Are There Any Takers for this GIFT, Gujarat International Finance Tec-City…pt 1

Markets regulator Securities and Exchange Board of India (Sebi) has issued a new framework for functioning of stock exchanges and clearing corporations that are setting up their operations in international financial services centers (IFSCs). The Securities and Exchange Board of India (Sebi) said all categories of exchange-traded products currently available for trading in stock exchanges will be eligible for trading in bourses operating in IFSCs. However, this is subject to prior approval of the market watchdog. Only non-agri commodity derivatives will be eligible for trading. Masala bonds, too, qualify, provided such bonds are listed. These exchange-traded products and masala bonds should be compliant with IOSCO (International Organization of Securities Commissions) and FATF (Financial Action Task Force) norms. With a single market structure to achieve synergies in terms of various operations, including ease of doing business, the regulator has asked bourses and corporations at IFSCs to ensure risk-management system and infrastructure as commensurate with trading hours at all times. Prior to commencement of their operations, exchanges at IFSCs would have to tie up with clearing corporations for clearing and settlement of their trades. Clearing corporations desirous of providing services at IFSCs will have to evolve a robust risk management framework in line with IOSCO principles for financial market infrastructures. Also, clearing corporations will have to comply with certain other norms, including margin framework. However, such corporations will have to require to conduct stress tests to ensure robustness of risk management framework. Clearing corporations will be ring-fenced down to the lowest level and their functions will be limited only to clearing and settlement and risk management. The capital of clearing corporations will not form part of net worth of their holding companies. Additionally, holding companies will not be allowed to extend any financial help to such clearing corporations if such entities become financially distressed. Market participants can avail of arbitration, mediation and other dispute resolution mechanisms offered by International Arbitration Centre to resolve disputes.

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But, why this framework is being talked of here? Thats because it has set the stage for the launch of Gujarat International Finance Tec-City, or GIFT City. The moot question is if trading firms will take the bait. More importantly, even if they do, will they just set up servers in GIFT City to establish a presence, or will they set up shop with a full-fledged team? The success of GIFT City, in a true sense, depends on whether top financial market professionals are willing to relocate. As things stand, that still looks like a pipe dream. Garnering trading volumes, on the other hand, may not be that much of a hurdle. Indian trading firms can potentially fund an IFSC subsidiary to the extent of 400% of their net worth through the overseas direct investment (ODI) route. Of course, unlike IFSCs such as Dubai and Singapore, the paperwork and the number of approvals required is far greater with Indian IFSCs such as GIFT City. But firms may be willing to overlook this, given the chance to be associated with one of the prime minister’s pet projects. Modi’s government has said its smart-cities initiative would involve building new cities, including satellites to existing metropolises and modernize existing midsize cities. It still hasn’t settled on a final list of locations. Jaijit Bhattacharya, a partner at KPMG India’s infrastructure division, estimates that it will cost $20 billion to create a smart city, so 100 cities would cost around $2 trillion—about the size of the Indian economy. India has so far budgeted $7.5 billion.

GIFT city achieved financial closure in 2014 in the month of June to be precise. The estimated cost of various infrastructure in Phase-I of the project is Rs 1818 crore. This cost is to be funded by debt of Rs 1,157 crore and balance Rs 661 crore by equity and internal accruals over the next three years, as was reported in 2014. The debt requirement for developing Phase-I infrastructure of GIFT City of Rs 1,157 crore has been tied up with consortium of five banks led by Syndicate Bank. The other consortium banks are Bank of India, Bank of Baroda, Punjab & Sindh Bank and Corporation Bank. The funds will be utilised for developing the state-of-the-art infrastructure planned for the city which includes Road Network, District Cooling System, Automated Solid Waste Management system, Utility Tunnel, Smart ICT, Master Balancing Reservoir, Water and Sewerage treatment plant, Power distribution system and other infrastructure components……….

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Funding Mechanisms for Delhi-Mumbai Industrial Corridor

In an Endeavour to raise the investment in infrastructure from its existing levels of 4.7% of GDP to around 8%, Government of India is actively promoting Public Private Partnerships (PPP) in the key infrastructure sectors viz. transport, power, urban infrastructure, tourism and railways. PPPs are seen as an important tool for producing an accelerated and larger pipeline of infrastructure investments, and catching up with the infrastructure deficit in the country. A PPP Cell has been established in the Department of Economic Affairs (DEA) and setting up of similar nodal agencies is being undertaken in each of the state across the country to administer various proposals and coordinate activities to promote PPPs. Further, GoI has initiated following funding schemes for development of infrastructure in the country:

Viability Gap Funding (VGF):

VGF is a special facility to support the financial viability of those infrastructure projects, which are economically justifiable but not viable commercially in the immediate future. It involves upfront grant assistance of up to 20% of the project cost for state or central PPP projects implemented by the private sector developer who is selected through competitive bidding. An Empowered Committee has been set up for quick processing of cases. Sectors shortlisted for availing Viability Gap Funding Assistance include Roads and bridges, railways, seaports, airports, inland waterways, Power, Urban transport, water supply, sewerage, solid waste management and other physical infrastructure in urban areas. Infrastructure projects in Special Economic Zones and International convention centers and other tourism infrastructure projects.

India Infrastructure Finance Company Limited (IIFCL):

GoI has established IIFCL as a wholly government-owned company, with an authorized capital of INR 1,000 Crore and paid-up capital of INR 100 Crore to provide long-term finance to infrastructure projects, either directly or through refinance. IIFCL caters to the financing gap in long-term financing of infrastructure projects in the public, private, or PPP sector. Any government project awarded to a private sector company for development, financing, and construction through PPP will have overriding priority under the scheme. IIFCL is an apex financial intermediary for the purpose of providing financial support to infrastructure projects and facilities in the country.

Funding from Japan Bank for International Cooperation (JBIC):

The Export-Import Bank of Japan (JEXIM) and the Overseas Economic Cooperation Fund (OECF) merged to form Japan Bank for International Cooperation (JBIC) under the JBIC Law on October 1, 1999. JBIC is a statutory mandate to conduct Japanese Government‟s external economic policy and economic cooperation. At present India accounts for the largest portion of JBIC funding (with 6.5% share constituting US$13.2 Billion) among the 27 countries, with an overall loan size of US $ 184.4 Billion. JBIC has two distinct operations as International Finance Operations (IFO) and Overseas Economic Cooperation Operations (OECO).

International Finance Operations (IFO):

Lends directly to borrowers or via financial intermediaries primarily to finance to: promote Japanese exports, imports and economic activities overseas and the stability of international financial order. It involves Export Loans to promote Japanese plant export to developing countries and Import Loans to promote import of natural resources and manufactured products to Japan. For this providing guarantees to Loans extended by Japanese Commercial banks and to bonds issued by developing countries. It is similar to Untied Funding.

Other Economic Cooperation Operations (OECO):

YEN Loans make development funds available to developing countries at low interest rates and with long repayment periods. These loans provide funds to develop and improve the economic and social infrastructure necessary to support self-help efforts and sustainable economic development for developing countries. The YEN loans are available in various forms like Project Loans, Engineering Services (E/S) Loans, Financial Intermediary Loans (Two-Step Loans), Structural Adjustment Loans (SAL), Commodity Loans and Sector Program Loans (SPL).

Japanese Depository Receipt:

A Japanese Depositary Receipt (JDR) represents ownership in the shares of a foreign company trading on Japanese financial markets. It is one of the funding options and very beneficial financial tool for Indian private companies. The merits of JDRs are financing, enhancing the credibility, expanding the base of shareholders, and promoting the company brand in Japan. JDRs trade on the Tokyo Stock Exchange (TSE) in yen, and in accordance with Japanese market conventions, enabling foreign issuers to tap the Japanese capital market and local investors to efficiently invest in quality international companies.

A depositary receipt (DR) program can help you access capital outside your home market, build corporate and brand visibility on an international basis, broaden and diversify your shareholder base, expand the market for your shares and even develop share plans for foreign-based employees. Japan’s depth of nancial resources, substantial economy and captive investor base make it an ideal market for DR issuance. Japan’s economy is the third largest in the world and the second largest in Asia in terms of nominal Gross Domestic Product (GDP). And as a major center for international nance and trade with a wide array of multinational corporations, and prominent institutional and retail investor communities, Japan is primed to support new opportunities for international investment.

Funding Covered By Nippon Export and Investment Insurance:

Trade and investment insurance of Nippon Export and Investment Insurance (NEXI) is insurance which covers the risks in overseas trading transactions conducted by Japanese companies, such as export, import, overseas investment, and financing. The role of trade and investment insurance is to mitigate a number of risks – political risks, such as restrictions on remittance of foreign currency, war, civil war; and commercial risks, such as nonpayment by the export counterpart buyer – that are inherent in overseas trading transactions and that cannot be covered ordinary marine insurance. It is also funding through Buyer‟s Credit Insurance, Overseas Untied Loan Insurance and Overseas Investment Insurance.

Funding from Multilateral Agencies: 

Multilateral agencies such as the Asian Development Bank and the World Bank have welcomed the recent steps taken by Government of India (GoI) with respect to VGF and IIFCL. These agencies are expected to assist GOI in promoting PPPs across sectors and regions of India, through a range of financing and advisory and technical assistance (TA) measures. Moreover, these agencies would also assist governments in tailoring the PPP solutions to specific demands of the individual states, sectors, and projects. pyramid

Bayesian Networks and Machine Learning

A Bayesian network (BN) is a probabilistic directed acyclic graph representing a set of random variables and their dependence on one another. BNs play an important role in machine learning as they can be used to calculate the probability of a new piece of data being sorted into an existing class by comparison with training data.

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Each variable requires a finite set of mutually exclusive (independent) states. A node with a dependent is called a parent node and each connected pair has a set of conditional probabilities defined by their mutual dependence. Each node depends only on its parents and has conditional independence from any node it is not descended from. Using this definition, and taking n to be the number of nodes in the set of training data, the joint probability of the set of all nodes, {X1, X2, · · · Xn}, is defined for any graph as

P(Xi) = ∏ni=1 P(Xii)

where πi refers to the set of parents of Xi. Any conditional probability between two nodes can then be calculated.

An argument for the use of BNs over other methods is that they are able to “smooth” data models, making all pieces of data usable for training. However, for a BN with m nodes, the number of possible graphs is exponential in n; a problem which has been addressed with varying levels of success. The bulk of the literature on learning with BNs utilises model selection. This is concerned with using a criterion to measure the fit of the network structure to the original data, before applying a heuristic search algorithm to find an equivalence class that does well under these conditions. This is repeated over the space of BN structures. A special case of BNs is the dynamic (time-dependent) hidden Markov model (HMM), in which only outputs are visible and states are hidden. Such models are often used for speech and handwriting recognition, as they can successfully evaluate which sequences of words are the most common.

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Quantum Bayesian networks (QBNs) and hidden quantum Markov models (HQMMs) have been demonstrated theoretically, but there is currently no experimental research. The format of a HMM lends itself to a smooth transition into the language of open quantum systems. Clark et al. claim that open quantum systems with instantaneous feedback are examples of HQMMs, with the open quantum system providing the internal states and the surrounding bath acting as the ancilla, or external state. This allows feedback to guide the internal dynamics of the system, thus conforming to the description of an HQMM.

Stochastic Quantum Walks and Artificial Neural Networks

Schuld et al. propose using quantum walks to construct a quantum ANN algorithm, specifi- cally with an eye to demonstrate associative memory capabilities. This is a sensible idea, as both discrete-time and continuous-time quantum walks are universal for quantum computation. In associative memories, a previously-seen complete input is retrieved upon presentation of an incomplete or noisy input.

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The quantum walker position represents the pattern of the “active” neurons (the firing pattern). That is, on an n-dimensional hypercube, if the walker is in a specific corner labelled with an n-bit string, then this string will have n corresponding neurons, each of which is “active” if the corresponding bit is 1. In a Hopfield network for a given input state x, the outputs are the minima of the energy function

E (x1,….,xn) = -1/2 Σni=1 Σnj=1 wijxixj + Σni=1 θixi

where xi is the state of the i-th neuron, wij is the strength of the inter-neuron link and θi is the activation threshold. Their idea is to construct a quantum walker such that one of these minima (dynamic attractor state) is the desired final state with high probability.

The paper examines two different approaches. First is the naïve case, where activation of a Hopfield network neuron is done using a biased coin. However they prove that this cannot work as the required neuron updating process is not unitary. Instead, a non-linearity is introduced through stochastic quantum walks (SQW) on a hypercube. To inject attractors in the walker’s hypercube graph, they remove all edges leading to/from the corners which represent them. This means that the coherent part of the walk can’t reach/leave these states, thus they become sink states of the graph. The decoherent part, represented by jump operators, adds paths leading to the sinks. A few successful simulations were run, illustrating the possibility of building an associative memory using SQW, and showing that the walker ends up in the sink in a time dependent on the decoherent dynamics. This might be a result in the right direction, but it is not a definitive answer to the ANN problem since Schuld et al. only demonstrate some associative memory properties of the walk. Their suggestion for further work is to explore open quantum walks for training feed-forward ANNs.