Modeling Financial Bubbles and Market Crashes

2017 ◽  
Author(s):  
Didier Sornette
Keyword(s):  
Stock Market ◽  
Hazard Rate ◽  
Market Price ◽  
Imitative Behavior ◽  
Rational Model ◽  
Basic Principles ◽  
Market Prices ◽  
Speculative Bubbles ◽  
Financial Bubbles ◽  
Market Crashes

This chapter considers two versions of a rational model of speculative bubbles and stock market crashes. According to the first version, stock market prices are driven by the crash hazard that may increase sometimes due to the collective behavior of “noise traders.” The second version assumes the opposite: the crash hazard is driven by prices that may soar sometimes, again due to investors' speculative or imitative behavior. The chapter first provides an overview of what a model is before discussing the basic principles of model construction in finance. It then describes the basic ingredients of the two models of speculative bubbles and market crashes, along with the main properties of the risk-driven model. It also examines how imitation and herding drive the crash hazard rate and concludes with an analysis of the price-driven model, how imitation and herding drive the market price, and how the price return drives the crash hazard rate.

scholarly journals Fractional differencing in stock market price and online presence of global tourist corporations

2019 ◽  
Vol 24 (48) ◽  
pp. 194-204 ◽  
Author(s):  
Francisco Flores-Muñoz ◽  
Alberto Javier Báez-García ◽  
Josué Gutiérrez-Barroso
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Market Price ◽  
Google Trends ◽  
Data Sets ◽  
Market Prices ◽  
Content Type ◽  
Search Results ◽  
Online Presence ◽  
Fractional Differencing

Purpose This work aims to explore the behavior of stock market prices according to the autoregressive fractional differencing integrated moving average model. This behavior will be compared with a measure of online presence, search engine results as measured by Google Trends. Design/methodology/approach The study sample is comprised by the companies listed at the STOXX® Global 3000 Travel and Leisure. Google Finance and Yahoo Finance, along with Google Trends, were used, respectively, to obtain the data of stock prices and search results, for a period of five years (October 2012 to October 2017). To guarantee certain comparability between the two data sets, weekly observations were collected, with a total figure of 118 firms, two time series each (price and search results), around 61,000 observations. Findings Relationships between the two data sets are explored, with theoretical implications for the fields of economics, finance and management. Tourist corporations were analyzed owing to their growing economic impact. The estimations are initially consistent with long memory; so, they suggest that both stock market prices and online search trends deserve further exploration for modeling and forecasting. Significant differences owing to country and sector effects are also shown. Originality/value This research contributes in two different ways: it demonstrate the potential of a new tool for the analysis of relevant time series to monitor the behavior of firms and markets, and it suggests several theoretical pathways for further research in the specific topics of asymmetry of information and corporate transparency, proposing pertinent bridges between the two fields.

scholarly journals The Dynamic Relationship between Crude Oil Prices and Stock Market Price Volatility in Nigeria: A Cointegrated VAR-GARCH Model

2019 ◽  
pp. 1-12
Author(s):  
David Adugh Kuhe
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Market Price ◽  
Garch Model ◽  
Price Volatility ◽  
Oil Prices ◽  
Market Prices ◽  
Dynamic Relationship ◽  
Crude Oil Prices ◽  
Long Run

This study investigates the dynamic relationship between crude oil prices and stock market price volatility in Nigeria using cointegrated Vector Generalized Autoregressive conditional Heteroskedasticity (VAR-GARCH) model. The study utilizes monthly data on the study variables from January 2006 to April 2017 and employs Dickey-Fuller Generalized least squares unit root test, simple linear regression model, unrestricted vector autoregressive model, Granger causality test and standard GARCH model as methods of analysis. Results shows that the study variables are integrated of order one, no long-run stable relationship was found to exist between crude oil prices and stock market prices in Nigeria. Both crude oil prices and stock market prices were found to have positive and significant impact on each other indicating that an increase in crude oil prices will increase stock market prices and vice versa. Both crude oil prices and stock market prices were found to have predictive information on one another in the long-run. A one-way causality ran from crude oil prices to stock market prices suggesting that crude oil prices determine stock prices and are a driven force in Nigerian stock market. Results of GARCH (1,1) models show high persistence of shocks in the conditional variance of both returns. The conditional volatility of stock market price log return was found to be stable and predictable while that of crude oil price log return was found to be unstable and unpredictable, although a dependable and dynamic relationship between crude oil prices and stock market prices was found to exist. The study provides some policy recommendations.

scholarly journals Non-transformed Principal Component Technique on Weekly Construction Stock Market Price

MATEMATIKA ◽  
2019 ◽  
Vol 35 (2) ◽  
pp. 139-147
Author(s):  
Yusrina Andu ◽  
Muhammad Hisyam Lee ◽  
Zakariya Yahya Algamal
Keyword(s):  
Stock Market ◽  
Mean Squared Error ◽  
Market Price ◽  
Principal Component ◽  
Construction Sector ◽  
Market Prices ◽  
Component Technique ◽  
Squared Error ◽  
The Mean ◽  
Explained Variance

The fast-growing urbanization has contributed to the construction sector becoming one of the major sectors traded in the world stock market. In general, non-stationarity is highly related to most of the stock market price pattern. Even though stationarity transformation is a common approach, yet this may prompt to originality loss of the data. Hence, the non-transformation technique using a generalized dynamic principal component (GDPC) were considered for this study. Comparison of GDPC was performed with two transformed principal component techniques. This is pertinent as to observe a larger perspective of both techniques. Thus, the latest weekly two-years observations of nine constructions stock market price from seven different countries were applied. The data was tested for stationarity before performing the analysis. As a result, the mean squared error in the non-transformed technique shows eight lowest values. Similarly, eight construction stock market prices had the highest percentage of explained variance. In conclusion, a non-transformed technique can also present a better resultoutcome without the stationarity transformation.

scholarly journals Modelling Macroeconomic Determinants Of Stock Market Prices: Evidence From Namibia

2012 ◽  
Vol 28 (5) ◽  
pp. 871 ◽  
Author(s):  
Joel Hinaunye Eita
Keyword(s):  
Stock Market ◽  
Interest Rates ◽  
Money Supply ◽  
Stock Prices ◽  
Economic Activity ◽  
Market Price ◽  
Font Size ◽  
Market Prices ◽  
Econometric Methodology ◽  
Macroeconomic Determinants

<span style="font-family: Times New Roman; font-size: small;"> </span><p style="margin: 0in 0.5in 0pt; text-align: justify; mso-pagination: none;" class="MsoNormal"><span lang="EN-GB" style="color: black; font-size: 10pt; mso-ansi-language: EN-GB; mso-themecolor: text1;"><span style="font-family: Times New Roman;">This paper investigates the macroeconomic determinants of stock market prices in Namibia. The investigation was conducted using a VECM econometric methodology and revealed that Namibian stock market prices are chiefly determined by economic activity, interest rates, inflation, money supply and exchange rates.<span style="mso-spacerun: yes;"> </span>An increase in economic activity and the money supply increases stock market prices, while increases in inflation and interest rates decrease stock prices.<span style="mso-spacerun: yes;"> </span>The results suggest that equities are not a hedge against inflation in Namibia, and contractionary monetary policy generally depresses stock prices.<span style="mso-spacerun: yes;"> </span>Increasing economic activity promotes stock market price development.</span></span></p><span style="font-family: Times New Roman; font-size: small;"> </span>

THE BEHAVIOR OF A THRESHOLD MODEL OF MARKET PRICE IN STOCK EXCHANGE

Fractals ◽  
1993 ◽  
Vol 01 (01) ◽  
pp. 29-40 ◽  
Author(s):  
TADASHI HIRABAYASHI ◽  
HIDEKI TAKAYASU ◽  
HITOSHI MIURA ◽  
KOICHI HAMADA
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Threshold Model ◽  
Market Price ◽  
Observation Time ◽  
Threshold Dynamics ◽  
Maximum Lyapunov Exponent ◽  
Stochastic Fluctuation ◽  
Market Prices ◽  
Global Trends

We analyze the behavior of deterministic threshold dynamics in a model of stock market. We observe global trends in the virtual market prices and find a kind of phase transition. At the critical region, the macroscopic variable of stock market price shows seemingly stochastic fluctuation with f-2 power spectrum consistent with real economic fluctuations. The maximum Lyapunov exponent is estimated to be slightly positive in short time steps (5 or 10 steps) and, as the observation time becomes longer, it converges to zero. This result indicates that the system is at the edge of chaos.

Net Worth

2017 ◽  
Author(s):  
Stefan Homburg
Keyword(s):  
Asymmetric Information ◽  
Stock Market ◽  
General Equilibrium ◽  
Economic Activity ◽  
Equilibrium Models ◽  
Market Prices ◽  
Net Worth ◽  
Unresolved Problem ◽  
Equilibrium Prices ◽  
Market Crashes

Chapter 5 focuses on producers’ net worth. It joins a large strand rooted in the financial literature, which points out that under asymmetric information, producers need own equity to obtain credit. Incorporating this assumption yields scenarios with endogenous borrowing limits and shows that small variations in credit requirements have large macroeconomic consequences. A second theme concerns an unresolved problem of general equilibrium models. These determine equilibrium prices from decisions of producers and consumers who are ostensibly aware only of market prices and their own characteristics, i.e., technologies and preferences. However, consumers must also know current profits because these enter their budget constraints. As profits are determined in equilibrium, a logical circle emerges. Stock manias can be interpreted as situations where consumers overestimate profits; conversely, stock market crashes may reflect underestimations of profits. The text shows that misguided profit expectations as such do not have the expected impacts on economic activity.

Stock Market Prediction Using Elliot Wave Theory and Classification

2021 ◽  
Vol 8 (1) ◽  
pp. 1-20
Author(s):  
Saeed Tabar ◽  
Sushil Sharma ◽  
David Volkman
Keyword(s):  
Stock Market ◽  
Market Price ◽  
Wave Theory ◽  
Support Vector ◽  
Valuable Insight ◽  
Stock Market Prediction ◽  
The Past ◽  
Market Crashes ◽  
Novel Method ◽  
Price Patterns

The area of stock market prediction has attracted a great deal of attention during the past decade especially after multiple market crashes. By analyzing market price fluctuations, we can achieve valuable insight regarding future trends. This research proposes a novel method for prediction using pattern analysis and classification. For the first part of the research, a trend analysis algorithm, Elliot wave theory, is used to classify price patterns for DJIA, S&P500, and NASDAQ into three categories: LONG, SHORT, and HOLD. After labeling patterns, classification learning algorithms including decision tree, naïve Bayes, and support vector machine (SVM) are used to learn from the patterns and make a prediction for the future. The algorithm is implemented during the market crashes of May 2010 and August 2015, and the obtained results show that it correctly identifies the market volatility by issuing HOLD and SHORT signals during those crashes.

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 CROBEX AND INDUSTRY PERFORMANCE IN THE CASE OF CROATIA: EVIDENCE FROM WAVELET COHERENCE

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ivan Novak
Keyword(s):  
Stock Market ◽  
Market Price ◽  
Wavelet Coherence ◽  
Stock Market Index ◽  
Market Prices ◽  
Market Index ◽  
Dependent Relationship ◽  
Novel Approach ◽  
Industry Performance ◽  
The Relationship

Determinants of the stock market index movement have always been of interest to scholars and practitioners. Various time series techniques have already been applied to determine the relationship between the stock market prices and macroeconomic variables. Many of these techniques are unable to reveal the frequency and time dependent relationship often leading to incorrect specification. Furthermore, results may differ depending on the level of the economic development. This paper is using the Wavelet coherence as a rather novel approach compensating for some limitations of the conventional approach bringing further insight into the stock market price movement of the Croatian capital market. Wavelet transform enables observation of the connection between the CROBEX and the industry performance across time and frequency domain. This relationship is tested using monthly data for CROBEX and industrial production index volume in the period from January 1998 to September 2019. Significant positive relationship was confirmed in the period from 1998 to 2015 identifying CROBEX as the leading variable.

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