Trading Period Research Articles

Assessing interdependence and contagion effects on the bond yield and stock returns nexus in Sub-Saharan Africa: Evidence from wavelet analysis

We examine the time- and frequency-dependent co-movements between stock and government bond yield returns for four major markets in sub-Saharan Africa. We employ the bi-, partial, and multiple wavelets techniques to evaluate the influence of COVID-19 cases on the fundamental relationship between bond and stock market returns. We further assess the lead-lag connectedness between these asset classes to ascertain their potency to offer diversification advantages in the COVID-19 pandemic era. From the bi-wavelet analysis, we find that the stock-bond dynamics in SSA markets mimic a non-homogeneous pattern that is attributable to interdependence. Our findings divulge that marginal COVID-19 cases do not drive the co-movement between stock and bond yield returns at all time scales. With no contagious spillovers found, the multiple wavelet correlations explicate that the interrelations between the two asset classes are attributed to their interdependence in the long term. We find safe-haven benefits for short- and medium-term investors during the COVID-19 pandemic. In the long term, for all market conditions, the extent of connectedness between stocks and bonds lessens any diversification prospects. We underscore the potency of South African bond and stock returns to lead and/or lag all other markets across investment horizons, both in normal and stressed trading periods. Devising measures that strengthen the markets for bonds to create consistent diversification opportunities for investors is essential to attracting international capital flows. We discuss the implications of our findings.

PORTFOLIO DIVERSIFICATION WITH BITCOIN. EVIDENCE FROM INSTITUTIONAL INVESTORS PERSPECTIVE

The paper investigates the empirical verification of the efficacy of investment diversification using the main stock exchange indices in the Eurozone countries and Bitcoin. The paper also investigates whether and to what extent it is desirable for institutional investors, in addition to traditional financial instruments, to invest in Bitcoin. The aim of the research is to examine whether it is justified and to what extent to include Bitcoin in the portfolio of an institutional investor. Through this research, an attempt is made to find an answer to the research question: “What share of Bitcoin in the portfolio structure is justified, taking into account the ratio of return and risk”? The analysis includes data on the daily movement of selected action indices as well as the movement of Bitcoin. The methodology involves the analysis of high-frequency data, given that daily trading data were used. The results show that it is justified to include Bitcoin in the portfolio structure. Also, the results show which share of Bitcoin in the portfolio is justified from the aspect of institutional investors. The data used in the analysis cover the period from 2019 and 2020. Two portfolios have been created, one without Bitcoin and the other with Bitcoin. The goal in optimization for both portfolios is to minimize risk. The observed period of the analysis is characterized by the crisis caused by the coronary virus pandemic and the period of active bitcoin trading. The results of the research show that Bitcoin is a good source of diversification in a portfolio that contains traditional financial instruments, both for an investor who is not prone to risk, and for those investors who have a greater appetite for risk. The conclusion is that the rational behavior of institutional investors requires consideration of investing in Bitcoin using the Markowitz model. However, given the high degree of volatility, investors should be careful when making decisions about including Bitcoin in their investment portfolio. Bitcoin is an extremely volatile instrument. Given that it is a speculative and highly volatile financial instrument, investors have different views on Bitcoin. First in terms of defining this cryptocurrency and then in terms of including this instrument in the investment portfolio. By including Bitcoin in the investment portfolio, the goal of diversification has been achieved. This is to reduce the risk of the institutional investor to a minimum. In practice, this means that it is possible to create a portfolio that carries an acceptable level of risk with the desired level of return. Given that Bitcoin is an extremely volatile and consequently - risky instrument, the expected return is also - high. The results of the research show that the cryptocurrency Bitcoin can serve as a desirable instrument for diversification of the investment portfolio when looking at a portfolio that includes stock indices. The results suggest that it is desirable to include in the structure of the portfolio a certain share of Bitcoin, about 6%.

A Multi-Stage Information Protection Scheme for CDA-Based Energy Trading Market in Smart Grids

Building a reliable information exchange scheme is the prerequisite for Peer-to-Peer (P2P) energy trading in the smart grid. This paper proposes a multi-stage information protection scheme to meet the data security requirements including personal privacy-preserving, message authentication and confidentiality. In this study, the adversary is supposed to attack the continuous double auction (CDA)-based energy trading system in both the bidding period and implementation period. To prevent the smart grid from all potential cyber-attacks, our protection scheme is proposed to deal with personal privacy leakage, malicious data injection and impersonation attack. Before the trading period, in the home energy data collection stage, we propose a dual privacy-preserving scheme based on the short key homomorphic encryption and the decentralized framework. In the trading period, an encryption-signature mixed (E-S) communication model is proposed to achieve the secure transmission of trading messages (e.g., energy bidding from prosumer). At last, in the implementation period, a decentralized field measurement monitoring scheme is proposed to detect the node compromise attack to the power measurements. In addition, the computational feasibility, time cost and communication cost are evaluated with the home-use PC. The anti-attack performance of the proposed scheme is verified on the IEEE 39-bus distribution network with the CDA-based P2P energy trading market.

Informativeness of trades around macroeconomic announcements in the foreign exchange market

• We study the extent of private information revealed through trades in the foreign exchange market. • We find the intraday variation in the proportion of permanent price variations driven by trade-related information has a repetitive periodic pattern, • We show the information share of private information in trades evolves across trading periods in the FX markets. • We find that, among market participants, heterogeneous interpretation of announcements and opinion disagreement after the announcement are revealed from order flow, and the efficient price is still driven by information implicit in trading activities after the announcement. We study the extent to which private information is revealed through trades in the foreign exchange market. The trade informativeness measured from the VAR framework of trades and quotes allows us to quantify the impact of asymmetric information on return variation. We find the intraday variation in the proportion of permanent price variation driven by trade-related information has a repetitive periodic pattern, and trades in the overlapping trading hours of London and New York carry most information than the other time segments. The results show the fraction of trade-related component to exchange rate variation positively relates to trading volume and reversely relates to effective spread. With a comprehensive set of 48 real-time US, European, and Japanese macroeconomic news surprises variables, and controlling for the effect of trading volume, spread, and volatility, we find news of production-related economic indicator, e.g., GDP and industrial production, has a positive relationship with the trade informativeness in a trading period, suggesting the increased private information share may be attributed to heterogeneous interpretations of the announcement among market participants. Announcement-related information is gradually resolved into the price, and the information is transited via the channel of trading activities.

A Comparative Study of the Functions of Gold and Bit coin in the Financial Industry

With the development of society, gold and "digital gold" Bit coin have been incorporated into the portfolio by investors. This paper will help traders predict the price of them, and plan the optimal portfolio strategy, so as to maximize the return and minimize the risk cost We have developed a model that uses only the past stream of daily prices to date to determine each day if the trader should buy, hold, or sell their assets in their portfolio. We will start with $1000 on 9/11/2016. We will use the five-year trading period, from 9/11/2016 to 9/10/2021. On each trading day, the trader will have a portfolio consisting of cash, gold, and bitcoin [C, G, B] in U.S. dollars, troy ounces, and bitcoins, respectively. The initial state is [1000, 0, 0]. The commission for each transaction (purchase or sale) costs α% of the amount traded. Assume α gold = 1% and α bitcoin = 2%. There is no cost to hold an asset. Note that bitcoin can be traded every day, but gold is only traded on days the market is open, as reflected in the pricing data files LBMA-GOLD.csv and BCHAIN-MKPRU.csv. Our model accounts for this trading schedule. To develop our model, we may only use the data in the two spreadsheets: LBMA-GOLD.csv and BCHAIN-MKPRU.csv.

The relationship between day-ahead and future prices in electricity markets: An empirical analysis on Italy, France, Germany, and Switzerland

We evaluate the relationship between electricity day-ahead and future prices following the hedging pressure theory, which explains the difference between future prices and expected spot prices regarding market players’ risk aversion. We calculate the sign and intensity of the ex-post risk premia in the electricity market of Italy, France, Switzerland, and Germany during the last decade and for all products traded: monthly, quarterly, and yearly futures and distinguish between base-load and peak-price futures. We test the impact of the trading period on the risk premia in panel regressions, controlling for seasonality and the impact of financial markets’ trades and primary energy prices. We show that, in all countries, there is no convergence of future prices to the underlying day-ahead ones; moreover, for most future contracts, the premium rises as contracts approach delivery. In addition, for Italy and Switzerland, there is an inversion of the sign of the premia (mostly for base-load products). Risk premia are negative at the beginning of the trading period and positive as the delivery period approaches. This indicates that, in these countries, premia are, on average, paid by power producers at the beginning of the period and by suppliers (i.e. power buyers) close to delivery. In contrast, in France and Germany, risk premia are both positive at the beginning and at the end of the trading period, signalling that, on average, buyers are willing to pay a premium to cover price volatility.

Dynamic connectedness and spillovers between Islamic and conventional stock markets: time- and frequency-domain approach in COVID-19 era

This study investigates the dynamic connectedness and spillovers between Islamic and conventional stock markets to reveal the time- and frequency-domain dynamics of the two asset classes under various market conditions. Using the spillover index of Baruník and Křehlík (2018), supplemented by the time-varying parameter vector autoregressions (TVP-VAR) connectedness model, we employ daily stock market indices for Islamic and conventional (G7) markets from November 23, 2015, to September 8, 2021. The findings explicate that the volatility spillovers across and within Islamic and/or G7 markets are time-varying and frequency-dependent but during market turbulences, the conventional stocks are prone to more volatilities than the Islamic stocks. Our findings additionally divulge contagious spillovers among Islamic and conventional stocks during Brexit and the studied COVID-19 period. Relative to mid-and long-term spillovers, we underscore the supremacy of short-term spillovers between Islamic and G7 markets. In turbulent trading periods, investors should utilise knowledge about market patterns and volatility to hedge their positions against lower stock returns, when spillover is more intense. Regulators should pay close attention to spillovers since they undermine cross-market connections. Intriguing findings and their implications are further discussed.

Data-driven optimal scheduling of multi-energy system virtual power plant (MEVPP) incorporating carbon capture system (CCS), electric vehicle flexibility, and clean energy marketer (CEM) strategy

The zero-carbon multi-energy systems (ZCMES) have received attention due to developed countries' promulgated carbon–neutral policy. Thus, This paper proposes a deep learning approach and optimization model for the optimal day-ahead scheduling of ZCMES virtual power plants. Technically, a carbon capture system (CCS) is introduced to harness the carbon emission associated with some equipment, consideration of electric vehicle multi-flexible potentials, followed by a clean energy marketer (CEM) strategy to ensure system reliability sustainably. For day-ahead multivariable time-series prediction, an integrated recurrent unit-bidirectional long-short term memory (GRU-BiLSTM) is developed. This is followed by an autoencoder (AE) for scenario generation and scene reduction using the fast forward reduction algorithm. A robust-stochastic modelling approach is then applied for optimal decision-making. As a case study, the proposed model is verified using accurate historical multi-energy data of a district in Arizona, the United States. The results show that the proposed model outperformed other scenarios by achieving a 76% average self-consumption ratio and 0.85 average multi-energy load cover ratio. Also, the proposed method obtains a 10.74% reduction in day-ahead scheduling cost by considering the CEM trading period and EV flexibility. Further, a 36% reduction is observed using a robust-stochastic approach, which is more robust and economical than deterministic, stochastic, and robust methods. Remarkably, it was observed that the CEM trading period restriction influenced the scheduling behaviour of ZCMES and the charging pattern of EVs. However, the integration of EV flexibility reduces dependency on the external grid and optimize the power consumption of CCS using part of cogeneration electrical output instead of total reliance on the external grid. Thus, the proposed model strengthens carbon–neutral feasibility in urban centres and serves as a reference tool for sustainable energy policymakers.

Statistical arbitrage trading on the intraday market using the asynchronous advantage actor–critic method

In this paper, we focus on statistical arbitrage trading opportunities involving the continuous exploitation of price differences arising during an intraday trading period with the option of closing positions on the balancing market. We aim to maximise the reward–risk ratio of an autonomous trading strategy. To find an optimal trading policy, we propose utilising the asynchronous advantage actor–critic (A3C) algorithm, a deep reinforcement learning method, with function approximators of two-headed shared deep neural networks. We enforce a risk-constrained trading strategy by limiting the maximum allowed position, and conduct state engineering and selection processes. We introduce a novel reward function and goal-based exploration, i.e. behaviour cloning. Our methodology is evaluated on a case study using the limit order book of the European single intraday coupled market (SIDC) available for the Dutch market area. The majority of hourly products on the test set return a profit. We expect our study to benefit electricity traders, renewable electricity producers and researchers who seek to implement state-of-art intelligent trading strategies.

Multidisciplinary investigation reveals an individual of West African origin buried in a Portuguese Mesolithic shell midden four centuries ago

Cabeço da Amoreira is a well-studied shell midden with a robust chronology based on a large number of radiocarbon dates on Mesolithic human burials. Surprisingly, we discovered one individual that lived about 400 years ago buried in this site. We employed a multidisciplinary approach integrating archaeology, historical records, genetics, radiocarbon dating and stable isotope analysis to investigate the biogeographic origins of this individual and burial circumstances. We could determine that this was a man of West African origin, probably from Senegambia, arriving in Portugal via the Trans-Atlantic Slave Trade. Our study provides new insights into aspects of the life and death of a first-generation African individual in Portugal during the Trans-Atlantic Slave Trade period and highlights the power of multidisciplinary research to unravel unwritten history.

A New Foundation for Freedom of Movement in an Age of Sovereign Control: The Liberal Jurisprudence of August Wilhelm Heffter

This article addresses how a once influential but now obscure jurist addressed a potential paradox in liberal thought—between democratic control over borders and transnational rights—as it arose in the mid-nineteenth-century, amid advocacy against authoritarianism and for free trade and movement, on the one hand, and the increasing calling into question of natural law theories that may have best facilitated free movement, on the other. While scholarship has increasingly shown how the boundaries between periods of natural law and positivist hegemony are difficult to distinguish, specific tensions in the mid-nineteenth-century called for an approach that preserved free movement in light of the growing appeal of empiricism and state sovereignty. In this context, August Wilhelm Heffter proposed that states were bound by higher law as a consequence of their free decision to enter international communities: these communities’ purpose, he wrote, bred customary laws facilitating interstate interaction. Heffter’s approximation of “natural” law in a more positivist context and his use of the period’s “customary” logic helps account for his influence not only in periods of free trade and movement’s ascendancy but also the survival of forms of his thought into periods of sovereigntist reaction against them. It therefore holds potential to address what scholarship has termed today’s “liberal paradox” between democracy and migration better than approaches that emphasize a more complete return to natural law.

Does systematic risk change when markets close? An analysis using stocks’ beta

Investors' behavior and news or events occurring during the market closure affect open price variation. For these reasons, daytime and overnight returns and volatilities move differently. In light of these effects, it is natural to ask whether systematic risk varies between trading and non-trading periods. We answer this question by evaluating the US stocks’ beta from 1992 to 2020. Computing daily, daytime, and overnight betas using a proper market index for each trading period, we show how market risk varies over time and is influenced by market shocks. We observe a high overnight risk concentrated within small and large stocks. We find that daytime systematic risk is generally higher, especially between 2001 and 2019. Furthermore, we show how the outbreak of the Covid-19 pandemic led to an increase in overnight risk implying that US stocks became more sensitive to the market closure.

Adaptation of urban distribution to the fluvial geomorphic environment and the reconstruction of the river system structure by urban distribution in Haihe Plain in the past 2,200 years

AbstractThe alluvial plain of many of the world's large rivers is generally an area with concentrated urban distribution and a long history of development. In eastern China, the alluvial plains downstream of large rivers such as the Haihe River are the most densely populated and economically developed regions. Beijing, the capital of China, is located on the Haihe Plain. It is a matter of great significance that the region's capacity for livability and sustainable development be improved. Using the river geomorphic data and historical town information, methods of spatial statistical analysis and historical literature research, this paper analyzes the spatial relationship between the distribution of urban centers and the fluvial geomorphology of the Haihe Plain over more than 2,200 years. The research finds that: first, different levels of cities and towns have led to varying choices regarding the fluvial geomorphic environment. Second, in different periods in which different transportations were dominant, the distribution of cities and towns has adapted to the fluvial geomorphology. In the land transportation period, urban settlements mainly concentrated on the diluvial‐alluvial fans at the foot of the Taihang Mountain; in the inland shipping period, they focused on gathering at the confluence of rivers on the central plain and along the canals; in the period of global trade, Tianjin, a trade center, established itself at the mouth of the Haihe River. Third, humans intend to rebuild water system structures (such as the Grand Canal) in the context of major water conservancy projects in the basin to meet the needs of the expanding regions and towns. The research shows that the urban development of the Haihe Plain should adapt to the natural regional pattern and establish a solution based on respecting the natural conditions of the basin, to make the regional sustainable development more resilient.

Price Index Modeling and Risk Prediction of Sharia Stocks in Indonesia

This study aimed to predict the JKII (Jakarta Islamic Index) price as a price index of sharia stocks and predict the loss risk. This study uses geometric Brownian motion (GBM) and Value at Risk (VaR; with the Monte Carlo Simulation approach) on the daily closing price of JKII from 1 August 2020–13 August 2021 to predict the price and loss risk of JKII at 16 August 2021–23 August 2021. The findings of this study were very accurate for predicting the JKII price with a MAPE value of 2.03%. Then, using VaR with a Monte Carlo Simulation approach, the loss risk prediction for 16 August 2021 (one-day trading period after 13 August 2021) at the 90%, 95%, and 99% confidence levels was 2.40%, 3.07%, and 4.27%, respectively. Most Indonesian Muslims have financial assets in the form of Islamic investments as they offer higher returns within a relatively short time. The movement of all Islamic stock prices traded on the Indonesian stock market can be seen through the Islamic stock price index, namely the JKII (Jakarta Islamic Index). Therefore, the focus of this study was predicting the price and loss risk of JKII as an index of Islamic stock prices in Indonesia. This study extends the previous literature to determine the prediction of JKII price and the loss risk through GBM and VaR using a Monte Carlo simulation approach.

Combining machine learning and effective feature selection for real-time stock trading in variable time-frames

The unpredictability and volatility of the stock market render it challenging to make a substantial profit using any generalised scheme. Many previous studies tried different techniques to build a machine learning model, which can make a significant profit in the US stock market by performing live trading. However, very few studies have focused on the importance of finding the best features for a particular trading period. Our top approach used the performance to narrow down the features from a total of 148 to about 30. Furthermore, the top 25 features were dynamically selected before each time training our machine learning model. It uses ensemble learning with four classifiers: Gaussian Naive Bayes, Decision Tree, Logistic Regression with L1 regularization, and Stochastic Gradient Descent, to decide whether to go long or short on a particular stock. Our best model performed daily trade between July 2011 and January 2019, generating 54.35% profit. Finally, our work showcased that mixtures of weighted classifiers perform better than any individual predictor of making trading decisions in the stock market.

Optimization of venture portfolio based on LSTM and dynamic programming

<abstract><p>A rational investor always pursues a portfolio with the greatest possible return and the least possible risk. Therefore, a core issue of investment decision analysis is how to make an optimal investment choice in the market with fuzzy information and realize the balance between maximizing the return on assets and minimizing the risk. In order to find optimal investment portfolios of financial assets with high volatility, such as gold and Bitcoin, a mathematical model for formulating investment strategies based on the long short-term memory time series and the dynamic programming model combined with the greedy algorithm has been proposed in this paper. The model provides the optimal daily strategy for the five-year trading period so that it can achieve the maximum expected return every day under the condition of a certain investment amount and a certain risk. In addition, a reasonable risk measure based on historical increases is established while considering the weights brought by different investment preferences. The empirical analysis results show that the optimal total assets and initial capital obtained by the model change in the same proportion, and the model is relatively stable and has strong adaptability to the initial capital. Therefore, the proposed model has practical reference value and research significance for investors and promotes a better combination of computer technology and financial investment decision.</p></abstract>

Research on the Trading Strategies for Maximizing the Value of Assets in Quantitative Trading

This study focuses on trading strategies for maximizing the value of assets, when traders buy, sell and hold bitcoin and gold daily. We have built a price forecasting model using Multiple Linear Regression (MLR) theory, and an executable trading model to provide daily trading orders. First, a 5-year data of daily prices were checked and few of missing values in gold price were filled with the value of the day before. Second, the MLR model was adopted using today's price, today's 5-day moving average (MA5) and today's 20-day moving average (MA20) to forecast prices in future days. The first 8% of the data was used as training set, and the goodness of fit is up to 99.64% on the remaining data. Third, we determined the overall trading strategy, the buying/selling point judgment strategy and the combination trading strategy for bitcoin and gold, based on the forecasting model. Finally, a trading model was built to solve daily trading orders (sell, buy or hold on) and the corresponding values for the three currencies (cash, bitcoin and gold), for a 5-year trading period. We have calculated that the value of assets increased from the initial $1,000 to $38.34 million, after 5 years of investment.

ÇALIŞMA SERMAYESİ YÖNETİMİNİN İŞLETME KARLILIĞINA ETKİSİ: BORSA İSTANBUL İMALAT SEKTÖRÜ UYGULAMASI

Efficient working capital management is of vital importance for companies to reach their goals, work at full capacity, and sustain their growth in a profitable way. In this study, the 2010-2018 quarterly data of the 102 manufacturing companies operating on BIST have been analyzed through panel data analysis. This study reveals that there is a negative relation between ROA, which is taken as a profitability metric, and Days Sales Outstanding (Average Collection Period), Accounts Payable Period, Cash Trading Period (Cash Cycle) and Leverage Ratio, which are among working capital metrics and that there is a positive relation between Current Ratio and Company Size.

Stochastic Analysis of the Impact of Growth-Rates on Stock Market Prices in Nigeria

A stochastic analysis of stock market expected returns and Growth-rates were investigated. The precise conditions for obtaining the drifts, volatilities and Growth-rates of four different stocks were also considered herein. From the stochastic analysis of the model; systems of non-linear stochastic differential equations were developed by means of covariance matrix solution on the stochastic part of the expected returns of investors while the deterministic part a function of the drift parameter as a mean column vector. A condition were imposed that multiplied drift parameter by one and solving simultaneously to obtain future stock prices. From the estimated growth-rates shows severe depletion of securities which are indexed by per thousands of naira leading to financial liquation. Also there are also good increases in growth rate values which dominantly indicate high stock returns over the trading period. This remark leads to a favorable profit margin on the aspect of investors. These are obtainable and conferred in this paper.

Teardrops at the Lake: Chemistry of New Kingdom to Makuria Glass Beads and Pendants Between the First and Second Nile Cataracts

International expeditions extensively excavated Lower Nubia (between the First and Second Nile Cataracts) before it was submerged under the waters of Lake Nasser and Lake Nubia. The expeditions concentrated on monumental architecture and cemeteries, including sites at Qustul and Serra East, where the New Kingdom, and Napatan, Meroitic, Nobadian, and Makurian-period elites and common people were buried, ca. 1400 BC–AD 1400. Although the finds abound in adornments, including bead imports from Egypt and South India/Sri Lanka, only a few traces of local glass bead-making have been recorded in Nubia so far. Based on results of laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS) analysis of 76 glass beads, pendants, and chunks from Qustul and Serra East contexts, dated between the New Kingdom and the Makuria Kingdom periods, this paper discusses the composition and provenance of two types of plant-ash soda-lime (v-Na-Ca) glass, two types of mineral soda-lime glass (m-Na-Ca), and two types of mineral-soda-high alumina (m-Na-Al) glass. It also presents the remains of a probable local glass bead-making workshop dated to the period of intensive long-distance bead trade in Northeast Africa, AD 400–600.