Time series momentum and moving average trading rules

Marshall, Ben R.; Nguyen, Nhut H.; Visaltanachoti, Nuttawat

doi:10.1080/14697688.2016.1205209

Cited by 58 publications

(37 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Following Marshall et al. ():

{MA}_{t, n} = \frac{P_{t - n + 1} + P_{t - n + 2} + \dots + P_{t}}{n}

A buy signal is generated on day t when:

{MAR}_{t, n} = P_{t} - {MA}_{t, n} > 0,

where n is the length of the MA, and we stay in the equity market as long as price is above the MA, or MAR t,n > 0. We examine the popular MA( n ) where N = 20, 50, 100, 150, and 200 days.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A technical approach to equity investing in emerging markets

2018

View full text Add to dashboard Cite

Technical analysis (TA) is used in evaluating its predictive power for the Morgan Stanley Capital International (MSCI) Emerging Market Index (EMI) that reflects 23 emerging market economies’ equity indices. We conclude strong predictive power of technical analysis for the EMI. Given this predictive power of TA, we then investigate whether investors can exploit this predictive power to beat the profitability of the Buy‐and‐Hold strategy considering both risk and transaction costs. Applying Moving Average, Relative Strength Index, Moving Average Convergence Divergence, and Rate of Change trading rules to the MSCI Emerging Market Index over the period of 11/1/1988 to 5/1/2017 reveals strong empirical evidence that investors could use TA to out‐perform the Buy‐and‐Hold strategy even when considering risk and transaction costs. This research provides evidence against the Efficient Market Hypothesis for EMI.

show abstract

“…Following Marshall et al. ():

{MA}_{t, n} = \frac{P_{t - n + 1} + P_{t - n + 2} + \dots + P_{t}}{n}

A buy signal is generated on day t when:

{MAR}_{t, n} = P_{t} - {MA}_{t, n} > 0,

where n is the length of the MA, and we stay in the equity market as long as price is above the MA, or MAR t,n > 0. We examine the popular MA( n ) where N = 20, 50, 100, 150, and 200 days.…”

Section: Methodsmentioning

confidence: 99%

“…Since Park and Irwin's survey, other studies on the profitability of TA have been conducted, including Zhou and Zhou (), Ülkü and Prodan (), Marshall, Nguyen, and Visaltanachoti (), and many others . About 25% of these research studies reject the predictive power of TA, while the other 75% support the predictive power of TA.…”

Section: Literature Reviewmentioning

confidence: 99%

A technical approach to equity investing in emerging markets

2018

View full text Add to dashboard Cite

show abstract

“…Marshall et al (2017) find that MA rules give an earlier signal than TSM, suggesting better returns for MA rules, but they both work best with large market value stocks. Moskowitz et al (2012) use monthly data from January 1965 to December 2009, and report that TSM provides significant positive excess returns in futures markets.…”

mentioning

confidence: 97%

Simple Market Timing with Moving Averages

2018

View full text Add to dashboard Cite

Consider using the simple moving average (MA) rule of Gartley (1935) to determine when to buy stocks, and when to sell them and switch to the risk-free rate. In comparison, how might the performance be affected if the frequency is changed to the use of MA calculations? The empirical results show that, on average, the lower is the frequency, the higher are average daily returns, even though the volatility is virtually unchanged when the frequency is lower. The volatility from the highest to the lowest frequency is about 30% lower as compared with the buy-and-hold strategy volatility, but the average returns approach the buy-and-hold returns when frequency is lower. The 30% reduction in volatility appears if we invest randomly half the time in stock markets and half in the risk-free rate. Keywords AbstractConsider using the simple moving average (MA) rule of Gartley (1935) to determine when to buy stocks, and when to sell them and switch to the risk-free rate. In comparison, how might the performance be affected if the frequency is changed to the use of MA calculations? The empirical results show that, on average, the lower is the frequency, the higher are average daily returns, even though the volatility is virtually unchanged when the frequency is lower. The volatility from the highest to the lowest frequency is about 30% lower as compared with the buy-and-hold strategy volatility, but the average returns approach the buy-and-hold returns when frequency is lower. The 30% reduction in volatility appears if we invest randomly half the time in stock markets and half in the risk-free rate.

show abstract

“… While Moskowitz et al () implement the momentum strategy with a short position, we follow Zakamulin () and Marshall et al () and substitute the short position with an allocation in cash. Thus, we implement the momentum strategies in the same way as our seasonality strategies, the moving average strategies, as well as our fundamental‐based predictive regression models.…”

mentioning

confidence: 99%

“…Thus, we implement the momentum strategies in the same way as our seasonality strategies, the moving average strategies, as well as our fundamental‐based predictive regression models. Marshall et al () additionally show the performance effects when the cash position is substituted with short sales in momentum and moving average strategies.…”

mentioning

confidence: 99%

Investing in the S&P 500 index: Can anything beat the buy‐and‐hold strategy?

Dichtl¹

2019

Rev Financ Econ

View full text Add to dashboard Cite

Determining whether investment strategies exist that provide higher (risk‐adjusted) returns than buying and holding the S&P 500 stock market index is not only highly relevant for finance theory, but also for the asset management industry. This study conducts a comprehensive test using realistic investment strategies based on monthly seasonalities, technical indicators, and fundamental factors (over 4,100 strategies in total). To assess statistical significance, we use Hansen's data‐snooping‐resistant SPA test. The results show that only investment strategies trying to exploit underreaction and overreaction effects with technical indicators dominate the buy‐and‐hold strategy in some simulation setups. These investment strategies are clearly superior to the strategies based on seasonalities and fundamental factors. Given that underreaction and overreaction effects are mainly justified with cognitive biases, our results support the economic relevance of behavioral finance insights.

show abstract

Time series momentum and moving average trading rules

Cited by 58 publications

References 29 publications

A technical approach to equity investing in emerging markets

A technical approach to equity investing in emerging markets

Simple Market Timing with Moving Averages

Investing in the S&P 500 index: Can anything beat the buy‐and‐hold strategy?

Contact Info

Product

Resources

About