scholarly journals An Analysis of the Random Walk Hypothesis based on Stock Prices, Dividends, and Earnings

2010 ◽  
Vol 6 ◽  
Author(s):  
Risa Leigh Kavalerchik
Keyword(s):  
Random Walk ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Future Price ◽  
Statistical Process ◽  
Random Walk Hypothesis ◽  
Deterministic Trend ◽  
Time Period ◽  
Available Information

This paper explores the stationarity of price movements, dividend yields, and earnings yields for stock market indices and individual stocks within the broader context of the random walk hypothesis. In general, in order for a stock’s price to follow a random walk, its future price must be unforecastable based on all currently available information in the stock market, including its price history. If a stock price is stationary in a given time period, its statistical process does not change over time, meaning that the series has a deterministic trend, which could even be flat. This investigation tests for stationarity in the time series of prices and dividend yields of the Dow Jones Industrial Average (DJIA), the S&P 500 Index, and their underlying component stocks based on the results of univariate and panel unit root tests. I also test for the stationarity of earnings yields for the components of the DJIA. I find that prices of the DJIA and its underlying components behave in a more stationary manner than do the prices of the S&P 500 and its underlying components. Dividend yields behave in an equally non-stationary fashion for the underlying components of both the DJIA and S&P 500. Interestingly, earnings yields for the DJIA prove to exhibit more stationarity than the dividend yields for the DJIA and S&P 500, suggesting that earnings data have some predictability for stock prices.

scholarly journals Random Walk Hypothesis for Stock Prices in Fiji

2021 ◽  
Vol 13 (2) ◽  
pp. 79-88
Author(s):  
Janesh Sami
Keyword(s):  
Random Walk ◽  
Stock Market ◽  
Unit Root ◽  
Stock Prices ◽  
Structural Break ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Random Walk Hypothesis ◽  
Augmented Dickey Fuller ◽  
Historical Movement

The main goal of this paper is to investigate the random walk hypothesis in Fiji using monthly data from January 2000 to October 2017. Applying augmented Dickey Fuller (ADF 1979, 1981) and Phillips-Perron (1988), Zivot-Andrews (1992), and Narayan and Popp (2010) unit root tests, this study finds that stock prices is best characterized as non-stationary. The estimated multiple structural break dates in the stock prices corresponds with devaluation of Fijian dollar by 20 percent in 2009 and General Elections in September 2014, which Fiji First Party won by majority votes. The empirical results indicate that stock prices are best characterized as a unit root (random walk) process, indicating that the weak-form efficient market hypothesis holds in Fiji’s stock market. Hence, it will be difficult to predict future returns based on historical movement of stock prices in Fiji’s stock market.

scholarly journals Stock Price Behavior of Nepalese Commercial Banks: Random Walk Hypothesis

2018 ◽  
Vol 5 ◽  
pp. 42-52
Author(s):  
Nirajan Bam ◽  
Rajesh Kumar Thagurathi ◽  
Bipin Shrestha
Keyword(s):  
Random Walk ◽  
Stock Prices ◽  
Commercial Banks ◽  
Stock Price ◽  
Fiscal Year ◽  
Data Set ◽  
Random Walk Hypothesis ◽  
The Past ◽  
Random Behavior ◽  
Run Test

Using the data set on daily stock prices during the fiscal year 2015/16 (Sept 23, 2015 through Dec 22, 2015), this paper attempts to analyze the random behavior of stock price of Nepalese Commercial Banks by using run test, serial correlation and run tests and martingale random walk hypothesis under heteroscedasticity assumption of standard error. The results conclude that the proposition of Random Walk Hypothesis (RWH) in Nepalese stock markets does not hold true. This conclusion corroborates with the conclusions of the past studies carried out in Nepalese context.

scholarly journals A Hidden Markov Model inference approach to testing the Random Walk Hypothesis: Empirical evidence from the Nigerian Stock Market

2016 ◽  
Vol 9 (3) ◽  
pp. 696-713
Author(s):  
Edesiri Nkemnole
Keyword(s):  
Random Walk ◽  
Stock Market ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Stock Prices ◽  
Stock Price ◽  
Hidden Markov ◽  
Random Walk Process ◽  
Hidden States ◽  
Stock Price Movements

The movement of stock prices, in capital markets across the world, has been found to be both random and non-random. Basically, for a stock price to follow a random walk, its future price changes randomly based on all currently available information in the stock market, its price history inclusive. Some research findings have shown that the existing traditional unit root tests have low statistical power and hence cannot capture gradual changes over successive observations. Consequently, there is a need to revisit the random walk theory in stock prices using other tests. This study employs a Hidden Markov Model (HMM) with time-varying parameters to assess whether the stock price movements of the Nigerian Stock Exchange (NSE) follow a random walk process, or otherwise. Via hidden states, the HMM allows for periods with different volatility levels characterised by the hidden states. By simply accounting for the non-constant variance of the data with a two-state Hidden Markov Model and taking estimation into account via the Sequential Monte Carlo Expectation Maximisation (SMCEM) technique, this study finds no support of randomness. In conclusion, the stock price movements of the NSE do not follow the random walk process.

scholarly journals An Empirical Investigation of the Random Walk Hypothesis in the Nigerian Stock Market

2021 ◽  
Vol 4 (1) ◽  
pp. 62-77
Author(s):  
DA Kuhe ◽  
J Akor
Keyword(s):  
Random Walk ◽  
Stock Market ◽  
Stock Returns ◽  
Unit Root ◽  
Stock Prices ◽  
Null Hypothesis ◽  
Serial Correlation ◽  
Ratio Test ◽  
Variance Ratio ◽  
Random Walk Hypothesis

The Random Walk Hypothesis (RWH) states that stock prices move randomly in the stock market without following any regular or particular pattern and as such historical information contained in the past prices of stocks cannot be used to predict current or future stock prices. Hence, stock prices are unpredictable and that investors cannot usurp any available information in the market to manipulate the market and make abnormal profits. This study empirically examines the random walk hypothesis in the Nigerian stock market using the daily quotations of the Nigerian stock exchange from 2nd January, 1998 to 31st December, 2019. The study employs Augmented Dickey-Fuller unit root test, the random walk model, Ljung-Box Q-statistic test for serial dependence, runs test of randomness, and the robust variance ratio test as methods of analyses. The result of the study rejected the null hypotheses of a unit root and random walk in the stock returns. The null hypothesis of no serial correlation in the residuals of stock returns was also rejected indicating the presence of serial correlation/autocorrelation in the residual series. The result of the runs test rejected the null hypothesis of randomness in the Nigerian stock returns. The results of the variance ratio test under homoskedasticity and heteroskedasticity assumptions both strongly rejected the null hypothesis of a random walk for both joint tests and test of individual periods. Based on the results of the four tests applied in this study, it is concluded that the Nigerian daily stock returns under the period of investigation do not follow a random walk and hence the null hypothesis of a random walk is rejected. The results of the study further revealed that the Nigerian stock market is weak-form inefficient indicating that prices in the Nigerian stock market are predictable, dependable, consistently mispriced, inflated, liable to arbitraging and left unprotected to speculations and market manipulations. The study provided some policy recommendations

ENDOGENOUS JUMPING AND ASSET PRICE DYNAMICS

1998 ◽  
Vol 2 (2) ◽  
pp. 213-237 ◽  
Author(s):  
G. C. Lim ◽  
Vance L. Martin ◽  
Leslie E. Teo
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Asset Prices ◽  
Asset Price ◽  
Price Dynamics ◽  
Stock Market Crash ◽  
Time Period ◽  
The U.S

A model of asset price dynamics is derived in which large jumps in stock prices are determined endogenously. An important property of the model is that it can lead to asset price distributions that are multimodal. The model can explain how relatively small changes in dividends can lead to relatively large changes in asset prices and it can be used to identify the time period in which bubbles begin and end. The framework is applied to modeling the U.S. stock market crash in October 1987. Some forecasting experiments also are conducted with the result that the model is able to predict the size of the eventual crash in the aggregate stock price.

Macroeconomic Factors and Pakistani Equity Market”

2004 ◽  
Vol 43 (4II) ◽  
pp. 619-637 ◽  
Author(s):  
Muhammad Nishat ◽  
Rozina Shaheen
Keyword(s):  
Stock Market ◽  
Industrial Production ◽  
Stock Prices ◽  
Stock Price ◽  
Consumer Price Index ◽  
Reverse Causality ◽  
Macroeconomic Factors ◽  
Macroeconomic Variables ◽  
Equilibrium Relationships

This paper analyzes long-term equilibrium relationships between a group of macroeconomic variables and the Karachi Stock Exchange Index. The macroeconomic variables are represented by the industrial production index, the consumer price index, M1, and the value of an investment earning the money market rate. We employ a vector error correction model to explore such relationships during 1973:1 to 2004:4. We found that these five variables are cointegrated and two long-term equilibrium relationships exist among these variables. Our results indicated a "causal" relationship between the stock market and the economy. Analysis of our results indicates that industrial production is the largest positive determinant of Pakistani stock prices, while inflation is the largest negative determinant of stock prices in Pakistan. We found that while macroeconomic variables Granger-caused stock price movements, the reverse causality was observed in case of industrial production and stock prices. Furthermore, we found that statistically significant lag lengths between fluctuations in the stock market and changes in the real economy are relatively short.

scholarly journals Building stock market resilience through digital transformation: using Google trends to analyze the impact of COVID-19 pandemic

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Ding Ding ◽  
Chong Guan ◽  
Calvin M. L. Chan ◽  
Wenting Liu
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Digital Transformation ◽  
Google Trends ◽  
Building Stock ◽  
Market Sentiment ◽  
Price Performance ◽  
Novel Coronavirus ◽  
The Impact

Abstract As the 2019 novel coronavirus disease (COVID-19) pandemic rages globally, its impact has been felt in the stock markets around the world. Amidst the gloomy economic outlook, certain sectors seem to have survived better than others. This paper aims to investigate the sectors that have performed better even as market sentiment is affected by the pandemic. The daily closing stock prices of a total usable sample of 1,567 firms from 37 sectors are first analyzed using a combination of hierarchical clustering and shape-based distance (SBD) measures. Market sentiment is modeled from Google Trends on the COVID-19 pandemic. This is then analyzed against the time series of daily closing stock prices using augmented vector autoregression (VAR). The empirical results indicate that market sentiment towards the pandemic has significant effects on the stock prices of the sectors. Particularly, the stock price performance across sectors is differentiated by the level of the digital transformation of sectors, with those that are most digitally transformed, showing resilience towards negative market sentiment on the pandemic. This study contributes to the existing literature by incorporating search trends to analyze market sentiment, and by showing that digital transformation moderated the stock market resilience of firms against concern over the COVID-19 outbreak.

scholarly journals The Impact of COVID-19 on the Stock Price of Socially Responsible Enterprises: An Empirical Study in Taiwan Stock Market

2021 ◽  
Vol 18 (4) ◽  
pp. 1398
Author(s):  
Kuo-Jung Lee ◽  
Su-Lien Lu
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Abnormal Returns ◽  
Recovery Phase ◽  
Socially Responsible ◽  
Cumulative Impact ◽  
Study Approach ◽  
Price Performance ◽  
The Impact

This study examines the impact of the COVID-19 outbreak on the Taiwan stock market and investigates whether companies with a commitment to corporate social responsibility (CSR) were less affected. This study uses a selection of companies provided by CommonWealth magazine to classify the listed companies in Taiwan as CSR and non-CSR companies. The event study approach is applied to examine the change in the stock prices of CSR companies after the first COVID-19 outbreak in Taiwan. The empirical results indicate that the stock prices of all companies generated significantly negative abnormal returns and negative cumulative abnormal returns after the outbreak. Compared with all companies and with non-CSR companies, CSR companies were less affected by the outbreak; their stock prices were relatively resistant to the fall and they recovered faster. In addition, the cumulative impact of the COVID-19 on the stock prices of CSR companies is smaller than that of non-CSR companies on both short- and long-term bases. However, the stock price performance of non-CSR companies was not weaker than that of CSR companies during times when the impact of the pandemic was lower or during the price recovery phase.

EVOLUTION OF SHANGHAI STOCK MARKET BASED ON MAXIMAL SPANNING TREES

2012 ◽  
Vol 27 (03) ◽  
pp. 1350022 ◽  
Author(s):  
CHUNXIA YANG ◽  
YING SHEN ◽  
BINGYING XIA
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Spanning Tree ◽  
Stock Price ◽  
Spanning Trees ◽  
Turning Points ◽  
Single Step ◽  
P Value ◽  
Power Law Distribution ◽  
Structure Variation

In this paper, using a moving window to scan through every stock price time series over a period from 2 January 2001 to 11 March 2011 and mutual information to measure the statistical interdependence between stock prices, we construct a corresponding weighted network for 501 Shanghai stocks in every given window. Next, we extract its maximal spanning tree and understand the structure variation of Shanghai stock market by analyzing the average path length, the influence of the center node and the p-value for every maximal spanning tree. A further analysis of the structure properties of maximal spanning trees over different periods of Shanghai stock market is carried out. All the obtained results indicate that the periods around 8 August 2005, 17 October 2007 and 25 December 2008 are turning points of Shanghai stock market, at turning points, the topology structure of the maximal spanning tree changes obviously: the degree of separation between nodes increases; the structure becomes looser; the influence of the center node gets smaller, and the degree distribution of the maximal spanning tree is no longer a power-law distribution. Lastly, we give an analysis of the variations of the single-step and multi-step survival ratios for all maximal spanning trees and find that two stocks are closely bonded and hard to be broken in a short term, on the contrary, no pair of stocks remains closely bonded for a long time.

scholarly journals Disposition Effect and Return Predictability in the Tunisian Stock Market

2017 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Cheïma Hmida ◽  
Ramzi Boussaidi
Keyword(s):  
Stock Market ◽  
Behavioral Finance ◽  
Stock Prices ◽  
Stock Price ◽  
Return Predictability ◽  
Capital Gains ◽  
Disposition Effect ◽  
Individual Investors ◽  
Long Run ◽  
Return Reversal

The behavioral finance literature has documented that individual investors tend to sell winning stocks more quickly than losing stocks, a phenomenon known as the disposition effect, and that such a behavior has an impact on stock prices. We examined this effect in the Tunisian stock market using the unrealized capital gains/losses of Grinblatt & Han (2005) to measure the disposition effect. We find that the Tunisian investors exhibit a disposition effect in the long-run horizon but not in the short and the intermediate horizons. Moreover, the disposition effect predicts a stock price continuation (momentum) for the whole sample. However this impact varies from an industry to another. It predicts a momentum for “manufacturing” but a return reversal for “financial” and “services”.

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