scholarly journals Wealth Share Analysis with “Fundamentalist/Chartist” Heterogeneous Agents

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Hai-Chuan Xu ◽  
Wei Zhang ◽  
Xiong Xiong ◽  
Wei-Xing Zhou
Keyword(s):  
Stock Prices ◽  
Heterogeneous Agents ◽  
Moving Average ◽  
Long Term Memory ◽  
Memory Length ◽  
Long Run ◽  
Increasing Trend ◽  
The Mean ◽  
Heterogeneous Agents Model

We build a multiassets heterogeneous agents model with fundamentalists and chartists, who make investment decisions by maximizing the constant relative risk aversion utility function. We verify that the model can reproduce the main stylized facts in real markets, such as fat-tailed return distribution and long-term memory in volatility. Based on the calibrated model, we study the impacts of the key strategies’ parameters on investors’ wealth shares. We find that, as chartists’ exponential moving average periods increase, their wealth shares also show an increasing trend. This means that higher memory length can help to improve their wealth shares. This effect saturates when the exponential moving average periods are sufficiently long. On the other hand, the mean reversion parameter has no obvious impacts on wealth shares of either type of traders. It suggests that no matter whether fundamentalists take moderate strategy or aggressive strategy on the mistake of stock prices, it will have no different impact on their wealth shares in the long run.

MULTIFRACTAL FLUCTUATIONS OF JIUZHAIGOU TOURISTS BEFORE AND AFTER WENCHUAN EARTHQUAKE

Fractals ◽  
2013 ◽  
Vol 21 (01) ◽  
pp. 1350001 ◽  
Author(s):  
KAI SHI ◽  
WEN-YONG LI ◽  
CHUN-QIONG LIU ◽  
ZHENG-WEN HUANG
Keyword(s):  
Wenchuan Earthquake ◽  
Moving Average ◽  
Long Term Memory ◽  
Short Term ◽  
Dynamical Mechanism ◽  
Before And After ◽  
Previous State ◽  
Multifractal Behavior ◽  
Multifractal Nature

In this work, multifractal methods have been successfully used to characterize the temporal fluctuations of daily Jiuzhai Valley domestic and foreign tourists before and after Wenchuan earthquake in China. We used multifractal detrending moving average method (MF-DMA). It showed that Jiuzhai Valley tourism markets are characterized by long-term memory and multifractal nature in. Moreover, the major sources of multifractality are studied. Based on the concept of sliding window, the time evolutions of the multifractal behavior of domestic and foreign tourists were analyzed and the influence of Wenchuan earthquake on Jiuzhai Valley tourism system dynamics were evaluated quantitatively. The study indicates that the inherent dynamical mechanism of Jiuzhai Valley tourism system has not been fundamentally changed from long views, although Jiuzhai Valley tourism system was seriously affected by the Wenchuan earthquake. Jiuzhai Valley tourism system has the ability to restore to its previous state in the short term.

scholarly journals The impact of Indonesian oil price (CPI) and macroeconomics on investments in the manufacturing sector in Indonesia

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 338
Author(s):  
Handri Handri ◽  
Hendrati Dwi Mulyaningsih ◽  
Achmad Kemal Hidayat ◽  
Rudi Kurniawan ◽  
Ani Wahyu Rachmawati
Keyword(s):  
Energy Source ◽  
Stock Prices ◽  
Manufacturing Sector ◽  
Granger Causality Test ◽  
Oil Price ◽  
Causality Test ◽  
Long Run ◽  
Short Run ◽  
The Impact

Background: Indonesia consumes oil as the main energy source in the production process and as a result of the development of the manufacturing industry. Thus, investment in manufacturing stocks will be affected by oil price fluctuations and macroeconomic conditions. Changes in oil prices will affect the performance of the manufacturing sector which in turn affects manufacturing stock prices. This paper aims to examine the impact of Indonesia's oil price shocks and macroeconomic factors on stock price movements in the manufacturing sector. Methods: This study uses monthly data for the 2009-2016 period in the manufacturing sector, and 67 stocks were selected on the basis consistently available in the period of the research. The cointegration and causality technique was used in this paper; firstly we applied a unit-panel root test, Secondly, we performed a residual test to indicate whether there was cointegration among variables in the long run equilibrium, and short the short run, we used a Granger causality test. Results: The panel unit root test (both Shin and Fisher) and the Pedroni cointegration residual test show that the data is stationary at 1%  level of significance, thus all variables simultaneously achieve long-run equilibrium, and in the short run, the Granger causality test shows that there is one way direction causality Conclusions: For long-term investment in manufacturing stocks, investors must consider the exchange rate, as it is also as a determining factor in influencing the movement of manufacturing stock prices, inflation, and the production index. Meanwhile, weakening of the rupiah in the short run will also determine investment conditions due to the dependency on raw materials for production from foreign sources. The price of oil as an energy source in the manufacturing sector does not have a long-term relationship with other variables.

The Exploratory Mindset and Corporate Innovation

2020 ◽  
pp. 1-43
Author(s):  
Zhaozhao He ◽  
David Hirshleifer
Keyword(s):  
Research And Development ◽  
Chief Executive Officer ◽  
Stock Prices ◽  
Operating Performance ◽  
Chief Executive ◽  
Market Reactions ◽  
Managerial Incentive ◽  
Corporate Innovation ◽  
Long Run

Abstract We propose that chief executive officer (CEO) exploratory mindset (inherent desire to search for novel ideas and long-term orientation) promotes innovation. Firms with CEOs with PhD degrees (PhD CEOs) produce more exploratory patents with greater novelty, generality, and originality. PhD CEOs engage less in managing earnings and stock prices, invest more in research and development (R&D) and alliances, generate higher long-term value of patents, and experience more positive market reactions to R&D alliances. Their firms achieve superior long-run operating performance. They tend to be hired by research-intensive firms with poor financial performance. Evidence from managerial incentive shocks and turnovers suggests that these effects do not derive solely from CEO–firm matching.

Long memory volatility in Asian stock markets

2017 ◽  
Vol 29 (3) ◽  
pp. 423-442 ◽  
Author(s):  
Geeta Duppati ◽  
Anoop S. Kumar ◽  
Frank Scrimgeour ◽  
Leon Li
Keyword(s):  
Long Memory ◽  
Moving Average ◽  
Realized Volatility ◽  
Theory And Practice ◽  
Long Term Memory ◽  
Conditional Heteroscedasticity ◽  
Term Memory ◽  
Content Type ◽  
Autoregressive Conditional Heteroscedasticity

Purpose The purpose of this paper is to assess to what extent intraday data can explain and predict long-term memory. Design/methodology/approach This article analysed the presence of long-memory volatility in five Asian equity indices, namely, SENSEX, CNIA, NIKKEI225, KO11 and FTSTI, using five-min intraday return series from 05 January 2015 to 06 August 2015 using two approaches, i.e. conditional volatility and realized volatility, for forecasting long-term memory. It employs conditional-generalized autoregressive conditional heteroscedasticity (GARCH), i.e. autoregressive fractionally integrated moving average (ARFIMA)-FIGARCH model and ARFIMA-asymmetric power autoregressive conditional heteroscedasticity (APARCH) models, and unconditional volatility realized volatility using autoregressive integrated moving average (ARIMA) and ARFIMA in-sample forecasting models to estimate the persistence of the long-term memory. Findings Given the GARCH framework, the ARFIMA-APARCH long-memory model gave the better forecast results signifying the importance of accounting for asymmetric information when modelling volatility in a financial market. Using the unconditional realized volatility results from the Singapore and Indian markets, the ARIMA model outperforms the ARFIMA model in terms of forecast performance and provides reasonable forecasts. Practical implications The issue of long memory has important implications for the theory and practice of finance. It is well-known that accurate volatility forecasts are important in a variety of settings including option and other derivatives pricing, portfolio and risk management. Social implications It could be said that using long-memory augmented models would give better results to investors so that they could analyse the market trends in returns and volatility in a more accurate manner and reach at an informed decision. This is useful to minimize the risks. Originality/value This research enhances the literature by estimating the influence of intraday variables on daily volatility. This is one of very few studies that uses conditional GARCH framework models and unconditional realized volatility estimates for forecasting long-term memory. The authors find that the methods complement each other.

scholarly journals Behavioral modeling and linearization of a millimeter-wave power amplifier

2009 ◽  
Vol 1 (2) ◽  
pp. 127-136
Author(s):  
Ilan Melczarsky ◽  
Pere L. Gilabert ◽  
Valeria Di Giacomo ◽  
Eduard Bertran ◽  
Fabio Filicori
Keyword(s):  
Millimeter Wave ◽  
Power Amplifier ◽  
Moving Average ◽  
Behavioral Model ◽  
Long Term Memory ◽  
Wave Power ◽  
Auto Regressive ◽  
Transistor Model ◽  
Short And Long Term

The use of digital predistortion for linearizing a millimeter-wave power amplifier (PA) is investigated. A PA operating at 38 GHz is designed using an accurate non-quasi-static transistor model, taking into account both short- and long-term memory effects. A realistic test signal is then used for the identification of a nonlinear auto-regressive moving average (NARMA) behavioral model of the PA. The NARMA-based digital predistorter is then derived and formulated in terms of basic predistortion cells, especially suitable for efficient implementation in an FPGA. The performance of the predistortion solution is preliminarily assessed by means of computer simulations.

scholarly journals Effects of Business and Finance Conditions on Tourism Firms’ Financial Performances: Evidence From Major Tourist Destinations

SAGE Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 215824402110401
Author(s):  
Farid Irani ◽  
Salih Katircioglu ◽  
Korhan K. Gokmenoglu
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Research Question ◽  
Ordinary Least Squares ◽  
Long Term Effects ◽  
Tourist Destinations ◽  
Long Run ◽  
Causality Tests ◽  
New Research

This study examines the effects of business and finance conditions on the stock performances of firms operating in the tourism, hospitality, and leisure industries. This research employs panel-based first- and second-generation estimators, such as Westerlund cointegration, dynamic ordinary least squares (DOLS), and Dumitrescu–Hurlin panel Granger causality tests, to explore long-term links between business conditions, financial development, and tourism growth in major tourist destination countries selected in this study. To our knowledge, this is the first study to attempt to explore this linkage. The long-run estimation underscores that business and finance environments are significant drivers of stock price movements in this industry. Therefore, any shock in business and finance activities will have long-term effects on tourism firms’ stock prices. Moreover, the results show that the most significant factor that explains changes in the tourism stock price is foreign tourist arrivals, indicating that the tourism stock price of major tourist countries is relatively more sensitive to changes in tourist arrivals to the country than other factors. This study proposes a new research question to estimate the effects of the business, financial conditions, and tourism growth on the stock performance of the tourism, hospitality, and leisure industries. Therefore, the results are likely to become vital for policymakers, managers, and asset pricing analysts.

Modeling long‐term memory effect in stock prices

2008 ◽  
Vol 25 (1) ◽  
pp. 38-48 ◽  
Author(s):  
Alper Ozun ◽  
Atilla Cifter
Keyword(s):  
Memory Effect ◽  
Stock Prices ◽  
Long Term Memory ◽  
Term Memory

Heterogeneous Dynasties and Long-Run Mobility

2021 ◽  
Author(s):  
Jess Benhabib ◽  
Alberto Bisin ◽  
Ricardo T Fernholz
Keyword(s):  
Heterogeneous Agents ◽  
Wealth Distribution ◽  
Empirical Work ◽  
Positive Correlation ◽  
Long Run ◽  
Heterogeneous Agents Model ◽  
Wealth Rank ◽  
Parent Child

Abstract Recent empirical work has demonstrated a positive correlation between grandparent-child wealth-rank, even after controlling for parent-child wealth-rank, and a positive correlation between dynastic wealth-ranks across almost 600 years. We show that a simple heterogeneous agents model with idiosyncratic wealth returns generates a realistic wealth distribution but fails to capture these long-run patterns of wealth mobility. An auto-correlated returns specification of this model also fails to capture both short and long-run mobility. However, an extension of the heterogeneous agents model which includes permanent heterogeneity in wealth returns is able to simultaneously match the wealth distribution and short- and long-run wealth mobility.

scholarly journals Capabilities of Statistical Residual-Based Control Charts in Short- and Long-Term Stock Trading

2016 ◽  
Vol 62 (1) ◽  
pp. 12-26 ◽  
Author(s):  
Berislav Žmuk
Keyword(s):  
Control Chart ◽  
Stock Prices ◽  
Control Charts ◽  
Stock Exchange ◽  
Moving Average ◽  
Stock Trading ◽  
Cusum Control Chart ◽  
Long Run ◽  
Short Run ◽  
Exponential Weighted Moving Average

Abstract The aim of this paper is to introduce and develop additional statistical tools to support the decision-making process in stock trading. The prices of CROBEX10 index stocks on the Zagreb Stock Exchange were used in the paper. The conducted trading simulations, based on the residual-based control charts, led to an investor’s profit in 67.92% cases. In the short run, the residual-based cumulative sum (CUSUM) control chart led to the highest portfolio profits. In the long run, when average stock prices were used and 2-sigma control limits set, the residual-based exponential weighted moving average control chart had the highest portfolio profit. In all other cases in the long run, the CUSUM control chart appeared to be the best choice. The acknowledgment that the SPC methods can be successfully used in stock trading will, hopefully, increase their use in this field.

scholarly journals S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Madhavi Latha Challa ◽  
Venkataramanaiah Malepati ◽  
Siva Nageswara Rao Kolusu
Keyword(s):  
Stock Returns ◽  
Stock Exchange ◽  
Moving Average ◽  
Arima Model ◽  
Descriptive Statistics ◽  
Autoregressive Integrated Moving Average ◽  
Augmented Dickey Fuller ◽  
The Mean ◽  
Daily Stock Returns

AbstractThis study forecasts the return and volatility dynamics of S&P BSE Sensex and S&P BSE IT indices of the Bombay Stock Exchange. To achieve the objectives, the study uses descriptive statistics; tests including variance ratio, Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski Phillips Schmidt and Shin; and Autoregressive Integrated Moving Average (ARIMA). The analysis forecasts daily stock returns for the S&P BSE Sensex and S&P BSE IT time series, using the ARIMA model. The results reveal that the mean returns of both indices are positive but near zero. This is indicative of a regressive tendency in the long-term. The forecasted values of S&P BSE Sensex and S&P BSE IT are almost equal to their actual values, with few deviations. Hence, the ARIMA model is capable of predicting medium- or long-term horizons using historical values of S&P BSE Sensex and S&P BSE IT.

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