Investigation of fractal market hypothesis and forecasting time series stock returns for Tehran Stock Exchange and London Stock Exchange

2019 ◽  
Author(s):  
Mahdi Moradi ◽  
Mehdi Jabbari Nooghabi ◽  
Mohammad Mahdi Rounaghi
Keyword(s):  
Time Series ◽  
Stock Returns ◽  
Stock Exchange ◽  
London Stock Exchange ◽  
Fractal Market Hypothesis

Do Financially Constrained Firms Earn Higher Returns than the Unconstrained Firms? A Comprehensive Evidence from Pakistan Stock Exchange

2020 ◽  
Keyword(s):  
Time Series ◽  
Asset Pricing ◽  
Capital Structure ◽  
Stock Returns ◽  
Financial Constraints ◽  
Stock Exchange ◽  
Cash Flows ◽  
Total Asset ◽  
Cash Holding ◽  
The Relationship

This paper examines the relationship between financial constraints and the stock returns explaining the pricing of stock through financially constrained and unconstrained firms in Pakistan. Three proxies; total assets, tangible to total assets and cash holding to total assets ratios) have been used for financial constraints and the study tried to investigate that either the investors are compensated for taking the extra risk or not in Pakistan Stock Exchange (PSX). We find that the financially constrained firms don’t earn higher returns when their capital structure is heavy with liquid assets and their cash flows are more than the unconstrained firms in PSX. Moreover, the time series results showed that the risk-adjusted returns of the most constrained firms give the mix and somewhat negative and significant and insignificant results for the Pakistani firms listed in PSX sorted based on tangible to total assets and Cash holding to total asset ratios. Keywords: Asset Pricing, Financial constraints, risk-adjusted performance of portfolios

scholarly journals Predicting Extreme Daily Regime Shifts in Financial Time Series Exchange/Johannesburg Stock Exchange—All Share Index

2021 ◽  
Vol 9 (2) ◽  
pp. 18
Author(s):  
Katleho Makatjane ◽  
Ntebogang Moroke
Keyword(s):  
Time Series ◽  
Stock Returns ◽  
Linear Models ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Moving Average ◽  
Autoregressive Integrated Moving Average ◽  
Financial Time ◽  
Johannesburg Stock Exchange ◽  
Non Linear

During the past decades, seasonal autoregressive integrated moving average (SARIMA) had become one of a prevalent linear models in time series and forecasting. Empirical research advocated that forecasting with non-linear models can be an encouraging alternative to traditional linear models. Linear models are often compared to non-linear models with mixed conclusions in terms of superiority in forecasting performance. Therefore, the aim of this study is to build an early warning system (EWS) model for extreme daily losses for financial stock markets. A logistic model tree (LMT) is used in collaboration with a seasonal autoregressive integrated moving average-Markov-Switching exponential generalised autoregressive conditional heteroscedasticity-generalised extreme value distribution (SARIMA-MS-EGARCH-GEVD) estimates. A time series of the study is a five-day financial time series exchange/Johannesburg stock exchange-all share index (FTSE/JSE-ALSI) for the period of 4 January 2010 to 31 July 2020. The study is set into a two-stage framework. Firstly, SARIMA model is fitted to stock returns in order to obtain independently and identically distributed (i.i.d) residuals and fit the MS(k)-EGARCH(p,q)-GEVD to i.i.d residuals; while, in the second stage, we set-up an EWS model. The results of the estimated MS(2)-EGARCH(1,1) -GEVD revealed that the conditional distribution of returns is highly volatile giving the expected duration to approximately 36 months and 4 days in regime one and 58 months and 2 days in regime two. We further found that any degree losses above 25% implies that there will be no further losses. Using the seven statistical loss functions, the estimated SARIMA(2,1,0)×(2,1,0)240−MS(2)−EGARCH(1,1)−GEVD proved to be the most appropriate model for predicting extreme regimes losses as it was ranked at 71%. Finally, the results of EWS model exhibit reasonably an overall performance of 98%, sensitivity of 79.89% and specificity of 98.40% respectively. The model further indicated a success classification rate of 89% and a prediction rate of 95%. This is a promising technique for EWS. The findings also confirmed 63% and 51% of extreme losses for both training sample and validation sample to be correctly classified. The findings of this study are useful for decision makers and financial sector for future use and planning. Furthermore, a base for future researchers for conducting studies on emerging markets, have been contributed. These results are also important to risk managers and and investors.

scholarly journals Media Coverage and Stock Returns on the London Stock Exchange, 1825–70*

2017 ◽  
Vol 22 (4) ◽  
pp. 1605-1629 ◽  
Author(s):  
John D Turner ◽  
Qing Ye ◽  
Clive B Walker
Keyword(s):  
Stock Returns ◽  
Media Coverage ◽  
Stock Exchange ◽  
London Stock Exchange

scholarly journals DAILY VARIATION AND PREDICTING STOCK MARKET RETURNS FOR THE FRANKFURTER BÖRSE (STOCK MARKET)

2008 ◽  
Vol 9 (3) ◽  
pp. 189-198 ◽  
Author(s):  
Jeffrey E. Jarrett ◽  
Janne Schilling
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Returns ◽  
Stock Prices ◽  
Stock Exchange ◽  
Arima Model ◽  
Unit Root Test ◽  
Unit Roots ◽  
Stock Market Returns ◽  
Daily Stock Returns

In this article we test the random walk hypothesis in the German daily stock prices by means of a unit root test and the development of an ARIMA model for prediction. The results show that the time series of daily stock returns for a stratified random sample of German firms listed on the stock exchange of Frankfurt exhibit unit roots. Also, we find that one may predict changes in the returns to these listed stocks. These time series exhibit properties which are forecast able and provide the intelligent data analysts’ methods to better predict the directive of individual stock returns for listed German firms. The results of this study, though different from most other studies of other stock markets, indicate the Frankfurt stock market behaves in similar ways to North American, other European and Asian markets previously studied in the same manner.

scholarly journals ESTIMATING STOCK RETURNS USING ARTIFICIAL NEURAL NETWORKS: AN EXPERIMENTAL DESIGN WITH AN EVIDENCE FROM IRAQ STOCK EXCHANGE

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Mohammed H Adnan ◽  
Mustafa Muneer Isma’eel
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Financial Markets ◽  
Stock Returns ◽  
Stock Exchange ◽  
Back Propagation ◽  
Error Back Propagation ◽  
Artificial Neural ◽  
Propagation Network

The research aims to estimate stock returns using artificial neural networks and to test the performance of the Error Back Propagation network, for its effectiveness and accuracy in predicting the returns of stocks and their potential in the field of financial markets and to rationalize investor decisions. A sample of companies listed on the Iraq Stock Exchange was selected with (38) stock for a time series spanning (120) months for the years (2010_2019). The research found that there is a weakness in the network of Error Back Propagation training and the identification of data patterns of stock returns as individual inputs feeding the network due to the high fluctuation in the rates of returns leads to variation in proportions and in different directions, negatively and positively.

scholarly journals Profits From Buying Losers And Selling Winners In The London Stock Exchange

2011 ◽  
Vol 1 (11) ◽  
Author(s):  
Antonios Antoniou ◽  
Emilios C. Galariotis ◽  
Spyros I. Spyrou
Keyword(s):  
Market Efficiency ◽  
Stock Returns ◽  
Stock Exchange ◽  
London Stock Exchange ◽  
Stock Portfolios ◽  
Contrarian Strategies ◽  
Subsequent Period ◽  
Investor Optimism ◽  
Investor Behaviour ◽  
Market Capitalisation

<p>DeBondt and Thaler (1985) have challenged the notions of market efficiency and of rational investor behaviour. According to their findings stock portfolios that experience negative returns tend to outperform portfolios that experience positive returns, during the subsequent period. In other words, stock returns may be predictable, and this may be due to excessive investor optimism and pessimism. This paper investigates the existence of such contrarian profits for stocks listed in the London Stock Exchange. The results indicate that contrarian strategies are profitable for UK stocks and more pronounced for extreme market capitalisation stocks. These profits persist even after the sample is adjusted for market frictions, and irrespective of whether raw or risk-adjusted returns are used.</p>

scholarly journals A study of the market reaction to CEO change

2021 ◽  
Vol 187 (1-2) ◽  
pp. 206-214
Author(s):  
Ali Khazaal Jabbar ◽  
◽  
Hussein Falah Hasan ◽  
Hudaa Nadhim Khalbas ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Stock Returns ◽  
Cross Sections ◽  
Significant Relationship ◽  
Fixed Effects ◽  
Stock Return ◽  
Stock Exchange ◽  
Panel Regression ◽  
Abnormal Stock Returns ◽  
Available Information

The purpose of this study is to investigate how market reacts to CEO changes and how it may lead to abnormal stock returns. The research is of retrospective character and is based on publicly available information published by listed companies in Tehran Stock Exchange during 2011-2015 taken from a sample of 102 companies. The hypotheses were tested using panel regression with fixed effects for time series and merged effects for cross sections. The results of hypothesis testing showed that there is a negative and significant relationship between CEO change and abnormal stock returns. In other words, it can be argued that at the time of CEO change, stocks are underrated by stockholders, as a result of which the estimated stock return will be lower than expected.

Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem

2021 ◽  
Vol 8 (55) ◽  
pp. 44-62
Author(s):  
Marcin Chlebus ◽  
Michał Dyczko ◽  
Michał Woźniak
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Stock Returns ◽  
Stock Exchange ◽  
Research Problem ◽  
Added Value ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Daily Stock Returns ◽  
Machine Learning Models

Abstract Statistical learning models have profoundly changed the rules of trading on the stock exchange. Quantitative analysts try to utilise them predict potential profits and risks in a better manner. However, the available studies are mostly focused on testing the increasingly complex machine learning models on a selected sample of stocks, indexes etc. without a thorough understanding and consideration of their economic environment. Therefore, the goal of the article is to create an effective forecasting machine learning model of daily stock returns for a preselected company characterised by a wide portfolio of strategic branches influencing its valuation. We use Nvidia Corporation stock covering the period from 07/2012 to 12/2018 and apply various econometric and machine learning models, considering a diverse group of exogenous features, to analyse the research problem. The results suggest that it is possible to develop predictive machine learning models of Nvidia stock returns (based on many independent environmental variables) which outperform both simple naïve and econometric models. Our contribution to literature is twofold. First, we provide an added value to the strand of literature on the choice of model class to the stock returns prediction problem. Second, our study contributes to the thread of selecting exogenous variables and the need for their stationarity in the case of time series models.

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