scholarly journals Implementation of Fibonacci Retracements and Exponential Moving Average (EMA) Trading Strategy in Indonesia Stock Exchange

2021 ◽  
Vol 6 (4) ◽  
pp. 402-408
Author(s):  
Lusindah Lusindah ◽  
Erman Sumirat
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Stock Exchange ◽  
Moving Average ◽  
Time Frame ◽  
Trading Strategy ◽  
Individual Investors ◽  
Individual Investor ◽  
Back Testing ◽  
The Individual

Based on KSEI statistic data on March 2021, IDX individual stock market investor is increasing 199% compared to 2018 becoming 4,848,954 number of investors. 56.9% population of the individual investor is having ages that less than 30 years. In the period where IDX was bullish in November 2020 - January 2021, there is a phenomenon where stocks influencers appeared in social media and impacted to the stock price movement after the announcement is done by the influencer. In contrary, during bearish and sideways condition, those influencers were gone and changed with bad news that went viral where many individual investors are lost their capital in IDX. They lose money since they are gambling in the stock market without any analysis and no establishment of trading plan. This research is aimed as a strategy to individual investors in IDX to implement trading strategy based on Fibonacci retracements and projections, EMA lines, trendlines, stochastic, and volume. Back testing is conducted in IDX SMC Liquid index constituents during January 2018 until December 2020 period. By implementing this trading strategy, return generated is 164% for 3 years trading time frame. Author also found that this trading strategy is effective in bullish trend condition especially for individual investors that have long position.

Survey of Trading Systems for Individual Investors

2011 ◽  
pp. 157-176 ◽  
Author(s):  
M. Kersch ◽  
G. Schmidt
Keyword(s):  
Financial Markets ◽  
Stock Exchange ◽  
Point Of View ◽  
Individual Investors ◽  
Individual Investor ◽  
Trading System ◽  
Trading Systems ◽  
Trading Decisions ◽  
The Individual

Trading decisions in financial markets can be supported by the use of trading algorithms. To evaluate trading algorithms and to generate orders to be executed on the stock exchange trading systems are used. In this chapter, we define the individual investors’ requirements on a trading system, and analyze 17 trading systems from an individual investor’s point of view. The results of our study point out that the best alternative for an individual investor is not one single trading system, but a combination of two different classes of trading systems.

scholarly journals Factors influencing Individual Investor Behaviour: Evidence from the Kuwait Stock Exchange

2019 ◽  
Vol 15 (3) ◽  
pp. 27 ◽  
Author(s):  
Sadeq J. Abul
Keyword(s):  
Psychological Factors ◽  
Stock Exchange ◽  
Individual Investors ◽  
Individual Investor ◽  
Factors Influencing ◽  
Risk Appetite ◽  
Kuwait Stock Exchange ◽  
Herd Behaviour ◽  
Investor Behaviour ◽  
The Individual

This study investigates the effects of psychological factors on investor behaviour regarding the Kuwait Stock Exchange (KSE). These psychological factors are, namely: excessive optimism vs pessimism, herd behaviour and risk appetite. The data for this study obtained from KSE and a survey of a random sample of 398 individual investors. By using qualitative analysis and based on the theory of behavioural finance, the study findings show that herd behaviour, optimism and psychology risk have an impact on the individual investors’ decisions. However, we did not find any evidence of overconfidence behaviour’s effects on investors’ decisions. To our knowledge, KSE has been examined by several researchers without taking into consideration the effects of psychological factors on individual investor decisions. This study finds that psychological factors play a significant role in individual investors’ decisions regarding KSE. This study might contribute positively to the development of this field of research in (KSE).

scholarly journals Determinants of Investment Decisions: A Study of Individual Investors on the Zimbabwe Stock Exchange

2020 ◽  
Vol 7 (5) ◽  
pp. 38
Author(s):  
Peter Hunguru ◽  
Vusumuzi Sibanda ◽  
Ruramayi Tadu
Keyword(s):  
Decision Making ◽  
Stock Exchange ◽  
Investment Decision ◽  
Investment Decisions ◽  
Positive Association ◽  
Individual Investors ◽  
Individual Investor ◽  
Behavioural Factors ◽  
The Individual ◽  
Regret Aversion

This study investigated factors that inform individual investors in their decision-making on the Zimbabwe Stock Exchange. The main objective was to identify and assess the effect of the behavioural factors on investment decisions of individual investors. A quantitative survey of 291 randomly selected individual Zimbabwe Stock Exchange investors was conducted. Multiple regression analysis was used to calculate the correlation coefficient of behavioural factors and investment decision while correlation analysis was used to measure the strength of the relationship between the independent variables. The findings of the study established that the predictor variables had a strong positive association between them and individual investor decision at a significant level of 0.01 and 0.005. The findings of the study revealed that individual investor decisions are influenced by the behavioural factors which are; anchoring, availability, gambler’s fallacy, overconfidence, herding, loss aversion, mental accounting, regret aversion and representativeness. The study recommends the need for improved information on the stock markets dynamics as well as training on investor awareness programmes to support the decision-making abilities of the individual investors on the ZSE to fully play its rightful role in the development of the economy.

scholarly journals Determining the Dependency Pattern of Daily Change (increase or decrease) of Dhaka Stock Exchange Index (DSEX) in Bangladesh by Markov Chain and Logistic Regression Model

2019 ◽  
Vol 8 (4) ◽  
pp. 5225-5229
Keyword(s):  
Logistic Regression ◽  
Markov Chain ◽  
Stock Price ◽  
Stock Exchange ◽  
Securities And Exchange Commission ◽  
Individual Investors ◽  
Corporate Managers ◽  
Time Period ◽  
Order Markov Chain ◽  
The Individual

Bangladesh's capital market is South Asia's third largest market with two stock exchanges controlled by the Securities and Exchange Commission (SEC), namely Dhaka stock exchange (DSE) and Chittagong stock exchange (CSE). DSE introducing DSE broad index (“DSEX”) and DSE 30 index (“DSE30”) that effect from 2013. In this study, DSEX which reflect 97% of the total equity market capitalization is considered and focused the effect of change of index on market investment. The logistic regression (LR) model is performed in conjunction with the Markov chain (MC) of different order to represent the dependence of change (increase or decrease) of the current index upon the change of the previous two-time period. It was shown that the increased index day of the preceding two-time period relative to the decreased index day of the preceding two-time period affects the increased index day of the present time period. We observed a dependency of increase-decrease index of spell for the occurrence of index in the Dhaka stock exchange from the period of 28/01/2013 to 30/04/2019. The result shows that the frequency of index shift follows a second order Markov chain and logistic regression suggests that decrease index day of index followed by decreased and increase day of index followed by increased is more likely for the index of Dhaka stock exchange. This research helps to the individual investors in predicting the next day’s stock price based on the prior two days index price. This study also contributes to researchers, corporate managers and other personnel to determine the dependency pattern of stock price.

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.

scholarly journals Disposition Effect and Return Predictability in the Tunisian Stock Market

2017 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Cheïma Hmida ◽  
Ramzi Boussaidi
Keyword(s):  
Stock Market ◽  
Behavioral Finance ◽  
Stock Prices ◽  
Stock Price ◽  
Return Predictability ◽  
Capital Gains ◽  
Disposition Effect ◽  
Individual Investors ◽  
Long Run ◽  
Return Reversal

The behavioral finance literature has documented that individual investors tend to sell winning stocks more quickly than losing stocks, a phenomenon known as the disposition effect, and that such a behavior has an impact on stock prices. We examined this effect in the Tunisian stock market using the unrealized capital gains/losses of Grinblatt & Han (2005) to measure the disposition effect. We find that the Tunisian investors exhibit a disposition effect in the long-run horizon but not in the short and the intermediate horizons. Moreover, the disposition effect predicts a stock price continuation (momentum) for the whole sample. However this impact varies from an industry to another. It predicts a momentum for “manufacturing” but a return reversal for “financial” and “services”.

scholarly journals Influencing Factors of Stock Price in Nepal

NCC Journal ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 113-120
Author(s):  
Krishna Bahadur Thapa
Keyword(s):  
Interest Rate ◽  
Stock Market ◽  
Influencing Factors ◽  
Stock Price ◽  
Stock Exchange ◽  
Positive Association ◽  
Share Price ◽  
Financial Statement ◽  
Inverse Association ◽  
Significant Positive Association

This paper explores the influencing factors of stock price in Nepal (with reference to Nepalese commercial banks) listed on the Nepal Stock Exchange Ltd. over the period of 2008 to 2018AD. The information were collected from questionnaire and financial statement of concerned organizations and analyzed using simple linear regression model. The conclusions of the work revealed that earning per share (EPS), dividend per share (DPS), effective rules and regulations, market whims and rumors, company profiles and success depend upon luck have the significant positive association with share price while interest rate (IR) and price to earnings ratio (PER), showed the significant inverse association with share price. Further, accessibility of liquidity, fundamental and technical analysis stimulates the performance of the Nepalese stock market. More importantly, stock market has been found to respond significantly to changes in dividend and interest rate.

scholarly journals Directional Movement Distribution in the Bitcoin Markets

2020 ◽  
Vol 31 (2) ◽  
pp. 188-196 ◽  
Author(s):  
Beata Szetela ◽  
Grzegorz Mentel ◽  
Urszula Mentel ◽  
Yuriy Bilan
Keyword(s):  
Stock Exchange ◽  
Time Frame ◽  
Wilcoxon Test ◽  
Downward Trend ◽  
Virtual Currency ◽  
Substantial Impact ◽  
Return To Equilibrium ◽  
Directional Movement ◽  
Significant Difference ◽  
The Individual

The crypto exchanges operate primarily on the internet, where the speed of information spreading is significant. Therefore, it is expected that there should be no significant differences among the individual exchanges concerning the same asset being traded. Prices should quickly reach comparable values on all stock exchanges, and they should return to equilibrium in a relative time frame. Hence, the investors, while making decisions on the selection of a cryptocurrency market, should be guided primarily by the exchange security considerations, its flexibility, availability of a product offer, and costs of order processing. The work aims to check whether virtual currency exchanges differ from each other in the context of directional movement, both in an upward and downward trend. To achieve the objective of the paper, we used Directional Movement Index, supported by the Directional Indicators, to compare the distribution of the strength of the directional movement across three different cryptocurrency exchanges (Bitstamp, Coinbase, Kraken) within the up and the downward price movement phase. The comparison is made based on the results of the non-parametrical tests such as Wilcoxon test, Hodges Lehmann test, Ansari-Bradley test, and Conover test. The results show that theoretically, the choice of a cryptocurrency exchange in an upward trend will cause no significant difference for an investor and its strategy. However, the choice of a stock exchange in a downward trend may have a substantial impact on the rates of return.

scholarly journals A Predictive Meta Model for Forecasting Stock Price using Time Series Data

2021 ◽  
Vol 10 (1) ◽  
pp. 345-356
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Price ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Meta Model ◽  
Price Range ◽  
Stock Traders ◽  
The Right

Stock market prediction through time series is a challenging as well as an interesting research areafor the finance domain, through which stock traders and investors can find the right time to buy/sell stocks. However, various algorithms have been developed based on the statistical approach to forecast the time series for stock data, but due to the volatile nature and different price ranges of the stock price one particular algorithm is not enough to visualize the prediction. This study aims to propose a model that will choose the preeminent algorithm for that particular company’s stock that can forecastthe time series with minimal error. This model can assist a trader/investor with or without expertise in the stock market to achieve profitable investments. We have used the Stock data from Stock Exchange Bangladesh, which covers 300+ companies to train and test our system. We have classified those companies based on the stock price range and then applied our model to identify which algorithm suites most for a particular range of stock price. Comparative forecasting results of all algorithms in diverse price ranges have been presented to show the usefulness of this Predictive Meta Model

In Search of Stock Market Proxy to Calculate Systematic Risk (Beta) of Stocks in Indonesia Stock Exchange

2020 ◽  
Vol 29 (2) ◽  
pp. 80-88
Author(s):  
Mochammad Chabachib
Keyword(s):  
Stock Market ◽  
Real World ◽  
Stock Price ◽  
Price Index ◽  
Stock Exchange ◽  
The Other ◽  
Stock Index ◽  
Market Return ◽  
Stock Market Return ◽  
Stock Index Returns

The calculation of beta stock in Indonesia is still debatable to this day. Though many researchers who have used sophisticated methods mathematically, the assumptions applied in developing the methods are impossible to happen in the real world, such as the ability of stock market return the day after (lead) affects the market return today. This study was conducted to assess the stock price index in Indonesia Stock Exchange that can be used as a proxy of stock market in Indonesia. The results of this study showed that there was a gap between beta stocks counted with JCI return as a market proxy with beta stocks counted with index returns of LQ-45, SRI-KEHATI, PEFINDO-25, BISNIS-27, IDX-30 and KOMPAS-100. This study has also found that the beta counted by using KOMPAS-100 return produced the smallest standard error of the estimate (SEE) that it was more applicable compared to the other stock index returns.

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