scholarly journals Neural Networks in Narrow Stock Markets

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1272
Author(s):  
Gerardo Alfonso ◽  
Daniel R. Ramirez
Keyword(s):  
Neural Networks ◽  
Stock Market ◽  
Stock Markets ◽  
Real Economy ◽  
Training Algorithm ◽  
High Concentration ◽  
Wide Range ◽  
Retail Investors ◽  
Local Stock ◽  
Literature Coverage

Narrow markets are typically considered those that due to limited liquidity or peculiarities in its investor base, such as a particularly high concentration of retail investors, make the stock market less efficient and arguably less predictable. We show in this article that neural networks, applied to narrow markets, can provide relatively accurate forecasts in narrow markets. However, practical considerations such as potentially suboptimal trading infrastructure and stale prices should be taken into considerations. There is ample existing literature describing the use of neural network as a forecasting tool in deep stock markets. The application of neural networks to narrow markets have received much less literature coverage. It is however an important topic as having reliable stock forecasting tools in narrow markets can help with the development of the local stock market, potentially also helping the real economy. Neural networks applied to moderately narrow markets generated forecasts that appear to be comparable, but typically not as accurate, as those obtained in deep markets. These results are consistent across a wide range of learning algorithms and other network features such as the number of neurons. Selecting the appropriate network structure, including deciding what training algorithm to use, is a crucial step in order to obtain accurate forecasts.

scholarly journals The joint dynamics of investor beliefs and trading during the COVID-19 crash

2021 ◽  
Vol 118 (4) ◽  
pp. e2010316118
Author(s):  
Stefano Giglio ◽  
Matteo Maggiori ◽  
Johannes Stroebel ◽  
Stephen Utkus
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Real Economy ◽  
Market Outcomes ◽  
Stock Market Crash ◽  
Long Run ◽  
Retail Investors ◽  
Joint Dynamics ◽  
Short Run ◽  
Investor Expectations

We analyze how investor expectations about economic growth and stock returns changed during the February−March 2020 stock market crash induced by the COVID-19 pandemic, as well as during the subsequent partial stock market recovery. We surveyed retail investors who are clients of Vanguard at three points in time: 1) on February 11–12, around the all-time stock market high, 2) on March 11–12, after the stock market had collapsed by over 20%, and 3) on April 16–17, after the market had rallied 25% from its lowest point. Following the crash, the average investor turned more pessimistic about the short-run performance of both the stock market and the real economy. Investors also perceived higher probabilities of both further extreme stock market declines and large declines in short-run real economic activity. In contrast, investor expectations about long-run (10-y) economic and stock market outcomes remained largely unchanged, and, if anything, improved. Disagreement among investors about economic and stock market outcomes also increased substantially following the stock market crash, with the disagreement persisting through the partial market recovery. Those respondents who were the most optimistic in February saw the largest decline in expectations and sold the most equity. Those respondents who were the most pessimistic in February largely left their portfolios unchanged during and after the crash.

scholarly journals Time–Scale Relationship between Securitized Real Estate and Local Stock Markets: Some Wavelet Evidence

2019 ◽  
Vol 12 (1) ◽  
pp. 16 ◽  
Author(s):  
Kim Hiang Liow ◽  
Xiaoxia Zhou ◽  
Qiang Li ◽  
Yuting Huang
Keyword(s):  
Real Estate ◽  
Stock Market ◽  
Time Scale ◽  
Stock Markets ◽  
Real Estate Markets ◽  
Long Run ◽  
Short Run ◽  
The Relationship ◽  
Local Stock ◽  
Domestic Stock

: This study revisits the relationship between securitized real estate and local stock markets by focusing on their time-scale co-movement and contagion dynamics across five developed countries. Since securitized real estate market is an important capital component of the domestic stock market in the respective economies, it is linked to the stock market. Earlier research does not have satisfactory results, because traditional methods average different relationships over various time and frequency domains between securitized real estate and local stock markets. According to our novel wavelet analysis, the relationship between the two asset markets is time–frequency varying. The average long run real estate–stock correlation fails to outweigh the average short run correlation, indicating the real estate markets examined may have become increasingly less sensitive to the domestic stock markets in the long-run in recent years. Moreover, securitized real estate markets appear to lead stock markets in the short run, whereas stock markets tend to lead securitized real estate markets in the long run, and to a lesser degree medium-term. Finally, we find incomplete real estate and local stock market integration among the five developed economies, given only weaker long-run integration beyond crisis periods.

scholarly journals CONFIGURING ARTIFICIAL NEURAL NETWORKS FOR STOCK MARKET PREDICTIONS

2014 ◽  
Vol 20 (1) ◽  
pp. 116-132 ◽  
Author(s):  
Gheorghe Ruxanda ◽  
Laura Maria Badea
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stock Market ◽  
Network Architecture ◽  
Good Choice ◽  
Training Algorithm ◽  
Market Values ◽  
Non Linear ◽  
Artificial Neural ◽  
Business Practitioners

Making accurate predictions for stock market values with advanced non-linear methods creates opportunities for business practitioners, especially nowadays, with highly volatile stock market evolutions. Well suited for approaching non-linear problems, Artificial Neural Networks provide a number of features which make possible reasonably accurate forecasts. But, like the old Latin saying “Primus inter pares”, not all Artificial Neural Networks perform the same, end results depending very much on the network architecture and, more specifically, on the chosen training algorithm. This paper provides suggestions on how to configure Artificial Neural Networks for performing stock market predictions, with an application on the Romanian BET index. Final results are confirmed by testing the trained networks on the Croatian Stock Market data. End remarks entitle Broyden-Fletcher-Goldfarb-Shanno training algorithm as a good choice in terms of model convergence and generalization capacity.

scholarly journals Government expenditures in the support of technological innovations and impact on stock market and real economy: the empirical evidence from the US and Germany

Equilibrium ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. 717-734
Author(s):  
Jana Kotlebova ◽  
Peter Arendas ◽  
Bozena Chovancova
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Industry 4.0 ◽  
Value Added ◽  
Fiscal Sustainability ◽  
Stock Index ◽  
Government Expenditures ◽  
Annual Data ◽  
Real Economy ◽  
The Us

Research background: The current changes in the global stock markets are related to the next wave of the industrial revolution ?Industry 4.0?. It is expected that the Industry 4.0 will lead to an acceleration of the innovation process and growth of volumes of tailor-made products. The stock markets started to react to the upcoming technological changes over the last decade, which are reflected by the changes in the composition of the major stock indices where the technological sector started to grow in importance. But innovations are not only connected with the specialized technological sector, but they are also of direct concern to the whole spectrum of economic entities. Besides the private investments that are usually allocated via the stock market, also the public sector investments play an important role. Purpose of the article: The aim of this paper is to investigate the relationship between government expenditures on research and development (R&D) and stock markets (and GDP) in the US and in Germany. Methods: We use the tools of descriptive analysis as well as correlation and regression methods of estimation. Findings & Value added: Our research confirms that the collection of data on R&D on annual basis for Germany and the US is insufficient for analytical and systemic management purposes. The real effects of investments in the R&D are time lagged. The regression analysis of annual data confirms only the statistical importance of patent applications as well as interest rate and stock index as independent variables in explanation of variability of real economy growth during the 1985?2017 period. Our model did not prove the significance of government expenditures. We can explain it, among others, by the fact that governments do not pay sufficient attention to the challenges yet, which are associated with the Industry 4.0, especially in the US, where the government expenditures in R&D gradually decrease. The governments in both economies try to increase their support, but fiscal sustainability is a limiting factor.

scholarly journals Spillovers of the COVID-19 Pandemic: Impact on Global Economic Activity, the Stock Market, and the Energy Sector

2021 ◽  
Vol 14 (5) ◽  
pp. 200
Author(s):  
Md. Bokhtiar Hasan ◽  
Masnun Mahi ◽  
Tapan Sarker ◽  
Md. Ruhul Amin
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Economic Activity ◽  
Energy Sector ◽  
Real Economy ◽  
Structural Vector Autoregression ◽  
Health Crisis ◽  
World Energy ◽  
Two Factors ◽  
Endogenous Variables

In this study, we examine the effect of the COVID-19 pandemic on global economic activity, the stock market, and the energy sector considering the sizable damaging impacts in these crucial aspects. Our results, based on the structural vector autoregression (SVAR) model for the data from 21 January 2020, to 26 February 2021, indicate that the COVID-19 cases significantly and negatively impact all the endogenous variables such as Baltic dry index (BDI), MSCI world index (MSCI), and MSCI world energy index (MSCIE). Our results also reveal that of the three variables, the stock markets indices (MSCI and MSCIE) are comparatively more affected by COVID-19 cases. The findings imply that the stock markets are more sensitive to the COVID-19 pandemic than the real economy. The results further indicate that of the three variables, the MSCIE index is the most affected by COVID-19 due to two factors: one is the dwindling power consumption caused by COVID-19 and the other is the decline in oil price because of the Russia–OPEC price war. Our findings enhance the understanding of the spillover impacts of the global health crisis on economic activity, the stock market, and the energy sector. Moreover, our study offers insights for policymakers and governments into the relationship dynamics of COVID-19 that would help them be more cautious in taking preventive measures against the health crisis to save the economy, the stock market, and the energy sector from falling into a more deepened crisis.

scholarly journals Stock Market Wealth and the Real Economy: A Local Labor Market Approach

2021 ◽  
Vol 111 (5) ◽  
pp. 1613-1657
Author(s):  
Gabriel Chodorow-Reich ◽  
Plamen T. Nenov ◽  
Alp Simsek
Keyword(s):  
Monetary Policy ◽  
Labor Market ◽  
Stock Market ◽  
Stock Prices ◽  
Market Analysis ◽  
Real Economy ◽  
Local Labor Market ◽  
Percent Increase ◽  
Market Approach ◽  
Local Stock

We provide evidence of the stock market consumption wealth effect by using a local labor market analysis. An increase in local stock wealth driven by aggregate stock prices increases local employment and payroll in nontradable industries and in total, with no effect on employment in tradable industries. In a model of geographic heterogeneity in stock wealth, these responses imply an MPC of 3.2 cents per year and that a 20 percent increase in stock valuations, unless countered by monetary policy, increases the aggregate labor bill by at least 1.7 percent and aggregate hours by at least 0.7 percent two years after the shock. (JEL E21, E24, E52, G12, G51, R22, R23 )

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.

Momentum Effect in Stock Market: Empirical Evidence from Pakistan Stock Exchange

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Shahid Rasheed ◽  
Umar Saood ◽  
Waqar Alam
Keyword(s):  
Stock Market ◽  
Empirical Evidence ◽  
Stock Markets ◽  
Stock Exchange ◽  
Momentum Effect ◽  
Using Data

This study aims to examine the momentum effect presence in selected stocks of Pakistan stock market using data from Jan 2007 to Dec 2016. This study constructed the strategies includes docile, equal weighted and full rebalancing techniques. Data was extracted from the PSX – 100 index ranging from 2007 to 2016. STATA coding ASM software was used for calculating momentum portfolios, finally top 25 stocks were considered as a winner stocks and bottom 25 stocks were taken as a loser stocks. In conclusion, the results of the study found a strong momentum effect in Pakistan stock exchange PSX 100- index. As by results it has been observed that a substantial profit can earn by the investors or brokers in constructing a portfolio with a short formation period of three months and hold for 3, 6 and 12 months. There is hardly a study is present on the same topic on Pakistan Stock Exchange as preceding studies were only conducted on individual stock markets before merger of stock markets in Pakistan while this study leads the explanation of momentum phenomenon in new dimension i.e. Pakistan Stock Exchange. Keywords: Momentum, Portfolio, Winner Stocks, Loser Stocks

An Improved Training Algorithm for Quantum Neural Networks

2014 ◽  
Vol 35 (7) ◽  
pp. 1630-1635
Author(s):  
Yi-peng Zhang ◽  
Liang Chen ◽  
Huan Hao
Keyword(s):  
Neural Networks ◽  
Training Algorithm
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