scholarly journals A stylized macro-model with interacting real, monetary and stock markets

2021 ◽  
Author(s):  
F. Cavalli ◽  
A. Naimzada ◽  
N. Pecora
Keyword(s):  
Stock Market ◽  
Interest Rates ◽  
Stock Prices ◽  
Stock Markets ◽  
Stock Price ◽  
Market Integration ◽  
Dynamic Properties ◽  
National Income ◽  
Money Market ◽  
The Business Cycle

AbstractWe propose a model economy consisting of interdependent real, monetary and stock markets. The money market is influenced by the real one through a standard LM equation. Private expenditures depend on stock prices, which in turn are affected by interest rates and real profits, as these contribute to determine the participation level in the stock market. An evolutionary mechanism regulates agents’ participation in the stock market on the basis of a fitness measure that depends on the comparison between the stock return and the interest rate. Relying on analytical investigations complemented by numerical simulations, we study the economically relevant static and dynamic properties of the equilibrium, identifying the possible sources of instabilities and the channels through which they spread across markets. We aim at understanding what micro- and macro-factors affect the dynamics and, at the same time, how the dynamics of asset prices, which are ultimately influenced by the money market, behave over the business cycle. Starting from isolated markets, we show the effect of increasing the market interdependence on the national income, the stock price and the share of agents that participate in the stock market at the equilibrium. Moreover, we investigate the stabilizing/destabilizing role of market integration and the possible emergence of out-of-equilibrium dynamics.

The stock market over the business cycle

2021 ◽  
pp. 126-141
Author(s):  
Jesper Rangvid
Keyword(s):  
Monetary Policy ◽  
Stock Market ◽  
Business Cycle ◽  
Interest Rates ◽  
Stock Prices ◽  
Stock Price ◽  
Academic Research ◽  
Central Banks ◽  
Discount Rates ◽  
The Business Cycle

This chapter describes how the stock market relates to the business cycle. Stocks do badly during recessions and excellently during expansions. Earnings of firms drop during recessions. Stock prices drop as well, whereas dividends do not. This means that the stock-price dividend multiple contracts during recessions. If stock prices drop by more than dividends, it must be because investors have increased their expectations of future discount rates and/or lowered their expectations to future dividend/earnings growth. The chapter discusses the academic research on this issue. The chapter also shows that bonds do better than stocks during recessions. This has not least to do with the fact that central banks lower the monetary policy rate during recessions.Lower interest rates lead to higher bond prices, causing bonds to perform well during recessions.

Monetary policy, interest rates and stock markets

2021 ◽  
pp. 155-171
Author(s):  
Jesper Rangvid
Keyword(s):  
Monetary Policy ◽  
Stock Market ◽  
Business Cycle ◽  
Interest Rates ◽  
Stock Markets ◽  
Economic Activity ◽  
Negative Impact ◽  
Financial Assets ◽  
Government Bonds ◽  
The Business Cycle

This chapter examines how monetary policy, in itself and through its dependence on the business cycle, affects prices on financial assets. The chapter shows that changes in the monetary policy rate affect yields on government bonds with longer maturity as well as corporate bonds. This typically dampens economic activity. Changes in monetary policy typically also have a negative impact on the stock market. The chapter discusses whether monetary policy in itself affects the stock market or whether it works via its effect on the business cycle. It turns out that economic activity in itself, and monetary policy in itself, both affect the stock market. It is important to be aware of both channels, i.e. how economic activity affects the stock market and how monetary policy affects the stock market.

Monetary policy and the business cycle

2021 ◽  
pp. 142-154
Author(s):  
Jesper Rangvid
Keyword(s):  
Monetary Policy ◽  
Stock Market ◽  
Business Cycle ◽  
Interest Rates ◽  
Stock Prices ◽  
House Prices ◽  
Policy Instruments ◽  
Central Banks ◽  
Consumer Prices ◽  
The Business Cycle

This chapter describes monetary policy. Monetary policy aims at keeping consumer prices stable and the financial system well-functioning. Monetary policy is conducted by central banks. To achieve their goals, central banks use their monetary policy instruments, the most important of which is the monetary policy rate. By changing the short interest rate, central banks influence financial markets, first via its influence over other interest rates (longer interest rates on government bonds, interest rates on commercial debt, mortgage rates, etc.) and then via spill-overs to other asset prices, such as stock prices, exchange rates, house prices, etc. Changes in monetary policy thereby influence the business cycle and its future path. When monetary policy influences the business cycle, and the business cycle influences the stock market, there are good reasons to believe that monetary policy also influences the stock market.

scholarly journals Stock Market Analysis: A Review and Taxonomy of Prediction Techniques

2019 ◽  
Vol 7 (2) ◽  
pp. 26 ◽  
Author(s):  
Dev Shah ◽  
Haruna Isah ◽  
Farhana Zulkernine
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Markets ◽  
Stock Price ◽  
Machine Learning Techniques ◽  
Great Promise ◽  
Stock Market Prediction ◽  
Stock Analysis ◽  
Prediction Techniques ◽  
Price Analysis

Stock market prediction has always caught the attention of many analysts and researchers. Popular theories suggest that stock markets are essentially a random walk and it is a fool’s game to try and predict them. Predicting stock prices is a challenging problem in itself because of the number of variables which are involved. In the short term, the market behaves like a voting machine but in the longer term, it acts like a weighing machine and hence there is scope for predicting the market movements for a longer timeframe. Application of machine learning techniques and other algorithms for stock price analysis and forecasting is an area that shows great promise. In this paper, we first provide a concise review of stock markets and taxonomy of stock market prediction methods. We then focus on some of the research achievements in stock analysis and prediction. We discuss technical, fundamental, short- and long-term approaches used for stock analysis. Finally, we present some challenges and research opportunities in this field.

scholarly journals Dynamic Communities in Stock Market

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Xiangquan Gui ◽  
Li Li ◽  
Jie Cao ◽  
Lian Li
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Markets ◽  
Stock Price ◽  
Competition And Cooperation ◽  
New Findings ◽  
Fundamental Understanding ◽  
Prediction And Control ◽  
Dynamic Communities ◽  
And Control

The stock market has the huge effect and influence on a country or region’s economic and financial activities. But we have found that it is very hard for the prediction and control. This illustrates a critical need for new and fundamental understanding of the structure and dynamics of stock markets. Previous research and analysis on stock markets often focused on some assumptions of the game of competition and cooperation. Under the condition of these assumptions, the conclusions often reflect just part of the problem. The stock price is the core reflections of a stock market. So, in this paper, the authors introduce a methodology for constructing stock networks based on stock prices in a stock market and detecting dynamic communities in it. This strategy will help us from a new macroperspective to explore and mine the characteristics and laws hiding in the big data of stock markets. Through statistical analysis of many characteristics of dynamic communities, some interesting phenomena are found in this paper. These results are new findings in finance data analysis field and will potentially contribute to the analysis and decision-making of a financial market. The method presented in this paper can also be used to analyze other similar financial systems.

scholarly journals Turn-of-the-month effects in European stock markets before and after the financial crisis – An evolutionary finance perspective

2017 ◽  
Vol 15 (1) ◽  
pp. 90-99
Author(s):  
Thomas Holtfort ◽  
Andreas Horsch ◽  
Steffen Hundt
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Stock Markets ◽  
Stock Price ◽  
Special Focus ◽  
Evolutionary Finance ◽  
Before And After ◽  
The Business Cycle ◽  
Study Techniques ◽  
European Stock Markets

The wealth of owners of stock corporations is exposed to various phenomena affecting stock market prices. Of these calendar anomalies, we examine the turn-of-the-month (TOM) effect. Previous literature reveals only mixed results with regard to (changes of) the TOM pattern. Therefore, this paper aims to provide further insights by a comparison of crisis and non-crisis periods, applying an evolutionary finance approach, which is based on computational agent-based modelling. We analyse stock price developments in six European stock markets for the period 2000-2014 with a special focus on the financial crisis. For this purpose, we apply parametric and nonparametric event study techniques and find explanations of this effect, like volatility, trade volume and the business cycle. After testing for external factors, the study takes an alternative perspective based on the evolutionary finance approach, which is based on the biological principles of selection, mutation and dependence and shows the effects of shifted investment capital induced by revised strategies of investors who enter and exit corporate ownership by buying and selling at the stock market.

Financial integration in emerging economies: an application of threshold cointegration

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Sajid Ali ◽  
Mobeen Ur Rehman ◽  
Syed Jawad Hussain Shahzad ◽  
Naveed Raza ◽  
Xuan Vinh Vo
Keyword(s):  
South Africa ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Markets ◽  
Stock Price ◽  
Financial Integration ◽  
Good News ◽  
Speed Of Adjustment ◽  
Long Run ◽  
Indian Stock Market

AbstractThe asymmetric short – and long-run relationships between BRICS stock markets are examined using monthly stock price data from January 2001 through December 2014. The asymmetric co-integration analysis confirms the presence of a long-run association between the BRICS stock markets; where, the speed of adjustment to the negative shocks is higher and statistically significant for the Brazil-India and China-India pairs, which indicates quick adjustment of stock prices to bad news compared to good news. Conversely, the speed of adjustment for Indian and South African stock markets is higher for positive shocks, while the relationship between the stock markets pair of Russia and South Africa is linear. The results of asymmetric error correction model (AECM) reveal evidence of bidirectional causality between China-India, India-South Africa and South Africa-Russia, while unidirectional causality runs from the Indian to Brazilian stock market. Thus, we can safely conclude that the Indian stock market has long-run and short-run relationships with most of the other stock markets. This suggests that investors should pay attention to the Indian stock market when investing in BRICS stock markets.

Macroeconomic Factors and Pakistani Equity Market”

2004 ◽  
Vol 43 (4II) ◽  
pp. 619-637 ◽  
Author(s):  
Muhammad Nishat ◽  
Rozina Shaheen
Keyword(s):  
Stock Market ◽  
Industrial Production ◽  
Stock Prices ◽  
Stock Price ◽  
Consumer Price Index ◽  
Reverse Causality ◽  
Macroeconomic Factors ◽  
Macroeconomic Variables ◽  
Equilibrium Relationships

This paper analyzes long-term equilibrium relationships between a group of macroeconomic variables and the Karachi Stock Exchange Index. The macroeconomic variables are represented by the industrial production index, the consumer price index, M1, and the value of an investment earning the money market rate. We employ a vector error correction model to explore such relationships during 1973:1 to 2004:4. We found that these five variables are cointegrated and two long-term equilibrium relationships exist among these variables. Our results indicated a "causal" relationship between the stock market and the economy. Analysis of our results indicates that industrial production is the largest positive determinant of Pakistani stock prices, while inflation is the largest negative determinant of stock prices in Pakistan. We found that while macroeconomic variables Granger-caused stock price movements, the reverse causality was observed in case of industrial production and stock prices. Furthermore, we found that statistically significant lag lengths between fluctuations in the stock market and changes in the real economy are relatively short.

scholarly journals Building stock market resilience through digital transformation: using Google trends to analyze the impact of COVID-19 pandemic

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Ding Ding ◽  
Chong Guan ◽  
Calvin M. L. Chan ◽  
Wenting Liu
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Digital Transformation ◽  
Google Trends ◽  
Building Stock ◽  
Market Sentiment ◽  
Price Performance ◽  
Novel Coronavirus ◽  
The Impact

Abstract As the 2019 novel coronavirus disease (COVID-19) pandemic rages globally, its impact has been felt in the stock markets around the world. Amidst the gloomy economic outlook, certain sectors seem to have survived better than others. This paper aims to investigate the sectors that have performed better even as market sentiment is affected by the pandemic. The daily closing stock prices of a total usable sample of 1,567 firms from 37 sectors are first analyzed using a combination of hierarchical clustering and shape-based distance (SBD) measures. Market sentiment is modeled from Google Trends on the COVID-19 pandemic. This is then analyzed against the time series of daily closing stock prices using augmented vector autoregression (VAR). The empirical results indicate that market sentiment towards the pandemic has significant effects on the stock prices of the sectors. Particularly, the stock price performance across sectors is differentiated by the level of the digital transformation of sectors, with those that are most digitally transformed, showing resilience towards negative market sentiment on the pandemic. This study contributes to the existing literature by incorporating search trends to analyze market sentiment, and by showing that digital transformation moderated the stock market resilience of firms against concern over the COVID-19 outbreak.

Through the Lens of Recession 2.0: Diversification Dynamics Between the Leading Asian Stock Markets

2021 ◽  
pp. 097226292098395
Author(s):  
Manu K. S. ◽  
Surekha Nayak ◽  
Rameesha Kalra
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Market Integration ◽  
Developing Economies ◽  
Positive Impact ◽  
Global Market ◽  
Stock Market Integration ◽  
Developed Economies ◽  
Market Participants ◽  
Causality Tests

The focus of this article is to analyse the inter-linkages between eight leading stock markets in Asian continent from the period of July 2011 to February 2018. This period holds relevance as this was the time when Recession 2.0 set in, which adversely affected the developed economies; however, the developing economies withstood the crisis without much of an impact. Co-integration and Granger causality tests were conducted to probe the inter-linkages. Study revealed a positive impact on Asian stock market indices collectively on each of the indexes. The highest number of unidirectional causalities was to KOPSI and NIFTY from rest of the stock indices. Results confirmed that no co-integration relationship existed among the selected indices indicating favourable diversification opportunities. Thus, the study fosters global market participants and policymakers to consider the nitty-gritties of stock market integration so as to benefit from international stock market diversification in the Asian region.

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