Financial Market Prices Research Articles

Herding behavior and financial market price behavior under the COVID-19 pandemic: implications for the Amman Stock Exchange

Purpose — This study investigates herding behavior and the link between herding behavior with the liquidity and volatility of financial markets amid the COVID-19 pandemic.Research method —This study employed the Cross-Sectional Absolute Deviation (CSAD) approach technique for each of the previous and COVID-19 phases, as well as for the complete sample period. The sample includes 172 securities that have been traded in the Amman Stock Exchange from January 2006 to February 2022. The full sample data was separated into three subsamples: 1) the whole period from January 1, 2006, to February 28, 2022; 2) prior the COVID-19 outbreak from January 1, 2006 to February 28, 2020; and 3) during the COVID-19 outbreak from March 1, 2020, to February 28, 2022.Result — The results for the whole sample period show that herding behavior may be detected in the Amman Stock Exchange during down-market intervals, and the result does not change before or during the COVID-19. Furthermore, both down liquidity and down volatility periods exhibit contrary herding behavior. The study found that financial market price behavior is an important factor that may contribute to herding behavior, which occurs when volatility increases and liquidity decreases in the Amman Stock Exchange, and the result does not change prior to the COVID-19 period. While liquidity has a negative and large influence throughout the COVID-19 period, herding tendency does not increase when volatility changes.Recommendation — This research assists traders in planning their purchasing and selling strategies in the COVID-19 various scenarios.

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.

Investigating the informativeness of technical indicators and news sentiment in financial market price prediction

Real-time market prediction tool tracking public opinion in specialized newsgroups and informative market data persuades investors of financial markets. Previous works mainly used lexicon-based sentiment analysis for financial markets prediction, while recently proposed transformer-based sentiment analysis promise good results for cross-domain sentiment analysis. This work considers temporal relationships between consecutive snapshots of informative market data and mood time series for market price prediction. We calculate the sentiment mood time series via the probability distribution of news embedding generated through a BERT-based transformer language model fine-tuned for financial domain sentiment analysis. We then use a deep recurrent neural network for feature extraction followed by a dense layer for price regression. We implemented our approach as an open-source API for real-time price regression. We build a corpus of financial news related to currency pairs in foreign exchange and Cryptocurrency markets. We further augment our model with informative technical indicators and news sentiment scores aligned based on news release timestamp. Results of our experiments show significant error reduction compared to the baselines. Our Financial News and Financial Sentiment Analysis RESTFul APIs are available for public use.

Model Selection and Post Selection to Improve the Estimation of the ARCH Model

The Autoregressive Conditionally Heteroscedastic (ARCH) model is useful for handling volatilities in economical time series phenomena that ARIMA models are unable to handle. The ARCH model has been adopted in many applications that contain time series data such as financial market prices, options, commodity prices and the oil industry. In this paper, we propose an improved post-selection estimation strategy. We investigated and developed some asymptotic properties of the suggested strategies and compared with a benchmark estimator. Furthermore, we conducted a Monte Carlo simulation study to reappraise the relative characteristics of the listed estimators. Our numerical results corroborate with the analytical work of the study. We applied the proposed methods on the S&P500 stock market daily closing prices index to illustrate the usefulness of the developed methodologies.

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.

Research on Financial Market Price Direction Based on ARIMA Model

With the development of financial markets, virtual currencies, represented by bitcoin, are gradually replacing the process of money anchored by gold, and more and more people are choosing these two assets for investment. In this paper, to predict and give trading decisions for bitcoin and gold, we calculate the assetsunit price, increase, and deviation rate. In order to predict the financial data, ARIMA model is used to predict the unit price, rising rate and other indexes. Finally achieved satisfactory results. These prediction results can provide valuable guidance for investors' strategies.

A continuous heterogeneous-agent model for the co-evolution of asset price and wealth distribution in financial market

The heterogeneous agent model (HAM) is a powerful tool to study the stock price dynamics and stylized anomalies in financial markets, such as the volatility clustering and long-range dependence of stock returns. The existing HAMs often require the perfect arbitrage condition and relatively strong behavioral assumptions on agents’ expectation formation, which are not sufficient to capture the complexity in real finance market. To fill the theoretical gap, we propose a novel continuous version of HAM that relies on weaker assumptions, in which heterogeneous agents are identified continuously by their wealth and the ex-post price expectations no matter how they are formed. Given the non-parametric distribution on the continuous agent space and the bounded arbitrage condition, we establish the joint evolution equations for stock price and wealth distribution. Within the family of mixed-exponential distributions, we derive an explicit expression for the equations and prove that the evolution equation of wealth distribution has the fascinating splitting-mixing property. An endogenous generating mechanism for agent types arises naturally from the splitting-mixing property, which links the continuous HAM to the classical HAMs with finite agent types. By numerical simulation, we show that the wealth dynamics tends to positively associate the weight of each generated agent type with their averaged investment performance, while the price dynamics experiences the period-doubling bifurcation and chaotic bifurcation. By introducing random shocks into the continuous HAM, we fit the resulting return series via a GARCH model. Based on the significance of the fitted GARCH parameters, a wide range of model parameters are identified by which the continuous HAM can simultaneously generate volatility clustering, long-range dependence and other stylized anomalies in financial markets.

Financial Literacy in the Age of Green Investment

Abstract We survey a large sample of Swedish households and connect the responses to administrative data to relate pro-environmental attitudes and values to actual investment decisions. Pro-environment households are not more likely to hold pro-environment portfolios. This results from financial disengagement: they are less likely to own stocks, check pension balances, or make green active retirement planning choices. Green financial engagement is stronger in settings where financial literacy is higher or where informational hurdles are lower. Informational barriers appear to prevent financial market prices and returns from fully reflecting household environmental preferences.

From Social Mood to Collective Events: Measuring the Path by Sociometers

How a population/group feels about its future, its so-called “social mood”, and how that mood biases collective events of all types is the focus of this article. Through a variety of “sociometers” running from fashion styles to financial markets behaviour, the paper argues that the social mood dramatically influences the types of events we can expect to see on all time scales. The financial market price movement serve as possibly the best sociometer for measuring the social mood on all time scales. Additionally, I present arguments showing that there is virtually no feedback from events to mood; that is, the social mood is endogeneous to the population and is not determined by any sort of “outside forces”. I hypothesize that it may be mainly determined by “internal ideology” of a group that may be unobvious to its members. Finally, the paper concludes with a research program for turning the hypotheses advanced here into a full-fledged scientific theory of collective human behaviour.

Stock market index prediction based on reservoir computing models

Prediction of the financial market price is critical for financial decision-making and market policy-making. Recently, various machine learning and deep learning methods have been adopted to predict financial markets’ movements using historical time series of prices. However, accurate prediction of financial prices is still a long-standing challenge that always calls for new approaches. In this study, a novel machine learning model of reservoir computing is developed to predict stock market indices. The performance of the proposed new model is systematically evaluated using the time series of daily closing prices of seven major international stock market indices including S&P500 Index, New York Stock Exchange Composite, Dow Jones Industrial Average, Nasdaq Composite Index, Financial Times Stock Exchange 100 Index, Nikkei 225 Index, and Shanghai Stock Exchange Index between January 4, 2010, and December 31, 2018 covering 2,272 trading days. The results show that our model outperforms the widely used deep learning methods of long short-term memory and recurrent neural network in most cases. To further evaluate the predictive capability of our model, we compare our model to the other two newly reported deep learning methods in recent studies. Comparative results also show that our model is competitive to those deep learning methods in predicting stock market indices. Our study contributes to the literature by developing novel reservoir computing models for financial market predictions. Meanwhile, our results also provide practical implications for financial practitioners of potential financial applications of reservoir computing in financial time series analysis and predictions.

When Can Decision Makers Learn from Financial Market Prices?

AbstractI analyze a general setting where a policymaker needs information that financial market traders have in order to implement optimal policy, and prices can potentially reveal this information. Policy decisions, in turn, affect asset values. I derive a condition for the existence of fully revealing equilibria in competitive financial markets, which identifies all situations where learning from prices for policy purposes works. I discuss the possibility of using market information for banking supervision and central banking, and the general problem of asset design. I also demonstrate that some corporate prediction markets are ill‐designed, and show how to fix it.

A novel improved trigonometric neural network algorithm for solving price-dividend functions of continuous time one-dimensional asset-pricing models

Asset pricing model is the pillar of modern financial market price theory. It is of great practical and theoretical significance to solve the equilibrium price-dividend function of the asset pricing model. To solve the asset pricing model, this paper develops a novel neural network method called improved trigonometric neural network, which consists of three parts: the improved trigonometric function, the initial-condition extreme learning machine algorithm and the reduction algorithm. First, the equilibrium price-dividend function is described as a stochastic differential equation and this function is translated into a second-order ordinary differential equation equivalently. Second, the improved trigonometric neural network is used to solve the differential equation with initial conditions, where the improved trigonometric function is used to serve as the activation function. The reduction algorithm is proposed to obtain a simpler network structure and a faster computing speed. Third, numerical experiments of the improved trigonometric neural network show that the numerical solution of the price-dividend function will be obtained precisely, quickly and feasibly. Compared with several methods to solve asset pricing model, the improved trigonometric neural network has the highest accuracy and fastest speed.

Do Inflation Expectations Improve Model-Based Inflation Forecasts?

Those of professional forecasters do. For a wide range of time series models for the euro area and its member states we find a higher average forecast accuracy of models that incorporate information on inflation expectations from the ECB’s SPF and Consensus Economics compared to their counterparts that do not. The gains in forecast accuracy from incorporating inflation expectations are typically not large but significant in some periods. Both short- and long-term expectations provide useful information. By contrast, incorporating expectations derived from financial market prices or those of firms and households does not lead to systematic improvements in forecast performance. Individual models we consider are typically better than univariate benchmarks but for the euro area the professional forecasters are more accurate, especially in recent years (not always for the countries). The analysis is undertaken for headline inflation and inflation excluding energy and food and both point and density forecast are evaluated using real-time data vintages over 2001-2019.

Sailing through the COVID-19 Crisis by Using AI for Financial Market Predictions

The outbreak of COVID-19 has brought the world to an unprecedented position where financial and mental resources are drying up. Livelihoods are being lost, and it is becoming tough to save lives. These are the times to think of unprecedented solutions to the financial challenges being faced. Artificial intelligence (AI) has provided a fresh approach to finance through its implementation in the prediction of financial market prices by promising more generalizable results for stock market forecasting. Immense literature has attempted to apply AI and machine learning for predicting stock market returns and volatilities. The research on the applications of AI in finance lacks a consolidated overview of different research directions, findings, methodological approaches, and contributions. Therefore, there is a need to consolidate the extant literature in this upcoming field to consolidate the findings, identify the research gaps in the existing literature, and set a research agenda for future researchers. This paper addresses this need by synthesizing the extant literature in the form of a systematic review for addressing the use of AI in stock market predictions and interpreting the results in a narrative review. The gap formed through this article is the use of a combination of AI as a subject with the neural network as another area and stock market forecasting as another theme, and it will pave the way for future research studies. The analyses help highlight four important gaps in the existing literature on the subject.

How Much Information Is Incorporated into Financial Asset Prices? Experimental Evidence

Abstract We investigate the informational content of prices in financial asset markets. To do so, we use a large number of market experiments in which the amount of information held by traders is precisely observed. We derive a new method to estimate how much of this information is incorporated into market prices. We find that public information is almost completely reflected in prices but that surprisingly little private information—less than 50%—is incorporated into prices. Our estimates therefore suggest that, while semistrong informational efficiency is consistent with the data, financial market prices may be very far from strong-form efficiency.

Network VAR models to measure financial contagion

Financial contagion among countries can arise from different channels, the most important of which are financial markets and bank lending. The paper aims to build an econometric network approach to understand the extent to which contagion spillovers (from one country to another) aris from financial markets, from bank lending, or from both. To achieve this aim we consider a model specification strategy which combines Vector Autoregressive models with network models. The paper contributes to the contagion literature with a model that can consider bank exposures and financial market prices, jointly and not only separately. From an empirical viewpoint, our results show that both bilateral exposures and market prices act as contagion channels in the transmission of shocks arising from a country to other countries.

Forecasting Prices in Financial Markets Using Artificial Neural Networks

Forecasting Prices in Financial Markets Using Artificial Neural Networks

The Big Shift: From Monetary to Fiscal Policy

This research note investigates why Congress is likely to gain importance with respect to financial market price impacts, relative to monetary policy. Economic theory that claims the primary constraint on government spending is inflation, rather than deficits, has rapidly moved into Overton window, freeing up fiscal policy to have nearly unlimited firepower for tackling underemployment, inequality, climate change, and healthcare. There's a potential Blue Wave eager to address social, economic, and environmental issues through sweeping legislation and rule-making. There's less room left to advance monetary policy, given actions taken already. There are practically infinite levers to pull through new legislation and regulation – this is not the case with the blunt instrument of monetary policy. Proposed policies have specific sets of winners and losers, which leads to more dispersion in prices across companies, relative to the blunter monetary policy tools. This is even more pronounced with Democratic proposals. Within-day-within-sector spread between winner and loser public companies is larger with Democratic proposed policies over the past 6 years of data. More fiscal policy (and especially more of it coming from Democrats) could lead to more policy-driven winners and losers in the public markets.

STOCK PRICE TREND PREDICTION USING SUPPORT VECTOR MACHINE AND CORAL REEF OPTIMIZATION ALGORITHM

Due to non-linearity and non-stationary characteristics of stock market time series data, prior approaches have not been adequate enough for predicting stock market prices. Support vector machines are classifier that have been reported in the literature as having good recognition accuracy and have been applied in the area of predicting financial stock market prices and was found efficient. It is however noted that the performance of the SVM is affected by the values of the hyper-parameters used by the SVM. There is the need to find a way for searching for the best hyper-parameters that optimizes the performance of an SVM model. Coral Reef Optimization (CRO) is one of many nature-inspired algorithms used extensively to solve optimization problems. It is very effective in solving optimization problems because it is able to achieve global optimization. This paper’s contribution is the development of Coral Reef search algorithms for the improvement of the hyper-parameters of the SVM used for stock price trend prediction. The Algorithm is validated using stock data of two banks. The results obtained out-performed un-optimized SVM, and have the same performance as that of SVM optimized with the FireFly optimization algorithm.
 

The Correlation Analysis of Futures Pricing Mechanism in China’s Carbon Financial Market

China, taking the concept of sustainable development as the premise, puts forward Intended Nationally Determined Contributions (INDC) to reduce the greenhouse gas emissions in response to climate change. In this context, with the purpose of seeking solutions to a carbon financial market pricing mechanism to build China’s carbon finance market actively and thus achieving the goal of sustainable development, this paper, based on the autoregressive integrated moving average (ARIMA) model, established a carbon price prediction model for the carbon financial market, and studied the relationship between Certified Emission Reduction (CER) futures prices and spot prices, as well as the relationship between European Union allowances (EUA) futures prices and CER futures prices in an empirical manner. In this paper, EUA and CER futures prices of the European Climate Exchange (ECX) and EUA and CER spot prices of the BlueNext Environmental Exchange were selected as research objects. Granger causality test, co-integration test, and ECM were used to form a progressive econometric analysis framework. The results show that firstly, the ARIMA model can effectively predict carbon futures prices; secondly, CER futures prices cannot guide spot price, and the futures pricing function does not play a role in this market; thirdly, EUA futures price can, in the short term, effectively guide the trend of CER futures prices, with the futures pricing function between the two markets. In the long run, however, the future pricing function of the two markets is not obvious. Therefore, great differences among maturity of the two markets, degree of policy influence, and market share lead to the failure of long-run futures pricing functions.