Real Financial Market Research Articles

The Application of Markowitz Model and Index Model in the Real Financial Market

This essay aims to analyze the optimal capital allocation in a ten-stock portfolio. The research paper will delve into two big aspects: the introduction of the Markowitz Model (“MM”) and Index Model (“IM”) and the implementation of MM and IM. A recent 20 years of historical daily total return data for ten different stocks, which belong to groups of three to four different sectors (according to Yahoo Finance), one (S&P 500) equity index (a total of eleven risky assets) and a proxy for risk-free rate (one-month Fed Funds rate) will be used. Utilizing these optimization inputs for the Markowitz Model and Index Model, I will find the regions of permissible portfolios (efficient frontier, minimal risk portfolio, optimal portfolio, and minimal return portfolios frontier) for the five cases of the additional constraints.

Exploring the Existence of Efficiency in the Financial Markets

Since the market efficiency hypothesis theory was introduced by Fama in 1970, many investors and researchers argued about the efficiency form of the real financial market. In the real financial market, there are solid pieces of evidence that the price of securities could reflect related public information. However, as real human beings, the investors in the financial market are not always rational and act according to the assumptions stated in the market efficiency theory, which results in a reduction in the efficiency of the real financial market. In this paper, our group tends to use two different views (investors and companies) to prove that all of the market efficiency hypothesis theory assumptions are violated in the real financial market, and the current market efficiency is weak. From an investors view, our group tends to analyze the effect of psychological factors from investors on market efficiency. Furthermore, from the companies side, our group tends to use case analysis to provide solid evidence for the market efficiency analysis.

Portfolio Optimization for 10 Stocks under Constraints

Portfolio optimization is now playing a key role in the financial sector. The aim of this article is to analyze asset allocation across a number of sectors including technology, financial services, and industrials. Ten representative companies from these industries are selected, and then the performance of the portfolios is predicted using the Markowitz model and the Index model. Also, four constraints quantified by real financial market factors are used to observe the differences between the two models. Finally, the comparison of the two models under the same constraint and the comparison of the same model under different constraints are illustrated. The results show that, firstly, The Markowitz and index models are very effective when applied to portfolios of risky assets when combined with capital market lines and efficient frontiers. Secondly, using the S&P 500 index as an indicator of the global stock market, the index is favorable for those who choose a low-risk portfolio. These findings may help investors with specific risk appetites make their own investment decisions.

Portfolio Analysis of Different Industries under Constraints

Building an effective portfolio is critical for it can decrease investment risk and the impact of fluctuations in the share price of a single firm. This paper selects five example stocks from diverse industries, using the mean variance model and the Index model to estimate the best combination of these stocks, then adds five significant limits to both models to see how components in the real financial market effect the portfolio. The Minimal Variance Frontier is used to compare the impact of five constraints in this paper, and it is discovered that different models have different effects under the same constraints, constrained portfolios have less volatility than unconstrained portfolios, and not allowing short positions reduces risk while also lowering the overall return rate. The findings in this paper are valuable to the industry's research on the best allocation of financial assets and can benefit financial market investors.

Analysis of the Limitations of Portfolio Theory

Portfolio theory quantifies the risks and returns in the portfolio for the first time, and puts forward effective frontier model to provide scientific and quantifiable indicators and basis for investors' investment decisions. However, the theory is based on a series of perfect assumptions, which are not true in reality. Therefore, the application of the theory in the real financial market will inevitably have limitations. This paper mainly demonstrates the defects of the hypothesis through the phenomena in China's financial market, and then explains the limitations of portfolio theory. The case study shows that the market is not completely effective. The market will cause non-random stock price fluctuations and the violation of the law of one price due to imperfect information disclosure. In addition, investors are affected by cognitive bias, and their investment behaviour is not completely rational. This study illustrates and confirms the limitations of portfolio theory, and puts forward the direction for improving the model assumptions.

Behavioral Finance: A Study of Literature

The basic objective of this paper is to introduce the definition of different psychological biases that included in behavioural finance, find out the reasons why people behave differently in real life financial market from what is understood by traditional finance, elaborate the meaning of apply behavioural finance in people’s daily life, explain the importance of behavioural finance to modern economics. According to different experiments in previous research papers include mug test, caterpillar effect and several famous tests to explain four different categories of behavioural finance which are framing effect, loss aversion, endowment effect and herd instinct. In each categories described the definition, to connect the behaviour with human habits and give the examples to explain the reason of this psychological biases relate to the real financial market. This paper also includes the analysis of the impact of behavioural finance on human decision-making process and the future prospects of behavioural finance to explain and excavate the future research direction, identify drawbacks in current research content.

Parameters identification for an inverse problem arising from a binary option using a Bayesian inference approach

No-arbitrage property provides a simple method for pricing financial derivatives. However, arbitrage opportunities exist in various fields, even for a very short time. By knowing that an arbitrage property exists, we can adopt a financial trading strategy. This paper investigates the inverse option problems (IOP) in the backward parabolic equation with a suitable initial condition in financial markets. We identify the coefficients of this problem from the measured data and attempt to find arbitrage opportunities in financial markets using a Bayesian inference approach, which is presented as an IOP solution. The posterior probability density function of the parameters is computed from the measured data. The statistics of the unknown parameters are estimated by a Markov Chain Monte Carlo (MCMC) algorithm, which exploits the posterior state space. The efficient sampling strategy of the MCMC algorithm enables us to solve inverse problems by the Bayesian inference technique. Our numerical results indicate that the Bayesian inference approach can simultaneously estimate the unknown trend and volatility coefficients from the artificial measured data and the real financial market data.

Deep reinforcement learning based on transformer and U-Net framework for stock trading

An effective stock-trading strategy offers investors as much profit and as little risk as possible. Capturing volatility trends from historical stock prices and determining trading strategies is extremely challenging. This study proposes an end-to-end model called DRL-UTrans for learning a single stock trading strategy that combines deep reinforcement learning, transformer layers, and a U-Net architecture. In particular, the transformer layer captures complex dynamic patterns in financial markets. The model structure based on the U-Net architecture contains multiple skip connections used to combine long- and short-term features. The input of the model is a windowed stock price sequence, and the output consists of a trading action and action weight. The benefit of having two outputs is that the agent can control the share of buys and sells to reduce investment risk. In addition, a reward function that is sensitive to market volatility is proposed to feed back the market state. Finally, trading data for 10 stocks is extracted from a real financial market to validate the proposed model. The results show that DRL-UTrans has a higher profitability compared with the seven baseline approaches; further, it is effective in sensing market volatility and hedging market risk when encountering stock crashes.

Improved Dynamic Portfolio Selection Model with DCC-GARCH: Evidence from the U.S. Stock Market

As an important financial instrument in real financial market, the selection and management of portfolio has always been a significant and hot issue in the field of economics. This study collects data of stocks daily returns in the real US stock market to test whether DCC-GARCH model can improve the performance of portfolio return compared with the original static model when the assumption of fixed covariance matrix is relaxed. This study also compares the performance of improved portfolio with that of S&P500. The result shows that portfolio returns obtained by the dynamic model outperform the original static portfolio and market performance by a large margin. This shows that, without taking into account transaction costs, an effective way to enhance the traditional mean-variance model is to fit and predict the dynamic covariance using DCC-GARCH model.

Applicability Analysis of CAPM Model in the Context of Epidemic

Under the impact of the epidemic, the economy of all countries has been affected to varying degrees, and the most obvious common one is that the global inflation rate has risen, thus resulting in the phenomenon of purchasing power deviation. The focus of this paper is that the traditional Capital Asset Pricing Model (CAPM) lacks the prediction of this part of the risk, so it can not realize the original intention of THE CAPM Model, completely to wash away this part of the risk. As well as the domestic and foreign research review in recent years, the literature analysis, the conclusion is drawn, in view of the traditional CAPM in the international portfolio investment deficiency, as well as the applicability in the real financial market, this paper suggests that under the purchasing power parity offset, the use of high-order CAPM through the international portfolio investment is a more stable choice.

Corporate Financing Constraints and Information Disclosure: An Analysis of Corporate Investment Dilemmas under the Wave of Counter-Globalization

Open and transparent market information is a prerequisite to maintain the order of financial market and guarantee the normal financing of enterprises. In the real financial market, there is inevitably market information asymmetry. Information asymmetry has become a key factor in corporate financing constraints, and capital market information disclosure is conducive to solve problems such as information asymmetry in the capital market and smooth the financing channels of companies. In the process of economic globalization, some developed countries and industrial classes with damaged interests have set off a wave of counter-globalization, under which export-oriented enterprises in various countries are facing a series of investment dilemmas, which affect their choice of investment direction. In this paper, we study the relationship between corporate financing constraints and information disclosure, introduce information disclosure quality indicators and financing constraint measurement models, and analyze corporate investment dilemmas based on international disputes under the wave of counter-globalization.

Investment Planning Model Based on Quantitative Trading Strategies

Investment, as a financial activity for the general public, has a wide influence on the development of national enterprises. In the research, the main object is Bitcoin and gold, and the investment prediction of short-term investment is made according to the real financial market, and the investment decision prediction model is established by using XGBoost, BP neural network model, entropy value method, coefficient of variation method, linear programming, and value-at-risk model to solve. The topic provides historical data related to bitcoin and gold. First, for the amount of gold and bitcoin increase, with the price deviation rate for data processing. For the amount and increase problem, which is difficult to judge the accuracy, both XGBoost and BP neural network models are used to predict and compare the results, and finally XGBoost, which has higher accuracy, is chosen as the result. After that, the market sentiment is judged according to the obtained data results, and the entropy value method is applied to calculate the weight of relevant indicators to establish the bull and bear market prediction model. Next, the value-at-risk model is applied to establish a risk prediction model. Finally, the predictions for price, market, and risk are combined, and the coefficient of variation method is used to determine the weights of each component to obtain the objective function and design the law of investment behavior as the result of the final prediction model. The initial amount is brought into the model for calculation, and the final return is found to be about $240,000. To assess the sensitivity of the model, three scenarios are discussed and analyzed in terms of changes in transaction costs, gold-only purchases, and bitcoin-only purchases. The three extreme cases are solved by dynamic programming in turn, and it is found that the decision model is very sensitive to changes in transaction costs. And for the case of only enough to buy gold, versus only buying bitcoin, the conclusion finds that the sensitivity is still high, and it can be judged that bitcoin has a high investment potential as an emerging investment product.

Simulation of Financial Market Performance and Algorithmic Economic Model based on Complex Network

At present, traditional macro-financial models such as random walk model and log-periodic power law model in academia cannot explain the stylized characteristics of financial markets. So, we propose a microscopic model that produces stylized characteristics of real financial markets. The model integrates the herding effect, the non-linear relationship of investors and the non-linear structure of the system very well. The research results show that the financial market model established in this paper can simulate most of the characteristics of the real financial market price time series relatively accurately. The model helps to understand the internal operating mechanism of the financial system and provides some basis and reference for the establishment of financial market forecasting models.

Nonlinear fluctuation analysis of statistical physics financial dynamics

Based on the multi-color contact process and the oriented percolation, this paper develops a novel statistical physics financial price model with jumps to simulate and understand the price fluctuation characteristics in the real market. In this model, the interaction between investors and the dynamic changes of their investment attitudes and strategies are simulated by the complex interactions between different states of particles in the multi-color contact process. This is considered to lead to the rise and fall of the prices of financial products and affect the financial market price fluctuations. In addition, the herd behavior of investors facing sudden information outside the market also leads to dramatic price jumps. The model uses the oriented percolation combined with the Poisson process to describe those sudden fluctuations, in which the magnitude of the price jump of financial product is reconstructed by the clusters of partial interactions in the system. Therefore, the model constructs a total price process by considering both normal price fluctuations caused by the interaction of investor attitudes and sudden price jump fluctuations caused by the herd behavior. Based on the proposed model, this paper selects different parameter sets to generate the simulated return series and studies some basic descriptive statistics, stylized facts, multi-fractal property and complexity behavior of the simulated return series. To verify the rationality of the model, this paper conducts the same analysis and comparison on the return series of the real financial market. The outcomes of analysis indicate that the model proposed in this paper can reproduce the main fluctuation characteristics in the real financial market to a certain extent.

Stock Movement Prediction Based on Bi-Typed Hybrid-Relational Market Knowledge Graph Via Dual Attention Networks

Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets. Recent financial studies show that the momentum spillover effect plays a significant role in stock fluctuation. However, previous studies typically only learn the simple connection information among related companies, which inevitably fail to model complex relations of listed companies in real financial market. To address this issue, we first construct a more comprehensive Market Knowledge Graph (MKG) which contains bi-typed entities including listed companies and their associated executives, and hybrid-relations including the explicit relations and implicit relations. Afterward, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DanSmp</small> , a novel Dual Attention Networks to learn the momentum spillover signals based upon the constructed MKG for stock prediction. The empirical experiments on our constructed datasets against nine SOTA baselines demonstrate that the proposed <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DanSmp</small> is capable of improving stock prediction with the constructed MKG.

Robust and accurate construction of the local volatility surface using the Black–Scholes equation

• We present an algorithm to reconstruct a local volatility (LV) function. • We reconstruct the local volatility surface using a nonlinear fitting function. • The results demonstrate the robustness and accuracy of the proposed algorithm. In this study, we develop a numerical method for the robust and accurate construction of a local volatility (LV) surface using the generalized Black–Scholes (BS) equation from the given option price data. The BS equation is a partial differential equation and has been used to model financial option pricing. Constant volatility was used in the classical BS model. However, it is well known that the constant volatility BS model is practically unsuitable because real financial market data demonstrate non-constant volatility behavior. The LV function is dependent on the asset prices and time. One of the difficulties in reconstructing an unknown LV surface is uniqueness. We extend a previous study of reconstructing time-dependent volatility, which is unique, to time- and space-dependent volatility surfaces. We propose an algorithm comprising four steps: the first step is estimating constant implied volatility; the second step is finding the influential region using the probability density function of a log-normal distribution; the third step is calculating the time-dependent volatility function; and the final step is reconstructing the LV surface. We use a finite difference method to numerically solve the BS model and a nonlinear fitting function to compute the LV surface. We perform computational experiments using synthetic and real market data. The numerical results demonstrate the robust and accurate construction of an unknown LV surface using the proposed method.

A Portfolio Selection Model Based on the Interval Number

Traditional portfolio theory uses probability theory to analyze the uncertainty of financial market. The assets’ return in a portfolio is regarded as a random variable which follows a certain probability distribution. However, it is difficult to estimate the assets return in the real financial market, so the interval distribution of asset return can be estimated according to the relevant suggestions of experts and decision makers, that is, the interval number is used to describe the distribution of asset return. Therefore, this paper establishes a portfolio selection model based on the interval number. In this model, the semiabsolute deviation risk function is used to measure the portfolio’s risk, and the solution of the model is obtained by using the order relation of the interval number. At the same time, a satisfactory solution of the model is obtained by using the concept of acceptability of the interval number. Finally, an example is given to illustrate the practicability of the model.

Monotonicity theorem for the uncertain fractional differential equation and application to uncertain financial market

Uncertain fractional differential equations (UFDEs) have non-locality features to reflect memory and hereditary characteristics for the asset price changes, thus are more suitable to model the real financial market. Based on this characteristic, this paper primarily investigates the monotonicity theorem for uncertain fractional differential equations in Caputo sense and its application. Firstly, monotonicity theorems for solutions of UFDEs are presented by using the α-path method. Secondly, as the application of the monotone function theorem, a novel uncertain fractional mean-reverting model with a floating interest rate is presented. Lastly, the pricing formulas of the European and American options are derived for the proposed model based on the monotone function and present extreme values and time integral theorems, respectively. In addition, numerical schemes are designed, and numerical calculations are illustrated concerning different parameters through the predictor–corrector method.

Exponential synchronization and stabilization of delayed feedback hyperchaotic financial system

In a real financial market, the delayed market feedback and the delayed effect of government macrocontrol are inevitable, and both bring mathematical difficulties in studying stabilization and synchronization of the hyperchaotic financial system. However, employing the Lyapunov function method, differential mean value theorem, and suitable bounded hypotheses and pulse control technology result in globally asymptotic stabilization and synchronization criteria. It is the first paper driving the stabilization and synchronization criteria under the assumptions of double delays. Finally, numerical examples illustrate the effectiveness of the proposed methods.

RELIABILITY INDEX AND OPTION PRICING FORMULAS OF THE FIRST-HITTING TIME MODEL BASED ON THE UNCERTAIN FRACTIONAL-ORDER DIFFERENTIAL EQUATION WITH CAPUTO TYPE

Since the ability to control the investor’s income or loss within a certain range, barrier option has been among the most popular path-dependent options where its payoff depends on whether or not the underlying asset’s price reaches a given “barrier”. First, assuming the underlying asset as an uncertain variable for the case that the Caputo fractional-order derivative is adopted instead of the ordinary derivative, the real financial market is better modeled by the uncertain fractional-order differential equation with Caputo type. Then, a first-hitting time model which can measure the exercise ability is innovatively presented. Second, based on the first-hitting time theorem of the uncertain fractional-order differential equation, the reliability index (including validity and survival index) for the proposed model is obtained, and four types of European barrier option (including up-and-in call, down-and-in put, up-and-out put, and down-and-out call options) pricing formulas are obtained accordingly. Lastly, applying the predictor–corrector method, numerical algorithms are provided for calculating European barrier and the reliability index, numerical experiments and corresponding sensitivity analysis are also illustrated concerning various conditions.