Mathur

(1997)

5 IMM foreign currency futures and spots: mark, yen, pound, Swiss franc, and Canadian dollar

/ Daily

1977-90

(1978-91)

Moving average

(short moving averages: 1 day to 9 days; long moving averages: 10, 15, 20, 25, and 30 days)

Zero mean profits

/ Optimized trading rules

0.1% per round-trip

In-sample results indicated that moving average rules generated both statistically and economically significant returns for all currency futures but the Canadian dollar. Similar results were reported for both out-of-sample data (annual net returns ranged from 5.5% to 9.6%) and spot rates. Further analyses showed that the moving average rule profits resulted from the central bank’s “leaning against the wind intervention.”

14. Goodacre,

Bosher, &

Dove (1999)

254 companies in the FTSE 350 Index and 64 option trades in the U.K.

/ Daily

Prior 200 days

(1988-96)

CRISMA (combination system of Cumulative volume, RelatIve Strength, and Moving Average)

FTSE All Share Index

/ Optimized parameters

0 to 2% per round-trip

The CRISMA trading system generated annualized profits ranging 6.9% to 19.3% depending on transaction costs, while an annualized return on the FTSE All Share Index over the same time period was 14.0%. When adjusted for market movements and risk, however, mean excess returns for nonzero levels of transaction costs were significantly negative. Moreover, performance of the trading system was not stable over time. With option trading, the system generated mean return of 10.2% per trade even in the presence of maximum retail costs, but only 55% of trades were profitable.

15. Kwan, Lam,

So, & Yu

(2000)

Hang Seng Index Futures

/ Daily

1986-97

(1990-98)

A statistical price-trend model

Buy & hold /

Optimized parameters

0.4 to 0.5% per one-way transaction

The price-trend model performed poorer than the B&H strategy in the periods 1991-93 and 1995-96 when the market was bullish. However, the trading rule produced larger profits than the B&H in the years, 90, 94, 97, and 98 when the market became up and down. Across all years and transaction costs considered, an average net return (10.1%) of the trading rule was slightly smaller than that (13.5%) of the B&H strategy.

Table 3 continued.

Criteria: Study	Markets considered / Frequency of data	In-sample period (Out-of-sample period)	Technical trading systems	Benchmark strategies / Optimization	Transaction costs	Conclusion
16. Maillet & Michel (2000)	12 exchange rates (combinations of U.S. dollar, mark, yen, pound, and France franc) / Daily	1974-79 (1979-96)	Moving average (short moving averages: 1 day to 14 days; long moving averages: 15 to 200 days)	Zero mean profits, buy & hold / Optimized trading rules	Not adjusted	Optimized moving average rules generated statistically significant returns and outperformed the corresponding B&H strategies with the exception of the mark/franc rate. Bootstrap tests generally confirmed the results with the rejection of higher returns only in 4 out of 12 rates: the mark/dollar, mark/franc, yen/dollar, and yen/franc. Moreover, riskiness of both moving average rules and the B&H strategy, which was measured by their standard deviations, appeared to be not much different.
17. Taylor (2000)	1) Financial Times (FT) All-Share index; 2) UK 12-share index; 3) 12 UK stocks; 4) FT 100 index and index futures; 5) DJIA index; 6) S&P 500 index and index futures / Daily	1), 2), and 3): 1972-91; 4): 1985-94; 5): 1897-1988; 6): 1982-92	Moving average (short moving averages: 1, 2, and 5 days; long moving averages: 50, 100, 150, and 200, with and without a 1% band)	/ Parameters are optimized for the DJIA data from 1897 to 1968.	Not adjusted	The results of optimized moving average rules indicated that differences of mean returns between buy and sell positions were substantially positive and statistically significant for the FTA index, all versions of the 12-share index, 4 of the 12 UK firms, and the DJIA index for 3 out of 5 subperiods. No significant results were found for the FTSE 100 and S&P 500 indices. Buy positions also appeared to have lower standard deviations than sell positions for all but two series. An average breakeven one-way transaction cost across all data series was 0.35%. In particular, for the DJIA index, a trading rule (a 5/200 moving average rule) optimized over the 1897-1968 period produced a breakeven one-way transaction cost of 1.07% during the 1968-88 period.
18. Goodacre & Kohn- Spreyer (2001)	A random sample of 322 companies from the S&P 500 / Daily	Prior 200 days (1988-96)	CRISMA (combination system of Cumulative volume, RelatIve Strength, and Moving Average)	The S&P 500 Index / Optimized parameters	0 to 2% per round-trip	The CRISMA system generated annualized profits ranging 6.2% to 17.6% depending on transaction costs, while the annualized return on the S&P 500 Index over the same time period was 14.2%. However, when adjusted for market movements and risk, mean excess returns for nonzero levels of transaction costs were significantly negative across all return-generating models. Moreover, the results were not stable over time, although trades on larger firms generally performed better than small ones.
19. Lee, Gleason, & Mathur (2001)	13 Latin American spot currencies / Daily	1992-99 (various periods from data available)	Moving average (short moving averages: 1 day to 9 days; long moving averages: 10 to 30 days) and channel (2 to 50 days)	Zero mean profits / Optimized trading rules	0.1% per round-trip	Out-of-sample results showed that moving average rules generated significantly positive returns for currencies of four countries: Brazil, Mexico, Peru, and Venezuela. Channel rules also produced significant profits for the same currencies except that of Peru. When only long positions were considered, there was a marginal improvement to five and four currencies for moving average rules and channel rules, respectively.

Criteria:

Study

Markets considered

/ Frequency of data

In-sample period (Out-of-sample period)

Technical trading systems

Benchmark strategies / Optimization

Transaction

costs

Conclusion

16. Maillet &

Michel

(2000)

12 exchange rates (combinations of U.S. dollar, mark, yen, pound, and France franc)

/ Daily

1974-79

(1979-96)

Moving average

(short moving averages: 1 day to 14 days; long moving averages: 15 to 200 days)

Zero mean profits, buy & hold

/ Optimized trading rules

Not adjusted

Optimized moving average rules generated statistically significant returns and outperformed the corresponding B&H strategies with the exception of the mark/franc rate. Bootstrap tests generally confirmed the results with the rejection of higher returns only in 4 out of 12 rates: the mark/dollar, mark/franc, yen/dollar, and yen/franc. Moreover, riskiness of both moving average rules and the B&H strategy, which was measured by their standard deviations, appeared to be not much different.

17. Taylor (2000)

1) Financial Times (FT) All-Share index; 2) UK 12-share index; 3) 12 UK stocks; 4) FT 100 index and index futures; 5) DJIA index; 6) S&P 500 index and index futures

/ Daily

1), 2), and 3): 1972-91;

4): 1985-94;

5): 1897-1988;

6): 1982-92

Moving average

(short moving averages: 1, 2, and 5 days; long moving averages: 50, 100, 150, and 200, with and without a 1% band)

/ Parameters are optimized for the DJIA data from 1897 to 1968.

Not adjusted

The results of optimized moving average rules indicated that differences of mean returns between buy and sell positions were substantially positive and statistically significant for the FTA index, all versions of the 12-share index, 4 of the 12 UK firms, and the DJIA index for 3 out of 5 subperiods. No significant results were found for the FTSE 100 and S&P 500 indices. Buy positions also appeared to have lower standard deviations than sell positions for all but two series. An average breakeven one-way transaction cost across all data series was 0.35%. In particular, for the DJIA index, a trading rule (a 5/200 moving average rule) optimized over the 1897-1968 period produced a breakeven one-way transaction cost of 1.07% during the 1968-88 period.

18. Goodacre &

Kohn-

Spreyer

(2001)

A random sample of 322 companies from the S&P 500

/ Daily

Prior 200 days

(1988-96)

CRISMA (combination system of Cumulative volume, RelatIve Strength, and Moving Average)

The S&P 500 Index

/ Optimized parameters

0 to 2% per round-trip

The CRISMA system generated annualized profits ranging 6.2% to 17.6% depending on transaction costs, while the annualized return on the S&P 500 Index over the same time period was 14.2%. However, when adjusted for market movements and risk, mean excess returns for nonzero levels of transaction costs were significantly negative across all return-generating models. Moreover, the results were not stable over time, although trades on larger firms generally performed better than small ones.

19. Lee,

Gleason,

& Mathur

(2001)

13 Latin American spot currencies

/ Daily

1992-99 (various periods from data available)

Moving average

(short moving averages: 1 day to 9 days; long moving averages: 10 to 30 days) and channel (2 to 50 days)

Zero mean profits

/ Optimized trading rules

0.1% per round-trip

Out-of-sample results showed that moving average rules generated significantly positive returns for currencies of four countries: Brazil, Mexico, Peru, and Venezuela. Channel rules also produced significant profits for the same currencies except that of Peru. When only long positions were considered, there was a marginal improvement to five and four currencies for moving average rules and channel rules, respectively.

Table 3 continued.

Criteria: Study	Markets considered / Frequency of data	In-sample period (Out-of-sample period)	Technical trading systems	Benchmark strategies / Optimization	Transaction costs	Conclusion
21. Martin (2001)	12 currencies in developing countries / Daily	1/92-6/92 (7/92-6/95)	Moving average (short moving averages: 1 day to 9 days; long moving averages: 10 to 30 days)	Short-selling strategy / Optimized trading rules	0.5% per one-way transaction	Out-of-sample, moving average rules generated positive mean net returns in 10 of 12 currencies, and the returns were greater than 0.14% daily (35% per year) in 5 currencies. However, Sharpe ratios indicated that moving average rules did not generate superior returns on a risk-adjusted basis.
22. Skouras (2001)	Dow Jones Industrial Average (DJIA) / Daily	1962-86 (1962-86)	Moving average (2 to 200 days with bands of 0, 0.5, 1, 1.5, and 2%)	Buy & hold / Optimized trading rules	Various levels from 0 to 0.1% per one-way transaction	Out-of-sample returns were estimated on a daily basis. Time-varying estimated rules (by an Artificial Technical Analyst) outperformed various fixed moving average rules employed by Brock et al. (1992) as well as the B&H strategy. When considering transaction costs, however, mean returns from the optimized trading rule were higher than the B&H mean return only after transaction costs of less than 0.06%.
23. Olson (2004)	18 exchange rates / Daily	5-year in-sample period from 1971-2000 (1976-2000)	Moving average (short moving averages: 1 day to 12 days; long moving averages: 5 to 200 days)	Buy & hold / Optimized trading rules	0.1% per round-trip	Out-of-sample results indicated that risk-adjusted trading profits for individual currencies and an equal-weighted 18-currency portfolio declined over time. For the 18-currency portfolio, annualized risk-adjusted returns decreased from an average of over 3% in the late 1970s and early 1980s to about zero percent in the late 1990s. Overall, profits of moving average rules in foreign exchange markets have declined over time.

Criteria:

Study

Markets considered

/ Frequency of data

In-sample period (Out-of-sample period)

Technical trading systems

Benchmark strategies / Optimization

Transaction

costs

Conclusion

20. Lee, Pan, &

Liu (2001)

9 exchange rates from Asian countries

1988-94 (1989-95)

The same trading rules as in Lee, Gleason, & Mathur (2001)

Zero mean profits

/ Optimized trading rules

0.1% per round-trip

Out-of-sample tests indicated that four exchange rates from Korea, New Zealand, Singapore, and Taiwan yielded positive profits for both moving average rules and channel rules. However, these profits were not significantly different from zero, except that of the Taiwan dollar.

21. Martin

(2001)

12 currencies in developing countries

/ Daily

1/92-6/92

(7/92-6/95)

Moving average

(short moving averages: 1 day to 9 days; long moving averages: 10 to 30 days)

Short-selling strategy

/ Optimized trading rules

0.5% per one-way transaction

Out-of-sample, moving average rules generated positive mean net returns in 10 of 12 currencies, and the returns were greater than 0.14% daily (35% per year) in 5 currencies. However, Sharpe ratios indicated that moving average rules did not generate superior returns on a risk-adjusted basis.

22. Skouras

(2001)

Dow Jones Industrial Average (DJIA)

/ Daily

1962-86

(1962-86)

Moving average

(2 to 200 days with bands of 0, 0.5, 1, 1.5, and 2%)

Buy & hold

/ Optimized trading rules

Various levels from 0 to 0.1% per one-way transaction

Out-of-sample returns were estimated on a daily basis. Time-varying estimated rules (by an Artificial Technical Analyst) outperformed various fixed moving average rules employed by Brock et al. (1992) as well as the B&H strategy. When considering transaction costs, however, mean returns from the optimized trading rule were higher than the B&H mean return only after transaction costs of less than 0.06%.

23. Olson (2004)

18 exchange rates

/ Daily

5-year in-sample period from 1971-2000 (1976-2000)

Moving average

(short moving averages: 1 day to 12 days; long moving averages: 5 to 200 days)

Buy & hold

/ Optimized trading rules

0.1% per round-trip

Out-of-sample results indicated that risk-adjusted trading profits for individual currencies and an equal-weighted 18-currency portfolio declined over time. For the 18-currency portfolio, annualized risk-adjusted returns decreased from an average of over 3% in the late 1970s and early 1980s to about zero percent in the late 1990s. Overall, profits of moving average rules in foreign exchange markets have declined over time.

Table 4 Summary of model-based bootstrap technical analysis studies published between 1988 and 2004

Criteria: Study	Markets considered / Frequency of data	In-sample period	Technical trading systems	Benchmark strategies / Optimization	Transaction costs	Conclusion
1. Brock, Lakonishok, & LeBaron (1992)	Dow Jones Industrial Average (DJIA) / Daily	1897-1986	Moving averages (1/50, 1/150, 5/150, 1/200, and 2/200 days with 0 and 1% bands) and trading range breakout (50, 150 and 200 days with 0 and 1% bands)	Unconditional 1- and 10-day returns	Not adjusted	Before transaction costs, buy (sell) positions across all trading rules consistently generated higher (lower) mean returns than unconditional mean returns, and these results were highly significant in most cases. For example, a mean buy return from variable moving average rules was about 12% per year and a mean sell return was about -7%. Moreover, the buy returns were even less volatile than the sell returns. Simulated series from a random walk with a drift, AR (1), GARCH-M, and EGARCH models using a bootstrap method could not explain returns and volatility of the actual Dow series.
2. Levich & Thomas (1993)	5 IMM currency futures: mark, yen, pound, Canadian dollar, and Swiss franc / Daily	1976-90	Filters (0.5, 1, 2, 3, 4, and 5%) and moving average (1/5, 5/20, 1/200 days)	Buy & hold	0.025% and 0.04% per one-way transaction	After adjustment for transaction costs and risk, every filter rule and moving average rule generated substantial positive mean net returns for all currencies but the Canadian dollar. Moreover, the results of the bootstrap simulation indicated that, for both trading systems, the null hypothesis that there is no information in the original time series was rejected in 25 of 30 cases.
3. Bessembinder & Chan (1995)	Asian stock indices: Hong Kong, Japan, Korea, Malaysia, Thailand, and Taiwan / Daily	1975-91	The same trading rules as in Brock et al. (1992)	Buy & hold	0.5, 1, and 2% per round-trip	Across all markets and trading rules tested, average mean returns on buy days exceeded those on sell days by 26.8% per year, and an average break-even round-trip transaction cost for the full sample was 1.57%. In particular, technical signals generated by the U.S. markets appeared to have substantial forecast power for returns in the Asian markets. Overall, trading rules generated higher net profits (12.2% to 21.2% per year) in the Malaysia, Thailand, and Taiwan stock markets.
4. Hudson, Dempsey, & Keasey (1996)	Financial Times Industrial Ordinary Index (FT30) in the U.K. / Daily	1935-94	The same trading rules as in Brock et al. (1992)	Unconditional mean returns	More than 1% per round-trip for large investing institutions	Before transaction costs, buy (sell) positions across all trading systems consistently generated higher (lower) returns than unconditional returns. However, an extra return per round-trip transaction averaged across all systems appeared to be about 0.8%, which was relatively smaller than the round-trip transaction costs of 1%.
5. Kho (1996)	4 currency futures from IMM: pound, mark, yen, and Swiss franc / Weekly	1980-91	Moving average (1/20, 1/30, 1/50, 2/20, 2/30, 2/50 weeks with bands of 0 and 1%)	Unconditional weekly mean return, Univariate GARCH-M	Not adjusted	Initially, moving average rules generated substantial mean returns between 9.9% and 11.1% per year from buy signals. These trading returns could not be explained by the empirical distribution of the univariate GARCH-M model as well as transaction costs or serial correlations in futures returns. However, the returns appeared to be insignificant when time-varying risk premia, which were estimated from a general model of the conditional CAPM, were taken into account.

Criteria:

Study

Markets considered

/ Frequency of data

In-sample period

Technical trading systems

Benchmark strategies / Optimization

Transaction

costs

Conclusion

1. Brock,

Lakonishok, &

LeBaron (1992)

Dow Jones Industrial Average (DJIA)

/ Daily

1897-1986

Moving averages

(1/50, 1/150, 5/150, 1/200, and 2/200 days with 0 and 1% bands) and trading range breakout (50, 150 and 200 days with 0 and 1% bands)

Unconditional 1- and 10-day returns

Not adjusted

Before transaction costs, buy (sell) positions across all trading rules consistently generated higher (lower) mean returns than unconditional mean returns, and these results were highly significant in most cases. For example, a mean buy return from variable moving average rules was about 12% per year and a mean sell return was about -7%. Moreover, the buy returns were even less volatile than the sell returns. Simulated series from a random walk with a drift, AR (1), GARCH-M, and EGARCH models using a bootstrap method could not explain returns and volatility of the actual Dow series.

2. Levich &

Thomas

(1993)

5 IMM currency futures: mark, yen, pound, Canadian dollar, and Swiss franc

/ Daily

1976-90

Filters (0.5, 1, 2, 3, 4, and 5%) and moving average (1/5, 5/20, 1/200 days)

Buy & hold

0.025% and 0.04% per one-way transaction

After adjustment for transaction costs and risk, every filter rule and moving average rule generated substantial positive mean net returns for all currencies but the Canadian dollar. Moreover, the results of the bootstrap simulation indicated that, for both trading systems, the null hypothesis that there is no information in the original time series was rejected in 25 of 30 cases.

3. Bessembinder

& Chan (1995)

Asian stock indices: Hong Kong, Japan, Korea, Malaysia, Thailand, and Taiwan

/ Daily

1975-91

The same trading rules as in Brock et al. (1992)

Buy & hold

0.5, 1, and 2% per round-trip

Across all markets and trading rules tested, average mean returns on buy days exceeded those on sell days by 26.8% per year, and an average break-even round-trip transaction cost for the full sample was 1.57%. In particular, technical signals generated by the U.S. markets appeared to have substantial forecast power for returns in the Asian markets. Overall, trading rules generated higher net profits (12.2% to 21.2% per year) in the Malaysia, Thailand, and Taiwan stock markets.

4. Hudson,

Dempsey,

& Keasey

(1996)

Financial Times Industrial Ordinary Index (FT30) in the U.K.

/ Daily

1935-94

The same trading rules as in Brock et al. (1992)

Unconditional mean returns

More than 1% per round-trip for large investing institutions

Before transaction costs, buy (sell) positions across all trading systems consistently generated higher (lower) returns than unconditional returns. However, an extra return per round-trip transaction averaged across all systems appeared to be about 0.8%, which was relatively smaller than the round-trip transaction costs of 1%.

5. Kho (1996)

4 currency futures from IMM: pound, mark, yen, and Swiss franc

/ Weekly

1980-91

Moving average

(1/20, 1/30, 1/50, 2/20, 2/30, 2/50 weeks with bands of 0 and 1%)

Unconditional weekly mean return, Univariate GARCH-M

Not adjusted

Initially, moving average rules generated substantial mean returns between 9.9% and 11.1% per year from buy signals. These trading returns could not be explained by the empirical distribution of the univariate GARCH-M model as well as transaction costs or serial correlations in futures returns. However, the returns appeared to be insignificant when time-varying risk premia, which were estimated from a general model of the conditional CAPM, were taken into account.

Table 4 continued.

Criteria: Study	Markets considered / Frequency of data	In-sample period	Technical trading systems	Benchmark strategies / Optimization	Transaction costs	Conclusion
6. Raj & Thurston (1996)	Hang Seng Futures Index of Hong Kong / Daily	1989-93	The same trading rules as in Brock et al. (1992), without 1/150 and 2/200 moving average rules

Criteria:

Study

Markets considered

/ Frequency of data

In-sample period

Technical trading systems

Benchmark strategies / Optimization

Transaction

costs

Conclusion

6. Raj & Thurston

(1996)

Hang Seng Futures Index of Hong Kong

/ Daily

1989-93

The same trading rules as in Brock et al. (1992), without 1/150 and 2/200 moving average rules

­

Out-of-sample tests