We have presented our ongoing work on a policy–driven tradingframework for evolving market–based spectrum assignment regimes. Our system is able to address the social, economic and technical requirements of different spectrum markets while taking in consideration contextual information from the environment to support trade deci- sions. Such complexities prohibit the reliance on static assignment processes and make our policy–based solution an attractive choice. While our prototype implementation is far from complete it demonstrates the feasibility of our approach. Our ongoing work is focused on a complete implementation of the context manager component and the IETF policy model.
Existence of the tradingframework can be felt and attested if there is consistency and stability of form and substance in aqad, legal documentation, financial reporting and Maqasid al-Shariah (Saiful Azhar Rosly, 2010). For instance in BBA, the contract is valid via aqad and the financial reporting of the bank should also show that the bank has initially hold ownership prior to the murabahah sale. The sale contract should grant legal protection to the customer in case the asset delivered was defective and with the same respect, bank should receive protection from the court if the customer defaulted on his murabahah debt obligation. The Maqasid approach should provide insights that the murabahah contract does not embrace riba values and lifestyles. If riba through interest-bearing lending and borrowing is responsible for financial turmoil, murabahah financing is not expected to produce economic bubbles leading to similar crises.
Though most of the financial time series analysis involve prediction of stock price or it fluctuation, but trading the stock market is another popular research area. Gaining profit or loss from stock trading ultimately depends on analysis of future movement of highly fluctuating and irregular stock price values. In literature a number of models combining technical analysis with computational intelligent techniques are available for prediction of stock price index move- ments and for stock trading. In Ref.  a new tradingframework enhancing the performance of reinforcement learning based trading systems is proposed to make buy and sell suggestions for investors in their daily stock trading so as to maximize their profit in the dynamic stock market. In Ref.  a new model using Piecewise Linear Repre- sentations (PLR) and Artificial Neural Networks (ANNs) is proposed to analyze the nonlinear relationships between the stock closed price and various technical indexes, and to capture the knowledge of trading signals that are hidden in historical data. The learned ANN model is used to predict the future trading signals on a daily basis. Secondly, a trading decision is triggered by developing a dynamic threshold decision system. Another forecasting model inte- grating the case based dynamic window (CBDW) and the neural network is applied by 13 to predict the right turning points in stock trading, so as to maximize the investing revenue. In Ref.  a method using together the well known k- NN classifier and some common tools of technical analysis, like technical indicators, stop loss, stop gain and RSI filters is proposed with the purpose of investigating the feasibility of using an intelligent trading system in real market conditions, considering real companies of S~ ao Paulo Stock Exchange and transaction costs. An effective trading signal detection system using Piecewise Linear Representations (PLR) and Artificial Neural Networks (ANNs) is proposed in 14
This chapter makes a comparative study on how other jurisdictions with good legislation on capital markets and securities regulate insider trading. The researcher has selected the English and South African insider trading legal frameworks for that purpose. Discussions are developed based on provisions prohibiting insider trading practices in these two jurisdictions. The researcher describes the experiences in the enforcement of insider trading legislation in the two selected countries, making their legal and regulatory frameworks the most adequate, effective and comparable to the highest standards. This chapter is concluded with guidelines for creating strong, efficient, effective and more competitive insider trading regulation. The investigations of this part have been useful to suggest whether Tanzania‟s insider trading regulatory framework can adopt and learn to amend its legislation to a more strong and reliable legislation as to align with the latest developments elsewhere. Something very important to note is that the study at this part does not explain the detailed historical perspectives of insider trading regulation in the chosen jurisdictions. The researcher examines and analyses the current pieces of legislation designed to regulate insider trading- their attempt to provide answers to questions such as: what is inside information? Who can be considered an insider? What activities related to using inside information are prohibited? How to prevent insider trading? What sanctions and enforcement measures should be implemented?
The rest of the paper is structured as follows. Section 2 provides information for the stock indices (FTSE100, DAX30 and CAC40) and the exchange rates (Euro to British Pound, US Dollar and Japanese Yen) that comprise the dataset and the construction of the annualized realized volatility measures. Section 3 illustrates the forecasting models and the distributional assumptions under investigation. The most widely applied models for realized volatility forecasting, i.e. AFRIMA and HAR specifications, are estimated assuming that the standardized unpredictable components are normally, Student t, GED, and skewed Student t distributed. Section 4 describes the statistical properties of the forecast errors estimated from two generic model frameworks widely applied in financial literature (i.e. regression and ARFIMA models with heteroscedastic residuals), as well as the exploitation of the standardized forecast errors for defining an evaluation procedure of volatility models’ predictability. Section 5 analyses the findings of the adopted forecasting evaluation method. In Section 6, we assume that the realized volatility measure is a tradeable asset and we define a tradingframework under which we will investigate whether a trader based on the standardized forecast errors achieves higher returns compared to a trader whose trading is based on the simple forecast errors. Finally Section 7 concludes the study.
Following a decade of economic decline and hyperinflation during 2007 to 2008, Zimbabwe dollarized its economy in 2009 which to some extent brought stability (Zimbabwe Fiscal Policy Review, 2010). However, the period following this was characterized by a shortage of hard currency in banks and other financing institutions, finally culminating in the scaling down of the productive sectors leading to high unemployment coupled with poverty and a general decline in the standards of living of the Zimbabwean populace (Noko, 2011).This view was supported by Hawkins (2012) who posed a question on whether dollarization was going to be sustainable in Zimbabwe. The study therefore comes against a backdrop of the shift from the Zimbabwe dollar in 2009 to the multi- currency tradingframework, also referred to as dollarization leading to current challenges facing the Zimbabwean economy.
We modify the structure of the model in PR (2006) in four aspects: …rst, we allow for daily auctions instead of one in the entire requirement period; second, we alter the timing of the model to have the auction after interbank trading has taken place, at any given day, and not before; third, the banks in our model optimally decide over the amount of reserves they accumulate each day to contribute to their reserve requirement, which is a residual in PR (2006). In this paper we present one of four possible timings for this decision to be made: simultaneously with the auction demand decision 4 . Fourth, along with
Ms Rana Usman is Senior Assistant Vice President and Head of the Northern region at India's leading stock exchange, the National Stock Exchange. She has been associated with NSE from its inception 20 years ago. Ms Usman has handled various portfolios in her earlier stints at the Head office at Mumbai, including trading, clearing and settlement at NSCCL; NSE's clearing arm, and new product development. She has handled critical functions of risk management at the exchange as well as the associated systems and processes. She has been heading the Northern region office for nearly four years, where she guides a team of more than 40 people to effectively handle Business development, operations, Corporate Affairs, SME listing and Education initiatives. She interacts closely with regulatory officials and supervises the redressal of investor complaints in North India.
iii) Where a strategy involves the trading of two or more different securities or derivative instruments, the smaller of the minimum volume thresholds of the securities or derivative instruments comprised in the block trade will be applied to each of these securities or derivative instruments. Where the strategy involves the trading of two or more different contract months and/or strike prices of the same contract month, the minimum volume threshold will apply to each leg of the trade, except where specific provision has been made within the published minimum thresholds. iv) Approved participants may not aggregate separate orders in order to meet the minimum volume
The disclosure framework addresses the information that managers will disclose to investors to provide them with information to allow them to make an investment decision and to monitor it over time. This includes the provision of a private placement memorandum, annual audited financial statements, periodic performance information and other investor communications, as well as timely disclosure of significant events in light of the circumstances. Managers should determine (based on the specific characteristics of their business) the manner and frequency with which these disclosures will be made and clearly communicate this to investors. Public companies produce independently audited, GAAP-compliant financial statements. Because hedge fund investors share the need for accurate, independently verified financial information, we recommend that all hedge funds do (or continue to do) the same.
The globalisation of business and finance has inevitably led to calls for a common set of high quality, global accounting standards. In this context, International Financial Reporting Standards (IFRS) have gained significant momentum in Europe, especially since the enforcement of the EU Regulation of 2002, which require listed companies to prepare their consolidated annual accounts using this accounting framework. Since then, the Luxembourg bank accounting law has been subsequently amended to introduce the possibility for Luxembourg credit institutions to use IFRS - in part or as a whole - in order to prepare their annual accounts.
emissions quota will be allocated to certain industries such as power industry, energy, cement. Qiu Lihui  proposed that the enterprise who exceed the allocated standard need to buy carbon quotas from carbon trading market. Because the carbon quota is invisible, to ensure that relevant data is true, basis for the carbon trading market running is reliable , it is need to establish strict standards and credible verification system, namely carbon audit. National regulatory authorities should find out the enterprise's "carbon footprint" before the enterprise carbon trading , and then determine the enterprise quota of carbon emissions combined with the future emission reduction requirements, and according to the carbon emission of each industry; at the same time, in the periodic evaluation, the government should supervise and evaluate the enterprise’s report of emissions behavior, the purpose is to guarantee carbon allocation is fair, impartial, and to improve government department or enterprise carbon quota management, performance and responsibility consciousness.
The proportions of volume accounted for by each trader type is relatively stable across the three market capitalisation categories, with HFTs making up approximately 10-15% of total trading, while IBs account for approximately 50% of all trades and brokers for the remaining 35%-40%. Crucially, these three categories of traders all have access to multiple trading venues as well as sophisticated order routing technology that is able to sweep across multiple venues in extremely short periods of time (e.g. within a second). In the case of IBs and HFTs, market makers within these firms (be they trading desks or market making algorithms) are also able to make markets across these different venues simultaneously. That these three types of traders constitute over 99% of total dark pool trading implies that these trading venues are less isolated from the primary exchange than they would be if dark pools were only populated by end-user firms with market makers confined to displayed venues. The trading of these three sophisticated trader types across the multiple venues can help integrate the multiple venues and facilitate the interaction of trading interest on separate venues. This feature of dark pools is also relevant for a number of recent theory papers that assume market makers do not have access to dark pools when modelling the interaction of such venues with lit markets (e.g. Hendershott and Mendelson (2000), Ye (2012), Zhu (2014)). In such an environment, concentration of informed trading on the displayed venue, combined with uninformed, liquidity traders using the displayed venue as a “market of last resort” (i.e. only in the event they cannot find a counterparty in the dark pool), can have very different consequences than in the case where liquidity providers are present on all types of venues.
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