Scaling in Stock Market Data
Scaling in Stock Market Data

Author: Rama Cont

Publisher:

Published: 1998

Total Pages: 11

ISBN-13:

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The concepts of scale invariance and scaling behavior are now increasingly applied outside their traditional domains of application, the physical sciences. Their application to financial markets, initiated by Mandelbrot in the 1960s, has experienced a regain of interest in the recent years, partly due to the abundance of high-frequency data sets and availability of computers for analyzing their statistical properties. This lecture is intended as an introduction and a brief review of current research in a field which is increasingly applied in the study of time aggregation properties of financial data. We will try to show how the concepts of scale invariance and scaling behavior may be usefully applied in the framework of a statistical approach to the study of financial data, pointing out at the same time the limits of such an approach.

Scale Invariance and Beyond
Scale Invariance and Beyond

Author: B. Dubrulle

Publisher: Springer Science & Business Media

Published: 2013-11-09

Total Pages: 291

ISBN-13: 3662097990

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This book is an excellent introduction to the concept of scale invariance, which is a growing field of research with wide applications. It describes where and how symmetry under scale transformation (and its various forms of partial breakdown) can be used to analyze solutions of a problem without the need to explicitly solve it. The first part gives descriptions of tools and concepts; the second is devoted to recent attempts to go beyond the invariance or symmetry breaking, to discuss causes and consequences, and to extract useful information about the system. Examples are carefully worked out in fields as diverse as condensed matter physics, population dynamics, earthquake physics, turbulence, cosmology and finance.

Stock Market Prediction and Efficiency Analysis Using Recurrent Neural Network
Stock Market Prediction and Efficiency Analysis Using Recurrent Neural Network

Author: Joish Bosco

Publisher:

Published: 2018-04-06

Total Pages: 84

ISBN-13: 9783668800465

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Project Report from the year 2018 in the subject Computer Science - Technical Computer Science, course: Computer Science, language: English, abstract: Modeling and Forecasting of the financial market have been an attractive topic to scholars and researchers from various academic fields. The financial market is an abstract concept where financial commodities such as stocks, bonds, and precious metals transactions happen between buyers and sellers. In the present scenario of the financial market world, especially in the stock market, forecasting the trend or the price of stocks using machine learning techniques and artificial neural networks are the most attractive issue to be investigated. As Giles explained, financial forecasting is an instance of signal processing problem which is difficult because of high noise, small sample size, non-stationary, and non-linearity. The noisy characteristics mean the incomplete information gap between past stock trading price and volume with a future price. The stock market is sensitive with the political and macroeconomic environment. However, these two kinds of information are too complex and unstable to gather. The above information that cannot be included in features are considered as noise. The sample size of financial data is determined by real-world transaction records. On one hand, a larger sample size refers a longer period of transaction records; on the other hand, large sample size increases the uncertainty of financial environment during the 2 sample period. In this project, we use stock data instead of daily data in order to reduce the probability of uncertain noise, and relatively increase the sample size within a certain period of time. By non-stationarity, one means that the distribution of stock data is various during time changing. Non-linearity implies that feature correlation of different individual stocks is various. Efficient Market Hypothesis was developed by Burton G. Malkiel in 1991.

Stock price analysis through Statistical and Data Science tools: An Overview
Stock price analysis through Statistical and Data Science tools: An Overview

Author: Vinaitheerthan Renganathan

Publisher: Vinaitheerthan Renganathan

Published: 2021-04-30

Total Pages: 107

ISBN-13: 9354579736

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Stock price analysis involves different methods such as fundamental analysis and technical analysis which is based on data related to price movement of the stock in the past. Price of the stock is affected by various factors such as company’s performance, current status of economy and political factor. These factors play an important role in supply and demand of the stock which makes the price to be volatile in the short term. Investors and stock traders aim to book profit through buying and selling the stocks. There are different statistical and data science tools are being used to predict the stock price. Data Science and Statistical tools assume only the stock price’s historical data in predicting the future stock price. Statistical tools include measures such as Graph and Charts which depicts the general trend and time series tools such as Auto Regressive Integrated Moving Averages (ARIMA) and regression analysis. Data Science tools include models like Decision Tree, Support Vector Machine (SVM), Artificial Neural Network (ANN) and Long Term and Short Term Memory (LSTM) Models. Current methods include carrying out sentiment analysis of tweets, comments and other social media discussion to extract the hidden sentiment expressed by the users which indicate the positive or negative sentiment towards the stock price and the company. The book provides an overview of the analyzing and predicting stock price movements using statistical and data science tools using R open source software with hypothetical stock data sets. It provides a short introduction to R software to enable the user to understand analysis part in the later part. The book will not go into details of suggesting when to purchase a stock or what at price. The tools presented in the book can be used as a guiding tool in decision making while buying or selling the stock. Vinaitheerthan Renganathan www.vinaitheerthan.com/book.php

A Wavelet Analysis of Scaling Laws and Long-Memory in Stock Market Volatility
A Wavelet Analysis of Scaling Laws and Long-Memory in Stock Market Volatility

Author: Tommi A. Vuorenmaa

Publisher:

Published: 2013

Total Pages: 44

ISBN-13:

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This paper investigates the dependence of average stock market volatility on the timescale or on the time interval used to measure price changes, which dependence is often referred to as the scaling law. Scaling factor, on the other hand, refers to the elasticity of the volatility measure with respect to the timescale. This paper studies, in particular, whether the scaling factor differs from the one in a simple random walk model and whether it has remained stable over time. It also explores possible underlying reasons for the observed behaviour of volatility in terms of heterogeneity of stock market players and periodicity of intraday volatility. The data consist of volatility series of Nokia Oyj at the Helsinki Stock Exchange at five minute frequency over the period from January 4, 1999 to December 30, 2002. The paper uses wavelet methods to decompose stock market volatility at different timescales. Wavelet methods are particularly well motivated in the present context due to their superior ability to describe local properties of times series. The results are, in general, consistent with multiscaling in Finnish stock markets. Furthermore, the scaling factor and the long-memory parameters of the volatility series are not constant over time, nor consistent with a random walk model. Interestingly, the evidence also suggests that, for a significant part, the behaviour of volatility is accounted for by an intraday volatility cycle referred to as the New York effect. Long-memory features emerge more clearly in the data over the period around the burst of the IT bubble and may, consequently, be an indication of irrational exuberance on the part of investors.

The Research Driven Investor
The Research Driven Investor

Author: Timothy Hayes

Publisher: McGraw-Hill Companies

Published: 2001

Total Pages: 328

ISBN-13:

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The editor of "Investment Strategy" shows how individual investors can access institutional-quality tools, data, and indicators and consistently beat the market. Hayes presents walk-through examples of a wide variety of investment models based on more than 100 years of stock market data and research from Ned Davis Research to achieve top results. 120 illustrations. 60 tables.

The Stock Market Is Predictable
The Stock Market Is Predictable

Author: Francis Yee

Publisher: Fhy Systems, LLC

Published: 2014-04-21

Total Pages: 100

ISBN-13: 9780991650217

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The Stock Market is Predictable: Exploit Proven Seasonal Patterns for Higher Returns details steps an investor can take in order to take advantage of predictable patterns. These patterns are proven by academic research through many published studies. Over one hundred years of historical data collected by the oldest and most trusted stock trading almanac support the fact that predictable seasonal patterns exist in the stock market. The book describes how to use four simple and easy-to-understand steps at two strategic periods in the calendar to profit from proven seasonal patterns when stock prices rise and when prices fall. By modifying a simple investing technique, positive returns from the stock market will be achieved 70-80% of the time over a sustained investing period. Learn the simple steps and when to use them to earn greater returns on your investments.

Long Term Memories of Developed and Emerging Markets
Long Term Memories of Developed and Emerging Markets

Author: Tiziana Di Matteo

Publisher:

Published: 2013

Total Pages:

ISBN-13:

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The scaling properties encompass in a simple analysis many of the volatility characteristics of financial markets. That is why we use them to probe the different degree of markets development. We empirically study the scaling properties of daily Foreign Exchange rates, Stock Market indices and fixed income instruments by using the generalized Hurst approach. We show that the scaling exponents are associated with characteristics of the specific markets and can be used to differentiate markets in their stage of development. The robustness of the results is tested by both Monte-Carlo studies and a computation of the scaling in the frequency-domain.

On the Time Scaling of Value at Risk with Trading
On the Time Scaling of Value at Risk with Trading

Author: Jimmy Skoglund

Publisher:

Published: 2013

Total Pages:

ISBN-13:

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Portfolio risk measures such as Value at Risk is traditionally measured using a buy and hold assumption on the portfolio. In particular the 10-day market risk capital is commonly measured as the 1-day Value at Risk scaled by the square root of 10. While this scaling is convenient to obtain n-day Value at Risk numbers from 1-day Value at Risk it has some deficiencies. This includes the implicit assumption of a normal iid distribution as well as the implicit assumption of a buy and hold portfolio with no management intervention. In this paper we examine the potential effect of the second implicit assumption i.e., that of assuming a buy and hold portfolio. Indeed, understanding the impact of an approximating buy and hold assumption is a key concern in validating the institutions Value at Risk model. Using stock data that covers the period from 6th April 2001 to 17th June 2009, including data for the recent crisis period, we compare the Value at Risk profiles for four different stylized daily trading methods in estimating 10-day Value at Risk. The trading methods are the convex, concave and volatility based trading methods. In our analysis we find that the trading strategy may have a substantial impact on the accuracy of the square root of time rule in scaling Value at Risk. This effect is especially pronounced in case of buy volatility trading strategies where risk is amplified by trading into volatile instruments - yielding significantly higher risk than under a buy and hold assumption, or, a square root of time rule. On the other hand, risk reduction versus buy and hold for strategies that trade into low volatility instruments may be small. Our findings strongly support that measures of risk should take into account traders style and portfolio level trading strategies if risk is to be accurately measured. This means that financial institutions need to validate their current Value at Risk model trading assumptions against the actual trade behavior.