scholarly journals Don’t fight the tape! Technical Analysis Momentum and Contrarian Signals as Common Cognitive Biases

2020 ◽  
Vol 28 (2) ◽  
pp. 98-110
Author(s):  
Piotr Zielonka ◽  
◽  
Wojciech Białaszek ◽  
Paweł Biedrzycki ◽  
Bartłomiej Dzik ◽  
...  
Keyword(s):  
Stock Market ◽  
Natural Environment ◽  
Stock Price ◽  
Predictive Power ◽  
Cognitive Biases ◽  
Technical Analysis ◽  
Market Participants ◽  
Trend Reversal

Purpose: Stock market participants use technical analysis to seek trends in stock price charts despite its doubtful efficiency. We tested whether technical analysis signals represent typical and common cognitive biases associated with the continuation or reversal of the trend. Methodology: We compared investors’ opinions about the predictive power of technical analysis signals grouped into five conditions: real technical analysis signals associated with trend continuation (real momentum signals) or trend reversal (real contrarian signals), fake momentum or fake contrarian signals, and fluctuation signals. Findings: Investors assigned larger predictive power to real and fake signals associated with trend continuation than to signals associated with trend reversal. Fake signals, which represented cognitive biases, elicited similar predictions about trend continuation or reversal to real technical analysis signals. Originality: Market players assess momentum signals to have greater predictive power than contrarian signals and neutral signals to have the least predictive power. These results are independent of whether technical analysis signals were well-known to investors or made up by experimenters. The hardwired propensity of our brains to detect patterns combined with the non-natural environment of the stock market creates the illusion of expertise that is not easy to dispel.

scholarly journals Use of Technical Analysis Tools in Nepalese Stock Market

2018 ◽  
Vol 11 (1) ◽  
pp. 55-64
Author(s):  
Rashesh Vaidya
Keyword(s):  
Stock Market ◽  
Investment Decisions ◽  
Technical Analysis ◽  
Trade Volume ◽  
Market Participants ◽  
Support Level ◽  
Analysis Tools

This paper has attempted to find the interest of Nepalese investors, brokers and depository participants on the use the technical tools for the analysis of the stock market. The use of technical analysis in context to Nepal shows that the participants in the Nepalese stock market are highly interested on the use of new Hi-Lo price while making their investment decisions. Another interest was seen for trade volume indicators. The Nepalese stock market participants are not seen interested in using the resistance and support level followed by the pattern i.e. candlestick charts while analyzing the stock market trend.

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 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 Securities Analysis and Portfolio Management of Selected NSE Securities

2021 ◽  
pp. 540-543
Author(s):  
Prof. (Dr) Pramod Sharma
Keyword(s):  
Stock Market ◽  
Portfolio Management ◽  
Intrinsic Value ◽  
Technical Analysis ◽  
Future Price ◽  
Market Participants ◽  
Major Market ◽  
Price Trends ◽  
Indian Stock Market ◽  
A Company

“Technical Analysis is the study of data generated by the action of markets and by the behaviour and psychology of market participants and observers”: -Constitution of the market technicians Association Technical analysis is a completely different approach to stock market investing- it doesn’t try to find the intrinsic value of a company or try to find whether a share is mispriced or undervalued. "Technical analysis is the study of market action, primarily through the use of charts, for the purpose of forecasting future price trends. “A technical analyst is interested only in the price movements in the market. So, it is all about analysing the demand and supply or a price volume analysis. Technical analysis considers only the actual price behaviour of the market or instrument, based on the premise that price reflects all relevant factors before an investor becomes aware of them through other channels. These stock market indicators would help the investor to identify major market turning points. This paper examines the technical analysis of selected companies which helps to understand the price behaviour of the shares, the signals given by them and to assist investment decisions in the Indian stock Market.

scholarly journals Stock Prediction Using Technical And Sentimental Analysis

2021 ◽  
pp. 584-588
Author(s):  
Prof. N.P. Kadale ◽  
Gavali Prajwal ◽  
Pratik Jadhav ◽  
Sachin Landge ◽  
Pratiksha Bhoite
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Multiple Kernel Learning ◽  
Technical Analysis ◽  
Regression Problem ◽  
Stock Market Prediction ◽  
The Public ◽  
Public Sentiment ◽  
Current Events

Predicting stock market movements is a well-known problem of interest. Now-a- days social media is perfectly representing the public sentiment and opinion about current events. Especially, Twitter has attracted a lot of attention from researchers for studying the public sentiments. Stock market prediction on the basis of public sentiments expressed on Twitter has been an intriguing field of research. The approach through sentimental analysis is to observe how well the changes in stock prices i.e. the rise and fall are correlated to the opinion of people that are expressed by them on Twitter. Sentimental analysis helps in analyzing the public sentiments on Twitter, this approach is our approach through using make of sentimental analysis. Another approach in the same topic of our project is using technical analysis. We model the stock price movement as a function of these input features and solve it as a regression problem in a multiple kernel learning regression framework. The machine learning coupled with fundamental and/ or technical analysis also yields satisfactory results for stock market prediction. We also evaluated the model for taking buy-sell decision at the end of day which is also known as intraday trading.

scholarly journals IS STOCK PRICES REFLECTED IN MARKET RATIOS?

2020 ◽  
Vol 2 (2) ◽  
pp. 123
Author(s):  
Tan Kwang En
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Technical Analysis ◽  
Composite Indicator ◽  
Financial Accounting ◽  
Financial Ratios ◽  
Macroeconomic Factor ◽  
Price Trends ◽  
Almost All

<p>The most fascinating thing in stock market world is forecasting stock prices. Almost all players in stock market race to find the best method for forecast stock prices. After years of researching and practicing, we can divide all methods into two main methods, fundamental and technical analysis. Fundamental analysis based its forecasting method on macroeconomic factor, industry analysis, and company internal factors, while technical analysis based on studying financial accounting numbers and stock price trends in the past and present. This study will be focusing in the uses of technical analysing in forecasting stock prices.</p><p>There are many ways in technical analysis to forecast stock prices. Investors and analysts usually use stock price trends or financial ratios to do that. The latest is the most simple and powerful tools that almost everyone can use it, regardless to its limitations. When it comes to use financial ratios, there are a lot of contradicting results that make its users need to make a comparation between ratios and make a decision. </p><p>This paper try to use another solution to overcome those problem with using a composite indicators. The composite indicator will be compared with another market ratio to find out which method is the best on forecasting stock prices.</p><p>The result is composite indicator is the best method on forecasting stock prices compared with price to sales ratio, price to book value ratio, price to earnings per share ratio, and price to operating cash flow ratio.</p>

scholarly journals Importance of Machine Learning in Making Investment Decision in Stock Market

2021 ◽  
pp. 025609092110599
Author(s):  
Akhilesh Prasad ◽  
Arumugam Seetharaman
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Stock Market ◽  
Stock Price ◽  
Learning Algorithms ◽  
Technical Analysis ◽  
Processing Unit ◽  
Accuracy Score ◽  
Research Papers ◽  
Price Trends

Executive Summary Predicting stock trends in the financial market is always demanding but satisfying as well. With the growing power of computing and the recent development of graphics processing unit and tensor processing unit, analysts and researchers are applying advanced techniques such as machine learning techniques more and more to predict stock price trends. In recent years, researchers have developed several algorithms to predict stock trends. To assist investors interested in investing in the stock market, preferably for a short period, it has become necessary to review research papers dealing on machine learning and analyse the importance of their findings in the context of how stock price trends generate trading signals. In this article, to achieve the stated task, authors scrutinized more than 50 research papers focusing on various machine learning algorithms with varied levels of input variables and found that though the performance of models measured by root-mean-square error (RMSE) for regression and accuracy score for classification models varied greatly, long short-term memory (LSTM) model displayed higher accuracy amongst the machine and deep learning models reviewed. However, reinforcement learning algorithm performance measured by profitability and Sharpe ratio outperformed all. In general, traders can maximize their profits by using machine learning instead of using technical analysis. Technical analysis is very easy to implement, but the profit based on it can vanish too soon or making a profit using technical analysis is almost difficult because of its simplicity. Hence, studying machine, deep and reinforcement learning algorithms is vital for traders and investors. These findings were based on the literature review consolidated in the result section.

scholarly journals HYPOTHESIS PASAR EFISIEN/EFFICIENT MARKET HYPOTHESIS (Pasal Modal menurut Teori Fama dan Pandangan Islam)

2017 ◽  
Vol 1 (1) ◽  
pp. 25-43
Author(s):  
Yenni Samri Juliati Nasution
Keyword(s):  
Stock Market ◽  
Capital Market ◽  
Stock Price ◽  
Efficient Market Hypothesis ◽  
Share Price ◽  
Annual Reports ◽  
Efficient Market ◽  
Market Participants ◽  
The Right ◽  
The Capital Market

In trading activity in the capital market, information is one of the important factors to be known by market participants. Information on the capital market with regard to decisions made by the investor to choose the investment portfolio efficiently. The success of a company seen from the full value. at companies that go public, seen enterprise value of the share price. The stock price reflects the value of the company when the stock market in an efficient state. An efficient market may indicate that stock price fully reflect available information, this information may include the company’s annual reports, the distribution of dividends, stock splits, stock market analysts report and so on Islamic Capital Market in the frame must be in accordance with Islamic principles which certainly puts the right information so that member for the good of the investors.Dalam kegiatan perdagangan di pasar modal, informasi merupakan salah satu faktor penting untuk diketahui oleh parapelaku pasar. Informasi tentang pasar modal berkaitan dengan pengambilan keputusan yang dilakukan oleh para investor untuk memilih portofolio investasi yang efisien. Keberhasilan suatu perusahaan dilihat dari nilai penuh. Pada perusahaan yang go public, nilai perusahaan dilihat dari harga sahamnya. Harga saham mencerminkan nilai perusahaan bila pasar modal dalam keadaan efisien. Pasar yang efisien dapat menunjukkan harga saham yang mencerminkan secara penuh informasi yang tersedia, informasi tersebut dapat berupa laporan tahunan perusahaan, pembagian deviden, pemecahan saham, laporan para analis pasar modal, dan sebagainya. Pasar Modal dalam bingkai Islam harus sesuai dengan prinsip syariah yang pasti mengedepankan informasi yang benar sehingga member kebaikan untuk para investor.

A Technical Analysis Information Fusion Approach for Stock Price Analysis and Modeling

2018 ◽  
Vol 17 (01) ◽  
pp. 1850007 ◽  
Author(s):  
Salim Lahmiri
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Information Fusion ◽  
Stock Price ◽  
Market Price ◽  
Technical Information ◽  
Technical Analysis ◽  
Stock Market Index ◽  
Approximation Coefficients ◽  
Index Level

In this paper, we address the problem of technical analysis information fusion in improving stock market index-level prediction. We present an approach for analyzing stock market price behavior based on different categories of technical analysis metrics and a multiple predictive system. Each category of technical analysis measures is used to characterize stock market price movements. The presented predictive system is based on an ensemble of neural networks (NN) coupled with particle swarm intelligence for parameter optimization where each single neural network is trained with a specific category of technical analysis measures. The experimental evaluation on three international stock market indices and three individual stocks show that the presented ensemble-based technical indicators fusion system significantly improves forecasting accuracy in comparison with single NN. Also, it outperforms the classical neural network trained with index-level lagged values and NN trained with stationary wavelet transform details and approximation coefficients. As a result, technical information fusion in NN ensemble architecture helps improving prediction accuracy.

scholarly journals HERD BEHAVIOR AND AGGREGATE FLUCTUATIONS IN FINANCIAL MARKETS

2000 ◽  
Vol 4 (2) ◽  
pp. 170-196 ◽  
Author(s):  
Rama Cont ◽  
Jean-Philipe Bouchaud
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Stock Price ◽  
Heavy Tails ◽  
Empirical Studies ◽  
Herd Behavior ◽  
Market Returns ◽  
Communication Structure ◽  
Stock Market Returns ◽  
Market Participants

We present a simple model of a stock market where a random communication structure between agents generically gives rise to heavy tails in the distribution of stock price variations in the form of an exponentially truncated power law, similar to distributions observed in recent empirical studies of high-frequency market data. Our model provides a link between two well-known market phenomena: the heavy tails observed in the distribution of stock market returns on one hand and herding behavior in financial markets on the other hand. In particular, our study suggests a relation between the excess kurtosis observed in asset returns, the market order flow, and the tendency of market participants to imitate each other.

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