scholarly journals IS TECHNICAL ANALYSIS PROFITABLE AND CAPABLE FOR STOCK PRICE PREDICTION? EVIDENCE FROM MALAYSIAN STOCK MARKET

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Kelvin Lee Yong MIng ◽  
Mohamad Jais ◽  
Bakri Abdul Karim
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Stock Return ◽  
Moving Average ◽  
Fibonacci Numbers ◽  
Technical Analysis ◽  
Least Square ◽  
Ordinary Least Square ◽  
Stock Price Prediction ◽  
The Mean

This study aims to test the ability of technical analysis in predicting the stock price and generating profits. This study employed two of the technical analysis indicators, which are (i) Variable Moving Average (VMA) rules and (ii) Elliot Wave Principle incorporated with Fibonacci numbers. Besides that, this study also examines the relationship between the signals emitted by VMA rules and the stock return by applying Ordinary Least Square (OLS) regression analysis. Among the 42 VMA rules tested, there were only 10 VMA rules shown that the mean returns generated from buy signals are significant higher than the unconditional return. While, the mean returns from sell signals are significant lesser than the unconditional return for all the VMA rules tested. As for Elliot Wave Principle incorporated with Fibonacci numbers indicator, the findings shows that impulsive wave is predictable, meanwhile the corrective wave is less predictable. Lastly, only the signals of 14 VMA rules had shown a significant relationship with the daily stock return. In conclusion, the VMA rules only able to generate profits for certain term of moving average, whereas the Elliot Wave Principle incorporated with Fibonacci numbers tools is useful in predicting the stock market trend.

scholarly journals A Study on Stock Price Prediction using LSTM Model

2021 ◽  
Vol 9 (VI) ◽  
pp. 2618-2622
Author(s):  
Koushal Saini
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Time Series Data ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Trend Detection ◽  
Series Data ◽  
Future Price ◽  
Stock Price Prediction

Predicting stock price of any stock is a challenging task because the Volatility of stock market the nature of stock price is dynamic, chaotic, noisy and sometimes totally unexpected. The other most difficult task is to analyze and decide financial time series data that improves investment returns and help in minimizing losses. Technical analysis is a method that help in analyzing a stock and predict its future price via evaluating securities. There are already many Indicators and other tools for technical analysis in stock market. Some famous indicators such as SMA (Simple Moving Average), EMA (Exponential Moving Average), WMA (Weight Moving Average), VWMA (Volume Weight Moving Average), DEMA (moving averages), MACD (Moving Average Convergence/Divergence), ADX (Average Di- reactional Movement Index), TDI (Trend Detection Index), Arun, VHF (trend indicators), stochastic, RSI (Relative Strength Index), SMI(Stochastic Momentum Index, volume indicators are also available for technical analysis. Here, we have used the LSTM Model to predict future price of some big companies of stock market in NSE.

scholarly journals Faktor-Faktor Yang Mempengaruhi Pergerakan Indeks Harga Saham Sektoral Pertanian di Bursa Efek Indonesia Periode 2014-2018

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Chikal Galih ◽  
Lies Sulistyowati
Keyword(s):  
Stock Price ◽  
Stock Return ◽  
Price Index ◽  
Composite Index ◽  
Least Square ◽  
Public Companies ◽  
Ordinary Least Square ◽  
Stock Investment ◽  
Index Stock ◽  
Stock Price Index

Indeks Harga Saham Gabungan (IHSG) adalah salah satu indikator perkembangan investasi saham di Indonesia, di mana ada indeks sektor yang mewakili perusahaan publik, salah satu indeks sektoral adalah Indeks Harga Saham Sektoral (IHSS) Pertanian. Fenomena yang terjadi pada periode 2014-2018 adalah tingkat pengembalian investasi di IHSS Pertanian menjadi yang terburuk dibandingkan dengan IHSG dan sektor lainnya sebesar -33,47%. Tujuan dari penelitian ini adalah untuk mengidentifikasi faktor-faktor yang mempengaruhi pergerakan IHSS Pertanian periode 2014 hingga 2018 secara bulanan. Analisis yang digunakan adalah analisis Ordinary Least Square (OLS) untuk mengidentifikasi faktor-faktor yang mempengaruhi pergerakan IHSS Pertanian. Hasil penelitian menunjukkan bahwa inflasi, nilai tukar USD/IDR, suku bunga bank sentral, IHSG, harga minyak kelapa sawit, dan harga emas berpengaruh signifikan terhadap pergerakan IHSS Pertanian dengan nilai pengaruh 88,6%.Kata Kunci: Indeks Harga Saham Sektoral Pertanian, Return Saham, Makroekonomi, Ordinary Least Square (OLS)AbstractJakarta Composite Index (IHSG) is an indicator of the development of stock investment in Indonesia, where there are indices of sectors that represent public companies, one of the sectoral indices is the Sectoral Stock Price Index (IHSS) of Agriculture. The phenomenon that occurred in the 2014-2018 period was the level of investment return in the IHSS of Agriculture being the worst compared to the IHSG and other sectors by -33.47%. The purpose of this study is to identify the factors that influence the movement of IHSS of Agriculture for the period of 2014 up to 2018 on monthly base. The analysis used is Ordinary Least Square (OLS) analysis to identify the factors that influence the movement of IHSS of Agriculture. The results showed that inflation, USD/IDR exchange rate, central bank interest rate, IHSG, palm oil prices, and gold prices significantly influence the movement of IHSS of Agriculture with an influence value of 88.6%. Keywords: Agricultural Sectoral Stock Price Index, Stock Return, Macroeconomics, Ordinary Least Square (OLS).

scholarly journals Stock Price Prediction using Technical Indicators: A Predictive Model using Optimal Deep Learning

2019 ◽  
Vol 8 (2) ◽  
pp. 2297-2305
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Stock Price ◽  
Short Term Memory ◽  
Technical Analysis ◽  
Learning Task ◽  
Correlation Tensor ◽  
Technical Indicators ◽  
Stock Price Prediction ◽  
Proposed Model

The stock market is highly volatile and complex in nature. Technical analysts often apply Technical Analysis (TA) on historical price data, which is an exhaustive task and might produce incorrect predictions. The machine learning coupled with fundamental and / or Technical Analysis also yields satisfactory results for stock market prediction. In this work an effort is made to predict the price and price trend of stocks by applying optimal Long Short Term Memory (O-LSTM) deep learning and adaptive Stock Technical Indicators (STIs). We also evaluated the model for taking buy-sell decision at the end of day. To optimize the deep learning task we utilized the concept of Correlation-Tensor built with appropriate STIs. The tensor with adaptive indicators is passed to the model for better and accurate prediction. The results are analyzed using popular metrics and compared with two benchmark ML classifiers and a recent classifier based on deep learning. The mean prediction accuracy achieved using proposed model is 59.25%, over number of stocks, which is much higher than benchmark approaches.

scholarly journals SMOOTHING TECHNIQUES FOR MARKET FLUCTUATION SIGNALS

2011 ◽  
Vol 12 (1) ◽  
pp. 63-74
Author(s):  
Audrius Dzikevičius ◽  
Svetlana Šaranda
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Stock Markets ◽  
Moving Average ◽  
Main Tool ◽  
Technical Analysis ◽  
Baltic States ◽  
Research Issues ◽  
The Mean ◽  
The Baltic

The financial crisis of 2008–2009 caused lots of discussions between Academia and as a result researches on financial crisis and bubble prediction possibilities appeared. Academia shows its growing interest in the issue during the last decade. The majority of researches made are based on different forms of forecast used. Some of previous studies claim that the trend of the stock market can be forecasted using moving average method. After the finance market crashed, a need to forecast further possible bubbles arises. As the economics of the Baltic States is very sensitive to such bubbles it is very important to forecast preliminary the trends of the finance markets ant to plan the right actions in order to temper such bubble influence on the national economics. Although economic theory is opposite to the technical analysis theory which is the main tool for traders in stock markets it is used widely. This paper examines whether a proper technical analysis rule such as Exponential Moving Average (EMA) has a predictive power on stock markets in the Baltic States. The method is applied to OMX Baltic Benchmark Index and industrial indexes as they are more or less sensitive to the main index fluctuations. The results were compared using systematic error (mean square error, the mean absolute deviation, mean forecast error, the mean absolute percentage error) and tracking signal evaluation, CAPM method and appropriate period of EMA finding for each market forecast. A graphical analysis was used in order to determine whether EMA can forecast the main trends of the stock market fluctuations. The conclusions made during the research suggest new research issues and new hypotheses for its further testing.

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.

MOMENTUM OSCILLATORS - A TOOL TO PREDICT STOCK PRICE BEHAVIOUR

2016 ◽  
Vol 12 (1) ◽  
Author(s):  
Shishir Kumar Gujrati
Keyword(s):  
Stock Price ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Future Price ◽  
Range Data ◽  
Strength Index ◽  
Price Trends ◽  
Past Data ◽  
Price Movements

Stock markets are always taken as the barometer of the economy. The price movement of their indices reflects every ups and downs of the economy. Although seem to be random, these price movements do follow a certain track which can be identified using appropriate tool over long range data. One such method is of Technical Analysis wherein future price trends are forecasted using past data. Momentum Oscillators are the important tools of technical analysis. The current paper aims to identify the previous price movements of sensex by using Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) tools and also aims to check whether these tools are appropriate in forecasting the price trends or not.

scholarly journals A graph-based CNN-LSTM stock price prediction algorithm with leading indicators

2021 ◽  
Author(s):  
Jimmy Ming-Tai Wu ◽  
Zhongcui Li ◽  
Norbert Herencsar ◽  
Bay Vo ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Financial Management ◽  
Stock Price ◽  
Short Term Memory ◽  
Prediction Algorithm ◽  
Leading Indicators ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Wealth Management

AbstractIn today’s society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, people’s favorite product of investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. More and more scholars have developed methods of prediction from multiple angles for the stock market. According to the feature of financial time series and the task of price prediction, this article proposes a new framework structure to achieve a more accurate prediction of the stock price, which combines Convolution Neural Network (CNN) and Long–Short-Term Memory Neural Network (LSTM). This new method is aptly named stock sequence array convolutional LSTM (SACLSTM). It constructs a sequence array of historical data and its leading indicators (options and futures), and uses the array as the input image of the CNN framework, and extracts certain feature vectors through the convolutional layer and the layer of pooling, and as the input vector of LSTM, and takes ten stocks in U.S.A and Taiwan as the experimental data. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly.

Islamic calendar and stock market behaviour in India

2018 ◽  
Vol 45 (11) ◽  
pp. 1550-1566
Author(s):  
Dharani Munusamy
Keyword(s):  
Stock Market ◽  
Least Square Method ◽  
Least Square ◽  
Stock Market Returns ◽  
Content Type ◽  
Ordinary Least Square ◽  
Average Return ◽  
Year Effect ◽  
The Common ◽  
The Impact

Purpose The purpose of this paper is to examine the behavior of the stock market returns in the different days of the week and different months of the year in accordance with the Islamic calendar. Further, the study estimates the risk-adjusted returns to test the performance of the indices during the Ramadan and non-Ramadan days. Finally, the study investigates the impact of Ramadan on the returns and the volatility of the stock market indices in India. Design/methodology/approach Initially, the study applies the Ordinary Least Square method to test the day-of-the-week and the month-of-the-year effect of the common and Shariah indices. Next, the study employs the risk-adjusted measurement to examine the underperformance and over-performance of the indices for both the periods. Finally, the study estimates the GARCH (1,1) and GJR-GARCH (1,1) models to observe the impact of Ramadan on the returns and the volatility of the Shariah indices in India. Findings The study finds that an average return of the indices during the Ramadan days are higher than non-Ramadan days. Further, the average returns of the Shariah indices are significantly higher on Wednesday than other days of the week. In addition, the highest and significant mean returns and mean risk-adjusted returns of the indices during the Ramadan days are observed. Finally, the study finds an evidence of the Ramadan effect on the returns and volatility of the indices in India. Originality/value The study observes evidence that the Ramadan effect influences the Shariah indices, but not the common indices in the stock market of the non-Muslim countries. It indicates that the Ramadan creates the positive mood and emotions in the investors buying and selling activities. The study suggests that investors can buy the shares before Ramadan period and sell them during the Ramadan days to get an abnormal return in the emerging markets.

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