Moving Average Trading Rules for NASDAQ Composite Index

Chen, Chien-Ping

doi:10.24135/afl.v5i2.54

Cited by 4 publications

(11 citation statements)

References 24 publications

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“…Importantly, there are over 5000 stock indices in the US among which, the NASDAQ Composite Index (Figure 1) is one of them (Chen, 2021). According to Duarte et al (2011), the NASDAQ (National Association of Securities Dealers Automated Quotation) Stock Market was founded in 1971 and was the world's first electronic stock market.…”

Section: The Nasdaq Composite Indexmentioning

confidence: 99%

Does Spatial Autocorrelation Hold for the Stock Market Application to the NASDAQ Composite Index

Karunamuni

2024

Preprint

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This study examines the spatial distribution of companies listed in the NASDAQ Composite Index from 2010 to 2019. The aim of this study is to understand how Tobler's First Law of Geography is reflected on the spatial pattern of headquarters of companies where their stock prices increased or decreased. The first research question is (1) if there is clustering of publicly traded companies (headquarters) associated with growth stocks and publicly traded companies associated with non-growth stocks in the mainland US. The second research question is (2) where are hotspots located in the US on a State-level? The values of daily mean change in closing price that was greater than 0 was considered a growth stock and a value less than 0 was considered a non-growth stock for this research. A test for spatial autocorrelation (Global Moran's I) was done using R, and local spatial autocorrelation (Getis Ord Gi*) was conducted to find hot spots using R with a distance threshold of 2000 km. Of 1496 stocks, 1028 stocks grew while 468 did not. The Global Moran's I for growth stocks is -0.0016 and 0.0001 in non-growth stocks. In addition, at a 2000 km threshold, California is the only hot spot in the US. Therefore, there was no spatial autocorrelation of publicly traded companies from 2010 to 2019. Spatial Autocorrelation in this study for the 1496 stocks has provided evidence that there is randomness of company locations in relation to the stock performance. The NASDAQ is a formidable marker of stock performance from 2010 to 2019 and location of the company relative to the stock is not an influence of how well the stock performs. Therefore, this study delivers empirical evidence based on the NASDAQ to confirm Tobler's second law of geography. Another possible theorization is that artificial features in space may not be related to one another either nearer or more distant and further research on man-made phenomena could be studied in future research.

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Section: The Nasdaq Composite Indexmentioning

confidence: 99%

Does Spatial Autocorrelation Hold for the Stock Market Application to the NASDAQ Composite Index

Karunamuni

2024

Preprint

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“…The investment strategies be mainly discussed in this article is Moving Average Crossover Strategy. The Moving Average (MA) is a common tool in technical analysis [3]. As a very simple mathematical model, the moving average is widely used in the analysis of financial markets such as stocks, futures, and foreign exchange.…”

Section: Moving Average Crossover Strategymentioning

confidence: 99%

“…It is simple, effective, and widely used. On the other hand, many other technical indicators are also based on moving averages, such as the famous MACD and EMA [3,4]. Therefore, moving averages occupy an important position in the field of technical analysis.…”

Section: Moving Average Crossover Strategymentioning

confidence: 99%

Research on Trend Investing Strategy and Hedging Strategy

Feng

2023

AEMPS

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An investment method based on traditional subjective judgments faces huge challenges, while the strategy of quantitative investment is to find the optimal alpha model to judge the best timing of selling and buying and designing the most effective investment portfolio. Therefore, compared with traditional investment methods, quantitative investment breaks through the limitations of subjective judgment with its qualitative and quantitative advantages. The futures market attracts many investors with its low transaction costs and the two-way nature of transactions. However, most of the investment strategies that are frequently used in the market have low stability and do not guarantee correlation with the general market trend. This makes the investments risky, particularly for individual investors. Therefore, this paper will explore both futures investment and risk hedging strategies, aiming to ensure that every buy and sell is the correct choice as much as possible, such that futures investment is relatively stable and has high returns. This article focuses on using the moving average cross strategy through the python programming language to achieve the judgment of buying and selling in the process of futures trading and drawing the net worth curve of the real-time futures market data and perform a correlation analysis to determine which commodity sector is the most suitable using the trend strategy. For risk hedging, the article mainly uses the correlation analysis to study the relation between the prices of different types of futures. Furthermore, the futures and options market price fluctuations are discussed in depth, and also analysed is how to use and balance hedge parameters for risk hedging.

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“…To assess whether the markets are at least weak form efficient, numerous studies have investigated whether investors can apply the simple moving average trading rules to gain abnormal profits. See for reference : Bessembinder, and Chan (1998), Brock, Lakonishok, and LeBaron (1992), Hudson, Dempsey, and Keasey (1996), Kwon, and Kish (2002), Metghalchi, Chang, and Du (2011), Metghalchi, Chen, and Hajilee (2016), and Yu, Nartea, Gan, and Yao (2013).…”

Section: Introductionmentioning

confidence: 99%

“…To test whether abnormal profits exist, three T-tests for the following hypotheses were tested (see Brock, et al, 1992, and numerous articles by Metghalchi, et al, 2011Metghalchi, et al, , 2016Metghalchi, et al, , 2018Metghalchi, et al, , and 2019.…”

Section: Introductionmentioning

confidence: 99%