scholarly journals Soft computing and bioinspired computing techniques for stock market prediction-a comprehensive survey

2018 ◽  
Vol 7 (3) ◽  
pp. 1836
Author(s):  
Dr S. Kumar Chandar
Keyword(s):  
Stock Market ◽  
Soft Computing ◽  
Prediction Method ◽  
Stock Market Index ◽  
Stock Market Prediction ◽  
Market Index ◽  
Soft Computing Method ◽  
Minimum Number ◽  
Comprehensive Survey ◽  
Input Variables

Stock Market Prediction (SMP) is one of the most important and hottest topics in business and finance. The main goal of SMP is to develop an efficient technique to predict stock values and achieves accurate results with minimum number of input data. This research paper reviews currently available SMP techniques based on soft computing and bio inspired computing algorithms. Many issues in-volved in the SMP are identified and different techniques are studied along with their merits and demerits to find the most suitable one. This paper also analyses the performance of various techniques with respect to some metrics including MSE, RMSE, MAD, MAPE, AAE and Hit ratio. The reviewed papers are classified in terms of number of input variables, prediction method and evaluation parame-ters used. A tabular representation of all the SMP techniques is presented to facilitate the future comparison. From the reviewed paper, it is noticed that the integration of soft computing with the bio inspired algorithms has the potential to predict the stock market index with high accuracy and achieves best result than soft computing method alone.  

scholarly journals An Improved Probabilistic Neural Network Model for Directional Prediction of a Stock Market Index

2019 ◽  
Vol 9 (24) ◽  
pp. 5334 ◽  
Author(s):  
Vasana Chandrasekara ◽  
Chandima Tilakaratne ◽  
Musa Mammadov
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Financial Market ◽  
Joint Distribution ◽  
Probabilistic Neural Network ◽  
Stock Market Index ◽  
Multivariate Distributions ◽  
Market Index ◽  
Input Variables ◽  
T Distribution

Financial market prediction attracts immense interest among researchers nowadays due to rapid increase in the investments of financial markets in the last few decades. The stock market is one of the leading financial markets due to importance and interest of many stakeholders. With the development of machine learning techniques, the financial industry thrived with the enhancement of the forecasting ability. Probabilistic neural network (PNN) is a promising machine learning technique which can be used to forecast financial markets with a higher accuracy. A major limitation of PNN is the assumption of Gaussian distribution as the distribution of input variables which is violated with respect to financial data. The main objective of this study is to improve the standard PNN by incorporating a proper multivariate distribution as the joint distribution of input variables and addressing the multi-class imbalanced problem persisting in the directional prediction of the stock market. This model building process is illustrated and tested with daily close prices of three stock market indices: AORD, GSPC and ASPI and related financial market indices. Results proved that scaled t distribution with location, scale and shape parameters can be used as more suitable distribution for financial return series. Global optimization methods are more appropriate to estimate better parameters of multivariate distributions. The global optimization technique used in this study is capable of estimating parameters with considerably high dimensional multivariate distributions. The proposed PNN model, which considers multivariate scaled t distribution as the joint distribution of input variables, exhibits better performance than the standard PNN model. The ensemble technique: multi-class undersampling based bagging (MCUB) was introduced to handle class imbalanced problem in PNNs is capable enough to resolve multi-class imbalanced problem persisting in both standard and proposed PNNs. Final model proposed in the study with proposed PNN and proposed MCUB technique is competent in forecasting the direction of a given stock market index with higher accuracy, which helps stakeholders of stock markets make accurate decisions.

On the sensibility of the Pairs Trading strategy: the case of the FTS stock market index

2020 ◽  
Vol 38 (3) ◽  
Author(s):  
Ainhoa Fernández-Pérez ◽  
María de las Nieves López-García ◽  
José Pedro Ramos Requena
Keyword(s):  
Standard Deviation ◽  
Stock Market ◽  
Trading Strategy ◽  
Stock Market Index ◽  
Pairs Trading ◽  
Empirical Application ◽  
Statistical Arbitrage ◽  
Market Index ◽  
Standard Deviations ◽  
Using Data

In this paper we present a non-conventional statistical arbitrage technique based in varying the number of standard deviations used to carry the trading strategy. We will show how values of 1 and 1,2 in the standard deviation provide better results that the classic strategy of Gatev et al (2006). An empirical application is performance using data of the FST100 index during the period 2010 to June 2019.

scholarly journals Investors’ Reactions to CSR News in Family Versus Nonfamily Firms: A Study on Signal (In)credibility

2021 ◽  
pp. 104225872110104
Author(s):  
Naciye Sekerci ◽  
Jamil Jaballah ◽  
Marc van Essen ◽  
Nadine Kammerlander
Keyword(s):  
Stock Market ◽  
Event Study ◽  
Family Firms ◽  
Signaling Theory ◽  
Family Firm ◽  
Stock Market Index ◽  
Important Condition ◽  
Post Hoc Analysis ◽  
Market Index ◽  
Post Hoc

We study family firm status as an important condition in signaling theory; specifically, we propose that the market reacts more positively to positive, and more negatively to negative, CSR news (i.e., signals) from family firms than to similar news from nonfamily firms. Moreover, we propose that during recessions, the direction of these relationships reverses. Based on an event study of 1247 positive and negative changes in the CSR ratings for all firms listed on the French SFB120 stock market index (2003-2013), we find support for our hypotheses. Moreover, a post hoc analysis reveals that the relationships are contingent on whether a family CEO leads the firm.

Linkages between cross-country business cycles, cross-country stock market cycles and cross-country real estate market cycles

2016 ◽  
Vol 9 (2) ◽  
pp. 123-146 ◽  
Author(s):  
Kim Hiang Liow
Keyword(s):  
Real Estate ◽  
Stock Market ◽  
Business Cycles ◽  
Spillover Effects ◽  
Real Estate Market ◽  
Stock Market Index ◽  
Content Type ◽  
Market Index ◽  
Cross Country ◽  
The Cross

Purpose This research aims to investigate whether and to what extent the co-movements of cross-country business cycles, cross-country stock market cycles and cross-country real estate market cycles are linked across G7 from February 1990 to June 2014. Design/methodology/approach The empirical approaches include correlation analysis on Hodrick–Prescott (HP) cycles, HP cycle return spillovers effects using Diebold and Yilmaz’s (2012) spillover index methodology, as well as Croux et al.’s (2001) dynamic correlation and cohesion methodology. Findings There are fairly strong cycle-return spillover effects between the cross-country business cycles, cross-country stock market cycles and cross-country real estate market cycles. The interactions among the cross-country business cycles, cross-country stock market cycles and cross-country real estate market cycles in G7 are less positively pronounced or exhibit counter-cyclical behavior at the traditional business cycle (medium-term) frequency band when “pure” stock market cycles are considered. Research limitations/implications The research is subject to the usual limitations concerning empirical research. Practical implications This study finds that real estate is an important factor in influencing the degree and behavior of the relationship between cross-country business cycles and cross-country stock market cycles in G7. It provides important empirical insights for portfolio investors to understand and forecast the differential benefits and pitfalls of portfolio diversification in the long-, medium- and short-cycle horizons, as well as for research studying the linkages between the real economy and financial sectors. Originality/value In adding to the existing body of knowledge concerning economic globalization and financial market interdependence, this study evaluates the linkages between business cycles, stock market cycles and public real estate market cycles cross G7 and adds to the academic real estate literature. Because public real estate market is a subset of stock market, our approach is to use an original stock market index, as well as a “pure” stock market index (with the influence of real estate market removed) to offer additional empirical insights from two key complementary perspectives.

Local Stock Market Integration with International Gold and Oil Price

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Kanon Kumar Sen ◽  
◽  
Md. Thasinul Abedin ◽  
Ratan Ghosh ◽  
◽  
...  
Keyword(s):  
Stock Market ◽  
Market Integration ◽  
Oil Price ◽  
Policy Implications ◽  
Bounds Testing ◽  
Stock Market Index ◽  
Data Set ◽  
Stock Market Integration ◽  
Market Index ◽  
Short Run

We look for the integration of Bangladesh Stock Market with international gold and oil price using most recent monthly data set from January 2003 to December 2020 (2003m1-2020m12). We employ the bounds-testing approach to cointegration between stock market index (DSEX) and international gold and oil price and eventually find an integration and dynamic significant impact of international gold and oil price on DSEX in the long and short-run. We discuss the important policy implications of the dynamic impact of international gold and oil price on stock market index.

scholarly journals The long-Run and Short-Run Analysis between Stock Market Index and Macroeconomic Variables in Jordan: Bounds Tests Approach

2019 ◽  
Vol 12 (4) ◽  
pp. 50
Author(s):  
Raed Walid Al-Smadi ◽  
Muthana Mohammad Omoush
Keyword(s):  
Stock Market ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Stock Market Index ◽  
Macroeconomic Variables ◽  
Market Index ◽  
Long Run ◽  
Equilibrium Relationship ◽  
Short Run

This paper investigates the long-run and short-run relationship between stock market index and the macroeconomic variables in Jordan. Annual time series data for the 1978–2017 periods and the ARDL bounding test are used. The results identify long-run equilibrium relationship between stock market index and the macroeconomic variables in Jordan. Jordanian policy makers have to pay more attention to the current regulation in the Amman Stock Exchange(ASE) and manage it well, thus ultimately helping financial development.

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