Real Stock Research Articles

Economic uncertainty, oil prices, hedging and U.S. stock returns of the airline industry

This paper examines the impacts of economic policy uncertainty and oil price shocks on stock returns of U.S. airlines using both industry and firm-level data. Our empirical approach considers a structural vector-autoregressive model with variables recognized to be important for airline returns including jet fuel price volatility. Empirical results confirm that oil price increase, economic uncertainty and jet fuel price volatility have significantly adverse effect on real stock returns of airlines both at industry and at firm level. In addition, we also find that hedging future fuel purchase has statistically positive impact on the smaller airlines. Our results suggest policy implications for practitioners, managers of airline industry and commodity investors.

Pricing ESG Equity Ratings and Underlying Data in Listed Real Estate Securities

By analyzing the adoption of the European Public Real Estate Association’s (EPRA) Sustainability Best Practices Recommendations (sBPR), we examine and discuss the application of transparent environmental, social and governance (ESG) ratings and their interaction with public real estate performance across European markets. Due to increasing concerns about the environment and the impact of investment on society at large, public property companies have made significant progress in improving transparency and enhancing the protection of shareholder value by sharing and reporting ESG best practices. We explore and review the EPRA sBPR database, which is highly useful for investors who are already screening listed real estate companies. Hence, in this project, we carefully study the diffusion process of this new ESG metric as a tool to enhance informational transparency regarding public real estate investment management and assess the effects of this transparency and ESG performance for the real estate stock returns. We find evidence of a sustainability premium that investors are willing to pay to access companies with better sustainable ratings.

Measurement and effects of euro/dollar exchange rate uncertainty

Abstract This paper analyzes the identification, drivers and effects of exchange rate uncertainty related to the euro/dollar exchange rate based on survey data. Our measure of common exchange uncertainty for the Euro/Dollar exchange rate is correlated with some alternative uncertainty measures but displays distinctive dynamics. We find that exchange rate uncertainty has significant effects on the real economy and stock markets in the US and Germany which go beyond the effects economic policy uncertainty by Baker et al. (2016) and option-based exchange rate volatility. Short-run exchange rate uncertainty is more informative compared to long-run exchange rate uncertainty and has the potential to capture the overall degree of uncertainty among forecasters.

Relative utility bounds for empirically optimal portfolios

We consider a single-period portfolio selection problem for an investor, maximizing the expected ratio of the portfolio utility to the utility of a best asset taken in hindsight. The decision rules are based on the history of stock returns with unknown distribution. Assuming that the utility function is Lipschitz or Holder continuous (the concavity is not required), we obtain high probability utility bounds under the sole assumption that the returns are independent and identically distributed. These bounds depend only on the utility function, the number of assets and the number of observations. For concave utilities similar bounds are obtained for the portfolios produced by the exponentiated gradient algorithm. Also we use statistical experiments to study risk and generalization properties of empirically optimal portfolios. Herein we consider a model with one risky asset and several datasets, containing real stock prices.

How Did COVID-19 Affect the Chinese Real Estate Stock Market: An Empirical Research

<p>COVID-19 has brought disruptions to various industries in China. The real estate industry can’t remain immune from the shock as well, which can be presented by the performance of real estate stocks. This study investigates the effects of COVID-19 on the Chinese real estate stocks. Statistical methods, such as OLS regression, are used to explore the effects of new cases, new deaths, new recoveries, bad and good news about COVID-19 of provinces where the headquarters of the sample companies lie on the daily stock returns as well as the changes of volatilities before and after COVID-19. Event study is employed to discover the effects of important events during COVID-19. Results suggest that positive information about COVID-19 significantly increased daily stock returns of the listed real estate company in that province. Total risk and idiosyncratic risk of real estate stocks have increased significantly since COVID-19, while systematic risk has decreased significantly. Among the crucial events during the pandemic, the lockdown of Wuhan significantly caused negative abnormal returns for real estate stocks.</p>

Real earnings management and stock returns: moderating role of cross-sectional effects

PurposeThe study aims at investigating the impact of real earnings management (REM) on the cross-sectional stock return after considering the moderating role of market effect, size effect, value effect and momentum effect.Design/methodology/approachThe study uses weekly and monthly data of 3,085 Bombay Stock Exchange listed stocks spanning over twenty years, from January 2000 to December 2019. REM is measured through metrics developed by Roychowdhury (2006), namely, abnormal levels of operating cash flows, production costs and discretionary expenditure. The study employs univariate and bivariate portfolio-level analysis.FindingsThe findings deduced from the empirical results demonstrate that investors perceive downward REM as an element of risk; hence, they discount the stock prices at a higher rate. On the contrary, results show that investors positively perceive upward REM; hence, they hold the stocks even at a lower rate of return. This anomaly is found to be robust for all kinds of considered moderations.Practical implicationsThe findings have important managerial implications as investors are found to assign different weights to different forms of REM, depending upon the perception regarding the magnitude of risk involved in different forms. Managers can accommodate this information during their short- and long-term corporate planning.Originality/valueFirst, the study is among the earlier attempts to examine the association between REM and stock returns by considering the moderating role of cross-sectional effects. Second, the study considers the direction and endogenous nature of REM while investigating the issue.

Stock Price Prediction Incorporating Market Style Clustering

Market style analysis is critical when designing a stock price prediction framework. Under different market styles, stocks may show quite different behaviors; thus, predictions will vary. Consequently, incorporating market styles into stock price predictions should help improve the prediction performance. In this paper, we investigate how to characterize market styles to improve stock prediction performance under varying market styles. First, stock time series data are divided into windows of different lengths. The windows are summarized and represented by technical indicators and news sentiment features. Second, hierarchical clustering is employed to cluster the windows and categorize their market styles; the window lengths and number of market styles are carefully tuned to achieve the best clustering results. Third, a distance measurement is proposed to distinguish among rotating patterns within the market styles to verify the usability of the market styles. Finally, a stock price prediction framework is constructed to predict future stock price trends based on data belonging to the same market styles. The experiments are conducted with five years of real Hong Kong Stock Exchange data that includes both stock prices and corresponding news. Two famous sentiment dictionaries (i.e., SenticNet 5 and the Loughran-McDonald financial sentiment dictionary 2018) are employed to analyze the news sentiments. Predictive models are compared both with and without incorporating market styles. The results demonstrate that the approach incorporating market styles outperforms the baseline, which does not incorporate market styles. There is a maximum 9 percent improvement in terms of accuracy and F1-score. Moreover, backtesting results show that incorporating market styles into trading signals earns trading strategies more profits on most stocks.

An endogenous mechanism of business cycles

This paper suggests that business cycles may be a manifestation of coupled real economy and stock market dynamics and describes a mechanism that can generate economic fluctuations consistent with observed business cycles. To this end, we seek to incorporate into the macroeconomic framework a dynamic stock market model based on opinion interactions (Gusev et al., 2015). We derive this model from microfoundations, provide its empirical verification, demonstrate that it contains the efficient market as a particular regime and establish a link through which macroeconomic models can be attached for the study of real economy and stock market interaction. To examine key effects, we link it with a simple macroeconomic model (Blanchard, 1981). The coupled system generates nontrivial endogenous dynamics, which exhibit deterministic and stochastic features, producing quasiperiodic fluctuations (business cycles). We also inspect this system’s behavior in the phase space. The real economy and the stock market coevolve dynamically along the path governed by a stochastically-forced dynamical system with two stable equilibria, one where the economy expands and the other where it contracts, resulting in business cycles identified as the coherence resonance phenomenon. Thus, the incorporation of stock market dynamics into the macroeconomic framework, as presented here, allows the derivation of realistic behaviors in a tractable setting.

Mean reversion in Asia-Pacific stock prices: New evidence from quantile unit root tests

We investigate the stationarity of the daily real stock prices in 12 Asia-Pacific countries over the period 1991–2020. The methodology employed is driven by the need to address three key concerns: (i) the identification of association between the size of shocks and stationarity; (ii) the identification of different speeds of adjustment towards long-run equilibrium; and (iii) the identification of mean reversion and potential asymmetric speed of adjustment before and after the 2008–2009 global financial crisis. To meet these concerns, we examine the time series properties of high frequency data within a quantile unit root testing framework. Our results generally indicate that stock prices are stationary at the higher quantiles. There is also evidence of asymmetries in stock price dynamic adjustments at the upper quantiles, in which larger shocks are associated with faster mean reversion, and conversely, smaller shocks are associated with non-stationarity. Further analysis indicates that stock prices became much more mean reverting and with a faster speed of adjustment after the global financial crisis for all sample countries.

ЕФЕКТ «БИЧАЧОГО БАТОГА» В ЛОГІСТИЧНІЙ ДІЯЛЬНОСТІ ПІДПРИЄМСТВА

This article is devoted to the development of international logistical activities, examination and prevention negative factors on any links of logistic chain. The important requirements to obtain maximum efficiency at all components of the logistic chain with minimal costs in each of its items, compliance with market interests and goals of each participant of logistic chain are considered. The simulated model has proved that the farther on logistic chain the information flow passes from consumer the more distorted information about the necessary stocks of products and real stocks in warehouse, and as a result – the extraordinary accumulation of mistakes in the forms of surplus of goods at all stages of its movement from producer to consumer. The similar phenomenon in logistics is called the effect of "The Bullwhip Effect". The method of practical analysis of the "bullwhip" effect was used in order to investigate resistance to this effect on the situation were simulated in «BEER GAME». The simulation method “BEER GAME” is the result of the work of the consulting system MA system, which works in the field of business logistics around the world. Four parts of the logistics chain took part in the simulation: a retailer, a distributor, a wholesaler and a manufacturer. They make a logistics chain 52 weeks long. The overall results of the game are presented in detail: the total cost of the simulated game, and manufacturer's game statistics, the statistics of game such as costs, orders and information of warehouse stock in terms of retailer, distributor and wholesaler. Furthermore, the article highlights other destabilizing effects that are closely related to the "bullwhip" effect, such as: the Forrester effect - a phenomenon in the sales channel, which predicts the inefficiency of the logistics chain; the Burbage effect - when the size of supplies can change in the direction of increase without managerial decisions; the Halligan effect - when there is a deviation from the existing supply and production plan; promotion effect - implicit price changes. On the basis of the conducted modeling and consideration of the reasons of occurrence of effect methods of stabilization of a logistic chain are offered.

Determinants of stock return of property and real estate companies in the developing market

Macroeconomic risk factors can determine the expected return on property and real estate stocks (Khan, Khan, Ahmad, & Bashir, 2021), in addition to other factors: property prices (Das, Füss, Hanle, & Russ, 2020) and financial performance (Medyawati & Yunanto, 2017). This study aims to empirically prove the effect of interest rates (SB), exchange rates (KURS), commercial property price index (IHPK), return on assets (ROA), debt-to-equity ratio (DER), and current ratio (CR) on stock returns estimated using panel data regression model. The sample of this research is 23 companies from 63 companies in the property and real estate industry which are listed on the Indonesia Stock Exchange (IDX) during the 2015–2019 period. The empirical findings of this study prove that the ROA, CR, IHPK, and KURS variables have a negative effect on stock returns, while the SB variable has a positive effect. The level of corporate debt (DER) was not proven to determine stock returns. The exchange rate has the greatest influence on stock returns, and the fact does show that the Indonesian stock market is dominated by foreign investors, so that every time foreign currencies appreciate because they leave the stock exchange, the stock price immediately declines. The results of this study have implications for investors that investment decisions to buy shares of property and real estate companies must understand the changes that occur, especially macroeconomic variables and also the company’s financial performance

The driving factors of real estate stock prices in Indonesia Stock Exchange

In this study, the factors to be researched are internal factors related to financial ratios such as market ratios namely Price Earnings Ratio, Leverage Ratio namely Debt to Equity Ratio, and Profitability Ratio namely Return on Assets (ROA) as variables that affect Stock Prices in real estate companies in Indonesia Stock Exchange. The data used are secondary data from time series that have been transformed from the Indonesia stock exchange. The method used is multiple regression with OLS, to find how much the influence of independent variables have as driving forces on stock prices. The results obtained from this study are price earnings ratio, debt to equity ratio, ROA simultaneously affect the stock prices of Real Estate companies in the Indonesian stock exchange. Meanwhile, partially, the three variables deserve to be the driving factor for share prices in real estate companies at the Indonesia Stock Exchange.

Textual Analysis of Short-seller Research Reports, Stock Prices, and Real Investment

We construct a comprehensive database of short-sell research reports and investigate to what extent investors pay attention to these reports. We find that, on average, target firms earn abnormal returns of -4% on the publication day, while the subsequent price revisions equal -20% and take up to 6 months to materialize. Analysts also react by lowering their price targets. By applying textual analysis, we show that a large percentage of the text relates to the revelation of accounting fraud. We document that firms mentioned in the reports end up significantly reducing their real investment and stock issuances. On average, each report is associated with a reduction of corporate investment equal to $118 million, and stock issuances equal to $179 million. Our findings suggest that short-seller research has significant consequences, both financially and in real terms.

Motivated Beliefs about Stock Returns

Does holding a stock bias one’s expectations about its future value? We find experimental evidence that it does. First, in a laboratory experiment we elicit peoples’ price predictions for simulated stocks and compare these with the Bayesian benchmark. Then in a second experiment, we elicit peoples’ daily price predictions for real stocks over a six-week period. In both experiments we find that people predict higher future values for losing stocks when they happen to hold them. This result provides the first direct evidence of a beliefs-based explanation for investors’ reluctance to sell losers, as in the well-known disposition effect.

Intelligent Algorithmic Trading Strategy Using Reinforcement Learning and Directional Change

Designing a profitable trading strategy plays a critical role in algorithmic trading, where the algorithm can manage and execute automated trading decisions. Determining a specific trading rule for trading at a particular time is a critical research problem in financial market trading. However, an intelligent, and a dynamic algorithmic trading driven by the current patterns of a given price time-series may help deal with this issue. Thus, Reinforcement Learning (RL) can achieve optimal dynamic algorithmic trading by considering the price time-series as its environment. A comprehensive representation of the environment states is indeed vital for proposing a dynamic algorithmic trading using RL. Therefore, we propose a representation of the environment states using the Directional Change (DC) event approach with a dynamic DC threshold. We refer to the proposed algorithmic trading approach as the DCRL trading strategy. In addition, the proposed DCRL trading strategy was trained using the Q-learning algorithm to find an optimal trading rule. We evaluated the DCRL trading strategy on real stock market data (S&P500, NASDAQ, and Dow Jones, for five years period from 2015-2020), and the results demonstrate that the DCRL state representation policies obtained more substantial trading returns and improved the Sharpe Ratios in a volatile stock market. In addition, a series of performance analyses demonstrate the robust performance and extensive applicability of the proposed DCRL trading strategy.

Theoretical and experimental evidence on stock market volatilities: a two-phase flow model

The volume–volatility relationship usually ignores possible effects of stock shares. This article proposes a two-phase flow model assuming that capital and stock flows determine stock price and return volatility. Computational simulations suggest that monodirectional capital or stock flows and collective flows exert different effects on stock return volatilities. Considering the impact of stock flows, the positive relationship between capital and return volatility is no longer guaranteed. The inflow of capital and the outflow of stock increase stock price similarly; but exhibit completely different effects on stock return volatilities. A persistent stock inflow (outflow) reduces (intensifies) return volatilities, whereas a monodirectional persistent capital outflow has no such effect. When capital and stock flows’ velocities satisfy critical values determined by the initial state of the market, the market enlargement accompanied with increasing stock and capital shows no impact on market stability because of stable return volatilities. Otherwise, stock flows drive return volatilities with stronger effects than capital flows. Further experimental studies that simulate the real stock market through a trading system provide strong evidence supporting the two-phase flow model. Given similar driving forces of capital and stock flows, the interaction of them should be considered in constructing investment strategies and setting policies.

Are the Predictions of the Mundell-Fleming Model Applicable to Mexico?

According to the Mundell-Fleming model (Romer, 2006) under a floating exchange rate system, fiscal expansion is ineffective in raising output and causes real appreciation whereas monetary expansion raises output and causes real depreciation. Applying an extended Mundell-Fleming model, this paper employs a simultaneous-equation model to test whether the predictions of the Mundell-Fleming model would apply to Mexico. The GARCH process is employed in empirical work. This study finds that fiscal expansion reduces output and causes real appreciation and that monetary expansion raises output and leads to real depreciation. In addition, a higher real interest rate reduces output and causes real appreciation, and a higher real stock price results in real appreciation. Therefore, except for the impact of fiscal expansion on output, the Mundell-Fleming model applies to Mexico.

Research on intelligent algorithms for solving portfolio problems

Investment is a behavior of coexistence of the benefits and risks. In the context of rich asset types, an effective investment portfolio can help investors obtain stable returns and diversify risks. In reality, portfolio problems often contain multiple constraints and objectives that cannot be effectively solved by traditional mathematical optimization methods. This paper proposes a hybrid beetle antennae search sine cosine algorithm based on non-linear inertia weight. Experiments are performed on five portfolio problem datasets in the real stock market. The results show that the proposed algorithm is effective and has some performance advantages in solving portfolio problems.

ANALISIS TEKNIKAL HARGA SAHAM PT. ADIRA DINAMIKA MULTI FINANCE Tbk. DI MASA COVID 19 DENGAN METODE ARIMA

This study aims to analyze and predict stock prices using technical analysis at PT. Adira Dinamika Multi Finance, Tbk by ARIMA method. Data collection technique used is archival data collection which comes from secondary data. Secondary data in this study’s from daily data on ADMF shares in 2nd January 2020-26th October 2020 which is sourced from finance.yahoo.com. The analysis technique used in this research is the Box-Jenkins method with three stages, i.e. the first stage of identification and specification of ARIMA model (p, d, q), the second stage is prediction stage, the third stage is diagnosis model. This study results show that the MA Model (2) is the best model whose data describes ADMF stock price movements during the pandemic period and produces a difference between the real data and forecast is not too different. Where on average there’s a difference about Rp. 43.5 between the real stock price data with forecast data. Suggestions for future researchers to use fundamental analysis to support investment decisions.

The Relationship Between Stock Prices and the Real Exchange Rate Index in BRICS Countries: Symmetric and Asymmetric Causality Analysis

This study investigates the causality relationship between the real exchange rate index and stock prices in Brazil, Russia, India, China, and South Africa (BRICS) countries over the period from March 2003 to June 2018. The relationship between the variables was made with symmetrical and asymmetrical causality tests within the framework of traditional approach and portfolio balance approach. Accordingly, Hacker-Hatemi-J (2006) test was used for symmetric analysis and Hatemi-J (2012) causality test was used for asymmetric analysis. According to the results of the analysis, the causality relationship between the mentioned variables is different in BRICS countries. The findings do not provide definitive evidence in favor of the traditional approach or portfolio balance approach. Also, it can be stated that there are noisy transactions in these two markets Key Words: BRICS, Stock Prices, Real Effective Exchange Rate, Symmetric and Asymmetric Causality AnalysisJEL Classification: C32, F31, G10, G15