Random Walk, Ergodicity versus Predictability – The Case of the Budapest Stock Exchange

Bélyácz, Iván; Zsolt, Nagy Bálint

doi:10.13189/aeb.2016.040403

Cited by 2 publications

(2 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kiragu and Mung'atu (2016) found generalized Pareto better fitted for daily returns from the Nairobi securities exchange. Iván and Zsolt (2016) found BUX daily return followed non-normality with flat-tailed. Corlu et al (2016) found generalized lambada better fitted than the normal distribution for daily returns from nineteen stock markets around the world.…”

Section: Introductionmentioning

confidence: 92%

Testing Normality for Daily Returns from the Nepalese Stock Market

Vaidya

Sharma

Dangol

2022

JBMR

View full text Add to dashboard Cite

Researchers and investors are interested in the normality of the return from the stock market and the establishment of the Efficient Market Hypothesis (EMH). Nevertheless, a statistical distribution of the normality of the return helps investors predict the stock market return with the help of the basic mean and standard deviation values of the return. Hence, the paper tested the normality of the daily returns from the Nepalese stock market, Nepal Stock Exchange Limited (NEPSE). The paper followed the statistical results and data visualization to determine the normality of the stock market return. The data visualization and statistical results have shown that the daily return from the NEPSE follows a normal distribution. The test statistics for the normality of the data also show a normal distribution for the NEPSE daily return. Similarly, the parameters for the fitted distribution also reflect normality. The daily transaction volume at the stock market normally leads the daily stock market return in normality as well. The fitting of a normal distribution for the daily returns reflects that the Nepalese investors could predict market risk and return using two statistical parameters, i.e., standard deviation and mean, respectively.

show abstract

Section: Introductionmentioning

confidence: 92%

Testing Normality for Daily Returns from the Nepalese Stock Market

Vaidya

Sharma

Dangol

2022

JBMR

View full text Add to dashboard Cite

show abstract

“…If technical analysis functions as a trading strategy for entry and exit, Machine Learning (ML) LSTM model can act as affirmative support for stock options. However, [27] research concludes that short-term predictability and forecast are impossible; the more time elapses between the appearance of choice and its consequences, the more certain it is for individuals to assume that their decisions must be made under ambiguous conditions circumstances.…”

Section: Introductionmentioning

confidence: 99%

Decision-Making Techniques using LSTM on Antam Mining Shares before and during the COVID-19 Pandemic in Indonesia

Badri

Heikal

Terah³

et al. 2021

ATM

View full text Add to dashboard Cite

Stocks, apart from having volatile and chaotic characteristics, also have various kinds of noise, non-linear and non-stationary movements, making them difficult to predict accurately. Therefore, the risk of investing in stocks depends on the skills of investors or traders in making judgments and decisions. This study aims to use Long Short-Term Memory (LSTM) as a decision-making technique with historical stock prices as the sole predictor, then implement it in conditions before and during the COVID-19 pandemic. The study results concluded that Long Short-Term Memory (LSTM) could be used as a decision-making technique in conditions before and during the COVID-19 pandemic with historical price inputs as the sole predictor. Based on the research that has been done, the following conclusions can be drawn: The LSTM model can predict stock prices well using historical stock prices as the sole predictor. The LSTM model can be used as a trading decision-making technique for day traders. The risk of stock prediction using the LSTM method in 2019 before the COVID pandemic was proven to be lower than in 2020 during the COVID pandemic. For further research, researchers can conduct more in-depth research on the risk criteria for making trading decisions as an essential reference that can be used to select the LSTM model.

show abstract

Random Walk, Ergodicity versus Predictability – The Case of the Budapest Stock Exchange

Cited by 2 publications

References 63 publications

Testing Normality for Daily Returns from the Nepalese Stock Market

Testing Normality for Daily Returns from the Nepalese Stock Market

Decision-Making Techniques using LSTM on Antam Mining Shares before and during the COVID-19 Pandemic in Indonesia

Contact Info

Product

Resources

About