Academic literature on the topic 'Intra- daily trading volumes'
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Journal articles on the topic "Intra- daily trading volumes"
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Brownlees, C. T., F. Cipollini, and G. M. Gallo. "Intra-daily Volume Modeling and Prediction for Algorithmic Trading." Journal of Financial Econometrics 9, no. 3 (July 5, 2010): 489–518. http://dx.doi.org/10.1093/jjfinec/nbq024.
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Li, Edward Xuejun, K. Ramesh, and Min Shen. "The Role of Newswires in Screening and Disseminating Value-Relevant Information in Periodic SEC Reports." Accounting Review 86, no. 2 (March 1, 2011): 669–701. http://dx.doi.org/10.2308/accr.00000023.
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ABSTRACT: We examine the role of newswires in identifying and conveying market-moving information in periodic SEC reports to capital market participants. Using data on Dow Jones Newswires, we find that newswires are more likely to send alerts on firms that do not release preliminary earnings, have credit ratings, are included in major market indices, have litigation exposure, or report losses. Reflective of the market’s focus on certain key events, firms with a nonstandard audit opinion, in the process of delisting, reporting unusual accounting items, or raising equity capital also receive alerts. Moreover, not only do we find significant price and volume reactions to the alerts at the daily level, but also we document immediate intra-day market activity triggered by the alerts, whereas we detect no similar reaction for SEC filings that trigger the alerts. Additional analysis suggests that the intra-day reaction is not driven by noise trading.
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Hahn, Sang Buhm, and Seung Hyun Oh. "The Impact of Program Trading on the Short-run and Long-run Volatility of Korean Stock Market." Journal of Derivatives and Quantitative Studies 15, no. 1 (May 31, 2007): 101–33. http://dx.doi.org/10.1108/jdqs-01-2007-b0004.
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This study investigates the impact of program trading on the market volatility by separating the volatility into long-run and short-run components using VA-CEGARCH model. This approach allows us to observe the two channels through which the program trading affects the market volatility. We have following results. Program trading and non-program trading both have no impact on the long-run component but do increase short-run component. In case of short-run component‘ program trading has a larger impact compared to non-program trading. Secondly, in both daily and intra-day analysis, arbitrage program trading is found to have a larger impact on short-run components than non-arbitrage program trading. Thirdly, ARCH effects are found in short-run components of daily analysis and long-run components of intra-day analysis. And the volatility’s asymmetric responses to good or bad news are introduced through long-run components. What is noteworthy is the fact that non-arbitrage program trading is actually found to reduce short-run volatility in the intra-day analysis. Which means that non-arbitrage program trading, such as hedging transactions, helps promote intra-day market stability. Our findings mean that the short-run component is the main channel by which program trading produce unnecessary market volatility.
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Kambeu, Edson. "Trading Volume as a Predictor of Market Movement." International Journal of Finance & Banking Studies (2147-4486) 8, no. 2 (July 20, 2019): 57–69. http://dx.doi.org/10.20525/ijfbs.v8i2.177.
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A logistic regression model is has also become a popular model because of its ability to predict, classify and draw relationships between a dichotomous dependent variable and dependent variables. On the other hand, the R programming language has become a popular language for building and implementing predictive analytics models. In this paper, we apply a logistic regression model in the R environment in order to examine whether daily trading volume at the Botswana Stock Exchange influence daily stock market movement. Specifically, we use a logistic regression model to find the relationship between daily stock movement and the trading volumes experienced in the recent five previous trading days. Our results show that only the trading volume for the third previous day influence current stock market index movement. Overall, trading volumes of the past five days were found not have an impact on today’s stock market movement. The results can be used as a basis for building a predictive model that utilizes trading as a predictor of stock market movement.
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Acker, Daniella, Mathew Stalker, and Ian Tonks. "Daily Closing Inside Spreads and Trading Volumes Around Earnings Announcements." Journal of Business Finance Accounting 29, no. 9&10 (November 2002): 1149–79. http://dx.doi.org/10.1111/1468-5957.00465.
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Muryani, Muryani, and Anisa Dyan Pratiwi. "Intra-Industry Trading Factors and Patterns in ASEAN-5 Region." Jurnal Global Strategis 12, no. 2 (November 30, 2018): 41. http://dx.doi.org/10.20473/jgs.12.2.2018.41-52.
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The large expansion of trade in the form of Intra-Industry Trade (IIT) in ASEAN is driving large volumes and variety of traded goods and is changing patterns of trade across members. This paper examines the factors affecting the level of IIT for ASEAN-5 countries (Indonesia, Malaysia, Philippines, Singapore, and Thailand) in the period of 2004-2014. IIT is measured with Grubel-Lloyd index covering ten different one-digit SITC categories. The result indicates a large Intra-Industry Trade among ASEAN countries and across most manufacturing sectors. IIT Index is employed as a dependent variable, and four variables are used as independent variables: 1) different GDP per capita, 2) foreign direct investment (FDI), 3) trade openness, and 4) distance. Different GDP per capita and trade openness have a positive effect on IIT. FDI does not affect IIT, and distance has a negative effect on IIT across intra-ASEAN trade. Keywords: International Trade, Intra-Industry Trade, Grubel-Lloyd, Panel Data Analysis
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Leung, Charles Ka Yui, Patrick Wai Yin Cheung, and Erica Jiajia Ding. "International Real Estate Review." International Real Estate Review 11, no. 2 (December 31, 2008): 47–74. http://dx.doi.org/10.53383/100097.
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Previous studies of the office market have tended to focus on either the rental market or the aggregate sales market. This paper focuses on the intra-metropolitan sales market and on office price and trading volume dynamics in Hong Kong. According to our findings, buildings trading at higher prices are not necessarily traded more often than those trading at lower prices. In addition, the price of offices in different categories does not necessarily move in tandem. The trading volumes of higher priced buildings tend to Granger cause the lower priced buildings, and this conclusion is robust to alternative classifications. The paper contrasts several existing theories. Suggestions for future research are also discussed.
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Lv, Qiuna, Liyan Han, Yipeng Wan, and Libo Yin. "Stock Net Entropy: Evidence from the Chinese Growth Enterprise Market." Entropy 20, no. 10 (October 19, 2018): 805. http://dx.doi.org/10.3390/e20100805.
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By introducing net entropy into a stock network, this paper focuses on investigating the impact of network entropy on market returns and trading in the Chinese Growth Enterprise Market (GEM). In this paper, indices of Wu structure entropy (WSE) and SD structure entropy (SDSE) are considered as indicators of network heterogeneity to present market diversification. A series of dynamic financial networks consisting of 1066 daily nets is constructed by applying the dynamic conditional correlation multivariate GARCH (DCC-MV-GARCH) model with a threshold adjustment. Then, we evaluate the quantitative relationships between network entropy indices and market trading-variables and their bilateral information spillover effects by applying the bivariate EGARCH model. There are two main findings in the paper. Firstly, the evidence significantly ensures that both market returns and trading volumes associate negatively with the network entropy indices, which indicates that stock heterogeneity, which is negative with the value of network entropy indices by definition, can help to improve market returns and increase market trading volumes. Secondly, results show significant information transmission between the indicators of network entropy and stock market trading variables.
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HE, LING-YUN, and XING-CHUN WEN. "PREDICTABILITY AND MARKET EFFICIENCY IN AGRICULTURAL FUTURES MARKETS: A PERSPECTIVE FROM PRICE–VOLUME CORRELATION BASED ON WAVELET COHERENCY ANALYSIS." Fractals 23, no. 02 (May 28, 2015): 1550003. http://dx.doi.org/10.1142/s0218348x15500036.
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In this paper, we use a time-frequency domain technique, namely, wavelet squared coherency, to examine the associations between the trading volumes of three agricultural futures and three different forms of these futures' daily closing prices, i.e. prices, returns and volatilities, over the past several years. These agricultural futures markets are selected from China as a typical case of the emerging countries, and from the US as a representative of the developed economies. We investigate correlations and lead–lag relationships between the trading volumes and the prices to detect the predictability and efficiency of these futures markets. The results suggest that the information contained in the trading volumes of the three agricultural futures markets in China can be applied to predict the prices or returns, while that in US has extremely weak predictive power for prices or returns. We also conduct the wavelet analysis on the relationships between the volumes and returns or volatilities to examine the existence of the two "stylized facts" proposed by Karpoff [J. M. Karpoff, The relation between price changes and trading volume: A survey, J. Financ. Quant. Anal.22(1) (1987) 109–126]. Different markets in the two countries perform differently in reproducing the two stylized facts. As the wavelet tools can decode nonlinear regularities and hidden patterns behind price–volume relationship in time-frequency space, different from the conventional econometric framework, this paper offers a new perspective into the market predictability and efficiency.
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Jain, Pawan, Spenser J. Robinson, Arjun J. Singh, and Mark Sunderman. "Hospitality REITs and financial crisis: a comprehensive assessment of market quality." Journal of Property Investment & Finance 35, no. 3 (April 3, 2017): 277–89. http://dx.doi.org/10.1108/jpif-08-2016-0068.
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Purpose The purpose of this paper is to examine market microstructure differences in stock market quality for hospitality real estate investment trusts (REITs) during the pre- and post-financial crisis eras. It provides insight on different trading strategies based on the underlying liquidity and volatility of hospitality REITs as compared traditional REITs and the broader market. Design/methodology/approach The paper uses established microstructure measures for liquidity, trading volumes and risk assessment and compares daily and intraday trading patterns of REITs, hospitality REITs and the broad market. Findings The results suggest a quicker recovery of performance for hospitality REITs and some fundamental increases in liquidity measures post-crisis. The results of the study highlight the differences in trading volumes, liquidity and risk profile of hospitality REITs compared to traditional REITs both in the pre- and post-financial crisis periods. Practical implications The quicker recovery of hospitality REITs in key trading measures may suggest flight to quality during periods of high volatility. Originality/value This study fills the gap in the literature relative to microstructure studies and provides information to help hotel firms and portfolio managers choose an appropriate organizational structure and investment vehicle, respectively.
Dissertations / Theses on the topic "Intra- daily trading volumes"
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Naimoli, Antonio. "Essays on the modelling and prediction of financial volatility and trading volumes." Doctoral thesis, Universita degli studi di Salerno, 2017. http://elea.unisa.it:8080/xmlui/handle/10556/3879.
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2015 - 2016Aim of this thesis is to propose and discuss novel model specifications for predicting financial volatility and trading volumes using intra-daily information. Chapter 1 provides a literature overview on modelling financial volatility and volumes and introduces thè most important contributions and findings of thè thesis. Chapter 2 presents an extension of thè Realized GARCH model by Hansen et al. (2012) along three different directions. First, we allow for heteroskedasticity of thè noise term in thè measurement equation, since it is assumed to be time-varying as a function of an estimator of thè integrated quarticity of intra-daily returns. Second, in order to account for attenuation bias effects, we let thè volatility dynamics to depend on thè accuracy of thè realized measure. This is achieved by leaving thè response coefficient of thè lagged realized measure, to depend on thè time-varying variance of thè volatility measurement error, giving more weight to lagged volatilities when they are more accurately measured. Finally, we account for jumps by introducing in thè measurement equation an additional explanatory variable aimed at quantify thè bias due to thè effect of jumps. Chapter 3 develops a further extension of thè Realized GARCH model of Hansen et al. (2012) for forecasting daily volatility incorporating information from multiple realized volatility measures computed at different sampling frequencies in order to achieve thè optimal trade-off between bias and efficiency. Namely, future volatility forecasts are determined by a weighted average of thè considered realized measures, where thè weights are time-varying and adaptively determined according to thè estimated amount of noise and jumps. This specification aims to reduce, in an adaptive fashion, bias effects related to thè different sampling frequency at which thè realized measure are computed. Chapter 4 proposes a novel approach for modelling and forecasting high-frequency trading volumes, extending thè logie of thè Component Multiplicative Error Model of Brownlees et al. (2011), by a more flexible specification of thè long-run component, since it is based on a MIDAS polynomial structure through an additive cascade of linear filters adopting heterogeneous components which can take on multiple frequencies, in order to reproduce thè strong persistent autocorrelation structure featuring intra-daily trading volumes. Finally, Appendix A presents an empirical application on tick-by-tick data filtering and highlights thè main features and issues surrounding ultra high-frequency datasets.
XXIX n.s.
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Groß-Klußmann, Axel. "An econometric analysis of intra-daily stock market liquidity, volatility and news impacts." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2012. http://dx.doi.org/10.18452/16572.
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In dieser Dissertation befassen wir uns mit ökonometrischen Modellen und empirischen Eigenschaften von Intra-Tages (Hochfrequenz-) Aktienmarktdaten. Der Fokus liegt hierbei auf der Analyse des Einflusses, den die Veröffentlichung von Wirtschaftsnachrichten auf die Aktienmarktaktivität hat, der Vorhersage der Geld-Brief-Spanne sowie der Modellierung von Volatilitätsmaßen auf Intra-Tages-Zeitintervallen. Zunächst quantifizieren wir die Marktreaktionen auf Marktneuigkeiten innerhalb eines Handelstages. Zu diesem Zweck benutzen wir linguistisch vorab bearbeitete Unternehmensnachrichtendaten mit Indikatoren über die Relevanz, Neuheit und Richtung dieser Nachrichten. Mit einem VAR Modell für 20-Sekunden Marktdaten der London Stock Exchange weisen wir durch Nachrichten hervorgerufene Marktreaktionen in Aktienkursrenditen, Volatilität, Handelsvolumina und Geld-Brief-Spannen nach. In einer zweiten Analyse führen wir ein long memory autoregressive conditional Poisson (LMACP)-Modell zur Modellierung hoch-persistenter diskreter positivwertiger Zeitreihen ein. Das Modell verwenden wir zur Prognose von Geld-Brief-Spannen, einem zentralen Parameter im Aktienhandel. Wir diskutieren theoretische Eigenschaften des LMACP-Modells und evaluieren rollierende Prognosen von Geld-Brief-Spannen an den NYSE und NASDAQ Börsenplätzen. Wir zeigen, dass Poisson-Zeitreihenmodelle in diesem Kontext signifikant bessere Vorhersagen liefern als ARMA-, ARFIMA-, ACD- und FIACD-Modelle. Zuletzt widmen wir uns der optimalen Messung von Volatilität auf kleinen 20 Sekunden bis 5 Minuten Zeitintervallen. Neben der Verwendung von realized volatility-Ansätzen konstruieren wir Volatilitätsmaße durch Integration von spot volatility-Schätzern, sodass auch Beobachtungen außerhalb der kleinen Zeitintervalle in die Volatilitätsschätzungen eingehen. Ein Vergleich der Ansätze in einer Simulationsstudie zeigt, dass Volatilitätsmaße basierend auf spot volatility-Schätzern den RMSE minimieren.In this thesis we present econometric models and empirical features of intra-daily (high frequency) stock market data. We focus on the measurement of news impacts on stock market activity, forecasts of bid-ask spreads and the modeling of volatility measures on intraday intervals. First, we quantify market reactions to an intraday stock-specific news flow. Using pre-processed data from an automated news analytics tool we analyze relevance, novelty and direction signals and indicators for company-specific news. Employing a high-frequency VAR model based on 20 second data of a cross-section of stocks traded at the London Stock Exchange we find distinct responses in returns, volatility, trading volumes and bid-ask spreads due to news arrivals. In a second analysis we introduce a long memory autoregressive conditional Poisson (LMACP) model to model highly persistent time series of counts. The model is applied to forecast quoted bid-ask spreads, a key parameter in stock trading operations. We discuss theoretical properties of LMACP models and evaluate rolling window forecasts of quoted bid-ask spreads for stocks traded at NYSE and NASDAQ. We show that Poisson time series models significantly outperform forecasts from ARMA, ARFIMA, ACD and FIACD models in this context. Finally, we address the problem of measuring volatility on small 20 second to 5 minute intra-daily intervals in an optimal way. In addition to the standard realized volatility approaches we construct volatility measures by integrating spot volatility estimates that include information on observations outside of the intra-daily intervals of interest. Comparing the alternative volatility measures in a simulation study we find that spot volatility-based measures minimize the RMSE in the case of small intervals.
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Garrett, Ian. "The pricing relationship between the FTSE 100 stock index and FTSE 100 stock index futures contract." Thesis, Brunel University, 1992. http://bura.brunel.ac.uk/handle/2438/5283.
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This thesis investigates the pricing relationship between the FTSE 100 Stock Index and the FTSE 100 Stock Index futures market. We develop and apply a framework in which it is possible to evaluate whether or not markets can be said to function effectively and efficiently. The framework is applied to both the daily and intra-daily pricing relationship between the aforementioned markets. In order to analyse the pricing relationship within days, we develop a new method to remove the effects of nonsynchronous trading from the FTSE 100 Index. We find that on a daily basis the markets generally function effectively, although this does not carryover to the intra-daily pricing relationship. This is especially true during the October 1987 stock market crash, where it is argued that a possible cause of the breakdown lies with the stock market. If this is the case, then any regulation should be aimed at the stock market, not the stock index futures market.
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Pan, Yu-You, and 潘俞佑. "Trading strategy of intra-daily trading volume burst production of TAIEX futures." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/99n25z.
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碩士國立高雄應用科技大學
金融系金融資訊碩士班
102
The subject of the relationship between trading volume and prices which has been academic and practical concerns since the capital market established. For technical analysis, price and trading volume are the important information. Clark (1973) first proposed the mixture of distribution hypothesis and then Copeland (1976) presented the sequential arrival of information hypothesis to explain the relationship between price and trading volume. They all consider that the absolute value of price change and trading volume are positive correlation. So this paper use Taiwan stock price index futures to construct a trading strategy, that is, when intra-daily trading volume burst production, immediately trading in the market. Following the current trend of candlestick to go long or short and with the same stop-loss and stop-profit to trade out the market. This research using two methods which are low amount of trading volume instantaneously turn into high amount of trading volume of absolute burst production and magnification of relative burst production to proceed empirical analysis. The empirical results show that first, the performances are better when the parameters of trading strategy change along with timeline; second, the parameters of moving window with different frequencies, which the training period of month corresponds to month is too short to make the profit and loss have large fluctuations and bad performances; third, when intra-daily trading volume burst production, using low amount of trading volume instantaneously turn into high amount of trading volume absolute burst production and magnification of relative burst production immediately trading in the market, and matching the parameters of moving window that both can get positive returns.
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Wang, Pin-Wei, and 王品媁. "Intelligent Intra-daily Trading Volume Modeling and Prediction in the Taiwan Stock and Futures Markets." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/8kp6mn.
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碩士國立交通大學
資訊管理研究所
104
The relationship between trading price and trading volume had been widely discussed by researchers and financial market practitioners. Traditional financial decision support system mostly focused on the market trend prediction and empirical tests between price and volume. This paper introduced an artificial intelligence system to model and predict the intra-daily trading volume in the Taiwan Stock and Futures Markets. We had implemented three different kinds of artificial neural networks and six regression models as the prediction kernels to substitute for simple linear regression model. In addition to the use of machine learning techniques, the clustering idea had also been applied to further improve the system performance. We found that the proposed trading volume forecasting model may outperform traditional approaches. The major contribution of this paper is to prove that the artificial intelligence and machine learning methods can represent the intra-daily volume changes better than simple linear regression. Moreover, the experimental results also show that the prediction can be further improved by three kinds of clustering model. It means that the adaptive model selection is required in this application to fit the complex and variant financial historical data. In summary, this paper proposed an effective trading volume prediction system based on various intelligent regression methods and clustering model.
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Terdudomtham, Thamavit. "The effects of ASEAN preferential trading arrangements on intra-ASEAN trade 1978-1985 /." 1988. http://catalog.hathitrust.org/api/volumes/oclc/32921512.html.
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Kung, Chin-Shu, and 宮欽恕. "The Impact of Daily Abatement System on Volatilities and Trading Volumes of Taiwan's Stock Index Futures and Options." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/31778160285057951991.
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碩士輔仁大學
金融研究所
97
This study is to investigate the impact on the market of futures and option after the margin abatement on-the-same-day system has been carried out. The intention is to understand whether this daily abatement system caused increased volatility and trading volume, and whether the relationship between futures and option markets changed because of it. The study showed the following results: 1. In futures market –volatility and trading volume both significantly increased, especially a 42% increase in trading volume, after the system of margin abatement on-the-same-day has been carried out. However Chow test (which could not prove a structure change) showed that the implementation of the daily abatement system was not the key factor for the increase of volatility rate and trading volume. This study could not prove that, in futures market, the implementation of the daily abatement system significantly affects volatility and trading volume, or at least the effect should be limited. 2. In option market – the influence is small since the abatement system of half margin on-the-same-day is currently implemented only on commodity futures, and Chow test did not prove a structure change either. Therefore, the daily abatement system should not be strongly correlated to volatility increase in option market; and its influence on trading volume is even less significant, trading volume appeared sliding down after the implementation of the system. This study also could not prove in option market that the implementation of the daily abatement system significantly affects volatility and trading volume, or at least the effect should be limited. 3. In relationship between futures and option – individual Chow tests on volatility and trading volume showed that the daily abatement system was not the key impact factor for causing the volatility varied, but both (futures and option) regression correlation coefficients appeared more various before and after implementation of the daily abatement system. In conclusion, the implementation of daily abatement system, whether in futures market or option market, did not significantly affect volatility and trading volume, or at least the effect should be limited. Therefore, in policy it is not appropriate to be used as a tool to control price volatility; and from Chow test, margin reduction did not prove to be able to stimulate trading volume of short-term traders.
Books on the topic "Intra- daily trading volumes"
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Hartmann, Philipp. Trading volumes and transaction costs in the foreign market: Evidence fron daily dollar-yen spot data. London: London School of Economics, Financial Markets Group, 1996.
Find full textBook chapters on the topic "Intra- daily trading volumes"
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Patra, Sudhakar. "Role of SAARC in Convergence of South Asian Economies." In Handbook of Research on Global Indicators of Economic and Political Convergence, 144–69. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0215-9.ch007.
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The objective of the chapter is to analyze the role of SAARC in regional integration, trade convergence in south Asia. It also highlights the pattern and direction of export and import, share in world trade, preferential trading in South Asian countries. With strong and improving macroeconomic fundamentals, the South Asian region is well established on a high growth path. Based on secondary data on South Asian trade collected from South Asian Economic reports and other statistical volumes, the study observes a decreasing trend of export during the period 1990-2011. Consequently, the overall intra-regional trade intensity index decreased to 1.5 in the year 2010 from 4.2 in the year 2005. SAARC and SAFTA have not contributed in integration and convergence of South Asian Countries rather lead to trade divergence.
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Hoekman, Bernard. "Trade in Services." In Industries without Smokestacks, 151–69. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198821885.003.0008.
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This chapter reviews some of the literature on trade in services, with an emphasis on recent analyses of services trade policies and their effects. African trade is heavily concentrated in agricultural and natural resource-based commodities; the agricultural sector continues to be a major source of employment and economic activity. Trade volumes have risen since the 1990s and exports of some industrial and processed products have been increasing, however, intra-regional trade remains well below potential and the challenge of diversification continues to prevail. There are encouraging prospects for accelerating trade growth as a result of policy reforms. A premise of this chapter is that a precondition for leveraging trade opportunities is a substantial reduction in trading and transaction costs for African firms beyond the current focus on actions to facilitate trade and focus more on improving the performance of a variety of services, including transport, logistics, and related services.
Conference papers on the topic "Intra- daily trading volumes"
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Poli, Michael, Jinkyoo Park, and Ilija Ilievski. "WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time Series." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/630.
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Finance is a particularly challenging application area for deep learning models due to low noise-to-signal ratio, non-stationarity, and partial observability. Non-deliverable-forwards (NDF), a derivatives contract used in foreign exchange (FX) trading, presents additional difficulty in the form of long-term planning required for an effective selection of start and end date of the contract. In this work, we focus on tackling the problem of NDF position length selection by leveraging high-dimensional sequential data consisting of spot rates, technical indicators and expert tenor patterns. To this end, we curate, analyze and release a dataset from the Depository Trust & Clearing Corporation (DTCC) NDF data that includes a comprehensive list of NDF volumes and daily spot rates for 64 FX pairs. We introduce WaveATTentionNet (WATTNet), a novel temporal convolution (TCN) model for spatio-temporal modeling of highly multivariate time series, and validate it across NDF markets with varying degrees of dissimilarity between the training and test periods in terms of volatility and general market regimes. The proposed method achieves a significant positive return on investment (ROI) in all NDF markets under analysis, outperforming recurrent and classical baselines by a wide margin. Finally, we propose two orthogonal interpretability approaches to verify noise robustness and detect the driving factors of the learned tenor selection strategy.
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Abd Razak, Rafidah, Andrew Chan, Ching Mei Pang, Jason Lew, and Siti Hajar Zamridin. "Integration of Dynamic, Static and Seismic Models: Developing High Precision Target Drilling for Matured Fields." In International Petroleum Technology Conference. IPTC, 2023. http://dx.doi.org/10.2523/iptc-22943-ea.
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Abstract Ageing fields are often in a race against depreciating Net Present Value (NPV) and decreasing production, advent of rising water contacts threatening daily and cumulative production. Static geological models form the primary input model into dynamic models which define and determine the development strategy of fields. Typically, dynamic models built from incipient seismic and geological models comprising of well data from early appraisal wells. In time, incorporation of production data refines the dynamic behaviors of field and in turn, these dynamic models are then used for subsequent re-development of the ageing fields. However, the incipient seismic models rarely receive similar updates as seismic acquisition are too costly to justify in an ageing asset with ever-depreciating NPV. This paper illustrates the process undertaken by subsurface team in identifying potential sites constraints by seismic and geological studies, in comparison with initial oilfield production incorporating pressure data to identify sweep efficiency. Starting with understanding a reservoir that experienced poor injection support, has led to unearthing the presence of faults that were not identified in previous seismic interpretations, and thus compartmentalizing the reservoir. The presence of the ‘unseen’ barriers in between an injector and an oil producer has hampered the injection effort towards the producer, and thus has been detrimental to the health of the reservoir. In lieu of the renewed understanding of the reservoir with regards to field compartmentalization which is recognized as key threat to water injection performance in Reservoir-L, usage of artificial intelligence machine-learning generated fault volumes helped define compartments better. The results indicate the NNW-SSE conjugate (shear) faults formed during the Mid-Late Miocene times which were not clearly demarcated on earlier seismic, play key contributing role with regards to connectivity and intra-field communication. This in turn led to revised understanding on areas of reduced production that led to model updates in line with production behaviors. Based on the new model, well location optimizations were undertaken for two water injectors in the infill drilling campaign for shallower Reservoir-K of Field S to avoid potential compartmentalization that would impact the sweep efficiency. Relocation of the water injectors into the same reservoir compartment as the target oil producer was expected to improve the EUR by ensuring direct sweep and good injectivity to the producer. In summary, by revisiting field models in advent of updating and recalibrating the dynamic model in view of historical production data, the team was able to update the model contributing towards field revitalization and field management in ensuring the field model delivers to the field development plan. The hard work from the team has paid out through the recent production performance of the reservoir.