Effect of Moving Averages in the Tickwise Tradings in the Stock Market

2006 ◽  
pp. 647-654
Author(s):  
Felix Streichert ◽  
Mieko Tanaka-Yamawaki ◽  
Masayuki Iwata
Keyword(s):  
Stock Market ◽  
Moving Averages

Is Technical Analysis Profitable even for an Amateur Investor?

2015 ◽  
pp. 255-269
Author(s):  
Vasileiou Evangelos
Keyword(s):  
Stock Market ◽  
Transaction Costs ◽  
Behavioral Finance ◽  
Investment Decisions ◽  
Technical Analysis ◽  
Moving Averages ◽  
Trading Rules ◽  
Trading Decisions ◽  
Transaction Fees

The purpose of this chapter is to examine if even the simplest trading rules could take advantage of the market's inefficiency and lead to profitable trading decisions. For this reason, this study examined the profitability of the simplest trading rules, using only the simple moving averages (SMA) rules that even an amateur investor could apply. In order to examine the specific issue a data sample from the Greek stock market during the period 2002-12 was used. The results suggest that even if one takes into account the most expensive transaction fees, the trading rules signal profitable investment decisions; therefore, even an amateur trader and/or investor who does not have a significant amount of money to invest (which may lead to reduced transaction costs) could take advantage of the market's inefficiency. Behavioral finance theories may provide some useful and alternative explanations regarding some of the reasons that contribute to the Greek stock market's inefficient environment.

Stock market stress from the central counterparty’s perspective

2019 ◽  
Vol 36 (1) ◽  
pp. 51-62
Author(s):  
Barbara Dömötör ◽  
Kata Váradi
Keyword(s):  
Risk Management ◽  
Stock Market ◽  
Stress Measurement ◽  
Risk Measure ◽  
Moving Averages ◽  
Stress Indicators ◽  
Content Type ◽  
Stress Signals ◽  
Relative Spread ◽  
Extreme Returns

Purpose The purpose of this paper is to investigate the possibility of monitoring stress on stock markets from the perspective of a central counterparty (CCP). Due to their balanced positions, CCPs are exposed to extreme price movements in both directions; thus, the major risk for them derives from extreme returns and market illiquidity. The authors examined the connection of the stress alarms of return- and liquidity-based measures to find an objective basis for stress measurement. Design/methodology/approach The authors defined two types of stress measures: indicators based on extreme returns and liquidity. It is suggested that the stress indicators should be based on the existing risk management methodology that examines different risk measure oversteps. The stress signals of the past nine years on the German stock market were analyzed. The authors investigated the connection between the chosen stress measures to obtain a robust measure for alarming stress. Findings Although extreme returns and illiquidity are both characteristics of stress, the correlation of returns- and liquidity-based stress indicators is low when taking daily values. On the other hand, the moving averages of the indicators correlate significantly in the case of measures of downward and upward extreme returns and liquidity measured by the relative spread. The results are robust enough to be used for monitoring stress periods. Originality/value This paper contributes to understanding the characteristics of stress periods and points to the fact that stress signals measured by different aspects can also differ within the same asset class. The moving averages of returns- and relative spread-based indicators, however, could provide a cost-effective quantitative support for the risk management of a CCP and make the margin calculation predictable for clearing members as well.

Prediction of Trends in Stock Market using Moving Averages and Machine Learning

2021 ◽  
Author(s):  
Shoban Dinesh ◽  
Nithin Rao R ◽  
S P Anusha ◽  
Samhitha R
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Moving Averages

The Predictability and Profitability of Simple Moving Averages and Trading Range Breakout Rules in the Pakistan Stock Market

2020 ◽  
Vol 23 (01) ◽  
pp. 2050001
Author(s):  
Salma Khand ◽  
Vivake Anand ◽  
Mohammad Nadeem Qureshi
Keyword(s):  
Stock Market ◽  
Transaction Costs ◽  
Empirical Evidence ◽  
Predictive Power ◽  
Moving Averages ◽  
Sample Period ◽  
Excess Returns ◽  
Full Sample ◽  
Fixed Length

This paper inspects whether variable- and fixed-length moving averages (VMA and FMA), and trading range breakout (TRB) rules have prognostic capability and can earn profits superior to buy-and-hold plan, when applied on KSE-100 index of Pakistan stock market during the full sample period January 1, 1997 to December 31, 2013. Full sample results provided empirical evidence for VMA rule that it has significant predictive power and is able to generate profits superior to simple buy-and-hold plan even after inclusion of transaction costs. The highest mean buy returns yielded by VMA, FMA and TRB rules are seen in noncrises periods. The overall implication of this study is that traders in the Pakistan stock market can utilize this information to obtain excess returns on a regular basis.

scholarly journals Accuracy of Moving Average Forecasting for NEPSE

2020 ◽  
Vol 13 (1) ◽  
pp. 62-76
Author(s):  
Rashesh Vaidya
Keyword(s):  
Stock Market ◽  
Graphical Representation ◽  
Moving Average ◽  
Annual Reports ◽  
Percentage Error ◽  
Moving Averages ◽  
Short Term ◽  
Time Duration ◽  
The Future

A simple moving average is one of the oldest and the simplest techniques of forecasting the trends of the stock market. The technical analysts follow mainly three types of moving averages, namely; simple, weighted, and exponential moving averages. Among these three types, as per the interest of investors, short-term and long-term time duration is used to calculate the trend using the moving average. All the mentioned moving averages are used by investors or analysts to predict the future trends of the market using historical data. Hence, for evaluating their forecasting accuracy, the paper has used both the short-term and the long-term moving average. The paper has used the NEPSE (closing) index values to calculate as well as plotted the moving averages to forecast the future trend and its accuracy with the help of Mean Absolute Percentage Error (MAPE). The paper found that there is a better crossover in the graphical representation of the moving average in the long-term moving average. In context to the Nepalese stock market, the MAPE results reflected a weekly (5-trading days) 5-SMA analysis of the market movement as the most relevant in short-term forecasting. Similarly, using the technique of moving average, 200-SMA (200-trading days of a year) was seen as the most effective to forecast long-term trends. The result of the long-term moving average MAPE pointed out that the annual reports of the listed companies better determine the trend of the market.

scholarly journals Profitability of The Moving Averages Technical Trading Rules in an Emerging Stock Market-A Study of Individual Stocks Listed On Pakistan Stock Exchange

2018 ◽  
Vol 14 (2) ◽  
pp. 67-76
Author(s):  
Muhammad Arif ◽  
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Exchange ◽  
Moving Average ◽  
Market Development ◽  
Moving Averages ◽  
Technical Trading ◽  
Technical Trading Rules ◽  
Trading Rules ◽  
Portfolio Returns

This paper investigates the gainfulness of moving averages (MA) timing method over the purchase and hold procedure for single stocks deal in Pakistan Stock Exchange. We used (Han et al., 2013) approach of single stock returns and indeterminate evidence of MA timing methodology insightful ability to increase higher returns over the strategy of purchase and hold. In addition, we report market risk-adjusted returns to expel any market development impacts and apply elective moving averages lag lengths to check the robustness of our outcomes. We look at that individual stock returns are noisier than portfolio returns and the fundamental technical exchanging principle of moving average don't be able to anticipate single stock returns. We propose the utilization of more perplexing trading rules in future investigations to determine the gainfulness of technical trading rules in individual stocks.

Further Mining the Predictability of Moving Averages: Evidence from the US Stock Market

2017 ◽  
Vol 19 (2) ◽  
pp. 413-433 ◽  
Author(s):  
Chaoqun Ma ◽  
Danyan Wen ◽  
Gang‐Jin Wang ◽  
Yong Jiang
Keyword(s):  
Stock Market ◽  
Moving Averages ◽  
The Us

Predicting Stock Market Performance

2020 ◽  
pp. 1-10
Author(s):  
Thomas Plieger ◽  
Thomas Grünhage ◽  
Éilish Duke ◽  
Martin Reuter
Keyword(s):  
Stock Market ◽  
Risk Taking ◽  
Life Stress ◽  
Price Volatility ◽  
Individual Characteristics ◽  
Self Report ◽  
Financial Behavior ◽  
Market Simulation ◽  
Stock Market Performance ◽  
Risk Taking Behavior

Abstract. Gender and personality traits influence risk proneness in the context of financial decisions. However, most studies on this topic have relied on either self-report data or on artificial measures of financial risk-taking behavior. Our study aimed to identify relevant trading behaviors and personal characteristics related to trading success. N = 108 Caucasians took part in a three-week stock market simulation paradigm, in which they traded shares of eight fictional companies that differed in issue price, volatility, and outcome. Participants also completed questionnaires measuring personality, risk-taking behavior, and life stress. Our model showed that being male and scoring high on self-directedness led to more risky financial behavior, which in turn positively predicted success in the stock market simulation. The total model explained 39% of the variance in trading success, indicating a role for other factors in influencing trading behavior. Future studies should try to enrich our model to get a more accurate impression of the associations between individual characteristics and financially successful behavior in context of stock trading.

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