Return Fluctuations Research Articles

Forecasting Bitcoin Spikes: A GARCH-SVM Approach

This study aims to forecast extreme fluctuations of Bitcoin returns. Bitcoin is the first decentralized and the largest, in terms of capitalization, cryptocurrency. A well-timed and precise forecast of extreme changes in Bitcoin returns is key to market participants since they may trigger large-scale selling or buying strategies that may crucially impact the cryptocurrency markets. We term the instances of extreme Bitcoin movement as ‘spikes’. In this paper, spikes are defined as the returns instances that outreach a two-standard deviations band around the mean value. Instead of the unconditional historic standard deviation that is usually used, in this paper, we utilized a GARCH(p,q) model to derive the conditional standard deviation. We claim that the conditional standard deviation is a more suitable measure of on-the-spot risk than the overall standard deviation. The forecasting operation was performed using the support vector machines (SVM) methodology from machine learning. The most accurate forecasting model that we created reached 79.17% out-of-sample forecasting accuracy regarding the spikes cases and 87.43% regarding the non-spikes ones.

An Empirical Study on Forecasting Stock Returns of Tata Consultancy Services

Forecasting of stock returns is always a vitally important financial notion confronted by investors. Due to existing fluctuations in the stock returns, investors are always keen to show their interest as they want to take the advantage of potential returns from the organization by way of investing in stocks. Hence, it becomes a matter of concern for investors to predict future stock returns so that they can attain their objective of wealth maximization. This reason creates an urge to explore the forecasting of stock returns empirically. This paper employed the ARIMA model, developed by Box and Jenkins in 1970, which relies on the previous values of the variable itself. In the paper, this methodology is applied to the stock returns of one of the top IT companies listed on NSE i.e. Tata Consultancy Services Ltd. Data of daily returns were collected from 1 April 2008 to 31st March 2021. Results concluded that the ARIMA model had a strong capability of forecasting in the short run.

Sensitivity of Stock Returns Volatility and Money Market Rates: Insight from Nigeria

The study focused on how money market factors affected the stock returnfluctuations in the Nigerian setting. The study specifically looked at the impact of Treasury bill rates and monetary policy rates on the return fluctuations in Nigeria from2002 - 2016 over the study period. Financial econometrics study was performed using descriptive statistics, unit root test, heteroscedasticity, autocorrelation, GARCH (1.1), and GARCH-X (1.1) models. The series' stationarity was confirmed using the PP test and the Equally Augmented Dickey-Fuller (ADF) test. Additionally, a Benchmark GARCH (1.1) model was estimated to study the volatility. A diagnostic test was run using the Ljung-Box Q-Statistics to determine the robustness of the calculated GARCH model. The overall finding indicated that there was significant volatility clustering that was still present in the Nigerian exchange group, suggesting that it would take some time for the market's reaction to volatility shocks from the prior period to be completely eliminated. The study also discovered that changes in treasury bill rates and monetary policy rates significantly reduce the volatility of returns on the stock in Nigeria, supporting the idea that there is an inverse link between the value of money and the value of capital markets.

The Interaction of Major Crypto-assets, Clean Energy, and Technology Indices in Diversified Portfolios

Cryptocurrencies have gained high interest from media, regulatory authorities, and both retailer and institutional investors especially in the COVID-19 pandemic followed by a tremendous academic interest. Classification of cryptos as an asset is one important issue while another crucial topic is the significantly high return and volatility fluctuations which does not make life easier for portfolio investors. High energy consumption to produce cryptos and carbon emission issues are also not developing the favor of cryptocurrencies. Moreover, in financial literature recent studies show that prices of renewable energy stocks have long-term cointegrating relationship with technology companies. In this context first we employ an asymmetric VAR-GARCH model to study spillover effects between major crypto-currencies, clean energy, and technology indices. Using daily data of the two major cryptocurrencies for the period of 1/1/2016 and 30/09/2021, we relate risk and return of different mean-variance portfolio strategies to Bitcoin (BTC), Etherium (ETH), S&P Global Clean Energy Index (SPGCE) and MSCI World Information Technology Index (MSCIWIT). Secondly, we apply the Markowitz mean-variance framework to assess risk-return benefits of cryptocurrency-portfolios. Our main goal is to offer optimal portfolio allocation approaches including cyptocurrencies with traditional financial assets. We will combine cryptocurrencies, clean energy, and technology indices to maximize return and Sharpe ratio. Furthermore, we will use our asymmetric VAR-GARCH models results to understand and cross check the Markowitz portfolio allocation results in details.

Pandemic or panic? A firm-level study on the psychological and industrial impacts of COVID-19 on the Chinese stock market

This study presents a thorough investigation of the relationship between the coronavirus disease 2019 (COVID-19) and daily stock price changes. We use several types of COVID-19 patients as indicators for exploring whether stock prices are significantly affected by COVID-19’s impact. In addition, using the Chinese stock market as an example, we are particularly interested in the psychological and industrial impacts of COVID-19 on the financial market. This study makes two contributions to the literature. First, from a theoretical perspective, it shows a novel quantitative relationship between the psychological response to the pandemic and stock prices. In addition, it depicts the mechanism of the shock to the stock market by pointing out the specific functional expression of the impulse reaction. To our knowledge, this is the first theoretical calculation of the impulse of a shock to the financial market. Second, this study empirically estimates the marginal effect of the COVID-19 pandemic on fluctuations in stock market returns. By controlling for stock fundamentals, this study also estimates diverse industrial responses to pandemic stock volatility. We confirm that the COVID-19 pandemic has caused panic in the stock market, which not only depresses stock prices but also inflates volatility in daily returns. Regarding the impulse of the shock, we identify the cumulative level of the pandemic variables as well as their incremental differences. As shown by our empirical results, the terms for these differences will eventually dominate the marginal effect, which confirms the fading impulse of the shock. Finally, this study highlights some important policy implications of stock market volatility and returns to work in the industry.

Non-equilibrium skewness, market crises, and option pricing: Non-linear Langevin model of markets with supersymmetry

This paper presents a tractable model of non-linear dynamics of market returns using a Langevin approach. Due to non-linearity of an interaction potential, the model admits regimes of both small and large return fluctuations. Langevin dynamics are mapped onto an equivalent quantum mechanical (QM) system. Borrowing ideas from supersymmetric quantum mechanics (SUSY QM), a parameterized ground state wave function (WF) of this QM system is used as a direct input to the model, which also fixes a non-linear Langevin potential. Using a two-component Gaussian mixture as a ground state WF with an asymmetric double well potential produces a tractable low-parametric model with interpretable parameters, referred to as the NES (Non-Equilibrium Skew) model. Supersymmetry (SUSY) is then used to find time-dependent solutions of the model in an analytically tractable way. Additional approximations give rise to a final practical version of the NES model, where real-measure and risk-neutral return distributions are given by three component Gaussian mixtures. This produces a closed-form approximation for option pricing in the NES model by a mixture of three Black–Scholes prices, providing accurate calibration to option prices for either benign or distressed market environments, while using only a single volatility parameter. These results stand in stark contrast to the most of other option pricing models such as local, stochastic, or rough volatility models that need more complex specifications of noise to fit the market data.

Industry-wise Dispersion of Investment Returns in Indian CPSEs: Experiential Confirmation on Aggregation

Purpose: The CPSEs play a vital role in the economic structure of the country by supplying necessary products and services. To sustain in the long run, it is essential to earn a reasonable amount of profit on a consistent basis. Thus, the higher the dispersion in investment returns, the higher is the risk and vice-versa. In this perspective, an industry-wise dispersion of investment returns in Indian CPSEs are carried out from 2010-11 to 2019-20. Design/Methodology/Approach: To fulfill the research objectives of the study, secondary data is used. Dispersion is calculated for the investment ratios by coefficient of variation. Furthermore, paired ‘t’ test is applied to locate any significant transform in the average dispersion of investment returns of all the selected industries taken together. One-way ANOVA is also applied to measure variation in investment returns among the selected industries. Findings/Result: The study results reveal that the rate of fluctuation in investment returns has varied extensively among the industries. Moreover, most of the selected industries in the 1st sub-period have shown better consistency in investment returns as compared to the 2nd sub-period. Further, significant differences are observed in investment returns among the selected industries, which implies that investment returns in each selected industry has a significant bearing on the aggregate investment returns of the CPSEs. Originality/Value: To recognize the rate of dispersal in investment returns generated by the CPSEs. Paper Type: Empirical Research.

The impact of supply chain finance on corporate social responsibility and creating shared value: a case from the emerging economy

PurposeAgriculture and cultivation firms are facing severe competition in the saturated market. Due to the characteristics of heavy assets, low investment return, long cycle and high price fluctuation, agri-food firms require innovations for capital support. The purpose of this paper is to provide valuable insights on how firms in the food/agricultural industry approach innovations and reinforce their advantages through functional and structural innovations by adopting supply chain finance (SCF).Design/methodology/approachThis research adopts a single-case study methodology to investigate the innovations and mechanisms taking place at H Corp Agriculture Group (H Corp hereafter), a Chinese egg company.FindingsThe findings of this paper indicate that SCF could have a great impact on supply chain management through functional and structural innovations throughout the supply chain and solve the capital constraint problems in the agricultural development process, promoting the implementation of the integration strategy as well as innovation in the agricultural industry chain. The research also shows that supply chain structural and functional innovations could promote corporate social responsibility (CSR) and creating shared value (CSV).Research limitations/implicationsThe research contributes to the application of SCF mechanisms and the realization of CSV and CSR jointly – both in the literature and in firms’ practices. It also contributes to the extension of structural and functional innovations and vertical integration of the supply chain. However, generalizability and universality are insufficient for a single case study in the specified industry. Data collection and quantitative analysis could be extended for further research.Originality/valueThe study addresses the need for comprehensive research on SCF and its applications. It proposes effective and efficient strategies for agri-food firms applying SCF to overcome industry capital constraints and develop competitiveness. It also provides a balanced and positive circulation between economic value and social value, realizing CSR and CSV.

THE ROLE OF INVESTOR SENTIMENT ON PRICING EFFECT OF PROFITABILITY RISK FACTOR IN KENYA

Emerging stock markets are characterized by strong investor sentiment and rapid fluctuations in returns. However, the role of investor sentiment on asset pricing has not been explored in these markets. This study sought to establish if the effect of the profitability risk factor on stock returns would vary with the level of investor sentiment at the Kenyan equity market. A quantitative causal time-series design was adopted to analyze the cause-effect relationship among the study variables. The study utilized monthly equity return data on 60 firms listed at the Nairobi Securities Exchange (NSE) from 2011 to 2019. Test portfolios were constructed following the Fama-French five-factor model framework. Auto-Regressive Distributed Lag (ARDL) and Vector Error Correction (VEC) estimation techniques show that profitability risk factor is a significant predictor of stock returns at a 5% level. Overall, though not consistent with valuation theory, the coefficient on profitability risk factor is negative, implying a high exposure to profitability risk results in low returns. Further, adding sentiment variables to the main effects model would enhance the significance of the profitability risk factor at the NSE. The evidence presented contributes to establishing investment strategies, estimating the required rate of return and assessing portfolio performance of collective investments.

Financial wealth, investment, and confidence in a DSGE model for China

Based on a DSGE model, we investigate the interconnection between China’s stock market and macroeconomic cycles. Our results show that consumption, investment, and capacity utilization exhibit significant and positive responses to stock market booms triggered by the financial and confidence shocks, while the responses of inflation are more muted and insignificant. There is a significant ‘leaning-against-the-wind’ reaction of China’s monetary policy to the credit-to-GDP gap at business cycle frequencies. Decomposing stock prices into fundamental values influenced by the financial shock and bubbles driven by the confidence shock, the confidence shock’s contribution to stock return fluctuations is estimated to be 14.8%.

Misperceptions of Bitcoin Volatility

Bitcoin market capitalization recently surpassed $1 trillion. While popular belief holds that a key characteristic of bitcoin is its excessive volatility, this article provides evidence that this is largely a misperception. We show that bitcoin return fluctuations are lower than those of roughly 900 stocks in the S&P1500 and 190 stocks in the S&P500. Moreover, we find that bitcoin is less volatile than commodities such as oil and silver, US Treasuries, AAA-rated corporate bonds, EU carbon credits, and some of the most popular technology and media stocks, including Apple, Twitter, and Netflix. Equally important, we find that during the March 2020 stock market crash triggered by COVID-19, the volatility of bitcoin was lower than that of most of these asset classes. The significant decline in bitcoin volatility over the past decade renders it more investable for conservative investors.

Dynamic Interdependence of Stock Returns Based on Information Transmission: Evidence from China and Latin America

This paper analyzes the degree of dynamic interdependence of stock returns between China and six Latin American countries from 2003 to 2018 using the DCC-MGARCH (1,1) model that reflects the information transmission process and the properties of time-varying correlation, such as sensitivity to new information, dependence of past volatility, and the persistence of volatility. It also reports the long-term trends of dynamic interdependence by using rolling window analysis corresponding to a window width of 175 days. The research presents the trends with the United States as a benchmark. First, this paper determined that the Chinese stock market’s influence on the stock markets of six Latin American countries was not decisive. Second, the Chilean and Peruvian stock markets were the most vulnerable to the fluctuations in the Chinese stock returns, while Brazil and Mexico were relatively less affected. Third, the long-term patterns of dynamic conditional correlation between China and the six Latin American countries except for Peru were generally placed between 0.1 and 0.3 Last, when considering the dynamic conditional correlation with the United States as a benchmark, the degree of dynamic interdependence between China and the six Latin American countries was not relatively high.

The Impact of the Exchange Rate Volatility on Stock Markets Dynamics in Tunisia and Turkey: An Artificial Neural Network Analysis

The present research provides an overview of links between exchange rate volatility and the dynamics of stock market returns in order to identify the influence of several macroeconomic variables on the volatility of stock markets, useful for political decision makers as well as investors to better control the portfolio risk level. More precisely, this research aims to identify the impact of exchange rate volatility on the fluctuations of stock market returns, considering two countries that belong to the Middle East and North Africa (MENA) zone: Tunisia and Turkey. Previous works in the literature used very specified and short periods of study, many important variables were neglected, and most of the earlier research was concentrated on the developed countries. In this research, we integrate several control variables of stock market returns that have not been simultaneously studied before. In addition, we spread out our research period up to 15 years including many events and dynamics. Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and multiple regression models are first employed. Then, an Artificial Neural Network (ANN) is used and compared with the results of the multiple regression. Hence, the results show that for both Tunisia and Turkey, exchange rate volatility has a significant effect on stock market fluctuations.

Forecasting stock returns on the Amman Stock Exchange: Do neural networks outperform linear regressions?

Different models have been used in the finance literature to predict the stock market returns. However, it remains an open question whether non-linear models can outperform linear models while providing accurate predictions for future returns. This study examines the prediction of the non-linear artificial neural network (ANN) models against the baseline linear regression models. This study aims specifically to compare the prediction performance of regression models with different specifications and static and dynamic ANN models. Thus, the analysis was conducted on a growing market, namely the Amman Stock Exchange. The results show that the trading volume and interest rates on loans tend to explain the monthly returns the most, compared to other predictors in the regressions. Moreover, incorporating more variables is not found to help in explaining the fluctuations in the stock market returns. More importantly, using the root mean square error (RMSE), as well as the mean absolute error statistical measures, the static ANN becomes the most preferred model for forecasting. The associated forecasting errors from these metrics become equal to 0.0021 and 0.0005, respectively. Lastly, the analysis conducted with the dynamic ANN model produced the highest RMSE value of 0.0067 since November 2018 following the amendment to the Jordanian income tax law. The same observation is also seen since the emerging of the COVID-19 outbreak (RMSE = 0.0042).

On the factors of Bitcoin\u2019s value at risk

This study investigates the factors of Bitcoin’s tail risk, quantified by Value at Risk (VaR). Extending the conditional autoregressive VaR model proposed by Engle and Manganelli (2004), I examine 30 potential drivers of Bitcoin’s 5% and 1% VaR. For the 5% VaR, quantity variables, such as Bitcoin trading volume and monetary policy rate, were positively significant, but these effects were attenuated when new samples were added. The 5% VaR responds positively to the Internet search index and negatively to the fluctuation of returns on commodity variables and the Chinese stock market index. For the 1% VaR, variables related to the macroeconomy play a key role. The consumer sentiment index exerts a strong positive effect on the 1% VaR. I also find that the 1% VaR has positive relationships with the US economic policy uncertainty index and the fluctuation of returns on the corporate bond index.

The Informational Response of Different Investor Types on Futures Return Fluctuations: Evidence from Taiwan’s Index Futures Market

This article examines the price impact of different types of investors' trading activities in the Taiwan index futures market on the market, and explores the information roles of different types of investors. We find the trading volumes of different investors in the index futures market affect futures returns through information. The impact on index futures returns in the current period is small, showing the ability of foreign institutional investors to quickly respond to negative news and obtain information advantages. Further, from the MSE and QLIKE loss functions, individual investors use EGARCH(1,1), domestic institutional investors TGARCH(1,1), and foreign institutional investors GARCH(1,1). Further, the imbalance of buy and sell orders is suitable for the fluctuation of futures returns using EGARCH(1,1).

Fama French 5 Faktör Modeline Karşı Alternatif Fama French 5 Faktör Modeli: Seçilmiş İslam Ülkeleri Uygulaması

In this study, the validity of the five-factor model in developing and underdeveloped countries was investigated in 2012-2020, as well as the validity of the model to be created by using the inflation rate instead of the risk-free interest rate, and the answers to the questions of its comparison with the original model. In seeking an answer to this question, Pakistan, Malaysia, Indonesia and Turkey were selected as the countries with interest-sensitive investors. In the study, the Kuala Lumpur Composite Index (KLCI) for Malaysia, the Jakarta Islamic Index (JKII) for Indonesia, the Karachi Meezan Index (KMI) for Pakistan and the Participation Index (KATLM) for Turkey were selected for the study and analysis was carried out on the top 30 companies in the index.In terms of the created portfolios, it is seen that different portfolios are effective in terms of the highest excess return in selected countries. In fact, it has been observed that the portfolio with the highest extreme return in a country has the lowest return in the other country. It can only be said that the excessive return of small firms is greater than that of large firms. Looking at the fluctuation of excess returns, it is seen that Pakistan and Indonesia markets are risky, while Malaysia is the least risky. As a result of the analysis, evidence has been obtained that the original model has little advantage over the inflation model and that they are very close to each other. It can be said that the FF5F model is also effective in developing and underdeveloped country markets, and interest-sensitive investors can model using the inflation rate.

Risk of Rice Cultivation under Current and Future Environment and Market

Risk is an inevitable feature of agriculture globally, and it might increase over time in the future. The study assesses the risk of rice cultivation in the three seasons under current and future conditions. The software programme @Risk version 7.6 was used to simulate the risk-return trade of rice cultivation in the three seasons in Bangladesh. The likelihood of having negative net income for the Aus (90%) and Boro (80%) rice was very high under the current market and environmental conditions. Besides, the chance of obtaining negative net income was notable for T. Aman (33%). Both the yield and price variation significantly contributed to the fluctuation of returns of rice production. However, with the current seasonal variation in yield, the probability of having negative net income for paddy rice was very low under the farmers’ expected price (Aman: 22.5 BDT kg-1, Boro: 25 BDT kg-1 and Aus: 24 BDT kg-1). The result indicates that only access to the fair price of the rough rice can ensure the economic sustainability of the rice production. Likewise, chances of having a negative net income of paddy rice in 2030 will be zero under the extrapolated yield (Aman: 5.3 kg ha-1, Boro: 6.3 kg ha-1 and Aus: 4.8 kg ha-1) and price (Aman: 31.75 BDT kg-1, Boro: 30.75 BDT kg-1 and Aus: 30 BDT kg-1). The findings indicate that rice cultivation in the three seasons will be economically sustainable, subject to achieving the expected genetic gain and ensuring access to the projected price. Thus, policy supports are needed to ensure farmers' access to a fair price, improve management practice, and strengthen research to enhance genetic gain for sustainable rice farming under future conditions. Bangladesh Rice J. 25 (1) : 101-110, 2021

Role of Cohort Size in Trends in Class and Occupational Returns to Education at First Job: The Case of Japan

AbstractDownward trends in class and occupational returns to education have mainly been attributed to educational expansion via credential inflation. However, despite this rapid educational expansion, trends in returns to education vary across societies. This study proposes that changes in population size can contribute to offsetting the impact of educational expansion, controlling for demand-side factors. Using nationally representative survey data from Japan, we analysed trends in class and occupational returns to education at the first job of individuals born between 1942 and 1986. We found that a smaller cohort size increased the advantage of the highly educated in reaching higher occupational positions and upper service class positions, while a higher university graduation rate decreased this advantage. A counterfactual simulation demonstrated that the fluctuation in returns to education was driven by the pace of educational expansion relative to reductions in cohort size. These findings suggest that declining cohort sizes have counterbalanced credential inflation and caused stable trends in returns to education. We argue that mixed trends in class and occupational returns to education can be explained by the different paces of population change across societies.

ANALISIS PROFITABILITAS DAN LEVERAGE TERHADAP RETURN SAHAM PADA SEKTOR PERTAMBANGAN BATU BARA

The current condition of the capital market is full of uncertainty, which can be seen from fluctuations in stock prices and Returns. The volatility of the company’s stock price can indicate that investors have not obtained optimal stock Returns. This study aims to examine the effect of profitability and Leverage on stock Returns in the coal mining sector listed on the IDX in 2017-2019. This research uses associative research with a quantitative approach. The population used is all coal mining sectors listed on the IDX in 2017-2019 and the sample is used for purposive sampling that is 16 companies. Secondary data is used by accessing the official IDX website then downloading the annual financial reports of the coal mining sector for 2017-2019. Multiple linear regression was used as analysis in this study with statistical software. The results of the study state that profitability and Leverage have no effect on stock Returns in the coal mining sector which is listed on the Indonesia Stock Exchange in 2017-2019.
 Keywords: Leverage, Profitabilitas, Return Saham