Assesing Performance of Moving Average Investment Timing Strategy Over the UK Stock Market

2017 ◽  
Vol 51 (3) ◽  
pp. 349-362 ◽  
Author(s):  
Muhammad Ishfaq Ahmad ◽  
Wang Guohui ◽  
Muhammad Yasir Rafiq ◽  
Mudassar Hasan ◽  
Ata-Ul-Haq Chohan ◽  
...  
Keyword(s):  
Stock Market ◽  
Moving Average ◽  
Investment Timing ◽  
The Uk

Performance of Moving Average Investment Timing Strategy in UK Stock Market: Individual Stocks versus Portfolios

2018 ◽  
Vol 7 (2) ◽  
Author(s):  
Muhammad Ishfaq Ahmed ◽  
Wang Ghohui ◽  
Mudassar Hasan ◽  
Anika Sattar ◽  
Munib Ahmed ◽  
...  
Keyword(s):  
Stock Market ◽  
Moving Average ◽  
Investment Timing

Maximal Overlap Discrete Wavelet Transform, Graph Theory And Backpropagation Neural Network In Stock Market Forecasting

2018 ◽  
Vol 5 (1) ◽  
pp. 41-46
Author(s):  
Rosalina Rosalina ◽  
Hendra Jayanto
Keyword(s):  
Neural Network ◽  
Graph Theory ◽  
Wavelet Transform ◽  
Stock Market ◽  
Discrete Wavelet Transform ◽  
Stock Price ◽  
Moving Average ◽  
Discrete Wavelet ◽  
Stock Market Forecasting ◽  
Artificial Neural

The aim of this paper is to get high accuracy of stock market forecasting in order to produce signals that will affect the decision making in the trading itself. Several experiments by using different methodologies have been performed to answer the stock market forecasting issues. A traditional linear model, like autoregressive integrated moving average (ARIMA) has been used, but the result is not satisfactory because it is not suitable for model financial series. Yet experts are likely observed another approach by using artificial neural networks. Artificial neural network (ANN) are found to be more effective in realizing the input-output mapping and could estimate any continuous function which given an arbitrarily desired accuracy. In details, in this paper will use maximal overlap discrete wavelet transform (MODWT) and graph theory to distinguish and determine between low and high frequencies, which in this case acted as fundamental and technical prediction of stock market trading. After processed dataset is formed, then we will advance to the next level of the training process to generate the final result that is the buy or sell signals given from information whether the stock price will go up or down.

Does the UK Stock Market Overreact? Some Further Evidence for Stock Market Efficiency

2005 ◽  
Author(s):  
Arief Daynes ◽  
Panagiotis Andrikopoulos ◽  
David Latimer ◽  
Paraskevas Pagas
Keyword(s):  
Stock Market ◽  
Market Efficiency ◽  
Stock Market Efficiency ◽  
The Uk

scholarly journals Investigating the Links between UK House Prices and Share Prices with Copulas

2021 ◽  
Author(s):  
Rakesh K. Bissoondeeal ◽  
Leonidas Tsiaras
Keyword(s):  
Stock Market ◽  
House Prices ◽  
National Level ◽  
Tail Dependence ◽  
Share Prices ◽  
Property Prices ◽  
Left Tail ◽  
The Uk ◽  
The Relationship ◽  
Shared Characteristic

AbstractWe investigate the nonlinear links between the housing and stock markets in the UK using copulas. Our empirical analysis is conducted at both the national and regional levels. We also examine how closely London house prices are linked to those in other parts of the UK. We find that (i) the dependence between the different markets exhibits significant time-variation, (ii) at the national level, the relationship between house prices and the stock market is characterised by left tail dependence, i.e., they are more likely to crash, rather than boom, together, (iii) although left tail dependence with the stock market is a prominent feature of some regions, it is by no means a universally shared characteristic, (iv) the dependence between property prices in London and other parts of the UK displays widespread regional variations.

scholarly journals Machine learning applied in the stock market through the Moving Average Convergence Divergence (MACD) indicator

2020 ◽  
Vol 17 (4) ◽  
pp. 44-60
Author(s):  
Alberto Antonio Agudelo Aguirre ◽  
Ricardo Alfredo Rojas Medina ◽  
Néstor Darío Duque Méndez
Keyword(s):  
Machine Learning ◽  
Genetic Algorithms ◽  
Stock Market ◽  
Moving Average ◽  
Asset Price ◽  
Technical Analysis ◽  
Investment Strategies ◽  
Historical Data Analysis ◽  
Financial Asset Returns ◽  
Technical Indicator

The implementation of tools such as Genetic Algorithms has not been exploited for asset price prediction despite their power, robustness, and potential application in the stock market. This paper aims to fill the gap existing in the literature on the use of Genetic Algorithms for predicting asset pricing of investment strategies into stock markets and investigate its advantages over its peers Buy & Hold and traditional technical analysis. The Genetic Algorithms strategy applied to the MACD was carried out in two different validation periods and sought to optimize the parameters that generate the buy-sell signals. The performance between the machine learning-based approach, technical analysis with the MACD and B&H was compared. The results suggest that it is possible to find optimal values of the technical indicator parameters that result in a higher return on investment through Genetic Algorithms, beating the traditional technical analysis and B&H by around 4%. This study offers a new insight for practitioners, traders, and finance researchers to take advantage of Genetic Algorithms for trading rules application in forecasting financial asset returns under a more efficient and robust methodology based on historical data analysis.

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