scholarly journals Generation of predictive price and trading volume patterns in a model of dynamically evolving free market supply and demand

2001 ◽  
Vol 6 (1) ◽  
pp. 37-47 ◽  
Author(s):  
J. K. Wang
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Free Market ◽  
Supply And Demand ◽  
Market Price ◽  
Efficient Market Hypothesis ◽  
Apparent Contradiction ◽  
Equity Values ◽  
Price Trends ◽  
Price History

I present a model of stock market price fluctuations incorporating effects of share supply as a history-dependent function of previous purchases and share demand as a function of price deviation from moving averages. Price charts generated show intervals of oscillations switching amplitude and frequency suddenly in time, forming price and trading volume patterns well-known in market technical analysis. Ultimate price trends agree with traditional predictions for specific patterns. The consideration of dynamically evolving supply and demand in this model resolves the apparent contradiction with the Efficient Market Hypothesis: perceptions of imprecise equity values by a world of investors evolve over non-negligible periods of time, with dependence on price history.

scholarly journals A Study of Black–Scholes Model’s Applicability in Indian Capital Markets

Paradigm ◽  
2020 ◽  
Vol 24 (1) ◽  
pp. 73-92
Author(s):  
Anubha Srivastava ◽  
Manjula Shastri
Keyword(s):  
Stock Market ◽  
Option Pricing ◽  
Trading Volume ◽  
Stock Exchange ◽  
Market Price ◽  
Significant Degree ◽  
Pricing Model ◽  
Fair Price ◽  
Black Scholes ◽  
Indian Stock Market

Derivative trading, started in mid-2000, has become an integral and significant part of Indian stock market. The tremendous increase in trading volume in Indian stock market has reflected into high volatility in the option prices. The pricing of options is very complex aspect of applied finance and has been subject of extensive research. Black–Scholes option model is a scientific pricing model which is applied for determining the fair price for option contracts. This article examines if Black–Scholes option pricing model (BSOPM) is a good indicator of option pricing in Indian context. The literature review highlights that various studies have been conducted on BSOPM in various stock exchange across the world with mixed outcome on its relevance and applicability. This article is an empirical study to test the relevance of BSOPM for which 10 most popular industry’s stock listed on National Stock Exchange have been taken. Then the BSOPM has been applied using volatility and risk-free rate. Furthermore, t-test has been used to test the hypothesis and determine the significant relationship between BS model values and actual model values. This study concludes that BSOPM involves significant degree of mispricing. Hence, this model alone cannot be adopted as an indicator for option pricing. The variation from market price is synchronised with respect to moneyness and time to maturity of the option.

Stock Market Prediction Using Machine Learning

2020 ◽  
Vol 17 (4) ◽  
pp. 1584-1589
Author(s):  
J. Shiva Nandhini ◽  
Chitrak Bari ◽  
Gareja Pradip
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Learning Algorithm ◽  
Market Price ◽  
Technical Analysis ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Machine Learning Techniques ◽  
Fundamental Analysis ◽  
Stock Market Prediction

The basic tool aimed at increasing the rate of investor’s interest in stock markets is by developing a vibrant application for analyzing and predicting stock market prices. In this report we explain, the development and implementation of a stock market price prediction application using machine learning algorithm. In this report, we try to analyze existing and new methods of stock market prediction. We take three different approaches for solving the problem: Fundamental analysis, Technical Analysis and The application of Machine Learning. We found evidence in support of the weak form of the Efficient Market Hypothesis. We can use Fundamental Analysis and Machine Learning to guide an investor’s decisions. We demonstrate a common flaw in Technical Analysis methodology to show that it produces limited useful information. Based on our findings, algorithmic trading programs are developed and simulated using Quant. During the past few decades, various machine learning techniques have been applied to study the highly theoretical and speculative nature of stock market by capturing and using repetitive patterns. Different companies use different types of analysis tools for forecasting and the main aim is the accuracy, with which they predict which set of stocks would yield the maximum amount of profit.

scholarly journals Stock market prediction using technical analysis

2006 ◽  
Vol 51 (170) ◽  
pp. 125-146 ◽  
Author(s):  
Aleksandra Bradic-Martinovic
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Moving Average ◽  
Supply And Demand ◽  
Wave Theory ◽  
Technical Analysis ◽  
Ease Of Use ◽  
Future Price ◽  
Mathematical Methods ◽  
Price Movements

Technical analysis (TA) is a form of analyzing market encompassing supply and demand of securities according to the study of their prices and trading volume. Using the appropriate methods, TA aims to identify price movements in the stock market, futures or currencies. In short, TA analysis is the process by which "future price movements are formulated according to the price history". TA originates from the work of Charles Dow and his conclusions about the global behavior of the market, as well as from Elliot Wave Theory. Dow did not regard its theory as a tool for stock market movement prediction, nor as a guide for investors, but as a kind of barometer of general market movements. The term TA methods encompasses all the methods used in tracking prices aiming to clearly predict future events. Many different methods, mainly statistical, are used in technical analysis, the most popular ones being: establishing and following trends using moving average, recognizing price momentum, calculating indicators and oscillators, as well as cycle analysis (structure indicators). It is also necessary to point out that TA is not a science in the true meaning of the term, and that methods it uses frequently deviate from the conventional manner of their use. The main advantage of these methods is their relative ease of use, aiming to give as clear picture as possible of price movements, while at the same time avoiding the use of complicated and complex mathematical methods. The reason for this is simple and is reflected in the dynamics of financial markets, where changes occur during short periods of time and where prompt decision-making is of vital importance.

scholarly journals Sistem Notifikasi Sms Hasil Prediksi Saham/Forex Menggunakan Raspberry Pi

2019 ◽  
Vol 6 (2) ◽  
pp. 113
Author(s):  
Kennardi Dewanto Tiono ◽  
Murtiyanto Santoso ◽  
Rayymond Sutjiadi ◽  
Resmana Lim
Keyword(s):  
Stock Market ◽  
Process Design ◽  
Market Price ◽  
User Profile ◽  
Experimental Results ◽  
Raspberry Pi ◽  
The Internet ◽  
Web Page ◽  
Price Trends ◽  
Sms Notification

<p class="Abstrak">Proyek ini dimaksudkan untuk membuat sebuah sistem yang dapat menganalisa data saham, membuat prediksi trend harga naik atau turun, dan mengirimkan notifikasi SMS mengenai hasil analisa dan prediksi kepada para pengguna. Sistem memanfaatkan Raspberry Pi yang dikoneksikan dengan Internet serta sebuah modul GSM untuk keperluan pengiriman hasil notifikasi SMS. Langkah-langkah yang dilakukan untuk dapat membuat sistem yang dimaksud adalah dengan membuat algoritma untuk proses analisa dan prediksi serta proses notifikasi, merancang dan membuat tampilan halaman web untuk <em>user profile</em>, dan menguji coba kelayakan dari program-program yang sudah dibuat. Dari hasil percobaan diketahui bahwa sistem yang telah dibuat mampu membuat analisa prediksi harga pasar saham serta menggunakan hasil dari prediksi tersebut untuk mengirimkan notifikasi berupa SMS kepada para <em>user</em>.</p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>This project is intended to create a system that can analyze and predict price trends up or down from a stock market, and send SMS notifications about the results of predictions to users. The system utilizes a Raspberry Pi that is connected to the Internet and a GSM module for sending SMS notification results. The steps taken to create the system in question is to create a program for the analysis and prediction process and notification process, design and create a web page display for the user profile, and test the feasibility of the programs that have been made. From the experimental results it is known that the system that has been made is able to analyze stock market price predictions and use the results of these predictions to send notifications in the form of SMS to users.</em><strong></strong></p><p class="Abstrak"><em><strong><br /></strong></em></p>

Predicting Stock Market Price Using Neural Network Model

2018 ◽  
Vol 9 (3) ◽  
pp. 84-94 ◽  
Author(s):  
Naliniprava Tripathy
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Network Model ◽  
Neural Network Model ◽  
Market Price ◽  
Efficient Market Hypothesis ◽  
Feedforward Neural Network ◽  
Neural Network Models ◽  
Daily Movement ◽  
Indian Stock Market

The present article predicts the movement of daily Indian stock market (S&P CNX Nifty) price by using Feedforward Neural Network Model over a period of eight years from January 1st 2008 to April 8th 2016. The prediction accuracy of the model is accessed by normalized mean square error (NMSE) and sign correctness percentage (SCP) measure. The study indicates that the predicted output is very close to actual data since the normalized error of one-day lag is 0.02. The analysis further shows that 60 percent accuracy found in the prediction of the direction of daily movement of Indian stock market price after the financial crises period 2008. The study indicates that the predictive power of the feedforward neural network models reasonably influenced by one-day lag stock market price. Hence, the validity of an efficient market hypothesis does not hold in practice in the Indian stock market. This article is quite useful to the investors, professional traders and regulators for understanding the effectiveness of Indian stock market to take appropriate investment decision in the stock market.

scholarly journals The Relationship of Stock Prices and Stock Market Performance Ratios in Companies Trading on Borsa Istanbul: An Application in Companies with the Highest Trading Volume

2021 ◽  
Author(s):  
Bilgehan Tekin ◽  
Seda Nur Bastak
Keyword(s):  
Regression Analysis ◽  
Stock Market ◽  
Stock Prices ◽  
Trading Volume ◽  
Market Price ◽  
Share Price ◽  
Panel Regression ◽  
Market Prices ◽  
Positive Direction ◽  
Stock Market Performance

In this study, the effect of certain ratios that investors pay attention to on stock prices in Borsa Istanbul is examined. For this purpose, 30 of the stocks with which the investors traded the most were taken as a sample. In the study, 30 companies with the highest average trading volume in the analysis period were selected according to their transactions in Borsa Istanbul. The study covers the period between 2010: 1Q-2019: 4Q. Variables included in the study are stock market price, P/E ratio, trading volume, market to book ratio, beta, free float percentage. In this study, it has been tried to understand at what level the stock market prices of companies' publicly traded stocks are affected by the indicators that emerge as a result of the transactions realized in the stock exchange, rather than the ratios discussed within the scope of financial analysis and ratio analysis, examples of which are very common in the literature. Panel regression analysis was performed in the study. Before proceeding to the panel regression analysis, preliminary tests were carried out and the model was tried to be given its most suitable form. For this purpose, multicollinearity tests, cross section dependency test, second generation unit root tests, varying variance test, panel regression model selection were made. The model created in the last stage was estimated. As a result of the study, it was seen that the Price/Earnings, Transaction Volume, Market Value/Book Value and Beta variables were significantly effective on the stock market prices of the companies' stocks. Among these variables, BETA affects negatively, while other variables affect positively. The variable with the highest effect on the share price is the negative BETA coefficient and the positive direction is the trading volume.

scholarly journals Contemporaneous Relationship between Trading Volume and Stock Returns Volatility : Evidence from Nepalese Stock Market

2017 ◽  
Vol 10 (1) ◽  
pp. 40-63
Author(s):  
Shivaram Shrestha
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Trading Volume ◽  
Stock Return ◽  
Efficient Market Hypothesis ◽  
Least Square ◽  
Return Volatility ◽  
Monthly Data ◽  
Stock Return Volatility ◽  
Mixture Of Distribution Hypothesis

This paper examines the contemporaneous relation between trading volume and stock returns volatility for Nepalese stock market using monthly data for the period 2005 mid-July to 2017 mid-April. The study uses ordinary least square method and analyzes whether rising price leads to higher volume or vice versa. The study also investigates the association between trading volume and stock returns volatility based on monthly data of NEPSE index and examines the effects of trading volume on stock returns volatility using GARCH (1, 1) model. The study finds positive contemporaneous relationship between trading volume and stock return volatility. The study result indicates that the relationship between trading volume and return volatility is asymmetric. The findings strongly support the hypothesis that higher trading volume is associated with an increase in stock return volatility, but offers little support to the sequential arrival hypothesis and the mixture of distribution hypothesis. Finally, the findings support the weak-form efficient market hypothesis in Nepalese stock market.

Predicting Stock Market Price Using Neural Network Model

2022 ◽  
pp. 1414-1426
Author(s):  
Naliniprava Tripathy
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Network Model ◽  
Neural Network Model ◽  
Market Price ◽  
Efficient Market Hypothesis ◽  
Feedforward Neural Network ◽  
Neural Network Models ◽  
Daily Movement ◽  
Indian Stock Market

The present article predicts the movement of daily Indian stock market (S&P CNX Nifty) price by using Feedforward Neural Network Model over a period of eight years from January 1st 2008 to April 8th 2016. The prediction accuracy of the model is accessed by normalized mean square error (NMSE) and sign correctness percentage (SCP) measure. The study indicates that the predicted output is very close to actual data since the normalized error of one-day lag is 0.02. The analysis further shows that 60 percent accuracy found in the prediction of the direction of daily movement of Indian stock market price after the financial crises period 2008. The study indicates that the predictive power of the feedforward neural network models reasonably influenced by one-day lag stock market price. Hence, the validity of an efficient market hypothesis does not hold in practice in the Indian stock market. This article is quite useful to the investors, professional traders and regulators for understanding the effectiveness of Indian stock market to take appropriate investment decision in the stock market.

Stock Market Prediction using Data Mining Techniques

2020 ◽  
pp. 310-313
Author(s):  
Karunesh Makker ◽  
Prince Patel ◽  
Hrishikesh Roy ◽  
Sonali Borse
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Prediction Model ◽  
Efficient Market Hypothesis ◽  
Deterministic System ◽  
Vast Number ◽  
Factors Influencing ◽  
Number Of Factors ◽  
Using Data ◽  
Robust Prediction

Stock market is a very volatile in-deterministic system with vast number of factors influencing the direction of trend on varying scales and multiple layers. Efficient Market Hypothesis (EMH) states that the market is unbeatable. This makes predicting the uptrend or downtrend a very challenging task. This research aims to combine multiple existing techniques into a much more robust prediction model which can handle various scenarios in which investment can be beneficial. Existing techniques like sentiment analysis or neural network techniques can be too narrow in their approach and can lead to erroneous outcomes for varying scenarios. By combing both techniques, this prediction model can provide more accurate and flexible recommendations. Embedding Technical indicators will guide the investor to minimize the risk and reap better returns.

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