Stochastic cost flow system for stock markets with an application in behavioral finance

2016 ◽  
Vol 03 (04) ◽  
pp. 1650026 ◽  
Author(s):  
Oliver Chan ◽  
Alfred Ka Chun Ma
Keyword(s):  
Probability Distribution ◽  
Behavioral Finance ◽  
Stock Markets ◽  
High Frequency ◽  
Flow System ◽  
Weighted Average ◽  
Financial Data ◽  
Purchase Price ◽  
Cost Flow ◽  
The Impact

We introduce a new stochastic cost flow system for stock markets in which the probability distribution of the weighted average purchase price of a stock among all of its shareholders can be explicitly determined. The stochastic system is illustrated in three empirical applications. Through the empirical results, we demonstrate the impact of choosing cost flow assumptions on the reference purchase price estimates, we show the value of high-frequency financial data, and we advocate the need for the stochastic cost flow system when estimating the proportion of gains realized (PGR) and the proportion of losses realized (PLR) which are the most important measures for the disposition effect.

Herd Behaviour: How Decisive is the Noise in the NSE and BSE Stock Markets?

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Paritosh Chandra Sinha
Keyword(s):  
Behavioral Finance ◽  
Stock Markets ◽  
High Frequency ◽  
Stock Exchange ◽  
Time Stamp ◽  
Trade Volume ◽  
Herding Behavior ◽  
Equilibrium Pricing ◽  
Indian Context ◽  
Herd Behaviour

Do investors in the stock markets act/react on true information or noise? Do they believe on their own information or simply herd? The study seeks to explore these typical research queries from the behavioral finance perspectives. In particular, it develops a new theory of herding behavior and extends the models of Banerjee (1992) and Bikhchandani, Hirshleifer, and Welch (1992). The study also empirically tests the same on the Indian context with the high frequency intraday trading data for the real trade-time or time-stamp, trade-volume, and trade-price of ten sample scripts listed for their trading in both markets - the Bombay Stock Exchange (BSE) and the National stock Exchange (NSE). The study contributes to the literature with original findings. It shows that investors in the two Indian stock markets show crowd of positive and negative herding as well significantly and there is huge noise along with information in the markets equilibrium pricing mechanism.

Herd Behaviour: How Decisive is the Noise in the NSE and BSE Stock Markets?

GIS Business ◽  
2016 ◽  
Vol 11 (6) ◽  
pp. 01-16
Author(s):  
Paritosh Chandra Sinha
Keyword(s):  
Behavioral Finance ◽  
Stock Markets ◽  
High Frequency ◽  
Stock Exchange ◽  
Time Stamp ◽  
Trade Volume ◽  
Herding Behavior ◽  
Equilibrium Pricing ◽  
Indian Context ◽  
Herd Behaviour

Do investors in the stock markets act/react on true information or noise? Do they believe on their own information or simply herd? The study seeks to explore these typical research queries from the behavioral finance perspectives. In particular, it develops a new theory of herding behavior and extends the models of Banerjee (1992) and Bikhchandani, Hirshleifer, and Welch (1992). The study also empirically tests the same on the Indian context with the high frequency intraday trading data for the real trade-time or time-stamp, trade-volume, and trade-price of ten sample scripts listed for their trading in both markets - the Bombay Stock Exchange (BSE) and the National stock Exchange (NSE). The study contributes to the literature with original findings. It shows that investors in the two Indian stock markets show crowd of positive and negative herding as well significantly and there is huge noise along with information in the markets equilibrium pricing mechanism.

Examining investors' sentiments, behavioral biases and investment decisions during COVID-19 in the emerging stock market: a case of Pakistan stock market

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shagufta Parveen ◽  
Zoya Wajid Satti ◽  
Qazi Abdul Subhan ◽  
Nishat Riaz ◽  
Samreen Fahim Baber ◽  
...  
Keyword(s):  
Developing Countries ◽  
Stock Market ◽  
Behavioral Finance ◽  
Stock Markets ◽  
Investment Decisions ◽  
Equation Modeling ◽  
Disposition Effect ◽  
Content Type ◽  
Behavioral Biases ◽  
The Impact

PurposeThis study investigates the impact of the COVID-19 pandemic on investors' sentiments, behavioral biases and investment decisions in the Pakistan Stock Exchange (PSX).Design/methodology/approachThe authors have assessed investors' behaviors and sentiments and the stock market overreaction during COVID-19 using a questionnaire and collected data from 401 investors trading in the PSX.FindingsResults of structural equation modeling revealed that the COVID-19 pandemic affected investors' behaviors, investment decisions and trade volume. It created feelings of fear and uncertainty among market participants. Evidence suggests that behavioral heuristics and biases, including representative heuristic, anchoring heuristic, overconfidence bias and disposition effect, negatively influenced investors' decisions at the PSX.Research limitations/implicationsThis study will contribute to behavioral finance literature in the context of developing countries as it has revealed the impact of COVID-19 on the emerging stock market, and its results are generalizable to other emerging stock markets.Practical implicationsThe findings of this study will help academicians, researchers and policymakers of developing countries. Academicians can formulate new behavioral models that can depict the solutions of dealing with an uncertain situation like COVID-19. Policymakers like the Securities Exchange Commission and the PSX can formulate crisis management strategies based on behavioral finance concepts to cope with situations like COVID-19 in the future and help lessen investors' losses in the stock markets. The role of the Securities Exchange Commission is crucial as it regulates the financial markets. It can arrange workshops to educate investors to manage their decisions during crisis time and focus on the best use of irrational and rational decision-making at the same time using Lo (2004) adaptive market hypothesis.Originality/valueThe novelty of the paper is that the authors have introduced overconfidence and disposition effect as mediators that create a connection between representative and anchoring heuristics and investment decisions using primary data collected from investors (institutional and retail) to demonstrate the presence of psychological biases during COVID-19, and it has been done for the first time according to authors' knowledge. It is a contribution and addition to the behavioral finance literature in the context of developing countries' stock markets and their efficiency.

scholarly journals Mixed-Stable Models: An Application to High-Frequency Financial Data

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 739
Author(s):  
Igoris Belovas ◽  
Leonidas Sakalauskas ◽  
Vadimas Starikovičius ◽  
Edward W. Sun
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
High Frequency ◽  
Financial Data ◽  
Stock Index ◽  
Parameter Estimates ◽  
Goodness Of Fit Test ◽  
The Impact ◽  
Stable Models

The paper extends the study of applying the mixed-stable models to the analysis of large sets of high-frequency financial data. The empirical data under review are the German DAX stock index yearly log-returns series. Mixed-stable models for 29 DAX companies are constructed employing efficient parallel algorithms for the processing of long-term data series. The adequacy of the modeling is verified with the empirical characteristic function goodness-of-fit test. We propose the smart-Δ method for the calculation of the α-stable probability density function. We study the impact of the accuracy of the computation of the probability density function and the accuracy of ML-optimization on the results of the modeling and processing time. The obtained mixed-stable parameter estimates can be used for the construction of the optimal asset portfolio.

High-Frequency Financial Econometrics

2014 ◽  
Author(s):  
Yacine Aït-Sahalia ◽  
Jean Jacod
Keyword(s):  
High Frequency ◽  
Practical Importance ◽  
Financial Econometrics ◽  
Trading Strategies ◽  
Financial Data ◽  
Jump Processes ◽  
High Frequency Trading ◽  
Microstructure Noise ◽  
Econometric Methods ◽  
Mathematical Foundations

High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been driven by the increasing availability of such data, the technological advancements that make high-frequency trading strategies possible, and the need of practitioners to analyze these data. This comprehensive book introduces readers to these emerging methods and tools of analysis. The book covers the mathematical foundations of stochastic processes, describes the primary characteristics of high-frequency financial data, and presents the asymptotic concepts that their analysis relies on. It also deals with estimation of the volatility portion of the model, including methods that are robust to market microstructure noise, and address estimation and testing questions involving the jump part of the model. As the book demonstrates, the practical importance and relevance of jumps in financial data are universally recognized, but only recently have econometric methods become available to rigorously analyze jump processes. The book approaches high-frequency econometrics with a distinct focus on the financial side of matters while maintaining technical rigor, which makes this book invaluable to researchers and practitioners alike.

Pemodelan Data Time Series Garch(1,1) Untuk Pasar Saham Indonesia

2015 ◽  
Vol 11 (1) ◽  
pp. 13
Author(s):  
Elfa Rafulta ◽  
Roni Tri Putra
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Markets ◽  
Financial Data ◽  
International Stock Markets

This paper introduced a method pengklusteran for financial data. By using the model Heteroskidastity Generalized autoregressive conditional (GARCH), will be estimated distance between the stock market using GARCH-based distance. The purpose of this method is mengkluster international stock markets with different amounts of data.

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