Financial Market Prices Research Articles

Are the leading indicators really leading? Evidence from mixed-frequency spillover approach

Leading indicators, usually measured at low frequency (e.g., monthly or quarterly), are widely used as pioneering indices to foresee the future perspective of macro economy and financial markets. However, it is not clear whether these low-frequency leading indicators really lead the actual performance of financial markets, which are usually measured at high frequency (e.g., daily or weekly). This paper uses a novel mixed-frequency spillover method of to examine the spillover effects between monthly leading indicators and weekly financial market prices in China, aiming to assess the pioneering function of these leading indicators. The empirical results suggest that, first, some monthly leading indicators can lead the monthly performance of financial markets, but all of them fail to lead the weekly performance of financial markets. Second, the mixed-frequency approach yields much higher spillover measures than the common-frequency method of Diebold and Yilmaz (2012). Third, China's stock and commodity markets are spillover transmitters to other financial markets in both common and mixed frequency conditions. Finally, the China Monetary Index (CMI) and the Manufacturing Purchasing Managers' Index (CPMI) are more effective leading indicators than the China Consumer Confidence Index (CCCI). These findings help investors and policymakers to adjust risk management strategies in a timely manner and facilitate more accurate financial forecasting.

Do regional trusts alleviate bond market risks? Evidence from the Chinese municipal corporate bond pricing

Municipal corporate bonds have attracted significant attention due to their pricing mechanisms and reflect China's distinct financial landscape. Existing research has highlighted the influence of informal institutional factors on bond pricing. This study introduces the social trust index to measure regional trust levels and assesses its correlation with municipal corporate bond issuance pricing. The empirical analysis indicates that higher regional trust levels are significantly associated with reduced issuance credit spreads for municipal corporate bonds. The relationship is moderated by local economic development and the strength of implicit government guarantees. Employing the “Yongmei” event as a case study, this research explores the dynamics of bond pricing mechanisms following a reduction in government implicit guarantees and finds that a weakening of these guarantees intensifies the influence of regional trust on credit spreads. Additionally, default events are observed to have spillover effects, impacting neighboring provinces and the overall market. This research extends the investigation of regional trust's pricing mechanisms to the municipal corporate bond market, thereby enriching the study of informal institutions' impact on financial market pricing. The findings underscore the importance of effectively harnessing market mechanisms by governments to mitigate risks in the municipal corporate bond sector.

A Girsanov transformed Clark-Ocone-Haussmann type formula for $$L^1$$-pure jump additive processes and its application to portfolio optimization

AbstractWe derive a Clark-Ocone-Haussmann (COH) type formula under a change of measure for $$ L^1 $$ L 1 -canonical additive processes, providing a tool for representing financial derivatives under a risk-neutral probability measure. COH formulas are fundamental in stochastic analysis, providing explicit martingale representations of random variables in terms of their Malliavin derivatives. In mathematical finance, the COH formula under a change of measure is crucial for representing financial derivatives under a risk-neutral probability measure. To prove our main results, we use the Malliavin-Skorohod calculus in $$ L^0 $$ L 0 and $$ L^1 $$ L 1 for additive processes, as developed by Di Nunno and Vives (2017). An application of our results is solving the local risk minimization (LRM) problem in financial markets driven by pure jump additive processes. LRM, a prominent hedging approach in incomplete markets, seeks strategies that minimize the conditional variance of the hedging error. By applying our COH formula, we obtain explicit expressions for locally risk-minimizing hedging strategies in terms of Malliavin derivatives under the market model underlying the additive process. These formulas provide practical tools for managing risks in financial market price fluctuations with $$L^1$$ L 1 -additive processes.

The Effect of Dividend Policy, Capital Structure, and Exchange Rate on Stock Returns in Industrial Sector Business Entities

The purpose of this study is to examine how Dividend Income, Capital Structure, and Exchange Rate affect Stock Market Performance. The sample consisted of 12 companies trading on the Indonesia Stock Exchange (IDX) selected using the Purposive Sampling method, resulting in 63 observations for each variable. Data from audited financial statements, tax reports, and stock market prices are published by Bursa Indonesia (IDX). Data analysis was performed using regression analysis techniques. The results of this study show that the Exchange Rate, Dividend Payout Ratio (DPR), and Debt to Equity ratio (DER) all have a significant impact on stock prices. Individually, the Exchange Rate and DER have significant effects, with the Exchange Rate having a negative effect and DER having a positive effect, but the DPR having no significant effect. The coefficient of determination shows that 10.8% of the stock price variation can be explained by the three variables studied, while 89.2% can be explained by other factors not included in the study.

Segmenting Bitcoin Transactions for Price Movement Prediction

Cryptocurrencies like Bitcoin have received substantial attention from financial exchanges. Unfortunately, arbitrage-based financial market price prediction models are ineffective for cryptocurrencies. In this paper, we utilize standard machine learning models and publicly available transaction data in blocks to predict the direction of Bitcoin price movement. We illustrate our methodology using data we merged from the Bitcoin blockchain and various online sources. This gave us the Bitcoin transaction history (block IDs, block timestamps, transaction IDs, senders’ addresses, receivers’ addresses, transaction amounts), as well as the market exchange price, for the period from 13 September 2011 to 5 May 2017. We show that segmenting publicly available transactions based on investor typology helps achieve higher prediction accuracy compared to the existing Bitcoin price movement prediction models in the literature. This transaction segmentation highlights the role of investor types in impacting financial markets. Managerially, the segmentation of financial transactions helps us understand the role of financial and cryptocurrency market participants in asset price movements. These findings provide further implications for risk management, financial regulation, and investment strategies in this new era of digital currencies.

Doubly reflected BSDEs driven by RCLL martingales under stochastic Lipschitz coefficient

In this paper, we study a backward stochastic differential equation driven by a Right Continuous with Left Limits (RCLL) martingale with two completely separated RCLL barriers. When the coefficient is stochastically Lipschitz, we demonstrate the existence and uniqueness of a square-integrable adapted solution using the penalization method. Additionally, we provide a fair price for a game contingent claim between two traders with additional exogenous knowledge of the same stock price in a public financial market driven by an Azéma’s martingale. We also determine a saddle point for the game when the obstacles are left upper semi-continuous along stopping times.

A Method for Assessing Financial Market Price Behavior: An Analysis of the Shanghai Stock Exchange Index

A Method for Assessing Financial Market Price Behavior: An Analysis of the Shanghai Stock Exchange Index

An Adaptive Multimodal Learning Model for Financial Market Price Prediction

An Adaptive Multimodal Learning Model for Financial Market Price Prediction

An integrated approach of ensemble learning methods for stock index prediction using investor sentiments

It has been evidenced by numerous studies that irrational investor sentiment is one of the critical factors leading to dramatic volatility in financial market prices. Therefore, how to effectively predict market prices by information on investor sentiment is a popular but complex topic for researchers, market investors, and financial regulators. In this research, we aim to investigate the effectiveness of stock index prediction using multiple investor sentiment features, and we propose an advanced price trend prediction and trading simulation approach for the Shanghai Stock Exchange index and the Shenzhen Component index by integrating the Boosting, Bagging, and NSGA-II methods. Additionally, the SHAP method is employed as a model interpretation approach to analyze the importance of the sentiment variables and quantify their contributions to the predictions from both local and global perspectives. According to the experimental results, it can be found that the proposed method outperforms the benchmark methods in terms of the hit ratio, accumulated return, and maximum drawdown. It indicates that the proposed method is capable of achieving high accuracy, low risk, and stable profit in price trend prediction and trading simulation of the Chinese stock indexes. Moreover, the SHAP approach incorporated in the proposed method improved the interpretability of the proposed model, which can provide a beneficial reference for market participants to clarify the important sentiment factors and make relative decisions.

PENGARUH KINERJA KEUANGAN DAN HARGA SAHAM TERHADAP RETURN SAHAM PADA PERUSAHAAN MANUFAKTUR YANG TERDAFTAR DI BEI PERIODE 2018-2021

The purpose of this study is to examine changes in financial data and market prices for producer shares on the IDX from 2018 to 2021, partially and simultaneously. Very extraordinary or volatile. In general, of course, it greatly influences the opinion of speculators or prospective economists from manufacturing issuers in Indonesia. The population is the total number of manufacturing companies on the IDX for the 2018–2021 period, or approximately 178 companies and a sample with a total of 112 units of analysis. Several linear regression analysis techniques are used in the research method. The results of the study are: Return on Investments, Earnings per Share, Dividend Yield and Share Prices have no partial or combined effect on Return of Shares of Industrial Companies Listed on the IDX for 2018-2021

Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins

This study analyzes the transmission of market uncertainty on key European financial markets and the cryptocurrency market over an extended period, encompassing the pre-, during, and post-pandemic periods. Daily financial market indices and price observations are used to assess the forecasting models. We compare statistical, machine learning, and deep learning forecasting models to evaluate the financial markets, such as the ARIMA, hybrid ETS-ANN, and kNN predictive models. The study results indicate that predicting financial market fluctuations is challenging, and the accuracy levels are generally low in several instances. ARIMA and hybrid ETS-ANN models perform better over extended periods compared to the kNN model, with ARIMA being the best-performing model in 2018–2021 and the hybrid ETS-ANN model being the best-performing model in most of the other subperiods. Still, the kNN model outperforms the others in several periods, depending on the observed accuracy measure. Researchers have advocated using parametric and non-parametric modeling combinations to generate better results. In this study, the results suggest that the hybrid ETS-ANN model is the best-performing model despite its moderate level of accuracy. Thus, the hybrid ETS-ANN model is a promising financial time series forecasting approach. The findings offer financial analysts an additional source that can provide valuable insights for investment decisions.

Simple Review of Extrapolation Bias

Extrapolation bias is a popular topic recently in economics and finance. Many researchers have become interested in this bias because it can explain many anomalies that a rational model cannot. This paper is a simple review of extrapolation bias, including its definition and bias. After reviewing past research on extrapolation, this paper selects a comprehensible definition, and three applications that can represent the recent research on extrapolation: the investor behavior model, the bubble model, and the house price model from three different top papers. After the review, it can be concluded that extrapolation bias has a wide applied range especially, in the financial market and asset pricing, and can explain many financial anomalies.

The Effect of Analyzing Financial Ratios on the Market Price of Profitable Companies and Allied Companies in The Arab Republic of Egypt


 
 Abstract
 This study aims to investigate how the adoption of the International Financial Reporting Standard (hereinafter referred to as IFRS) affected the market price of shares of a select group of profitable companies in In the Arab Republic of Egypt between 2017 and 2022. This study was conducted when the country's financial statements were made in accordance with its accounting standard. Since the Arab Republic of Egypt implemented IFRS in 2017, there has been a knowledge gap about the relationship between Financial Ratio Analysis and Market Price of Share. This study aimed to fill that gap. It only used data from the financial statements of the chosen firms and utilized multiple regression analysis. Earnings per share, net assets per share, liquidity ratio, debt ratio, return on asset, and return on equity were all employed as financial ratio proxies. The outcome demonstrates a good and significant relationship between profitable companies and affiliated companies in the Arab Republic of Egypt firms Market Price of Share for Earnings per Share, Net Assets per Share, Debt Ratio, and Return on Asset Ratio. Additionally, it was discovered that the in the Arab Republic of Egypt profitable enterprises, affiliated companies, and Agro-Allied Quoted firms' Market Price of Share are not significantly correlated with the liquidity ratio and return on equity ratio.
 Keywords: Returns on asset; Returns of equity; Agriculture and agro-allied; Market price; Earnings per share; Net assets per share; Debt ratio; Liquidity ratio

Nexus Between Travel & Leisure Stocks and Uncertainties: An Extreme Risk Spillover Analysis

The paper examines the presence of risk spillover to the US travel & leisure industry from the extreme changes in the uncertainties. More specifically, using a time-varying copula based conditional Value-at-Risk (CoVaR) framework, we evaluate the dynamic impact of the uncertainties on the extreme risk of the US travel & leisure industry by taking into consideration the uncertainty in economic policy, equity market conditions, and crude oil prices. The findings indicate a significant dynamic inverse relationship between the returns of travel & leisure industry and changes in uncertainty variables. The results further indicate the stronger sensitivity of the travel & leisure industry toward the uncertainties in financial market and crude oil prices. We find significant evidence of extreme upside and downside risk spillover to the US travel & leisure industry from excessive downward and upward changes in uncertainties respectively. The findings also demonstrate the asymmetric effect of extreme movements in uncertainty factors on the tail risk of the US travel & leisure industry. The findings of the study have ramifications for risk managers, portfolio managers and investors.

Analysis on the correlation between credit scale of financial institutions and stock market price data

This paper analyzes the degree of association between credit size of financial institutions and stock market prices in China based on monthly data from November 2006 to November 2016 using impulse response function because of VAR time series model. The results show that there is a two-way Granger causality between the credit size of financial institutions and stock market prices when the lag order is 2. The impact of credit size of financial institutions on stock market price shocks is larger in the short-term, and this effect rapidly decreases to near zero after the med-long term. Conversely, the med-long term shocks to the credit size of financial institutions are larger than the short-term shocks by stock market prices.

Can gold and bitcoin hedge against the COVID-19 related news sentiment risk? New evidence from a NARDL approach

The COVID-19 pandemic has led to extensive news coverage, causing investor sentiment to swing, which has further increased financial market price volatility. There is an increasing need to find a hedge against sentiment risk. This paper examines the hedge capabilities of gold and Bitcoin against COVID-19-related news sentiment (CNS) risk under a nonlinear autoregressive distributed lag (NARDL) model. Our empirical results reveal that there is an obvious asymmetric effect from the CNS on gold prices in the short run and that the decrease in the COVID-19-related news index would have a greater impact on gold prices than when it increases. The impact of CNS on Bitcoin prices is asymmetric in the long and short term, especially in the long term. In addition, we conclude that gold is a hedge against CNS risk in the long term, and the hedging effect of Bitcoin is mainly reflected in the short-term.

A survey on machine learning models for financial time series forecasting

Financial time series (FTS) are nonlinear, dynamic and chaotic. The search for models to facilitate FTS forecasting has been highly pursued for decades. Despite major related challenges, there has been much interest in this topic, and many efforts to forecast financial market pricing and the average movement of various financial assets have been implemented. Researchers have applied different models based on computer science and economics to gain efficient information and earn money through financial market investment decisions. Machine learning (ML) methods are popular and successful algorithms applied in the FTS domain. This paper provides a timely review of ML’s adoption in FTS forecasting. The progress of FTS forecasting models using ML methods is systematically summarized by searching articles published from 2011 to 2021. Focusing on the analysis of ML methods applied to the theoretical basis and empirical application of FTS data forecasting, this paper provides a relevant reference for FTS forecasting and interdisciplinary fusion research against the background of computational intelligence and big data. The literature survey reveals that the most commonly used models for prediction involve long short-term memory (LSTM) and hybrid methods. The main contribution of this paper is not only building a systematic program to compare the merits and demerits of specific FTS forecasting models but also detecting the importance and differences of each model to help researchers and practitioners make good choices. In addition, the limitations to be addressed and future research directions of ML models’ adoption in FTS forecasting are identified.

Empirical mode decomposition using deep learning model for financial market forecasting.

Financial market forecasting is an essential component of financial systems; however, predicting financial market trends is a challenging job due to noisy and non-stationary information. Deep learning is renowned for bringing out excellent abstract features from the huge volume of raw data without depending on prior knowledge, which is potentially fascinating in forecasting financial transactions. This article aims to propose a deep learning model that autonomously mines the statistical rules of data and guides the financial market transactions based on empirical mode decomposition (EMD) with back-propagation neural networks (BPNN). Through the characteristic time scale of data, the intrinsic wave pattern was obtained and then decomposed. Financial market transaction data were analyzed, optimized using PSO, and predicted. Combining the nonlinear and non-stationary financial time series can improve prediction accuracy. The predictive model of deep learning, based on the analysis of the massive financial trading data, can forecast the future trend of financial market price, forming a trading signal when particular confidence is satisfied. The empirical results show that the EMD-based deep learning model has an excellent predicting performance.

Fiscal uncertainty and sovereign credit risk

Periods of economic crisis and large fiscal interventions are often associated with elevated levels of uncertainty about governments’ future fiscal positions. This paper studies the impact of fiscal uncertainty on sovereign credit risk, and on the attention paid to expert assessments of such risk, using the example of sovereign credit ratings. Employing data that covers the Global Financial Crisis, a measure of fiscal uncertainty is constructed that is comparable across time and a range of advanced economies. To estimate the determinants of sovereign credit risk, an empirical framework is developed that accounts for the high stability of credit ratings over time by way of a new estimator for ordered and partially unobserved outcomes. Attention to credit rating announcements is measured using movements in financial market prices and internet search volume. Results suggest that fiscal uncertainty increases the predictive power of a model of sovereign credit ratings and can explain why credit ratings are often changed more frequently during crises. Sovereign credit ratings also gain more attention when uncertainty is high with the potential to aggravate sovereign distress.

Information effects of euro area monetary policy

This paper provides evidence for central bank information effects in the euro area. ECB announcements seem to convey information not only about monetary policy, but also about economic fundamentals. I separate these “information surprises” from “pure policy surprises” via sign restrictions and find intuitive effects of both surprise on a wide set of financial market prices, survey expectations and macroeconomic aggregates. Both surprise series are updated and made publicly available.