scholarly journals Multi-Factorized Semi-Covariance of Stock Markets and Gold Price

2021 ◽  
Vol 14 (4) ◽  
pp. 172
Author(s):  
Yun Shi ◽  
Lin Yang ◽  
Mei Huang ◽  
Jun Steed Huang
Keyword(s):  
Stock Market ◽  
Financial Market ◽  
Gold Price ◽  
Fractional Dimension ◽  
Significant Interest ◽  
Calculation Results ◽  
Complex Models ◽  
Market Leader ◽  
Market Trends ◽  
Insight Into

Complex models have received significant interest in recent years and are being increasingly used to explain the stochastic phenomenon with upward and downward fluctuation such as the stock market. Different from existing semi-variance methods in traditional integer dimension construction for two variables, this paper proposes a simplified multi-factorized fractional dimension derivation with the exact Excel tool algorithm involving the fractional center moment extension to covariance, which is a complex parameter average that is a multi-factorized extension to Pearson covariance. By examining the peaks and troughs of gold price averages, the proposed algorithm provides more insight into revealing underlying stock market trends to see who is the financial market leader during good economic times. The calculation results demonstrate that the complex covariance is able to distinguish subtle differences among stock market performances and gold prices for the same field that the two variable covariance may overlook. We take London, Tokyo, Shanghai, Toronto, and Nasdaq as the representative examples.

scholarly journals Multi-Factorized Semi-covariance of Stock Markets and Gold Price

2021 ◽  
Author(s):  
Jun Huang ◽  
Mei Huang ◽  
Lin Yang ◽  
Yun Shi
Keyword(s):  
Stock Market ◽  
Financial Market ◽  
Gold Price ◽  
Fractional Dimension ◽  
Significant Interest ◽  
Calculation Results ◽  
Complex Models ◽  
Market Leader ◽  
Market Trends ◽  
Insight Into

Complex models have received significant interest in recent years and are being increasingly used to explain the stochastic phenomenon with upward and downward fluctuation such as the stock market. Different from existing semi-variance methods in traditional integer dimension construction for two variables, this paper proposes a simplified multi-factorized fractional dimension derivation with the exact Excel tool algorithm involving the fractional center moment extension to covariance, which is a complex parameter average that is a multi-factorized extension to Pearson covariance. By examining the peaks and troughs of gold price averages, the proposed algorithm provides more insight into revealing underlying stock market trends to see who is the financial market leader during good economic times. The calculation results demonstrate that the complex covariance is able to distinguish subtle differences among stock market performances and gold prices for the same field that the two variable covariance may overlook. We take the London, Tokyo, Shanghai, Toronto and Nasdaq as the representative examples.

scholarly journals A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3094
Author(s):  
Li-Chen Cheng ◽  
Yu-Hsiang Huang ◽  
Ming-Hua Hsieh ◽  
Mu-En Wu
Keyword(s):  
Stock Market ◽  
Financial Market ◽  
Stock Prices ◽  
Trading Volume ◽  
Prediction Models ◽  
Trading Strategies ◽  
Q Learning ◽  
Price Prediction ◽  
Market Trends ◽  
Stock Market Investors

The prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Investors put their money into the financial market, hoping to maximize profits by understanding market trends and designing trading strategies at the entry and exit points. Most studies propose machine learning models to predict stock prices. However, constructing trading strategies is helpful for traders to avoid making mistakes and losing money. We propose an automatic trading framework using LSTM combined with deep Q-learning to determine the trading signal and the size of the trading position. This is more sophisticated than traditional price prediction models. This study used price data from the Taiwan stock market, including daily opening price, closing price, highest price, lowest price, and trading volume. The profitability of the system was evaluated using a combination of different states of different stocks. The profitability of the proposed system was positive after a long period of testing, which means that the system performed well in predicting the rise and fall of stocks.

Methods for Determining Stock Market Trends

1946 ◽  
Vol 2 (3) ◽  
pp. 12-21
Author(s):  
Ralph Rotnem
Keyword(s):  
Stock Market ◽  
Market Trends

Magical banking capital: Neo-endogenous money (NEM)

2020 ◽  
Vol 1 (1) ◽  
pp. 27-35
Author(s):  
Sovanbrata Talukdar
Keyword(s):  
Stock Market ◽  
Central Bank ◽  
Financial Market ◽  
Positive Relationship ◽  
Excess Return ◽  
Control Individual ◽  
Financial Constraint ◽  
Imperfect Market ◽  
External Finance ◽  
Endogenous Money

This research emerges with internal financial constraint. How financial constraint may lead to economic recess or back. This financial constraint is different than external finance constraint, and is not due to lack of gold, etc. It explains the positive relationship between excess return in stock market (ERSM) and non-real funding or riskier credit. The matter comes under imperfect market banking. It includes subsequently banking behavior and failure of central bank policy to control individual banks under these circumstances. In addition, it presents measures to get awareness before default comes, as financial default rare and crisis in financial market comes much before that.

scholarly journals Sugar Prices vs. Financial Market Uncertainty in the Time of Crisis: Does COVID-19 Induce Structural Changes in the Relationship?

Agriculture ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 93
Author(s):  
Pavel Kotyza ◽  
Katarzyna Czech ◽  
Michał Wielechowski ◽  
Luboš Smutka ◽  
Petr Procházka
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Financial Market ◽  
Structural Breaks ◽  
Structural Changes ◽  
Global Financial Crisis ◽  
Market Uncertainty ◽  
Volatility Index ◽  
The Global Financial Crisis ◽  
The Relationship

Securitization of the agricultural commodity market has accelerated since the beginning of the 21st century, particularly in the times of financial market uncertainty and crisis. Sugar belongs to the group of important agricultural commodities. The global financial crisis and the COVID-19 pandemic has caused a substantial increase in the stock market volatility. Moreover, the novel coronavirus hit both the sugar market’s supply and demand side, resulting in sugar stock changes. The paper aims to assess potential structural changes in the relationship between sugar prices and the financial market uncertainty in a crisis time. In more detail, using sequential Bai–Perron tests for structural breaks, we check whether the global financial crisis and the COVID-19 pandemic have induced structural breaks in that relationship. Sugar prices are represented by the S&P GSCI Sugar Index, while the S&P 500 option-implied volatility index (VIX) is used to show stock market uncertainty. To investigate the changes in the relationship between sugar prices and stock market uncertainty, a regression model with a sequential Bai–Perron test for structural breaks is applied for the daily data from 2000–2020. We reveal the existence of two structural breaks in the analysed relationship. The first breakpoint was linked to the global financial crisis outbreak, and the second occurred in December 2011. Surprisingly, the COVID-19 pandemic has not induced the statistically significant structural change. Based on the regression model with Bai–Perron structural changes, we show that from 2000 until the beginning of the global financial crisis, the relationship between the sugar prices and the financial market uncertainty was insignificant. The global financial crisis led to a structural change in the relationship. Since August 2008, we observe a significant and negative relationship between the S&P GSCI Sugar Index and the S&P 500 option-implied volatility index (VIX). Sensitivity analysis conducted for the different financial market uncertainty measures, i.e., the S&P 500 Realized Volatility Index confirms our findings.

scholarly journals Factors Affecting the Performance of Kuwait Stock Market

2016 ◽  
Vol 9 (5) ◽  
pp. 23
Author(s):  
Ebrahim Merza ◽  
Sayed-Abbas Almusawi
Keyword(s):  
Interest Rate ◽  
Real Estate ◽  
Stock Market ◽  
Banking Sector ◽  
Stock Exchange ◽  
Consumer Price Index ◽  
Gold Price ◽  
Oil Price ◽  
Factors Affecting ◽  
Insurance Sector

<p>This paper aims at finding the effective factors that influence three sectors in Kuwait stock exchange market (KSE) in addition to the whole stock market. The three sectors are banking, real estate and insurance sectors. The paper measures KSE performance through the average share prices calculated on a quarterly basis starting from 2005 until first quarter of 2015. It is found that each sector behaves differently towards macroeconomic variables. The most important determinants for the KSE overall market performance were found to be gold price and the deposits rate. Individually, the banking sector is influenced by consumer price index, interest rate on loans, oil price and gold price. The insurance sector is influenced by money supply, residential real estate price and oil price. The real estate sector is influenced by the exchange rate with respect to US dollars, interest rate on loans, oil price and gold price.</p>

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