Real Stock Market Research Articles

The Fisher’s hedge hypothesis: what about homogeneity and stability properties?

The purpose of this study is to see if the Fisher’s hypothesis validation is robust in year or/and countries dimensions. We investigate whether nominal or real stock market returns are hedged against inflation rate, so as to determine the appropriate time and markets to invest in (from the 32 countries) over a period covering the 2008 global finance crisis (GFC) and the Covid 19 outbreak. Hedging property is found to be homogenous within countries and stable in time. Using either nominal or real return, based on cross-sectional data results, Fisher’s hypothesis is generally validated with a few exceptions, while the time-series based results show that the hedge property is robust only in some countries. Using time series data (cross section data), in terms of homogeneity (homogeneity and stability), there is no difference between hedge property between Euro and non-Euro countries (groups of countries or between sub-periods) for both periods covering either 2008 GFC or the Covid 19 outbreak. Robust results are also the outcome of panel data investigations with or without the interest rate role as macro control variable.

Identifying crucial financial markets in the flows of cross-border capital – evidence from China's financial risk network

With the increasing openness of China's financial markets, it is important to address cross-border capital risks. This paper first calculates the stress indicators of financial markets by 24 variables, and then utilizes the Diebold-Yilmaz connectedness approach to examine the spillover effects among the volatility of cross-border capital and the stress indicators of various financial markets. We show both the real estate and stock market are crucial for cross-border capital flow. Using the rolling window method, we find significant time-varying spillovers between cross-border capital and financial markets, which peak during the periods with significant events such as the global financial crisis, the COVID-19 epidemic, and the shift in currency regime. This paper further applies the Markov-switching autoregressive model to identify three different states for the synthetic financial stress. After re-analyzing the subsample data, we observe that stock and real estate markets play significant roles in the low-stress state, foreign exchange and real estate markets in the medium-stress state, and stock and money markets in the high-stress state.

Volatility Spillovers in Real Estate, Virtual Assets, and Stock Markets

Volatility Spillovers in Real Estate, Virtual Assets, and Stock Markets

Mechanisms of investors’ bounded rationality and market herding effect by the stochastic Ising financial model

The widespread herding effect prevalent in China’s stock market is caused by investors’ bounded rationality behavior. However, the underlying mechanisms are not fully understood. This article aims to use the stochastic Ising financial model, which explains this problem from the perspective of Econophysics. The probability density function and power-law distribution of return data simulated by the stochastic Ising financial model show sharp peaks and fat tails, similar to the stylized facts of real Chinese stock market data. Further, we use this financial model to simulate herding behavior in the market. The results show that when the degree of investor irrationality (represented by β) increases, the Herd Effect Index (HERDI) also rises. When β exceeds a critical point, the HERDI index fluctuates more violently. Through empirical analysis, we find that there is an interactive relationship between investors’ bounded rational behavior and market herding effects, and this model can effectively explain these two phenomena.

Unveiling investors' substitution behavior: Stock trading decisions in response to housing market dynamics

Utilizing positive quasi-random shocks to local housing prices in Shanghai, we show that stock investors who experienced significant returns from the real estate market traded less actively, took less risk, and spent less effort trading. We confirm the effect of housing price changes on investors' trading behavior in a national sample. Our findings suggest a substitution effect between the real estate and stock market and highlight the importance of understanding investors' trading behavior in light of intertemporal variations in other asset market.

Research on the application of deep learning techniques in stock market prediction and investment decision-making in financial management

Introduction: This paper introduces a deep learning approach based on Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory Networks (BiLSTM), and attention mechanism for stock market prediction and investment decision making in financial management. These methods leverage the advantages of deep learning to capture complex patterns and dependencies in financial time series data. Stock market prediction and investment decision-making have always been important issues in financial management.Methods: Traditional statistical models often struggle to handle nonlinear relationships and complex temporal dependencies, thus necessitating the use of deep learning methods to improve prediction accuracy and decision effectiveness. This paper adopts a hybrid deep learning model incorporating CNN, BiLSTM, and attention mechanism. CNN can extract meaningful features from historical price or trading volume data, while BiLSTM can capture dependencies between past and future sequences. The attention mechanism allows the model to focus on the most relevant parts of the data. These methods are integrated to create a comprehensive stock market prediction model. We validate the effectiveness of the proposed methods through experiments on real stock market data. Compared to traditional models, the deep learning model utilizing CNN, BiLSTM, and attention mechanism demonstrates superior performance in stock market prediction and investment decision-making.Results and Discussion: Through ablation experiments on the dataset, our deep learning model achieves the best performance across all metrics. For example, the Mean Absolute Error (MAE) is 15.20, the Mean Absolute Percentage Error (MAPE) is 4.12%, the Root Mean Square Error (RMSE) is 2.13, and the Mean Squared Error (MSE) is 4.56. This indicates that these methods can predict stock market trends and price fluctuations more accurately, providing financial managers with more reliable decision guidance. This research holds significant implications for the field of financial management. It offers investors and financial institutions an innovative approach to better understand and predict stock market behavior, enabling them to make wiser investment decisions.

Behavioral factors influencing investment decision-making in the Nigerian real estate stock market

This paper examines the behavioral factors that influence investment decision-making in the Nigerian property stock market, with a survey of registered stockbrokers with the Nigeria Exchange Group (NGX), to provide information for optimal investment decision-making. The study adopted a direct survey method. Relevant information was obtained from registered Nigerian stockbrokers via a closed-end questionnaire, and their responses were analyzed using principal component factor (PCF), and ordinary least square (OLS) regression analyses. The study confirms the influence of behavioral factors on investment decisions in the real estate stock market. Buying decisions dominate during bull markets, but investors prefer selling decisions when the market is down. Six behavioral principal factors were extracted. Investor responsiveness factor explains the dynamics of investment decisions in the optimistic property stock market, while herd effect factor predicts investors' investment behavior in the pessimistic market. The empirical evidence of emotional trading underscores the importance of behavioral factors in achieving significant property stock market performance and optimal investment decision making in the Nigeria real estate stock market.

Developing Life-long Investment Literacy of Students through Initial Public Offerings (IPO) One-Stop Support Center and Stock Trading Simulation

Initial Public Offerings (IPO) One-Stop Support Center website is created to help students, the public and academic researchers. One of the significant features of this website is a stock trading simulation which enables stock trading practice in a simulated or virtual environment. It simulates the experience of trading equities with virtual currency. The main aim of this trading simulation is to offer students and the public (potential retail investors) a risk-free environment to learn and practice trading before entering the real stock market. This study aims to understand the influence of trading simulation as an educational tool in improving students’ lifelong learning skills through the development of investment literacy. This study was conducted using a qualitative approach. To fulfill the objective of this study, an online survey was distributed through a Google Form. A total of 110 respondents consisting of students enrolling in FIN768 (Investment and Portfolio Analysis) course, public, and academics in Universiti Teknologi MARA (UiTM), Shah Alam and Puncak Alam, Selangor took part in the online survey. This study finds that 89.10 percent of the respondents agree that this stock trading simulation is very useful for their life-long learning. This study contributes mainly to the students by providing them with valuable learning experiences and practical skills that extend beyond traditional classroom teachings.

A Comparative Study on Jorion’s Estimator in Contemporary Market

In knowledge of the prominence of Markowitz model in modern portfolio theory, and the following controversy on the appropriate choices of input parameters, this study examines the practical performance of Jorion's estimator as one of the estimation strategies to reduce the estimation error inherent in traditional input parameter. A comprehensive dataset of real stock market returns, encompassing 7 assets and 2 industries, is employed. Jorion's estimator entails the adjustment of the mean of sample return towards the mean of minimum variance portfolio. The results are then integrated into the optimization phase of Markowitz model. To evaluate the effectiveness of Jorion’s estimator, realized returns and return volatility of Markowitz model adapted by Jorion’s estimator are compared against the traditional model. The findings provide insights into the practicability of Jorion's estimator for portfolio management in contemporary capital market. After comparisons, all 3 measures of realized returns from Jorion’s estimator outperforms the counterpart from traditional parameter. This study affirms that the classic sample mean is inadequate as an estimator for future performance, and sophisticated techniques, such as Jorion’s estimator should be employed in real-world investment strategies.

Enhancing Financial Literacy and Investment Skills through a Virtual Stock Trading Application

Currently, there are many real trading applications but there are not many simple stock market simulation applications where users can practice trading in the Indian stock market. We all know about the Stock Market and its losses. we also have heard stories about scams and huge losses in stocks from relatives, friends, and family, But not trying is not the solution to the problem, then what can we do about it? We can learn how it works, practice and then try it in the real market. Then our solution to this problem is to create an application where users can trade stocks with the demo (Virtual) money with real-time experience so that they can learn and afford to make mistakes and learn from those mistakes and make some good fortune in the real stock market and this initiative is focused on Indian audience for there are 58% of US citizens take part in trading activities while in India it is only 2% so by this initiative we think it can India to make more in the Sector of finance

Enhancing Financial Literacy and Investment Skills through a Virtual Stock Trading Application

Currently, there are many real trading applications but there are not many simple stock market simulation applications where users can practice trading in the Indian stock market. We all know about the Stock Market and its losses. we also have heard stories about scams and huge losses in stocks from relatives, friends, and family, But not trying is not the solution to the problem, then what can we do about it? We can learn how it works, practice and then try it in the real market. Then our solution to this problem is to create an application where users can trade stocks with the demo (Virtual) money with real-time experience so that they can learn and afford to make mistakes and learn from those mistakes and make some good fortune in the real stock market and this initiative is focused on Indian audience for there are 58% of US citizens take part in trading activities while in India it is only 2% so by this initiative we think it can India to make more in the Sector of finance.

Enhancing Financial Literacy and Investment Skills through a Virtual Stock Trading Application

Currently, there are many real trading applications but there are not many simple stock market simulation applications where users can practice trading in the Indian stock market. We all know about the Stock Market and its losses. we also have heard stories about scams and huge losses in stocks from relatives, friends, and family, But not trying is not the solution to the problem, then what can we do about it? We can learn how it works, practice and then try it in the real market. Then our solution to this problem is to create an application where users can trade stocks with the demo (Virtual) money with real-time experience so that they can learn and afford to make mistakes and learn from those mistakes and make some good fortune in the real stock market and this initiative is focused on Indian audience for there are 58% of US citizens take part in trading activities while in India it is only 2% so by this initiative we think it can India to make more in the Sector of finance. Key Words: Virtual stock trading, Stock market simulation, Financial literacy, Investment education, Online trading platforms, Stock market knowledge, Risk management, Investment skills, User experience, Financial education, Stock trading simulation, Virtual investing, Educational technology, Portfolio management.

Using The Regression Method to Discover the Relationship Between Real Estate and Stock Market

Recently, with the economic fluctuation, the relationship between house price and the stock market index has been paying much attention. However, this relationship has not been fully clarified yet through historical research. In this paper, several regression models including linear regression model and non-linear regression model are constructed to test for both single and dual relationship between the house price and market index. The assumption and method used in building the model will also be discussed. From testing, there is a positive linear effect on house price from the market index (approximately 80% of house price variation has been explained by the market index). On the opposite side, house price affects 93% of the market index in a positive non-linear pattern. Besides that, GDP can explain around 90% of house price variation in a positive linear relationship. This paper aims to help investors to have a better understanding of the relationship between these two and to help them build a better investment portfolio.

A study of financial market resilience in China — From a hot money shock perspective

Given the huge impact of hot money on the China's financial market, it is crucial to evaluate and improve the ability to withstand such shocks. This paper measures the resilience of China's financial markets to hot money shocks, and examines the connectedness mechanism among submarket resilience. Our findings indicate that, first, China's overall financial market resilience is characterized by cyclical changes. Among all markets, the bond market exhibits the strongest ability to absorb shocks, while the real estate market shows the weakest. Second, there is a considerable association between financial submarket resilience, which is further strengthened by panic and pessimistic expectations in tail scenarios. The stock market holds the central position in the resilience connectedness structure and shows the greatest impact on other markets. This study suggests that focusing on real estate and stock market conditions is the key to accelerating financial market recovery from hot money shocks.

Neural Networks for Portfolio Analysis With Cardinality Constraints.

Portfolio analysis is a crucial subject within modern finance. However, the classical Markowitz model, which was awarded the Nobel Prize in Economics in 1991, faces new challenges in contemporary financial environments. Specifically, it fails to consider transaction costs and cardinality constraints, which have become increasingly critical factors, particularly in the era of high-frequency trading. To address these limitations, this research is motivated by the successful application of machine learning tools in various engineering disciplines. In this work, three novel dynamic neural networks are proposed to tackle nonconvex portfolio optimization under the presence of transaction costs and cardinality constraints. The neural dynamics are intentionally designed to exploit the structural characteristics of the problem, and the proposed models are rigorously proven to achieve global convergence. To validate their effectiveness, experimental analysis is conducted using real stock market data of companies listed in the Dow Jones Index (DJI), covering the period from November 8, 2021 to November 8, 2022, encompassing an entire year. The results demonstrate the efficacy of the proposed methods. Notably, the proposed model achieves a substantial reduction in costs (which combines investment risk and reward) by as much as 56.71% compared with portfolios that are averagely selected.

Machine learning-based analysis of volatility quantitative investment strategies for American financial stocks

<abstract><p>Volatility, a pivotal factor in the financial stock market, encapsulates the dynamic nature of asset prices and reflects both instability and risk. A volatility quantitative investment strategy is a methodology that utilizes information about volatility to guide investors in trading and profit-making. With the goal of enhancing the effectiveness and robustness of investment strategies, our methodology involved three prominent time series models with six machine learning models: K-nearest neighbors, AdaBoost, CatBoost, LightGBM, XGBoost, and random forest, which meticulously captured the intricate patterns within historical volatility data. These models synergistically combined to create eighteen novel fusion models to predict volatility with precision. By integrating the forecasting results with quantitative investing principles, we constructed a new strategy that achieved better returns in twelve selected American financial stocks. For investors navigating the real stock market, our findings serve as a valuable reference, potentially securing an average annualized return of approximately 5 to 10% for the American financial stocks under scrutiny in our research.</p></abstract>

DTSMLA: A dynamic task scheduling multi-level attention model for stock ranking

Predicting stock ranking is a complex and challenging task due to the intricate nature of real stock market systems. There are two main obstacles for current methods to directly using historical trading data to predict stock ranking: (1) Predicting stock rankings is a complex process influenced by a variety of factors, such as the temporal dependence of each stock, the spatial correlation between stocks, and the influence of the market on the stock; (2) The stock market’s considerable noise creates volatility in directly predicting stock rankings on a given day, presenting difficulties in training models. To overcome these two challenges, a dynamic task scheduling multi-level attention model (DTSMLA) is proposed to enhance stock ranking prediction through market index and multi-task learning. In response to complexity of stock data, we synthesize the factors affecting stocks and propose a multi-level attention prediction framework. To address the noise issue, we introduce multi-task learning and use a task scheduler algorithm to dynamically select auxiliary tasks for model training. Experimental results conducted on four real-world stock datasets have demonstrated that our method outperforms several state-of-the-art approaches.

Ascending-price mechanism for general multi-sided markets

We present an ascending-price mechanism for a multi-sided market with a variety of participants, such as manufacturers, logistics agents, insurance providers, and assemblers. Each deal in the market may consist of a combination of agents from separate categories, and different such combinations are simultaneously allowed. This flexibility lets multiple intersecting markets be resolved as a single global market. Our mechanism is obviously-truthful, strongly budget-balanced, individually rational, and attains almost the optimal gain-from-trade when the market for every allowed combination of categories is sufficiently large. We evaluate the performance of the suggested mechanism with experiments on real stock market data and synthetically produced data.

Forecasting Stock Market Volatility via Causal Reasoning

Abstract Studies have shown that Internet financial news has become an important reference for investors in investment behavior. In order to simulate trading experiments that mimic the real stock market, this paper develops a stock volatility prediction model based on causal reasoning. It also gathers and cleans news and stock market data from the Internet, such as opening price, closing price, and change. The findings of the study indicate that the level of stock market volatility can be significantly influenced by online financial news. The proposed model can analyze the effects of news and stock market data in an explainable manner.

Corporation Selection in Terms of Payout, Growth, Profitability and Valuation Indicators

Investment strategy and corporation selection are the important measures to help investors to beat the market by using their limited capital. Financial indicator measure is used as a common tool in corporation stock selection to help the investors to build their portfolio. This paper covers the data from 2015 to 2021 to examine the key financial ratios which include payout, growth, profitability, and corporation valuation of Procter & Gamble and its competitors in the industry. After the comparison of payout policy, profitability, capitalization and growth, PG will be the best corporation to invest. However, the research is limited due to the short run time and ignores the business cycle factors (e.g., inflation and acquires and merges). In the future research, it is suggested that a more complicated model would be used to help investors to examine more indicators. Overall, PG is one optimal investment decision in the industry which could help investors to win in the short run decision. These results shed light on corporation selection, portfolio management and financial ratio analysis in the real stock market practice.